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train · 44.6k rows

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fc29227e-8c35-44ae-bf31-4ee9ad12a879	dual-scale-single-image-dehazing-via-neural	2209.05913	null	https://arxiv.org/abs/2209.05913v1	https://arxiv.org/pdf/2209.05913v1.pdf	Dual-Scale Single Image Dehazing Via Neural Augmentation	Model-based single image dehazing algorithms restore haze-free images with sharp edges and rich details for real-world hazy images at the expense of low PSNR and SSIM values for synthetic hazy images. Data-driven ones restore haze-free images with high PSNR and SSIM values for synthetic hazy images but with low contrast, and even some remaining haze for real world hazy images. In this paper, a novel single image dehazing algorithm is introduced by combining model-based and data-driven approaches. Both transmission map and atmospheric light are first estimated by the model-based methods, and then refined by dual-scale generative adversarial networks (GANs) based approaches. The resultant algorithm forms a neural augmentation which converges very fast while the corresponding data-driven approach might not converge. Haze-free images are restored by using the estimated transmission map and atmospheric light as well as the Koschmiederlaw. Experimental results indicate that the proposed algorithm can remove haze well from real-world and synthetic hazy images.	['Shiqian Wu', 'Haiyan Shu', 'Chaobing Zheng', 'Zhengguo Li']	2022-09-13	null	null	null	null	['image-dehazing']	['computer-vision']	[ 4.47871506e-01 -2.29325831e-01 8.14972818e-01 -7.85267130e-02 -4.41324353e-01 -1.51103750e-01 5.58575630e-01 -5.19165337e-01 -3.37381303e-01 9.66537476e-01 -3.37103456e-02 -3.00271362e-02 4.50906483e-03 -1.14963520e+00 -6.80942416e-01 -1.51852810e+00 1.71318620e-01 3.19573842e-02 3.68871719e-01 -6.54888153e-01 9.32618007e-02 2.48052761e-01 -1.75698996e+00 8.79227184e-03 1.48387790e+00 7.80120790e-01 3.63897562e-01 1.06084216e+00 9.19662118e-02 1.06519234e+00 -7.39032567e-01 -2.20651612e-01 6.46624327e-01 -6.70261800e-01 -1.04017675e-01 3.31124336e-01 6.35974407e-01 -6.47907615e-01 -5.66771209e-01 1.48848021e+00 4.19161737e-01 2.86885768e-01 5.36679387e-01 -1.19494629e+00 -1.06809378e+00 -1.33615240e-01 -5.98199606e-01 1.73568755e-01 -2.49817163e-01 4.73624736e-01 1.80563498e-02 -8.32717240e-01 3.33190948e-01 1.03477466e+00 4.22043979e-01 3.85864884e-01 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82835303-cb50-469f-b141-31e10bc245c2	non-crossing-quantile-regression-for	null	null	http://proceedings.neurips.cc/paper/2020/hash/b6f8dc086b2d60c5856e4ff517060392-Abstract.html	http://proceedings.neurips.cc/paper/2020/file/b6f8dc086b2d60c5856e4ff517060392-Paper.pdf	Non-Crossing Quantile Regression for Distributional Reinforcement Learning	Distributional reinforcement learning (DRL) estimates the distribution over future returns instead of the mean to more efficiently capture the intrinsic uncertainty of MDPs. However, batch-based DRL algorithms cannot guarantee the non-decreasing property of learned quantile curves especially at the early training stage, leading to abnormal distribution estimates and reduced model interpretability. To address these issues, we introduce a general DRL framework by using non-crossing quantile regression to ensure the monotonicity constraint within each sampled batch, which can be incorporated with any well-known DRL algorithm. We demonstrate the validity of our method from both the theory and model implementation perspectives. Experiments on Atari 2600 Games show that some state-of-art DRL algorithms with the non-crossing modification can significantly outperform their baselines in terms of faster convergence speeds and better testing performance. In particular, our method can effectively recover the distribution information and thus dramatically increase the exploration efficiency when the reward space is extremely sparse.	['Xingdong Feng', 'Jianing Wang', 'Fan Zhou']	2020-12-01	null	null	null	neurips-2020-12	['distributional-reinforcement-learning']	['methodology']	[-0.42875347 -0.13021821 -0.5398829 -0.26357245 -1.0888683 -0.89242864 0.35574874 0.16542505 -0.45418265 0.96965647 0.08360851 -0.65057176 -0.5398738 -0.8430365 -0.9318759 -0.5905549 -0.30041143 0.5578212 0.10485997 0.01685627 0.24233578 0.30262893 -1.2770243 -0.07540521 1.1171504 1.20227 -0.02658761 0.38216814 0.16575985 0.8387227 -0.38963842 -0.38736773 0.49229765 -0.16424741 -0.2730287 -0.19604987 -0.05777888 -0.9426109 -0.23417906 1.0989186 0.448737 0.20075579 0.62458795 -1.2476609 -0.28178295 0.98983115 -1.1735541 0.24640445 0.03664735 0.18096204 1.3793182 -0.5201275 0.1381379 1.2589015 0.5574129 0.16878712 -1.3738667 -0.8047772 0.3281068 0.03395929 -1.1315678 -0.14464071 0.38691816 -0.3246532 0.7121516 -0.0242902 0.4860243 0.88531464 0.1832722 1.0319988 1.0798278 0.04474517 0.64345104 -0.10163703 0.02017248 0.3648901 0.6263728 0.5730018 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97e17a86-9a41-40a3-b528-1122d849c5c8	anatomy-guided-multimodal-registration-by	2104.07056	null	https://arxiv.org/abs/2104.07056v1	https://arxiv.org/pdf/2104.07056v1.pdf	Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registration	Multimodal image registration has many applications in diagnostic medical imaging and image-guided interventions, such as Transcatheter Arterial Chemoembolization (TACE) of liver cancer guided by intraprocedural CBCT and pre-operative MR. The ability to register peri-procedurally acquired diagnostic images into the intraprocedural environment can potentially improve the intra-procedural tumor targeting, which will significantly improve therapeutic outcomes. However, the intra-procedural CBCT often suffers from suboptimal image quality due to lack of signal calibration for Hounsfield unit, limited FOV, and motion/metal artifacts. These non-ideal conditions make standard intensity-based multimodal registration methods infeasible to generate correct transformation across modalities. While registration based on anatomic structures, such as segmentation or landmarks, provides an efficient alternative, such anatomic structure information is not always available. One can train a deep learning-based anatomy extractor, but it requires large-scale manual annotations on specific modalities, which are often extremely time-consuming to obtain and require expert radiological readers. To tackle these issues, we leverage annotated datasets already existing in a source modality and propose an anatomy-preserving domain adaptation to segmentation network (APA2Seg-Net) for learning segmentation without target modality ground truth. The segmenters are then integrated into our anatomy-guided multimodal registration based on the robust point matching machine. Our experimental results on in-house TACE patient data demonstrated that our APA2Seg-Net can generate robust CBCT and MR liver segmentation, and the anatomy-guided registration framework with these segmenters can provide high-quality multimodal registrations. Our code is available at https://github.com/bbbbbbzhou/APA2Seg-Net.	['James S. Duncan', 'Chi Liu', 'S. Kevin Zhou', 'Julius Chapiro', 'Zachary Augenfeld', 'Bo Zhou']	2021-04-14	null	null	null	null	['liver-segmentation']	['medical']	[ 1.44987360e-01 -5.12763783e-02 -3.74341100e-01 -3.32772017e-01 -1.16876543e+00 -8.48731577e-01 2.62741208e-01 2.59570271e-01 -3.46365392e-01 4.28469092e-01 2.92929143e-01 -7.40861237e-01 -1.59806117e-01 -7.73780227e-01 -4.27237689e-01 -8.94838035e-01 -2.22650431e-02 5.80078185e-01 2.27432296e-01 7.82305524e-02 -1.01358399e-01 5.72610557e-01 -4.03963983e-01 1.34852886e-01 1.22120428e+00 1.00072873e+00 3.12562138e-01 5.81400156e-01 -1.48536608e-01 3.69849741e-01 -2.03617066e-01 -9.85487774e-02 4.91156220e-01 -6.78210020e-01 -8.02857339e-01 -1.47977516e-01 2.49650478e-01 -1.90198272e-01 -4.74881738e-01 1.08201432e+00 6.10466540e-01 -9.14016888e-02 6.67490959e-01 -7.55566418e-01 -3.83889943e-01 6.37241006e-01 -6.42282903e-01 2.03023508e-01 -1.09460549e-02 5.00546932e-01 1.62456304e-01 -6.91699564e-01 3.76929849e-01 2.40794465e-01 8.94041896e-01 3.82538736e-01 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5505a90d-9533-45c9-99fe-044daa85380f	controlling-text-edition-by-changing-answers	2105.11018	null	https://arxiv.org/abs/2105.11018v1	https://arxiv.org/pdf/2105.11018v1.pdf	Controlling Text Edition by Changing Answers of Specific Questions	In this paper, we introduce the new task of controllable text edition, in which we take as input a long text, a question, and a target answer, and the output is a minimally modified text, so that it fits the target answer. This task is very important in many situations, such as changing some conditions, consequences, or properties in a legal document, or changing some key information of an event in a news text. This is very challenging, as it is hard to obtain a parallel corpus for training, and we need to first find all text positions that should be changed and then decide how to change them. We constructed the new dataset WikiBioCTE for this task based on the existing dataset WikiBio (originally created for table-to-text generation). We use WikiBioCTE for training, and manually labeled a test set for testing. We also propose novel evaluation metrics and a novel method for solving the new task. Experimental results on the test set show that our proposed method is a good fit for this novel NLP task.	['Thomas Lukasiewicz', 'Patrick Hohenecker', 'Lei Sha']	2021-05-23	null	https://aclanthology.org/2021.findings-acl.110	https://aclanthology.org/2021.findings-acl.110.pdf	findings-acl-2021-8	['table-to-text-generation']	['natural-language-processing']	[ 6.10233188e-01 2.96565384e-01 9.98969898e-02 -3.84893358e-01 -6.88142657e-01 -8.46298516e-01 6.78598762e-01 3.83138925e-01 -6.10148013e-01 1.16480744e+00 2.99321055e-01 -4.98029560e-01 -1.81894720e-01 -9.56671894e-01 -7.08796501e-01 -2.83875108e-01 5.97388029e-01 6.97633564e-01 7.11509049e-01 -4.68338251e-01 6.00291789e-01 -4.44858260e-02 -1.30253017e+00 4.43138331e-01 1.27685547e+00 6.79170668e-01 4.98279244e-01 4.01837140e-01 -4.64748561e-01 7.06919491e-01 -9.68242049e-01 -7.26078272e-01 8.81891325e-02 -6.91215158e-01 -1.15775084e+00 -1.99182797e-02 1.08070478e-01 9.04076993e-02 1.33519009e-01 1.07691681e+00 4.53910321e-01 3.19096327e-01 8.57394397e-01 -9.73263443e-01 -4.84311730e-01 1.17034459e+00 -1.84321240e-01 1.67100102e-01 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474744e9-2980-4fad-be18-149c2d1f0340	boosting-novel-category-discovery-over	2211.11262	null	https://arxiv.org/abs/2211.11262v2	https://arxiv.org/pdf/2211.11262v2.pdf	Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All-in-One Classifier	Unsupervised domain adaptation (UDA) has been highly successful in transferring knowledge acquired from a label-rich source domain to a label-scarce target domain. Open-set domain adaptation (ODA) and universal domain adaptation (UNDA) have been proposed as solutions to the problem concerning the presence of additional novel categories in the target domain. Existing ODA and UNDA approaches treat all novel categories as one unified unknown class and attempt to detect this unknown class during the training process. We find that domain variance leads to more significant view-noise in unsupervised data augmentation, affecting the further applications of contrastive learning~(CL), as well as the current closed-set classifier and open-set classifier causing the model to be overconfident in novel class discovery. To address the above two issues, we propose Soft-contrastive All-in-one Network~(SAN) for ODA and UNDA tasks. SAN includes a novel data-augmentation-based CL loss, which is used to improve the representational capability, and a more human-intuitive classifier, which is used to improve the new class discovery capability. The soft contrastive learning~(SCL) loss is used to weaken the adverse effects of the data-augmentation label noise problem, which is amplified in domain transfer. The All-in-One~(AIO) classifier overcomes the overconfidence problem of the current mainstream closed-set classifier and open-set classifier in a more human-intuitive way. The visualization results and ablation experiments demonstrate the importance of the two proposed innovations. Moreover, extensive experimental results on ODA and UNDA show that SAN has advantages over the existing state-of-the-art methods.	['Stan Z. Li', 'Baigui Sun', 'Senqiao Yang', 'Lei Shang', 'Zelin Zang']	2022-11-21	null	null	null	null	['novel-class-discovery', 'universal-domain-adaptation', 'novel-class-discovery']	['computer-vision', 'computer-vision', 'methodology']	[ 4.13176417e-01 5.45366853e-02 -2.87453264e-01 -3.39731753e-01 -3.84855747e-01 -5.94271660e-01 5.01520753e-01 4.76964861e-02 -4.72965986e-01 1.11703169e+00 -8.48327428e-02 -1.16946809e-01 -6.38626590e-02 -8.46408248e-01 -4.77971911e-01 -8.02479923e-01 2.35004514e-01 5.86181462e-01 3.48406971e-01 -2.96037108e-01 -1.65784746e-01 4.31185961e-01 -1.68574309e+00 2.37441927e-01 1.22582984e+00 8.73015285e-01 2.49896422e-01 -1.39393911e-01 -6.01819038e-01 3.70633036e-01 -6.66328490e-01 -1.82744190e-01 2.08743364e-01 -5.14052987e-01 -9.27626669e-01 -1.75476130e-02 1.79882109e-01 7.03598782e-02 6.97668344e-02 1.22672808e+00 6.27138913e-01 1.67700142e-01 9.65350926e-01 -1.54610074e+00 -6.36643887e-01 4.45506692e-01 -8.30363810e-01 2.07119957e-01 1.75748661e-01 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2c893d45-ee20-4d4c-a024-8a7b3311d735	devise-a-deep-visual-semantic-embedding-model	null	null	http://papers.nips.cc/paper/5204-devise-a-deep-visual-semantic-embedding-model	http://papers.nips.cc/paper/5204-devise-a-deep-visual-semantic-embedding-model.pdf	DeViSE: A Deep Visual-Semantic Embedding Model	Modern visual recognition systems are often limited in their ability to scale to large numbers of object categories. This limitation is in part due to the increasing difficulty of acquiring sufficient training data in the form of labeled images as the number of object categories grows. One remedy is to leverage data from other sources -- such as text data -- both to train visual models and to constrain their predictions. In this paper we present a new deep visual-semantic embedding model trained to identify visual objects using both labeled image data as well as semantic information gleaned from unannotated text. We demonstrate that this model matches state-of-the-art performance on the 1000-class ImageNet object recognition challenge while making more semantically reasonable errors, and also show that the semantic information can be exploited to make predictions about tens of thousands of image labels not observed during training. Semantic knowledge improves such zero-shot predictions by up to 65%, achieving hit rates of up to 10% across thousands of novel labels never seen by the visual model.	["Marc'Aurelio Ranzato", 'Jeff Dean', 'Samy Bengio', 'Jon Shlens', 'Greg S. Corrado', 'Andrea Frome', 'Tomas Mikolov']	2013-12-01	null	null	null	neurips-2013-12	['zero-shot-action-recognition']	['computer-vision']	[ 3.54169905e-01 3.05456847e-01 -2.96293527e-01 -6.10017657e-01 -6.08046472e-01 -6.79702699e-01 6.53551519e-01 2.76869863e-01 -5.98977864e-01 4.82567996e-01 1.72707215e-01 -1.33715183e-01 3.48864347e-01 -5.17565310e-01 -8.59329641e-01 -1.72533303e-01 2.25052431e-01 7.61258602e-01 3.75803769e-01 -5.69678806e-02 2.94263631e-01 3.69526923e-01 -1.86873007e+00 6.01211131e-01 2.76558489e-01 1.05495775e+00 3.01190108e-01 5.73240399e-01 -4.11986351e-01 1.00268567e+00 -2.90284693e-01 -3.72789979e-01 3.17490816e-01 -2.38916129e-01 -8.71963859e-01 2.43065640e-01 8.48023653e-01 -4.02959168e-01 -3.31282705e-01 1.01044917e+00 6.16371296e-02 8.50208029e-02 7.25275874e-01 -1.40534174e+00 -1.16749597e+00 3.29168618e-01 -4.10723180e-01 2.53120095e-01 -5.60163669e-02 2.38127246e-01 1.20840478e+00 -1.14249122e+00 8.77205729e-01 1.20430398e+00 5.47815025e-01 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7283f11a-298c-4aa3-b2c2-e3439001704a	how-much-can-clip-benefit-vision-and-language-1	null	null	https://openreview.net/forum?id=zf_Ll3HZWgy	https://openreview.net/pdf?id=zf_Ll3HZWgy	How Much Can CLIP Benefit Vision-and-Language Tasks?	Most existing Vision-and-Language (V&L) models rely on pre-trained visual encoders, using a relatively small set of manually-annotated data (as compared to web-crawled data), to perceive the visual world. However, it has been observed that large-scale pretraining usually can result in better generalization performance, e.g., CLIP (Contrastive Language-Image Pre-training), trained on a massive amount of image-caption pairs, has shown a strong zero-shot capability on various vision tasks. To further study the advantage brought by CLIP, we propose to use CLIP as the visual encoder in various V&L models in two typical scenarios: 1) plugging CLIP into task-specific fine-tuning; 2) combining CLIP with V&L pre-training and transferring to downstream tasks. We show that CLIP significantly outperforms widely-used visual encoders trained with in-domain annotated data, such as BottomUp-TopDown. We achieve competitive or better results on diverse V&L tasks, while establishing new state-of-the-art results on Visual Question Answering, Visual Entailment, and V&L Navigation tasks.	['Anonymous']	2021-09-29	null	https://openreview.net/forum?id=I0tnw1fYFEN	https://openreview.net/pdf?id=I0tnw1fYFEN	iclr-2022	['visual-entailment']	['reasoning']	[ 4.74890471e-02 1.23009287e-01 -1.70507833e-01 -2.84906894e-01 -8.23494971e-01 -7.12205052e-01 8.41824353e-01 2.06158847e-01 -6.72166526e-01 3.15386593e-01 2.44179696e-01 -5.59270918e-01 5.84651589e-01 -5.13363540e-01 -1.31765544e+00 -1.74682900e-01 3.62201542e-01 4.10817742e-01 4.29108232e-01 -3.09205115e-01 1.51460275e-01 -6.07160181e-02 -1.46994984e+00 6.94052815e-01 6.72155917e-01 1.14429677e+00 6.84159636e-01 8.28762949e-01 -3.87449354e-01 1.31946766e+00 -3.39037895e-01 -7.24180460e-01 1.87672570e-01 -4.21865761e-01 -8.20646286e-01 1.41639739e-01 8.21563959e-01 -4.54442680e-01 -4.61170584e-01 9.43441749e-01 3.15235227e-01 2.24002730e-02 6.42803431e-01 -1.41829455e+00 -1.66316330e+00 3.57301831e-01 -6.55942976e-01 2.72101671e-01 3.81551564e-01 5.83982050e-01 1.13435161e+00 -1.36023915e+00 9.12961960e-01 1.51470208e+00 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70d9781a-7106-4fbb-8ac0-a9c64680598a	mvpsnet-fast-generalizable-multi-view	2305.11167	null	https://arxiv.org/abs/2305.11167v1	https://arxiv.org/pdf/2305.11167v1.pdf	MVPSNet: Fast Generalizable Multi-view Photometric Stereo	We propose a fast and generalizable solution to Multi-view Photometric Stereo (MVPS), called MVPSNet. The key to our approach is a feature extraction network that effectively combines images from the same view captured under multiple lighting conditions to extract geometric features from shading cues for stereo matching. We demonstrate these features, termed `Light Aggregated Feature Maps' (LAFM), are effective for feature matching even in textureless regions, where traditional multi-view stereo methods fail. Our method produces similar reconstruction results to PS-NeRF, a state-of-the-art MVPS method that optimizes a neural network per-scene, while being 411$\times$ faster (105 seconds vs. 12 hours) in inference. Additionally, we introduce a new synthetic dataset for MVPS, sMVPS, which is shown to be effective to train a generalizable MVPS method.	['Soumyadip Sengupta', 'Jan-Michael Frahm', 'Pierre-Nicolas Perrin', 'Daniel Lichy', 'Dongxu Zhao']	2023-05-18	null	null	null	null	['stereo-matching-1']	['computer-vision']	[ 3.17085296e-01 -2.86745787e-01 5.54754734e-01 -5.67167222e-01 -1.11208856e+00 -5.36675990e-01 4.97923702e-01 -5.69948137e-01 -2.09305957e-01 5.06873727e-01 7.80452192e-02 -5.43690622e-02 -2.48931032e-02 -8.13234270e-01 -1.10053241e+00 -5.73514700e-01 3.84090751e-01 3.97366077e-01 3.95301223e-01 -2.59398431e-01 5.39235532e-01 8.75649035e-01 -1.97665131e+00 3.68021548e-01 4.66556430e-01 1.08626282e+00 6.60423338e-01 7.90816247e-01 3.00340474e-01 4.91840839e-01 4.97138649e-02 -4.10887748e-01 7.89245069e-01 2.72519648e-01 -7.28506327e-01 9.12406519e-02 1.66036475e+00 -6.04088068e-01 -5.26000381e-01 7.71592855e-01 6.35312498e-01 2.05901295e-01 3.41698408e-01 -9.05070186e-01 -3.02894562e-01 -4.00425971e-01 -6.85876608e-01 1.23865977e-01 7.64529109e-01 2.73740679e-01 1.08665907e+00 -1.28288114e+00 9.58778620e-01 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0d1ecb30-fd11-4ff7-a0a7-086205fd7080	text-and-code-embeddings-by-contrastive-pre	2201.10005	null	https://arxiv.org/abs/2201.10005v1	https://arxiv.org/pdf/2201.10005v1.pdf	Text and Code Embeddings by Contrastive Pre-Training	Text embeddings are useful features in many applications such as semantic search and computing text similarity. Previous work typically trains models customized for different use cases, varying in dataset choice, training objective and model architecture. In this work, we show that contrastive pre-training on unsupervised data at scale leads to high quality vector representations of text and code. The same unsupervised text embeddings that achieve new state-of-the-art results in linear-probe classification also display impressive semantic search capabilities and sometimes even perform competitively with fine-tuned models. On linear-probe classification accuracy averaging over 7 tasks, our best unsupervised model achieves a relative improvement of 4% and 1.8% over previous best unsupervised and supervised text embedding models respectively. The same text embeddings when evaluated on large-scale semantic search attains a relative improvement of 23.4%, 14.7%, and 10.6% over previous best unsupervised methods on MSMARCO, Natural Questions and TriviaQA benchmarks, respectively. Similarly to text embeddings, we train code embedding models on (text, code) pairs, obtaining a 20.8% relative improvement over prior best work on code search.	['Lilian Weng', 'Peter Welinder', 'Joanne Jang', 'Toki Sherbakov', 'Tabarak Khan', 'Madeleine Thompson', 'Kenny Hsu', 'Felipe Petroski Such', 'David Schnurr', 'Gretchen Krueger', 'Girish Sastry', 'Tyna Eloundou Nekoul', 'Boris Power', 'Pranav Shyam', 'Johannes Heidecke', 'Chris Hallacy', 'Jong Wook Kim', 'Nikolas Tezak', 'Qiming Yuan', 'Jerry Tworek', 'Jesse Michael Han', 'Alec Radford', 'Raul Puri', 'Tao Xu', 'Arvind Neelakantan']	2022-01-24	null	null	null	null	['code-search', 'code-search', 'triviaqa', 'passage-ranking']	['computer-code', 'computer-vision', 'miscellaneous', 'natural-language-processing']	[ 1.57810375e-02 -3.07082236e-02 -4.73114997e-01 -3.59263659e-01 -1.15691602e+00 -6.11054063e-01 6.76142097e-01 8.08453858e-01 -7.46851325e-01 4.74821106e-02 4.74896729e-01 -4.17135715e-01 -3.23876888e-01 -5.31910896e-01 -3.62178922e-01 -2.60715932e-01 2.08013743e-01 1.09296799e+00 2.01451331e-01 -2.99421877e-01 7.09818542e-01 -1.57816410e-01 -1.44121921e+00 2.32066303e-01 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5ccf0cdd-582e-4d53-8056-f7d27223a6ab	analyzing-the-sample-complexity-of-self	2305.19079	null	https://arxiv.org/abs/2305.19079v1	https://arxiv.org/pdf/2305.19079v1.pdf	Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods	Supervised training of deep neural networks on pairs of clean image and noisy measurement achieves state-of-the-art performance for many image reconstruction tasks, but such training pairs are usually difficult to collect. A variety of self-supervised methods enable training based on noisy measurements only, without clean images. In this work, we investigate the cost of self-supervised training by characterizing its sample complexity. We focus on a class of self-supervised methods that enable the computation of unbiased estimates of gradients of the supervised loss, including noise2noise methods. We first analytically show that a model trained with such self-supervised training is as good as the same model trained in a supervised fashion, but self-supervised training requires more examples than supervised training. We then study self-supervised denoising and accelerated MRI empirically and characterize the cost of self-supervised training in terms of the number of additional samples required, and find that the performance gap between self-supervised and supervised training vanishes as a function of the training examples, at a problem-dependent rate, as predicted by our theory.	['Reinhard Heckel', 'Dogukan Atik', 'Tobit Klug']	2023-05-30	null	null	null	null	['image-reconstruction']	['computer-vision']	[ 3.12481672e-01 2.18033910e-01 2.32349828e-01 -6.68200135e-01 -1.35510242e+00 -2.41162971e-01 4.20257598e-01 -5.99691197e-02 -6.58474684e-01 8.21272731e-01 2.26970688e-01 3.61244120e-02 -1.92714781e-01 -6.00727260e-01 -1.07755387e+00 -1.03987348e+00 -2.12956101e-01 2.14291319e-01 -2.60827750e-01 1.00863606e-01 -1.96642831e-01 1.96758971e-01 -1.13603437e+00 -8.61210972e-02 8.79051089e-01 1.13694894e+00 2.04859957e-01 7.05842912e-01 1.55633196e-01 7.62267947e-01 -4.09154743e-01 -2.29290932e-01 4.80319321e-01 -8.29026997e-01 -6.23955071e-01 2.45197982e-01 5.50464988e-01 -5.26035726e-01 -5.23024857e-01 1.37823534e+00 7.77283549e-01 1.13876015e-01 6.32400751e-01 -5.84962308e-01 -4.57319617e-01 5.58103561e-01 -1.74926579e-01 3.62305194e-01 -2.82517046e-01 2.43328601e-01 6.56982124e-01 -7.47958302e-01 5.11189401e-01 8.08488369e-01 1.08646488e+00 5.76135695e-01 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d8a6be8b-27ce-4ea2-a192-0fa57da72a3a	independently-recurrent-neural-network-indrnn	1803.04831	null	http://arxiv.org/abs/1803.04831v3	http://arxiv.org/pdf/1803.04831v3.pdf	Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN	Recurrent neural networks (RNNs) have been widely used for processing sequential data. However, RNNs are commonly difficult to train due to the well-known gradient vanishing and exploding problems and hard to learn long-term patterns. Long short-term memory (LSTM) and gated recurrent unit (GRU) were developed to address these problems, but the use of hyperbolic tangent and the sigmoid action functions results in gradient decay over layers. Consequently, construction of an efficiently trainable deep network is challenging. In addition, all the neurons in an RNN layer are entangled together and their behaviour is hard to interpret. To address these problems, a new type of RNN, referred to as independently recurrent neural network (IndRNN), is proposed in this paper, where neurons in the same layer are independent of each other and they are connected across layers. We have shown that an IndRNN can be easily regulated to prevent the gradient exploding and vanishing problems while allowing the network to learn long-term dependencies. Moreover, an IndRNN can work with non-saturated activation functions such as relu (rectified linear unit) and be still trained robustly. Multiple IndRNNs can be stacked to construct a network that is deeper than the existing RNNs. Experimental results have shown that the proposed IndRNN is able to process very long sequences (over 5000 time steps), can be used to construct very deep networks (21 layers used in the experiment) and still be trained robustly. Better performances have been achieved on various tasks by using IndRNNs compared with the traditional RNN and LSTM. The code is available at https://github.com/Sunnydreamrain/IndRNN_Theano_Lasagne.	['Wanqing Li', 'Shuai Li', 'Ce Zhu', 'Chris Cook', 'Yanbo Gao']	2018-03-13	independently-recurrent-neural-network-indrnn-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Li_Independently_Recurrent_Neural_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Independently_Recurrent_Neural_CVPR_2018_paper.pdf	cvpr-2018-6	['sequential-image-classification']	['computer-vision']	[ 1.38813347e-01 2.40226109e-02 1.19534872e-01 -2.09227756e-01 -4.09325324e-02 -2.29202569e-01 3.41350615e-01 -3.16279083e-01 -5.83179653e-01 8.08521867e-01 -7.42468536e-02 -4.01304275e-01 1.50188342e-01 -6.41222119e-01 -8.93898010e-01 -9.31010127e-01 -2.22684324e-01 -4.40087309e-03 6.17618859e-01 -4.56084788e-01 1.60183445e-01 5.93095124e-01 -1.38836777e+00 4.16596383e-01 7.29272544e-01 8.29011858e-01 7.52382159e-01 6.96588397e-01 -1.01545140e-01 1.34735823e+00 -5.19480050e-01 1.18523389e-01 -3.24097811e-03 -4.25063163e-01 -7.38390267e-01 -3.50697815e-01 -1.98275551e-01 -6.84897527e-02 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0e47bf7f-f208-4a21-a8b5-cf0021199592	a-conformer-based-waveform-domain-neural	2205.03481	null	https://arxiv.org/abs/2205.03481v1	https://arxiv.org/pdf/2205.03481v1.pdf	A Conformer-based Waveform-domain Neural Acoustic Echo Canceller Optimized for ASR Accuracy	Acoustic Echo Cancellation (AEC) is essential for accurate recognition of queries spoken to a smart speaker that is playing out audio. Previous work has shown that a neural AEC model operating on log-mel spectral features (denoted "logmel" hereafter) can greatly improve Automatic Speech Recognition (ASR) accuracy when optimized with an auxiliary loss utilizing a pre-trained ASR model encoder. In this paper, we develop a conformer-based waveform-domain neural AEC model inspired by the "TasNet" architecture. The model is trained by jointly optimizing Negative Scale-Invariant SNR (SISNR) and ASR losses on a large speech dataset. On a realistic rerecorded test set, we find that cascading a linear adaptive AEC and a waveform-domain neural AEC is very effective, giving 56-59% word error rate (WER) reduction over the linear AEC alone. On this test set, the 1.6M parameter waveform-domain neural AEC also improves over a larger 6.5M parameter logmel-domain neural AEC model by 20-29% in easy to moderate conditions. By operating on smaller frames, the waveform neural model is able to perform better at smaller sizes and is better suited for applications where memory is limited.	['Alexander Gruenstein', 'James Walker', 'Alex Park', 'Nathan Howard', 'Shuai Shao', 'Turaj Zakizadeh Shabestary', 'Arun Narayanan', 'Sankaran Panchapagesan']	2022-05-06	null	null	null	null	['acoustic-echo-cancellation', 'acoustic-echo-cancellation']	['medical', 'speech']	[ 5.43538272e-01 -2.06780344e-01 5.17551780e-01 -3.75354081e-01 -1.32823229e+00 -3.43102485e-01 1.81144565e-01 -1.53366357e-01 -7.67477512e-01 2.48284891e-01 4.43103254e-01 -5.83238900e-01 1.06758642e-04 4.23420779e-02 -5.77363133e-01 -6.42513335e-01 -1.95253655e-01 -3.53215970e-02 1.58018339e-02 -3.99732709e-01 8.91618058e-02 5.63136578e-01 -1.48338580e+00 2.01628059e-01 5.82075477e-01 1.18642366e+00 4.01762664e-01 1.36763787e+00 2.56670505e-01 5.73385119e-01 -1.08059728e+00 -1.48931339e-01 3.53964299e-01 -3.69699746e-01 -1.96364760e-01 -4.49439734e-01 5.40311694e-01 -2.35153466e-01 -4.33881640e-01 9.91208911e-01 1.25583422e+00 3.90345544e-01 4.46771204e-01 -5.91711521e-01 -1.57417744e-01 5.44495225e-01 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22733130-f57e-4baf-ab60-33b6e5543e37	query2gmm-learning-representation-with	2306.10367	null	https://arxiv.org/abs/2306.10367v1	https://arxiv.org/pdf/2306.10367v1.pdf	Query2GMM: Learning Representation with Gaussian Mixture Model for Reasoning over Knowledge Graphs	Logical query answering over Knowledge Graphs (KGs) is a fundamental yet complex task. A promising approach to achieve this is to embed queries and entities jointly into the same embedding space. Research along this line suggests that using multi-modal distribution to represent answer entities is more suitable than uni-modal distribution, as a single query may contain multiple disjoint answer subsets due to the compositional nature of multi-hop queries and the varying latent semantics of relations. However, existing methods based on multi-modal distribution roughly represent each subset without capturing its accurate cardinality, or even degenerate into uni-modal distribution learning during the reasoning process due to the lack of an effective similarity measure. To better model queries with diversified answers, we propose Query2GMM for answering logical queries over knowledge graphs. In Query2GMM, we present the GMM embedding to represent each query using a univariate Gaussian Mixture Model (GMM). Each subset of a query is encoded by its cardinality, semantic center and dispersion degree, allowing for precise representation of multiple subsets. Then we design specific neural networks for each operator to handle the inherent complexity that comes with multi-modal distribution while alleviating the cascading errors. Last, we define a new similarity measure to assess the relationships between an entity and a query's multi-answer subsets, enabling effective multi-modal distribution learning for reasoning. Comprehensive experimental results show that Query2GMM outperforms the best competitor by an absolute average of $5.5\%$. The source code is available at \url{https://anonymous.4open.science/r/Query2GMM-C42F}.	['Ying Zhang', 'Wenjie Zhang', 'Yuanyuan Xu', 'Yuhan Wu']	2023-06-17	null	null	null	null	['knowledge-graphs']	['knowledge-base']	[-3.94786090e-01 2.99936473e-01 -4.45270479e-01 -3.89030486e-01 -7.74719596e-01 -5.60091436e-01 3.52231950e-01 2.25598827e-01 2.29067709e-02 4.55030680e-01 1.56031951e-01 -2.33196765e-01 -6.49510801e-01 -1.34426856e+00 -7.06613004e-01 -3.98568928e-01 6.22816309e-02 9.29212272e-01 3.53353858e-01 -2.31316969e-01 -4.60584462e-02 2.36967877e-01 -1.34811532e+00 3.67078662e-01 1.11219716e+00 1.04363823e+00 8.82361829e-02 2.93416172e-01 -6.97111011e-01 9.64609385e-01 -7.42712557e-01 -8.82398307e-01 -7.03050420e-02 -2.37790450e-01 -1.15554786e+00 -3.99706036e-01 3.17925096e-01 -1.35450065e-01 -6.45034254e-01 1.21477652e+00 4.03313845e-01 1.29101887e-01 8.96677554e-01 -1.62475729e+00 -1.13244414e+00 7.38730669e-01 -1.88159540e-01 1.00285463e-01 5.66153288e-01 -3.24312299e-01 1.39226258e+00 -8.67555380e-01 4.12232280e-01 1.51144636e+00 4.91567045e-01 2.43507504e-01 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2d4445a2-d0f2-41aa-9f96-4fe256a7cf2e	correntropy-maximization-via-admm-application	1602.01729	null	http://arxiv.org/abs/1602.01729v1	http://arxiv.org/pdf/1602.01729v1.pdf	Correntropy Maximization via ADMM - Application to Robust Hyperspectral Unmixing	In hyperspectral images, some spectral bands suffer from low signal-to-noise ratio due to noisy acquisition and atmospheric effects, thus requiring robust techniques for the unmixing problem. This paper presents a robust supervised spectral unmixing approach for hyperspectral images. The robustness is achieved by writing the unmixing problem as the maximization of the correntropy criterion subject to the most commonly used constraints. Two unmixing problems are derived: the first problem considers the fully-constrained unmixing, with both the non-negativity and sum-to-one constraints, while the second one deals with the non-negativity and the sparsity-promoting of the abundances. The corresponding optimization problems are solved efficiently using an alternating direction method of multipliers (ADMM) approach. Experiments on synthetic and real hyperspectral images validate the performance of the proposed algorithms for different scenarios, demonstrating that the correntropy-based unmixing is robust to outlier bands.	['Paul Honeine', 'Abderrahim Halimi', 'Fei Zhu', 'Badong Chen', 'Nanning Zheng']	2016-02-04	null	null	null	null	['hyperspectral-unmixing']	['computer-vision']	[ 8.46128762e-01 -3.98314863e-01 1.75478131e-01 3.40818204e-02 -2.87992835e-01 -5.19112170e-01 3.11585963e-01 4.57056910e-02 -3.01874369e-01 7.27793634e-01 -7.11417496e-02 4.60953899e-02 -7.97107279e-01 -4.98793811e-01 -3.07450145e-01 -1.31363118e+00 -3.34719419e-02 8.28664470e-03 -6.34520292e-01 2.35963147e-02 1.57899424e-01 4.45574939e-01 -1.61520863e+00 -1.34453952e-01 1.29820859e+00 1.05322850e+00 2.92402476e-01 5.46000600e-01 2.52982169e-01 3.37422311e-01 -2.65167922e-01 2.19085813e-01 6.70500100e-01 -4.87505972e-01 -1.90669268e-01 8.31329525e-01 4.38678771e-01 -5.53561784e-02 8.72372761e-02 1.72316837e+00 4.76092160e-01 4.23235983e-01 5.84766150e-01 -1.34669363e+00 -3.71765971e-01 2.18395710e-01 -1.13526165e+00 -1.00119352e-01 -1.11975871e-01 -1.98977873e-01 9.77251768e-01 -8.98330271e-01 2.30281174e-01 7.88405836e-01 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1ce28e81-0d50-45a6-abf5-e682e1ed5482	contrastive-semantic-guided-image-smoothing	2209.00977	null	https://arxiv.org/abs/2209.00977v1	https://arxiv.org/pdf/2209.00977v1.pdf	Contrastive Semantic-Guided Image Smoothing Network	Image smoothing is a fundamental low-level vision task that aims to preserve salient structures of an image while removing insignificant details. Deep learning has been explored in image smoothing to deal with the complex entanglement of semantic structures and trivial details. However, current methods neglect two important facts in smoothing: 1) naive pixel-level regression supervised by the limited number of high-quality smoothing ground-truth could lead to domain shift and cause generalization problems towards real-world images; 2) texture appearance is closely related to object semantics, so that image smoothing requires awareness of semantic difference to apply adaptive smoothing strengths. To address these issues, we propose a novel Contrastive Semantic-Guided Image Smoothing Network (CSGIS-Net) that combines both contrastive prior and semantic prior to facilitate robust image smoothing. The supervision signal is augmented by leveraging undesired smoothing effects as negative teachers, and by incorporating segmentation tasks to encourage semantic distinctiveness. To realize the proposed network, we also enrich the original VOC dataset with texture enhancement and smoothing labels, namely VOC-smooth, which first bridges image smoothing and semantic segmentation. Extensive experiments demonstrate that the proposed CSGIS-Net outperforms state-of-the-art algorithms by a large margin. Code and dataset are available at https://github.com/wangjie6866/CSGIS-Net.	['Mingqiang Wei', 'Fu Lee Wang', 'Haoran Xie', 'Xuefeng Yan', 'Lina Gong', 'Yidan Feng', 'Yongzhen Wang', 'Jie Wang']	2022-09-02	null	null	null	null	['image-smoothing']	['computer-vision']	[ 5.78632116e-01 4.25840676e-01 -1.50044501e-01 -5.60913742e-01 -4.78846669e-01 8.04945305e-02 5.90991676e-01 -1.65708140e-01 -3.44952792e-01 5.59007525e-01 3.45341384e-01 1.17931880e-01 9.21464115e-02 -6.49544835e-01 -7.33651340e-01 -8.35923910e-01 2.78266370e-01 -4.22362387e-01 7.99928367e-01 -3.93675953e-01 3.42309654e-01 2.27396086e-01 -1.38235533e+00 2.11529508e-01 1.50687778e+00 1.11218512e+00 6.21289313e-01 1.33210167e-01 -4.14216340e-01 6.69310629e-01 -2.80828714e-01 -3.25957179e-01 2.45831802e-01 -2.40566641e-01 -6.83291793e-01 3.64613086e-01 5.81033468e-01 -1.39640927e-01 -3.06502789e-01 1.79577315e+00 3.16383928e-01 2.97408670e-01 3.68307889e-01 -9.75597262e-01 -9.86537755e-01 4.89675134e-01 -9.20967638e-01 1.25584677e-01 -2.90002316e-01 3.99329484e-01 5.37903070e-01 -7.80330002e-01 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2f75b9e2-674b-49e2-874d-1aaca9fbbff7	real-world-image-dehazing-with-improved-joint	null	null	https://www.sciencedirect.com/science/article/pii/S1047320322002401	https://www.sciencedirect.com/science/article/pii/S1047320322002401	Real-world image dehazing with improved joint enhancement and exposure fusion	In this work, a single image dehazing method that improves the haze removal capacity of the Joint Contrast Enhancement and Exposure Fusion (CEEF) method with Smoothing-Sharpening Image Filter (SSIF) is presented. In this method, the hazy image is first sharpened with SSIF to obtain a sharper image. In this way, the difference between haze and objects is amplified. Then, the AHE procedure in CEEF is replaced by CLAHE to obtain an enhanced CEEF. The enhanced CEEF is applied to the filtering result to obtain the final dehazed image. Observations demonstrate that the proposed method obtains enhanced results while reducing the amount of haze. The visual and quantitative comparisons between the proposed method and state-of-the-art dehazing methods show that the proposed method has better dehazing performance and has a 50% improvement in terms of the FADE metric compared to the closest result.	['Nur Huseyin KAPLAN']	2023-12-09	null	null	null	journal-of-visual-communication-and-image-2	['image-dehazing']	['computer-vision']	[ 4.28120971e-01 -5.92530787e-01 8.20823371e-01 1.29153073e-01 -1.26266167e-01 -7.03961030e-02 3.80729526e-01 -7.14507550e-02 -4.26317543e-01 7.12501585e-01 2.11162224e-01 1.00127108e-01 -2.08175600e-01 -8.69095087e-01 -2.08724573e-01 -1.35026765e+00 3.42501968e-01 -6.27039909e-01 7.52271295e-01 -5.17594278e-01 3.80720407e-01 3.12994897e-01 -1.67947042e+00 5.57073131e-02 1.45945096e+00 9.91051257e-01 4.56231892e-01 5.17150104e-01 7.82780051e-02 6.61535144e-01 -8.08102250e-01 -5.02617173e-02 3.34613383e-01 -6.02954388e-01 -2.13863686e-01 4.28554803e-01 2.96978295e-01 -4.32068348e-01 -2.86312938e-01 1.33194995e+00 4.02034253e-01 4.40107167e-01 6.15093112e-01 -8.13928246e-01 -6.72795296e-01 -3.15405220e-01 -9.65934217e-01 5.71461678e-01 -2.03848541e-01 -1.44658178e-01 9.14430693e-02 -1.03907931e+00 1.37171775e-01 1.01952088e+00 2.66166955e-01 8.37298855e-02 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-1.10880864e+00 4.30663735e-01 8.49470437e-01 -6.84550047e-01 -9.36919928e-01 3.74235451e-01]	[10.889721870422363, -3.089564800262451]
c5ef5975-ef7f-4bda-9fec-dbbcc05c6f1e	neuroscience-inspired-perception-action-in	2105.04261	null	https://arxiv.org/abs/2105.04261v1	https://arxiv.org/pdf/2105.04261v1.pdf	Neuroscience-inspired perception-action in robotics: applying active inference for state estimation, control and self-perception	Unlike robots, humans learn, adapt and perceive their bodies by interacting with the world. Discovering how the brain represents the body and generates actions is of major importance for robotics and artificial intelligence. Here we discuss how neuroscience findings open up opportunities to improve current estimation and control algorithms in robotics. In particular, how active inference, a mathematical formulation of how the brain resists a natural tendency to disorder, provides a unified recipe to potentially solve some of the major challenges in robotics, such as adaptation, robustness, flexibility, generalization and safe interaction. This paper summarizes some experiments and lessons learned from developing such a computational model on real embodied platforms, i.e., humanoid and industrial robots. Finally, we showcase the limitations and challenges that we are still facing to give robots human-like perception	['Marcel van Gerven', 'Pablo Lanillos']	2021-05-10	null	null	null	null	['industrial-robots']	['robots']	[ 2.35492557e-01 7.45543778e-01 6.91336766e-02 7.22234771e-02 5.07751346e-01 -6.04734898e-01 6.52314544e-01 -3.95405948e-01 -4.06211615e-01 9.14923072e-01 2.43746154e-02 3.58577937e-01 -3.93274724e-01 -3.74892771e-01 -8.59555542e-01 -7.51853287e-01 -3.08502734e-01 4.32630718e-01 8.64088163e-02 -3.58176559e-01 2.60269701e-01 6.39116406e-01 -1.99489748e+00 -2.64654607e-01 8.91062140e-01 6.06722772e-01 7.04335332e-01 6.07974827e-01 8.55985522e-01 8.27899933e-01 -2.23287582e-01 5.31874783e-02 3.77453148e-01 -3.22918832e-01 -7.29705453e-01 7.59667754e-02 -1.12379670e-01 -2.36045718e-01 -2.47888058e-01 9.22917008e-01 4.77837950e-01 4.35457796e-01 7.77181268e-01 -1.37583458e+00 -7.21279800e-01 5.49403131e-01 1.77202582e-01 -2.69419968e-01 5.99314034e-01 6.22987688e-01 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36a00f8f-40db-452b-9a54-75c7ae27aa46	fv2es-a-fully-end2end-multimodal-system-for	2209.10170	null	https://arxiv.org/abs/2209.10170v1	https://arxiv.org/pdf/2209.10170v1.pdf	FV2ES: A Fully End2End Multimodal System for Fast Yet Effective Video Emotion Recognition Inference	In the latest social networks, more and more people prefer to express their emotions in videos through text, speech, and rich facial expressions. Multimodal video emotion analysis techniques can help understand users' inner world automatically based on human expressions and gestures in images, tones in voices, and recognized natural language. However, in the existing research, the acoustic modality has long been in a marginal position as compared to visual and textual modalities. That is, it tends to be more difficult to improve the contribution of the acoustic modality for the whole multimodal emotion recognition task. Besides, although better performance can be obtained by introducing common deep learning methods, the complex structures of these training models always result in low inference efficiency, especially when exposed to high-resolution and long-length videos. Moreover, the lack of a fully end-to-end multimodal video emotion recognition system hinders its application. In this paper, we designed a fully multimodal video-to-emotion system (named FV2ES) for fast yet effective recognition inference, whose benefits are threefold: (1) The adoption of the hierarchical attention method upon the sound spectra breaks through the limited contribution of the acoustic modality and outperforms the existing models' performance on both IEMOCAP and CMU-MOSEI datasets; (2) the introduction of the idea of multi-scale for visual extraction while single-branch for inference brings higher efficiency and maintains the prediction accuracy at the same time; (3) the further integration of data pre-processing into the aligned multimodal learning model allows the significant reduction of computational costs and storage space.	['Yuan Zhang', 'Xuling Huang', 'Qinglan Wei']	2022-09-21	null	null	null	null	['video-emotion-recognition', 'multimodal-emotion-recognition', 'multimodal-emotion-recognition']	['computer-vision', 'computer-vision', 'speech']	[-1.51216179e-01 -3.84600997e-01 -8.31370130e-02 -3.22681367e-01 -5.66486120e-01 -2.60043710e-01 4.75718617e-01 -3.61944526e-01 -5.15592039e-01 5.59625745e-01 2.11167306e-01 2.26921275e-01 2.03718748e-02 -4.33248669e-01 -3.54463518e-01 -7.27436125e-01 1.68995425e-01 -1.41301945e-01 -2.72940490e-02 -1.98529050e-01 -4.28992957e-02 4.04884100e-01 -1.96737432e+00 3.40744942e-01 8.06526005e-01 1.48789442e+00 2.40306273e-01 6.09413445e-01 -4.16879982e-01 8.72733772e-01 -3.42144936e-01 -7.95344234e-01 -2.43947253e-01 -3.19387883e-01 -3.93779963e-01 3.07002794e-02 1.64175615e-01 -4.79754239e-01 -3.73661101e-01 1.06208718e+00 8.15504611e-01 1.96590066e-01 4.75592971e-01 -1.45022643e+00 -4.31492835e-01 2.65754074e-01 -4.68890131e-01 -2.60449141e-01 5.59106588e-01 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cc739e15-4175-441e-9f24-492e405c677b	llm-as-a-robotic-brain-unifying-egocentric	2304.09349	null	https://arxiv.org/abs/2304.09349v4	https://arxiv.org/pdf/2304.09349v4.pdf	LLM as A Robotic Brain: Unifying Egocentric Memory and Control	Embodied AI focuses on the study and development of intelligent systems that possess a physical or virtual embodiment (i.e. robots) and are able to dynamically interact with their environment. Memory and control are the two essential parts of an embodied system and usually require separate frameworks to model each of them. In this paper, we propose a novel and generalizable framework called LLM-Brain: using Large-scale Language Model as a robotic brain to unify egocentric memory and control. The LLM-Brain framework integrates multiple multimodal language models for robotic tasks, utilizing a zero-shot learning approach. All components within LLM-Brain communicate using natural language in closed-loop multi-round dialogues that encompass perception, planning, control, and memory. The core of the system is an embodied LLM to maintain egocentric memory and control the robot. We demonstrate LLM-Brain by examining two downstream tasks: active exploration and embodied question answering. The active exploration tasks require the robot to extensively explore an unknown environment within a limited number of actions. Meanwhile, the embodied question answering tasks necessitate that the robot answers questions based on observations acquired during prior explorations.	['Bernard Ghanem', 'Mohamed Elhoseiny', 'Guocheng Qian', 'Bing Li', 'Jun Chen', 'Jinjie Mai']	2023-04-19	null	null	null	null	['embodied-question-answering']	['computer-vision']	[-2.07120359e-01 6.99470580e-01 6.72009587e-02 -1.95143431e-01 -1.74623892e-01 -5.90249658e-01 9.98653829e-01 -2.57787585e-01 -4.14268076e-01 4.29819167e-01 1.75558835e-01 5.18363044e-02 -1.55526742e-01 -8.84478927e-01 -6.99219823e-01 -5.43717682e-01 -1.21492818e-01 3.91622722e-01 -1.11016914e-01 -6.58730090e-01 4.22486275e-01 2.19051361e-01 -1.45519245e+00 -1.05573706e-01 6.36026084e-01 6.51383162e-01 9.64777648e-01 9.09783244e-01 -1.12290032e-01 1.46943617e+00 -1.02011524e-01 3.67670000e-01 -2.47824103e-01 -3.25586379e-01 -1.30625021e+00 1.35317728e-01 -5.93538880e-01 -3.10748816e-01 -6.42344475e-01 8.10778916e-01 2.57241100e-01 6.12675905e-01 6.08442187e-01 -1.38344145e+00 -9.27693844e-01 4.34994757e-01 1.37173846e-01 -3.28359514e-01 6.51513219e-01 3.13347846e-01 6.41691148e-01 -8.57204139e-01 5.66797495e-01 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-1.17781627e+00 -4.62808341e-01 -1.07423306e+00 -4.80691463e-01 -2.22079322e-01 5.09516060e-01 -9.15348828e-01 -1.89576551e-01 -2.41374075e-02]	[4.330359935760498, 0.879243016242981]
9ecfd238-449e-4017-982f-851f67268a9e	evaluation-tuning-and-interpretation-of	2005.03126	null	https://arxiv.org/abs/2005.03126v1	https://arxiv.org/pdf/2005.03126v1.pdf	Evaluation, Tuning and Interpretation of Neural Networks for Meteorological Applications	Neural networks have opened up many new opportunities to utilize remotely sensed images in meteorology. Common applications include image classification, e.g., to determine whether an image contains a tropical cyclone, and image translation, e.g., to emulate radar imagery for satellites that only have passive channels. However, there are yet many open questions regarding the use of neural networks in meteorology, such as best practices for evaluation, tuning and interpretation. This article highlights several strategies and practical considerations for neural network development that have not yet received much attention in the meteorological community, such as the concept of effective receptive fields, underutilized meteorological performance measures, and methods for NN interpretation, such as synthetic experiments and layer-wise relevance propagation. We also consider the process of neural network interpretation as a whole, recognizing it as an iterative scientist-driven discovery process, and breaking it down into individual steps that researchers can take. Finally, while most work on neural network interpretation in meteorology has so far focused on networks for image classification tasks, we expand the focus to also include networks for image translation.	['Imme Ebert-Uphoff', 'Kyle A. Hilburn']	2020-05-06	null	null	null	null	['network-interpretation']	['computer-vision']	[ 6.66707754e-01 -2.55985111e-01 -7.27890655e-02 -7.60181785e-01 -6.27725348e-02 -6.11230314e-01 6.54571891e-01 -2.14304388e-01 -5.54933727e-01 7.15083182e-01 -3.91150601e-02 -1.11442780e+00 -1.95223853e-01 -7.37217724e-01 -4.48771507e-01 -8.86433482e-01 -3.46288085e-01 1.88406304e-01 -5.51374316e-01 -3.19966882e-01 2.01121405e-01 7.95851111e-01 -1.49995399e+00 -2.57898327e-02 6.06993377e-01 8.14265847e-01 -4.70963791e-02 8.29650879e-01 1.24072775e-01 4.37065333e-01 -7.50173867e-01 2.79623181e-01 3.07861507e-01 -4.29433823e-01 -6.16237760e-01 7.26580173e-02 4.58908319e-01 -3.48188907e-01 2.81536225e-02 9.48895812e-01 4.73747671e-01 4.21152413e-01 7.17835724e-01 -8.95752013e-01 -7.59892404e-01 3.67314845e-01 -3.33361328e-01 7.73263872e-01 -5.44148862e-01 1.92877173e-01 6.86807036e-01 -7.33180821e-01 1.50664076e-01 1.10125589e+00 5.33439040e-01 3.73993188e-01 -1.28737152e+00 -6.64333284e-01 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a7871782-f1ab-4fe7-b2a7-d2e7679656a1	prototypical-logic-tensor-networks-proto-ltn	2207.00433	null	https://arxiv.org/abs/2207.00433v1	https://arxiv.org/pdf/2207.00433v1.pdf	PROTOtypical Logic Tensor Networks (PROTO-LTN) for Zero Shot Learning	Semantic image interpretation can vastly benefit from approaches that combine sub-symbolic distributed representation learning with the capability to reason at a higher level of abstraction. Logic Tensor Networks (LTNs) are a class of neuro-symbolic systems based on a differentiable, first-order logic grounded into a deep neural network. LTNs replace the classical concept of training set with a knowledge base of fuzzy logical axioms. By defining a set of differentiable operators to approximate the role of connectives, predicates, functions and quantifiers, a loss function is automatically specified so that LTNs can learn to satisfy the knowledge base. We focus here on the subsumption or \texttt{isOfClass} predicate, which is fundamental to encode most semantic image interpretation tasks. Unlike conventional LTNs, which rely on a separate predicate for each class (e.g., dog, cat), each with its own set of learnable weights, we propose a common \texttt{isOfClass} predicate, whose level of truth is a function of the distance between an object embedding and the corresponding class prototype. The PROTOtypical Logic Tensor Networks (PROTO-LTN) extend the current formulation by grounding abstract concepts as parametrized class prototypes in a high-dimensional embedding space, while reducing the number of parameters required to ground the knowledge base. We show how this architecture can be effectively trained in the few and zero-shot learning scenarios. Experiments on Generalized Zero Shot Learning benchmarks validate the proposed implementation as a competitive alternative to traditional embedding-based approaches. The proposed formulation opens up new opportunities in zero shot learning settings, as the LTN formalism allows to integrate background knowledge in the form of logical axioms to compensate for the lack of labelled examples.	['Lia Morra', 'Lamberti Fabrizio', 'Francesco Manigrasso', 'Simone Martone']	2022-06-26	null	null	null	null	['generalized-zero-shot-learning', 'tensor-networks', 'generalized-zero-shot-learning']	['computer-vision', 'methodology', 'methodology']	[ 2.55253673e-01 7.04030454e-01 -3.11598718e-01 -5.49711406e-01 -5.53466454e-02 -2.93270707e-01 8.17766547e-01 4.01718616e-01 -3.11567575e-01 4.74677742e-01 -1.05560988e-01 -1.83747172e-01 -5.74746370e-01 -1.33223283e+00 -9.33017910e-01 -7.39538372e-01 -1.42546445e-01 8.07043016e-01 3.26055586e-01 -4.90878522e-01 -1.84939146e-01 3.82645220e-01 -1.85268593e+00 6.21047974e-01 7.57289708e-01 1.44879496e+00 -1.29162356e-01 1.42851681e-01 -4.74332601e-01 1.02299094e+00 -4.51249272e-01 -6.82158649e-01 1.28219068e-01 -2.33907610e-01 -8.99934471e-01 -9.81546938e-02 5.38145840e-01 -1.50782093e-01 -2.03486353e-01 1.36895394e+00 -1.97911873e-01 3.03765088e-01 7.16974914e-01 -1.68451262e+00 -1.08099711e+00 8.81387115e-01 3.47824275e-01 -1.39959872e-01 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58963d70-6519-4e00-bb3c-e791761d7d30	augmenting-greybox-fuzzing-with-generative-ai	2306.06782	null	https://arxiv.org/abs/2306.06782v1	https://arxiv.org/pdf/2306.06782v1.pdf	Augmenting Greybox Fuzzing with Generative AI	Real-world programs expecting structured inputs often has a format-parsing stage gating the deeper program space. Neither a mutation-based approach nor a generative approach can provide a solution that is effective and scalable. Large language models (LLM) pre-trained with an enormous amount of natural language corpus have proved to be effective for understanding the implicit format syntax and generating format-conforming inputs. In this paper, propose ChatFuzz, a greybox fuzzer augmented by generative AI. More specifically, we pick a seed in the fuzzer's seed pool and prompt ChatGPT generative models to variations, which are more likely to be format-conforming and thus of high quality. We conduct extensive experiments to explore the best practice for harvesting the power of generative LLM models. The experiment results show that our approach improves the edge coverage by 12.77\% over the SOTA greybox fuzzer (AFL++) on 12 target programs from three well-tested benchmarks. As for vulnerability detection, \sys is able to perform similar to or better than AFL++ for programs with explicit syntax rules but not for programs with non-trivial syntax.	['Heng Yin', 'Qian Zhang', 'Jie Hu']	2023-06-11	null	null	null	null	['vulnerability-detection']	['miscellaneous']	[ 2.99853653e-01 3.80405337e-01 -2.88180321e-01 -2.67249048e-01 -7.70615578e-01 -6.79460347e-01 2.99865305e-01 -9.96038318e-02 2.30159611e-01 5.05399287e-01 -3.80330890e-01 -8.91205370e-01 3.80985916e-01 -1.20309126e+00 -1.18284225e+00 -3.58328945e-03 -1.57131881e-01 4.04295385e-01 5.90999544e-01 -7.40103424e-01 4.66502905e-01 -2.74163544e-01 -1.71523178e+00 7.99566984e-01 1.41708922e+00 3.82141948e-01 2.66732574e-01 8.78832102e-01 -3.95487636e-01 8.42213154e-01 -1.06390882e+00 -6.96129799e-01 1.56910896e-01 -7.08641469e-01 -6.99298322e-01 -3.74012440e-01 2.76046306e-01 3.52285244e-02 1.47987843e-01 1.43289459e+00 4.63119522e-02 -5.42091727e-01 1.94536373e-01 -1.26148939e+00 -6.60936952e-01 1.63334894e+00 -4.36409801e-01 7.82886967e-02 3.51291329e-01 5.89981794e-01 8.97980690e-01 -2.70578206e-01 7.24870503e-01 1.35854578e+00 5.62449574e-01 9.03807044e-01 -1.39006507e+00 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8.53081420e-02 3.34279314e-02 -2.63838798e-01 -3.03852737e-01 1.48682162e-01]	[7.786173343658447, 7.607961177825928]
a46e1b82-9954-4735-b411-4285bae561b5	the-asvspoof-2019-database	1911.01601	null	https://arxiv.org/abs/1911.01601v4	https://arxiv.org/pdf/1911.01601v4.pdf	ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech	Automatic speaker verification (ASV) is one of the most natural and convenient means of biometric person recognition. Unfortunately, just like all other biometric systems, ASV is vulnerable to spoofing, also referred to as "presentation attacks." These vulnerabilities are generally unacceptable and call for spoofing countermeasures or "presentation attack detection" systems. In addition to impersonation, ASV systems are vulnerable to replay, speech synthesis, and voice conversion attacks. The ASVspoof 2019 edition is the first to consider all three spoofing attack types within a single challenge. While they originate from the same source database and same underlying protocol, they are explored in two specific use case scenarios. Spoofing attacks within a logical access (LA) scenario are generated with the latest speech synthesis and voice conversion technologies, including state-of-the-art neural acoustic and waveform model techniques. Replay spoofing attacks within a physical access (PA) scenario are generated through carefully controlled simulations that support much more revealing analysis than possible previously. Also new to the 2019 edition is the use of the tandem detection cost function metric, which reflects the impact of spoofing and countermeasures on the reliability of a fixed ASV system. This paper describes the database design, protocol, spoofing attack implementations, and baseline ASV and countermeasure results. It also describes a human assessment on spoofed data in logical access. It was demonstrated that the spoofing data in the ASVspoof 2019 database have varied degrees of perceived quality and similarity to the target speakers, including spoofed data that cannot be differentiated from bona-fide utterances even by human subjects.	['Zhen-Hua Ling', 'Jing-Xuan Zhang', 'Avashna Govender', 'Jean-Francois Bonastre', 'Driss Matrouf', 'Ingmar Steiner', 'Tomoki Toda', 'Wen-Chin Huang', 'Yi-Chiao Wu', 'Li-Juan Liu', 'Sebastien Le Maguer', 'Hsin-Min Wang', 'Hsin-Te Hwang', 'Yu-Huai Peng', 'Ye Jia', 'Tomi Kinnunen', 'Takashi Kaneda', 'Nicholas Evans', 'Kou Tanaka', 'Andreas Nautsch', 'Paavo Alku', 'Lauri Juvela', 'Kong Aik Lee', 'Junichi Yamagishi', 'Hector Delgado', 'Rob Clark', 'Massimiliano Todisco', 'Markus Becker', 'Koji Mushika', 'Kai Onuma', 'Hirokazu Kameoka', 'Yu Zhang', 'Yu Tsao', 'Yuan Jiang', 'Xin Wang', 'Ville Vestman', 'Srikanth Ronanki', 'Quan Wang', 'Md Sahidullah', 'Fergus Henderson']	2019-11-05	null	null	null	null	['person-recognition']	['computer-vision']	[ 1.99873134e-01 -2.95595497e-01 3.82761806e-02 -2.25282565e-01 -5.54916143e-01 -9.87309337e-01 7.10987866e-01 3.52640939e-03 -6.86098188e-02 4.27398413e-01 3.30521435e-01 -6.64917767e-01 1.00144617e-01 -2.93113112e-01 -3.33333999e-01 -3.87335539e-01 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c8fac812-10f9-4b0a-bff9-0403626b0e97	regressing-a-3d-face-shape-from-a-single	null	null	http://openaccess.thecvf.com/content_iccv_2015/html/Tulyakov_Regressing_a_3D_ICCV_2015_paper.html	http://openaccess.thecvf.com/content_iccv_2015/papers/Tulyakov_Regressing_a_3D_ICCV_2015_paper.pdf	Regressing a 3D Face Shape From a Single Image	In this work we present a method to estimate a 3D face shape from a single image. Our method is based on a cascade regression framework that directly estimates face landmarks locations in 3D. We include the knowledge that a face is a 3D object into the learning pipeline and show how this information decreases localization errors while keeping the computational time low. We predict the actual positions of the landmarks even if they are occluded due to face rotation. To support the ability of our method to reliably reconstruct 3D shapes, we introduce a simple method for head pose estimation using a single image that reaches higher accuracy than the state of the art. Comparison of 3D face landmarks localization with the available state of the art further supports the feasibility of a single-step face shape estimation. The code, trained models and our 3D annotations will be made available to the research community.	['Sergey Tulyakov', 'Nicu Sebe']	2015-12-01	null	null	null	iccv-2015-12	['head-pose-estimation']	['computer-vision']	[-3.49985451e-01 3.81479442e-01 1.53381312e-02 -8.44130397e-01 -8.35863948e-01 -6.35769546e-01 4.51537192e-01 -1.51912838e-01 -3.02987903e-01 1.13979228e-01 2.94275850e-01 -8.64081234e-02 4.30841774e-01 -3.19212645e-01 -8.66801560e-01 -1.60253167e-01 6.39713416e-03 8.14620435e-01 6.03217408e-02 2.67532974e-01 2.32177287e-01 1.16119158e+00 -1.53778839e+00 5.33460546e-03 -1.09077841e-01 8.83169532e-01 -9.20462832e-02 5.91913223e-01 2.22537611e-02 -1.22241624e-01 -3.58801812e-01 -3.65608901e-01 3.66607815e-01 -9.15804598e-03 -6.06157184e-01 3.23849618e-01 1.07915759e+00 -7.38638997e-01 1.39890268e-01 4.95844483e-01 6.76254511e-01 -2.90068775e-01 6.34460151e-01 -1.13123322e+00 -8.21891651e-02 -1.57075897e-01 -7.43783116e-01 -1.25788078e-01 7.07887888e-01 -3.81843269e-01 3.89937460e-01 -1.33367741e+00 7.35523701e-01 1.31500006e+00 1.10972798e+00 6.56454206e-01 -1.21222007e+00 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f2f1eff5-3d43-4637-8f3b-41c12fe83a6f	crt-6d-fast-6d-object-pose-estimation-with	2210.11718	null	https://arxiv.org/abs/2210.11718v1	https://arxiv.org/pdf/2210.11718v1.pdf	CRT-6D: Fast 6D Object Pose Estimation with Cascaded Refinement Transformers	Learning based 6D object pose estimation methods rely on computing large intermediate pose representations and/or iteratively refining an initial estimation with a slow render-compare pipeline. This paper introduces a novel method we call Cascaded Pose Refinement Transformers, or CRT-6D. We replace the commonly used dense intermediate representation with a sparse set of features sampled from the feature pyramid we call OSKFs(Object Surface Keypoint Features) where each element corresponds to an object keypoint. We employ lightweight deformable transformers and chain them together to iteratively refine proposed poses over the sampled OSKFs. We achieve inference runtimes 2x faster than the closest real-time state of the art methods while supporting up to 21 objects on a single model. We demonstrate the effectiveness of CRT-6D by performing extensive experiments on the LM-O and YCBV datasets. Compared to real-time methods, we achieve state of the art on LM-O and YCB-V, falling slightly behind methods with inference runtimes one order of magnitude higher. The source code is available at: https://github.com/PedroCastro/CRT-6D	['Tae-Kyun Kim', 'Pedro Castro']	2022-10-21	null	null	null	null	['6d-pose-estimation']	['computer-vision']	[-1.75746039e-01 -5.58343045e-02 1.33062184e-01 -1.50016427e-01 -1.25896442e+00 -6.20853424e-01 7.31933534e-01 6.97384849e-02 -8.80772397e-02 3.17341626e-01 4.55675535e-02 2.28560895e-01 2.92337053e-02 -5.66166341e-01 -1.08989310e+00 -4.13752735e-01 -1.65354893e-01 1.27775097e+00 6.31623268e-01 1.43530279e-01 3.94586116e-01 8.73445392e-01 -1.78169811e+00 1.54895306e-01 4.49253291e-01 1.35682416e+00 1.86754867e-01 9.80933487e-01 1.20025508e-01 1.64127514e-01 -2.52111912e-01 -1.54881909e-01 5.49428582e-01 2.49534115e-01 -6.95277810e-01 8.24852437e-02 1.24184239e+00 -6.98731899e-01 -6.14081584e-02 5.33238590e-01 5.35910070e-01 6.40039220e-02 6.23149335e-01 -1.17433548e+00 -1.17400527e-01 4.26258259e-02 -5.92699468e-01 -6.16313554e-02 5.11928916e-01 4.25987877e-02 8.46315980e-01 -1.57257712e+00 8.29955041e-01 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-2.08864498e+00 -4.24955964e-01 -7.48317420e-01 -1.58964261e-01 8.87747779e-02 3.70166808e-01 -1.10393643e+00 -1.74595818e-01 3.13759944e-03]	[7.600530624389648, -2.647589683532715]
fe8b338c-8cb2-4e24-b68c-80bc8443f07c	revealing-the-invisible-with-model-and-data	2006.09674	null	https://arxiv.org/abs/2006.09674v1	https://arxiv.org/pdf/2006.09674v1.pdf	Revealing the Invisible with Model and Data Shrinking for Composite-database Micro-expression Recognition	Composite-database micro-expression recognition is attracting increasing attention as it is more practical to real-world applications. Though the composite database provides more sample diversity for learning good representation models, the important subtle dynamics are prone to disappearing in the domain shift such that the models greatly degrade their performance, especially for deep models. In this paper, we analyze the influence of learning complexity, including the input complexity and model complexity, and discover that the lower-resolution input data and shallower-architecture model are helpful to ease the degradation of deep models in composite-database task. Based on this, we propose a recurrent convolutional network (RCN) to explore the shallower-architecture and lower-resolution input data, shrinking model and input complexities simultaneously. Furthermore, we develop three parameter-free modules (i.e., wide expansion, shortcut connection and attention unit) to integrate with RCN without increasing any learnable parameters. These three modules can enhance the representation ability in various perspectives while preserving not-very-deep architecture for lower-resolution data. Besides, three modules can further be combined by an automatic strategy (a neural architecture search strategy) and the searched architecture becomes more robust. Extensive experiments on MEGC2019 dataset (composited of existing SMIC, CASME II and SAMM datasets) have verified the influence of learning complexity and shown that RCNs with three modules and the searched combination outperform the state-of-the-art approaches.	['Huai-Qian Khor', 'Zhaoqiang Xia', 'Xiaoyi Feng', 'Wei Peng', 'Guoying Zhao']	2020-06-17	null	null	null	null	['micro-expression-recognition']	['computer-vision']	[-3.54570597e-02 -2.05931425e-01 -1.19642273e-01 -2.93960482e-01 -5.01414776e-01 -2.53883839e-01 3.75601351e-01 -4.01337087e-01 -4.09961671e-01 4.85791683e-01 1.67414412e-01 7.06741810e-02 -2.94032216e-01 -6.27230465e-01 -5.46504498e-01 -9.32520986e-01 2.37544566e-01 1.62699029e-01 1.79578736e-01 -6.56426132e-01 7.54465088e-02 4.65273917e-01 -1.70065022e+00 5.12311995e-01 1.01812828e+00 1.04803205e+00 5.05459368e-01 -1.74403060e-02 -3.61403793e-01 6.68012977e-01 -5.36730587e-01 -3.91675383e-01 6.83106109e-02 -3.07429314e-01 -5.22938192e-01 -2.41605327e-01 -1.74955092e-02 -1.93664562e-02 -3.54196936e-01 1.09342968e+00 8.13441336e-01 1.08664520e-01 6.08178377e-01 -9.59223032e-01 -6.46490812e-01 6.01806641e-01 -7.76966155e-01 4.47031647e-01 -1.40839815e-01 6.01337068e-02 8.01805794e-01 -1.07374108e+00 4.55344170e-01 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9bf43499-6d70-4e4d-bae3-ec37bb6967c0	two-stream-consensus-network-submission-to	2106.10829	null	https://arxiv.org/abs/2106.10829v3	https://arxiv.org/pdf/2106.10829v3.pdf	Two-Stream Consensus Network: Submission to HACS Challenge 2021 Weakly-Supervised Learning Track	This technical report presents our solution to the HACS Temporal Action Localization Challenge 2021, Weakly-Supervised Learning Track. The goal of weakly-supervised temporal action localization is to temporally locate and classify action of interest in untrimmed videos given only video-level labels. We adopt the two-stream consensus network (TSCN) as the main framework in this challenge. The TSCN consists of a two-stream base model training procedure and a pseudo ground truth learning procedure. The base model training encourages the model to predict reliable predictions based on single modality (i.e., RGB or optical flow), based on the fusion of which a pseudo ground truth is generated and in turn used as supervision to train the base models. On the HACS v1.1.1 dataset, without fine-tuning the feature-extraction I3D models, our method achieves 22.20% on the validation set and 21.68% on the testing set in terms of average mAP. Our solution ranked the 2nd in this challenge, and we hope our method can serve as a baseline for future academic research.	['Junsong Yuan', 'David Doermann', 'Le Wang', 'Yuanhao Zhai']	2021-06-21	null	null	null	null	['weakly-supervised-temporal-action']	['computer-vision']	[ 3.53113353e-01 4.94703799e-02 -6.15150094e-01 -1.95003837e-01 -1.09594107e+00 -4.35722321e-01 6.34904742e-01 -3.89288396e-01 -4.16240036e-01 7.94369817e-01 4.56629008e-01 4.08846326e-02 3.06482404e-01 -1.32451490e-01 -8.27334940e-01 -8.55990171e-01 -2.52212673e-01 1.48248285e-01 5.86665809e-01 1.91036314e-01 7.55111426e-02 1.65636167e-01 -1.47914612e+00 7.13607013e-01 3.90575469e-01 1.49718559e+00 2.21249573e-02 7.47799039e-01 4.02595788e-01 1.62951577e+00 -9.78615656e-02 1.00698829e-01 4.44546700e-01 -4.69182879e-01 -1.18076837e+00 1.14215985e-01 7.06325352e-01 -5.34982920e-01 -5.68457663e-01 6.23533010e-01 2.46640295e-01 3.05082202e-01 4.57705617e-01 -1.56952465e+00 -3.70455608e-02 3.13804179e-01 -3.03116232e-01 2.40036204e-01 4.97622788e-01 4.74597961e-01 9.21967924e-01 -1.00121152e+00 9.20084715e-01 9.18205082e-01 5.55867314e-01 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-2.71216452e-01 4.07478750e-01 5.61428368e-01 -7.87119389e-01 -4.75953788e-01 6.90975845e-01]	[8.455156326293945, 0.5696932077407837]
410a3d90-cd08-43cd-a4df-547f1049c97c	rbc-rectifying-the-biased-context-in	2203.08404	null	https://arxiv.org/abs/2203.08404v1	https://arxiv.org/pdf/2203.08404v1.pdf	RBC: Rectifying the Biased Context in Continual Semantic Segmentation	Recent years have witnessed a great development of Convolutional Neural Networks in semantic segmentation, where all classes of training images are simultaneously available. In practice, new images are usually made available in a consecutive manner, leading to a problem called Continual Semantic Segmentation (CSS). Typically, CSS faces the forgetting problem since previous training images are unavailable, and the semantic shift problem of the background class. Considering the semantic segmentation as a context-dependent pixel-level classification task, we explore CSS from a new perspective of context analysis in this paper. We observe that the context of old-class pixels in the new images is much more biased on new classes than that in the old images, which can sharply aggravate the old-class forgetting and new-class overfitting. To tackle the obstacle, we propose a biased-context-rectified CSS framework with a context-rectified image-duplet learning scheme and a biased-context-insensitive consistency loss. Furthermore, we propose an adaptive re-weighting class-balanced learning strategy for the biased class distribution. Our approach outperforms state-of-the-art methods by a large margin in existing CSS scenarios.	['Xi Li', 'Xinghe Fu', 'Fengyu Yang', 'Hanbin Zhao']	2022-03-16	null	null	null	null	['continual-semantic-segmentation']	['computer-vision']	[ 7.95685053e-01 -7.15551823e-02 -2.25999996e-01 -6.81017756e-01 -4.73004013e-01 -3.32828313e-01 2.22394183e-01 1.47871688e-01 -8.77940476e-01 8.17653656e-01 -2.20343798e-01 -7.50181172e-03 4.48594093e-02 -8.27431798e-01 -7.62956440e-01 -9.54037905e-01 5.96557200e-01 4.18326594e-02 7.73933649e-01 -4.20492217e-02 2.11438775e-01 2.22864747e-01 -1.65438426e+00 1.81267276e-01 1.20246291e+00 1.17635953e+00 6.08476698e-01 2.60425329e-01 -4.45923775e-01 3.66912842e-01 -6.65300906e-01 -3.36046934e-01 1.32217288e-01 -3.13874871e-01 -7.97309101e-01 4.43382621e-01 6.27500534e-01 -1.86524205e-02 -2.36419007e-01 1.27213919e+00 1.97566032e-01 4.11801279e-01 2.13327393e-01 -1.29821730e+00 -6.01819813e-01 4.41825420e-01 -8.28998148e-01 1.62831292e-01 -3.66800904e-01 7.31142424e-03 6.35908306e-01 -8.84758115e-01 7.16757417e-01 9.33260322e-01 5.40274799e-01 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-6.18286610e-01 -7.63621807e-01 5.24755597e-01 4.22648787e-01 -7.63874829e-01 -3.88188183e-01 1.01976074e-01]	[9.456804275512695, 1.9013073444366455]
6eedd36e-897b-4fd1-a24d-1bb1b1e32f02	cipher-construction-of-differentially-private	1812.05671	null	https://arxiv.org/abs/1812.05671v3	https://arxiv.org/pdf/1812.05671v3.pdf	Construction of Differentially Private Empirical Distributions from a low-order Marginals Set through Solving Linear Equations with l2 Regularization	We introduce a new algorithm, Construction of dIfferentially Private Empirical Distributions from a low-order marginals set tHrough solving linear Equations with l2 Regularization (CIPHER), that produces differentially private empirical joint distributions from a set of low-order marginals. CIPHER is conceptually simple and requires no more than decomposing joint probabilities via basic probability rules to construct a linear equation set and subsequently solving the equations. Compared to the full-dimensional histogram (FDH) sanitization, CIPHER has drastic\-ally lower requirements on computational storage and memory, which is practically attractive especially considering that the high-order signals preserved by the FDH sanitization are likely just sample randomness and rarely of interest. Our experiments demonstrate that CIPHER outperforms the multiplicative weighting exponential mechanism in preserving original information and has similar or superior cost-normalized utility to FDH sanitization at the same privacy budget.	['Fang Liu', 'Evercita C. Eugenio']	2018-12-12	null	null	null	null	['l2-regularization']	['methodology']	[ 2.59406179e-01 2.07444802e-01 -8.95480141e-02 -2.01455101e-01 -1.43724430e+00 -6.75403416e-01 5.61510503e-01 4.93567856e-03 -4.99893188e-01 1.07337320e+00 2.01091096e-01 -4.42534268e-01 -1.71462834e-01 -7.18352199e-01 -8.81868303e-01 -1.43578506e+00 -5.19015908e-01 3.42821300e-01 -1.09163761e-01 2.92250991e-01 1.74114048e-01 7.85130024e-01 -1.33514059e+00 3.96636538e-02 8.50099027e-01 9.18682337e-01 -4.03358847e-01 7.24838853e-01 3.65034342e-01 4.04966861e-01 -5.54263055e-01 -8.36910129e-01 5.97788274e-01 -7.58027613e-01 -4.75816905e-01 -2.91451842e-01 4.45088148e-01 -4.58049238e-01 -6.25020385e-01 1.34667885e+00 6.27502263e-01 -8.02611336e-02 9.90842462e-01 -1.38524520e+00 -5.52646101e-01 4.07467335e-01 -7.29183197e-01 -2.08741114e-01 2.69035310e-01 -1.24825105e-01 1.04310989e+00 -1.92148045e-01 5.99885523e-01 1.16769195e+00 9.02998984e-01 3.74151915e-01 -1.97442389e+00 -7.78493106e-01 -5.86838424e-01 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550ee2e8-4452-4537-af0f-1aef7b029f09	approximate-thompson-sampling-via-epistemic	2302.09205	null	https://arxiv.org/abs/2302.09205v1	https://arxiv.org/pdf/2302.09205v1.pdf	Approximate Thompson Sampling via Epistemic Neural Networks	Thompson sampling (TS) is a popular heuristic for action selection, but it requires sampling from a posterior distribution. Unfortunately, this can become computationally intractable in complex environments, such as those modeled using neural networks. Approximate posterior samples can produce effective actions, but only if they reasonably approximate joint predictive distributions of outputs across inputs. Notably, accuracy of marginal predictive distributions does not suffice. Epistemic neural networks (ENNs) are designed to produce accurate joint predictive distributions. We compare a range of ENNs through computational experiments that assess their performance in approximating TS across bandit and reinforcement learning environments. The results indicate that ENNs serve this purpose well and illustrate how the quality of joint predictive distributions drives performance. Further, we demonstrate that the \textit{epinet} -- a small additive network that estimates uncertainty -- matches the performance of large ensembles at orders of magnitude lower computational cost. This enables effective application of TS with computation that scales gracefully to complex environments.	['Benjamin Van Roy', 'Xiuyuan Lu', 'Morteza Ibrahimi', 'Vikranth Dwaracherla', 'Seyed Mohammad Asghari', 'Zheng Wen', 'Ian Osband']	2023-02-18	null	null	null	null	['thompson-sampling']	['methodology']	[ 2.69360602e-01 1.69536158e-01 -3.33222508e-01 -2.29669154e-01 -1.31342280e+00 -3.97567302e-01 6.55016482e-01 -2.51105368e-01 -4.56806153e-01 1.37681592e+00 1.41211480e-01 -3.94966453e-01 -5.32229185e-01 -7.13899910e-01 -9.75042343e-01 -6.60697222e-01 -9.85517800e-02 5.90869963e-01 1.28707558e-01 2.01430112e-01 2.27461115e-01 1.79387853e-01 -1.65627956e+00 2.14562014e-01 1.08544469e+00 1.08346641e+00 -4.22608256e-02 7.53310859e-01 2.91004449e-01 1.08149898e+00 -7.53203154e-01 -3.38994056e-01 2.73515105e-01 -6.86235249e-01 -3.66510659e-01 -5.08539379e-01 1.34389594e-01 -7.65629172e-01 -3.13656271e-01 1.08373189e+00 3.92397940e-01 5.83638549e-01 1.10134935e+00 -1.19824874e+00 -4.34089124e-01 1.17565680e+00 -8.57265294e-02 -3.03243957e-02 -2.32926272e-02 5.75673938e-01 1.11684692e+00 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3.77243906e-01 3.31194013e-01 -9.89796996e-01 -1.70886517e+00 -1.16379678e+00 -9.74111319e-01 -1.75626308e-01 4.09568101e-02 -8.47121000e-01 -5.98873734e-01 -3.83060992e-01]	[4.174543857574463, 2.519291639328003]
adfab46d-e464-4898-81de-097a538ec8ef	language-in-a-search-box-grounding-language	2104.08874	null	https://arxiv.org/abs/2104.08874v1	https://arxiv.org/pdf/2104.08874v1.pdf	Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction	We investigate grounded language learning through real-world data, by modelling a teacher-learner dynamics through the natural interactions occurring between users and search engines; in particular, we explore the emergence of semantic generalization from unsupervised dense representations outside of synthetic environments. A grounding domain, a denotation function and a composition function are learned from user data only. We show how the resulting semantics for noun phrases exhibits compositional properties while being fully learnable without any explicit labelling. We benchmark our grounded semantics on compositionality and zero-shot inference tasks, and we show that it provides better results and better generalizations than SOTA non-grounded models, such as word2vec and BERT.	['Jacopo Tagliabue', 'Ciro Greco', 'Federico Bianchi']	2021-04-18	null	https://aclanthology.org/2021.naacl-main.348	https://aclanthology.org/2021.naacl-main.348.pdf	naacl-2021-4	['grounded-language-learning']	['natural-language-processing']	[ 1.05050497e-01 6.22841060e-01 -9.95232835e-02 -4.74211603e-01 -2.80243933e-01 -6.75711513e-01 1.18614805e+00 5.63974977e-01 -6.36920035e-01 6.80156410e-01 7.51054108e-01 -2.91042179e-01 -6.30306974e-02 -1.24304879e+00 -1.00791395e+00 -5.53866625e-01 -2.61625528e-01 1.06328762e+00 1.55797392e-01 -7.71406591e-01 -1.46606326e-01 5.28500043e-02 -1.87421215e+00 3.84631544e-01 1.03200150e+00 4.98137563e-01 3.59134048e-01 5.03836453e-01 -5.12350976e-01 9.68270242e-01 -2.76535094e-01 -5.22126734e-01 1.77398533e-01 -4.10524249e-01 -1.12786710e+00 -5.82110137e-02 4.24773067e-01 -2.01256379e-01 -4.17217344e-01 1.14905691e+00 1.80500969e-01 6.67434990e-01 7.75251687e-01 -1.32168376e+00 -1.34073865e+00 1.36181152e+00 3.29368085e-01 9.34691355e-03 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d6e67d56-1719-45bc-b6e2-4ca7035c55cb	generalization-in-text-based-games-via	2109.09968	null	https://arxiv.org/abs/2109.09968v1	https://arxiv.org/pdf/2109.09968v1.pdf	Generalization in Text-based Games via Hierarchical Reinforcement Learning	Deep reinforcement learning provides a promising approach for text-based games in studying natural language communication between humans and artificial agents. However, the generalization still remains a big challenge as the agents depend critically on the complexity and variety of training tasks. In this paper, we address this problem by introducing a hierarchical framework built upon the knowledge graph-based RL agent. In the high level, a meta-policy is executed to decompose the whole game into a set of subtasks specified by textual goals, and select one of them based on the KG. Then a sub-policy in the low level is executed to conduct goal-conditioned reinforcement learning. We carry out experiments on games with various difficulty levels and show that the proposed method enjoys favorable generalizability.	['Chengqi Zhang', 'Yali Du', 'Ling Chen', 'Meng Fang', 'Yunqiu Xu']	2021-09-21	null	https://aclanthology.org/2021.findings-emnlp.116	https://aclanthology.org/2021.findings-emnlp.116.pdf	findings-emnlp-2021-11	['text-based-games']	['playing-games']	[-7.74800479e-02 2.30462868e-02 -1.43203139e-01 6.89604282e-02 -4.46109295e-01 -4.37385648e-01 6.35302663e-01 4.40314002e-02 -5.13701141e-01 9.16588068e-01 8.15778971e-03 -3.67619187e-01 -2.80989617e-01 -1.18697226e+00 -3.09317172e-01 -7.50194907e-01 -3.69268209e-01 6.89059079e-01 5.14226437e-01 -5.98341107e-01 2.24178180e-01 1.46256648e-02 -1.05890584e+00 1.11093432e-01 1.00738728e+00 7.70326853e-01 4.19649303e-01 6.73294008e-01 -2.29670882e-01 1.37454629e+00 -6.46391809e-01 -3.76969814e-01 1.61844075e-01 -8.28181744e-01 -9.34947789e-01 2.74348676e-01 -5.77495217e-01 -3.21324795e-01 -2.17630163e-01 1.19669199e+00 3.34992588e-01 3.82418126e-01 5.34614742e-01 -1.34238005e+00 -2.60760427e-01 8.40535522e-01 -3.49739015e-01 -8.20187330e-02 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649d17dd-9a73-45a2-b9bf-f03fa9bd01b1	person-search-with-natural-language	1702.05729	null	http://arxiv.org/abs/1702.05729v2	http://arxiv.org/pdf/1702.05729v2.pdf	Person Search with Natural Language Description	Searching persons in large-scale image databases with the query of natural language description has important applications in video surveillance. Existing methods mainly focused on searching persons with image-based or attribute-based queries, which have major limitations for a practical usage. In this paper, we study the problem of person search with natural language description. Given the textual description of a person, the algorithm of the person search is required to rank all the samples in the person database then retrieve the most relevant sample corresponding to the queried description. Since there is no person dataset or benchmark with textual description available, we collect a large-scale person description dataset with detailed natural language annotations and person samples from various sources, termed as CUHK Person Description Dataset (CUHK-PEDES). A wide range of possible models and baselines have been evaluated and compared on the person search benchmark. An Recurrent Neural Network with Gated Neural Attention mechanism (GNA-RNN) is proposed to establish the state-of-the art performance on person search.	['Shuang Li', 'Tong Xiao', 'Hongsheng Li', 'Dayu Yue', 'Bolei Zhou', 'Xiaogang Wang']	2017-02-19	person-search-with-natural-language-1	http://openaccess.thecvf.com/content_cvpr_2017/html/Li_Person_Search_With_CVPR_2017_paper.html	http://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Person_Search_With_CVPR_2017_paper.pdf	cvpr-2017-7	['nlp-based-person-retrival', 'person-search']	['computer-vision', 'computer-vision']	[-5.70050962e-02 -5.91384172e-01 -3.74933749e-01 -6.01480007e-01 -1.04935074e+00 -3.76895279e-01 9.12323356e-01 -1.99827909e-01 -7.05316544e-01 7.38464653e-01 6.15400255e-01 4.45582330e-01 -6.37176111e-02 -6.29815280e-01 -2.64801413e-01 -4.92092222e-01 2.83811390e-01 7.77192175e-01 1.02852814e-01 -5.16925156e-02 -7.10522011e-02 1.53623894e-01 -1.54111040e+00 2.31415123e-01 1.38879210e-01 9.87616122e-01 2.75416493e-01 7.47563303e-01 2.63293564e-01 9.80190575e-01 -8.99041951e-01 -7.50762284e-01 2.56529510e-01 -4.56846833e-01 -1.00517583e+00 2.06605747e-01 8.82180870e-01 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417e3b82-ce26-4544-9d89-08acfa9433ca	claws-clustering-assisted-weakly-supervised-1	2011.12077	null	https://arxiv.org/abs/2011.12077v4	https://arxiv.org/pdf/2011.12077v4.pdf	CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection	Learning to detect real-world anomalous events through video-level labels is a challenging task due to the rare occurrence of anomalies as well as noise in the labels. In this work, we propose a weakly supervised anomaly detection method which has manifold contributions including1) a random batch based training procedure to reduce inter-batch correlation, 2) a normalcy suppression mechanism to minimize anomaly scores of the normal regions of a video by taking into account the overall information available in one training batch, and 3) a clustering distance based loss to contribute towards mitigating the label noise and to produce better anomaly representations by encouraging our model to generate distinct normal and anomalous clusters. The proposed method obtains83.03% and 89.67% frame-level AUC performance on the UCF Crime and ShanghaiTech datasets respectively, demonstrating its superiority over the existing state-of-the-art algorithms.	['Seung-Ik Lee', 'Marcella Astrid', 'Arif Mahmood', 'Muhammad Zaigham Zaheer']	2020-11-24	claws-clustering-assisted-weakly-supervised	https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4066_ECCV_2020_paper.php	https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123670358.pdf	eccv-2020-8	['supervised-anomaly-detection']	['computer-vision']	[ 2.66564906e-01 -1.65306002e-01 2.26946086e-01 -4.30083632e-01 -6.61498070e-01 -2.56044298e-01 6.19280934e-01 5.72965503e-01 -2.89561450e-01 4.54566836e-01 2.45304495e-01 1.11145720e-01 5.03101619e-03 -3.21226716e-01 -6.50396049e-01 -8.60052466e-01 -3.37791413e-01 4.71778549e-02 4.75389957e-01 2.08370592e-02 3.54891956e-01 3.37519795e-01 -1.63767111e+00 5.62167943e-01 1.17542756e+00 1.32094300e+00 -4.41330075e-01 4.51163143e-01 -1.69721127e-01 1.26965451e+00 -5.04162610e-01 -1.16514914e-01 3.35546017e-01 -4.90734398e-01 -3.83335799e-01 4.70996171e-01 5.19342840e-01 -2.96342403e-01 -1.88488394e-01 1.11970699e+00 3.38686645e-01 3.41555297e-01 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3f823cc4-da56-4f86-8c54-fb9a348065fb	sdf-srn-learning-signed-distance-3d-object	2010.10505	null	https://arxiv.org/abs/2010.10505v1	https://arxiv.org/pdf/2010.10505v1.pdf	SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images	Dense 3D object reconstruction from a single image has recently witnessed remarkable advances, but supervising neural networks with ground-truth 3D shapes is impractical due to the laborious process of creating paired image-shape datasets. Recent efforts have turned to learning 3D reconstruction without 3D supervision from RGB images with annotated 2D silhouettes, dramatically reducing the cost and effort of annotation. These techniques, however, remain impractical as they still require multi-view annotations of the same object instance during training. As a result, most experimental efforts to date have been limited to synthetic datasets. In this paper, we address this issue and propose SDF-SRN, an approach that requires only a single view of objects at training time, offering greater utility for real-world scenarios. SDF-SRN learns implicit 3D shape representations to handle arbitrary shape topologies that may exist in the datasets. To this end, we derive a novel differentiable rendering formulation for learning signed distance functions (SDF) from 2D silhouettes. Our method outperforms the state of the art under challenging single-view supervision settings on both synthetic and real-world datasets.	['Simon Lucey', 'Chaoyang Wang', 'Chen-Hsuan Lin']	2020-10-20	null	http://proceedings.neurips.cc/paper/2020/hash/83fa5a432ae55c253d0e60dbfa716723-Abstract.html	http://proceedings.neurips.cc/paper/2020/file/83fa5a432ae55c253d0e60dbfa716723-Paper.pdf	neurips-2020-12	['3d-object-reconstruction', '3d-object-reconstruction-from-a-single-image']	['computer-vision', 'computer-vision']	[ 3.38767290e-01 7.73455575e-02 1.16709024e-01 -6.24877393e-01 -6.73774004e-01 -7.87535489e-01 4.25520509e-01 -2.44251341e-01 -1.78837314e-01 5.61787605e-01 -1.26185492e-01 -5.45389876e-02 1.39853299e-01 -6.36367857e-01 -1.01263475e+00 -5.02114296e-01 2.24871814e-01 8.97899210e-01 1.25145644e-01 -4.71446589e-02 1.44647852e-01 9.49554443e-01 -1.51247573e+00 5.59416134e-04 5.91688156e-01 1.07519937e+00 2.72675931e-01 3.62572491e-01 -1.39501646e-01 2.24642947e-01 -3.05456281e-01 -4.56111282e-01 7.07006633e-01 -2.97497325e-02 -4.44291264e-01 5.37224412e-01 8.93483281e-01 -5.64544022e-01 -2.50476122e-01 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5d1d94a8-7154-4285-8fe2-a6b044dc3067	joint-optimization-in-edge-cloud-continuum	2108.06493	null	https://arxiv.org/abs/2108.06493v1	https://arxiv.org/pdf/2108.06493v1.pdf	Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identification	Person re-identification (ReID) aims to re-identify a person from non-overlapping camera views. Since person ReID data contains sensitive personal information, researchers have adopted federated learning, an emerging distributed training method, to mitigate the privacy leakage risks. However, existing studies rely on data labels that are laborious and time-consuming to obtain. We present FedUReID, a federated unsupervised person ReID system to learn person ReID models without any labels while preserving privacy. FedUReID enables in-situ model training on edges with unlabeled data. A cloud server aggregates models from edges instead of centralizing raw data to preserve data privacy. Moreover, to tackle the problem that edges vary in data volumes and distributions, we personalize training in edges with joint optimization of cloud and edge. Specifically, we propose personalized epoch to reassign computation throughout training, personalized clustering to iteratively predict suitable labels for unlabeled data, and personalized update to adapt the server aggregated model to each edge. Extensive experiments on eight person ReID datasets demonstrate that FedUReID not only achieves higher accuracy but also reduces computation cost by 29%. Our FedUReID system with the joint optimization will shed light on implementing federated learning to more multimedia tasks without data labels.	['Shuai Zhang', 'Yonggang Wen', 'Weiming Zhuang']	2021-08-14	null	null	null	null	['unsupervised-person-re-identification']	['computer-vision']	[-4.34505671e-01 -2.88339257e-01 1.07083973e-02 -7.82327592e-01 -5.33876657e-01 -8.26702476e-01 1.69816181e-01 -1.39972165e-01 -5.85250378e-01 6.88699603e-01 2.73325205e-01 2.18607649e-01 -8.50233138e-02 -7.13480413e-01 -6.02510095e-01 -8.01624358e-01 -1.55937653e-02 6.00283206e-01 -4.51905638e-01 7.75442243e-01 -2.99349397e-01 6.26191676e-01 -1.38303900e+00 7.29396790e-02 8.12266946e-01 6.89210594e-01 -4.27260488e-01 3.03393513e-01 -2.21811552e-02 5.10645390e-01 -4.09693152e-01 -1.16255832e+00 7.79983044e-01 1.61042660e-01 -6.49950802e-01 -7.20562339e-02 6.60944939e-01 -5.88888228e-01 -5.75386286e-01 1.26689637e+00 8.26036394e-01 1.01596475e-01 3.99083823e-01 -1.76160359e+00 -9.76419210e-01 4.68948305e-01 -8.05943906e-01 -9.19767544e-02 1.96760669e-01 1.49349356e-02 1.83617562e-01 -4.98164535e-01 6.93985581e-01 9.24727499e-01 9.54621375e-01 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ecb9ea49-9715-4c2a-9c20-8851145002ef	improving-information-extraction-from-images	1808.08941	null	http://arxiv.org/abs/1808.08941v1	http://arxiv.org/pdf/1808.08941v1.pdf	Improving Information Extraction from Images with Learned Semantic Models	Many applications require an understanding of an image that goes beyond the simple detection and classification of its objects. In particular, a great deal of semantic information is carried in the relationships between objects. We have previously shown that the combination of a visual model and a statistical semantic prior model can improve on the task of mapping images to their associated scene description. In this paper, we review the model and compare it to a novel conditional multi-way model for visual relationship detection, which does not include an explicitly trained visual prior model. We also discuss potential relationships between the proposed methods and memory models of the human brain.	['Yunpu Ma', 'Stephan Baier', 'Volker Tresp']	2018-08-27	null	null	null	null	['visual-relationship-detection']	['computer-vision']	[ 3.97629410e-01 9.88396108e-02 9.31197628e-02 -8.00455272e-01 -3.55127864e-02 -4.06601369e-01 9.75138664e-01 3.67686838e-01 -5.15681088e-01 3.51442903e-01 4.61772308e-02 -7.88594037e-02 -1.03053607e-01 -6.06467843e-01 -5.67419827e-01 -2.97338694e-01 2.56853789e-01 5.44034600e-01 7.37840831e-01 2.40202099e-02 5.53523719e-01 5.15325963e-01 -1.63499010e+00 4.52722490e-01 2.84539729e-01 8.49115193e-01 9.14306521e-01 6.46891415e-01 -4.99648675e-02 8.77025545e-01 -4.33875293e-01 -6.13337867e-02 -1.98113397e-01 -3.27193588e-01 -1.00495088e+00 4.27840441e-01 5.14138043e-01 -2.05365077e-01 -3.75862062e-01 1.03955507e+00 -9.07347277e-02 4.05545026e-01 9.57504749e-01 -1.20304322e+00 -6.60516202e-01 -2.94684321e-02 -5.39083779e-01 4.90004182e-01 3.82863164e-01 -3.87334406e-01 8.48287404e-01 -1.04879832e+00 6.26628339e-01 1.28918910e+00 4.14289892e-01 1.85778067e-01 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41b566e4-3f92-429a-91b4-b4b253baddcb	enhancing-predictive-skills-in-physically	2104.11009	null	https://arxiv.org/abs/2104.11009v1	https://arxiv.org/pdf/2104.11009v1.pdf	Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for Hydrological Processes	Current modeling approaches for hydrological modeling often rely on either physics-based or data-science methods, including Machine Learning (ML) algorithms. While physics-based models tend to rigid structure resulting in unrealistic parameter values in certain instances, ML algorithms establish the input-output relationship while ignoring the constraints imposed by well-known physical processes. While there is a notion that the physics model enables better process understanding and ML algorithms exhibit better predictive skills, scientific knowledge that does not add to predictive ability may be deceptive. Hence, there is a need for a hybrid modeling approach to couple ML algorithms and physics-based models in a synergistic manner. Here we develop a Physics Informed Machine Learning (PIML) model that combines the process understanding of conceptual hydrological model with predictive abilities of state-of-the-art ML models. We apply the proposed model to predict the monthly time series of the target (streamflow) and intermediate variables (actual evapotranspiration) in the Narmada river basin in India. Our results show the capability of the PIML model to outperform a purely conceptual model ($abcd$ model) and ML algorithms while ensuring the physical consistency in outputs validated through water balance analysis. The systematic approach for combining conceptual model structure with ML algorithms could be used to improve the predictive accuracy of crucial hydrological processes important for flood risk assessment.	['Udit Bhatia', 'Jenil Vagadiya', 'Pravin Bhasme']	2021-04-22	null	null	null	null	['physics-informed-machine-learning']	['graphs']	[ 5.52057400e-02 6.97674975e-02 -2.82959253e-01 -3.12475592e-01 -1.53688610e-01 -4.32791561e-01 7.10113108e-01 5.96766829e-01 4.50396277e-02 1.03960347e+00 -2.33953502e-02 -1.13874769e+00 -6.16510689e-01 -1.48291361e+00 -4.79042381e-01 -7.60771215e-01 -4.47723120e-01 4.88819838e-01 -1.25176877e-01 -5.99156559e-01 4.86203462e-01 8.59991908e-01 -1.30538416e+00 -2.59942096e-02 1.37655962e+00 4.99037802e-01 3.85066330e-01 6.25442624e-01 -6.30675912e-01 6.20633066e-01 -5.19718863e-02 3.27006094e-02 1.79402187e-01 -2.99238265e-01 -4.07018125e-01 -4.62631553e-01 -9.61487442e-02 -2.73926586e-01 -1.14780419e-01 5.76011658e-01 2.19160885e-01 -1.22886407e-03 7.87516832e-01 -1.27429104e+00 -4.91290510e-01 6.80858970e-01 -5.89391351e-01 4.23224410e-03 -2.29849927e-02 4.27661985e-01 8.24381292e-01 -5.39523304e-01 -2.17533298e-03 1.65241456e+00 8.65676105e-01 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-1.09875846e+00 -7.87060201e-01 -8.40361491e-02 1.59455106e-01 -7.21771896e-01 -3.81144166e-01 3.91250402e-02]	[6.566445827484131, 3.0224015712738037]
5b1920a0-adff-4aef-99ee-d600f7c1aecb	exploiting-observation-bias-to-improve-matrix	2306.04775	null	https://arxiv.org/abs/2306.04775v1	https://arxiv.org/pdf/2306.04775v1.pdf	Exploiting Observation Bias to Improve Matrix Completion	We consider a variant of matrix completion where entries are revealed in a biased manner, adopting a model akin to that introduced by Ma and Chen. Instead of treating this observation bias as a disadvantage, as is typically the case, our goal is to exploit the shared information between the bias and the outcome of interest to improve predictions. Towards this, we propose a simple two-stage algorithm: (i) interpreting the observation pattern as a fully observed noisy matrix, we apply traditional matrix completion methods to the observation pattern to estimate the distances between the latent factors; (ii) we apply supervised learning on the recovered features to impute missing observations. We establish finite-sample error rates that are competitive with the corresponding supervised learning parametric rates, suggesting that our learning performance is comparable to having access to the unobserved covariates. Empirical evaluation using a real-world dataset reflects similar performance gains, with our algorithm's estimates having 30x smaller mean squared error compared to traditional matrix completion methods.	['Devavrat Shah', 'Charlotte Park', 'Sean Mann']	2023-06-07	null	null	null	null	['matrix-completion']	['methodology']	[ 5.61997235e-01 4.07919586e-01 -5.47207773e-01 -4.79759991e-01 -1.22286129e+00 -4.75618511e-01 5.89763463e-01 1.05759747e-01 -3.42651725e-01 7.42438555e-01 7.20884442e-01 -4.13224012e-01 -2.47697085e-01 -3.71734142e-01 -9.16291177e-01 -6.72607899e-01 -3.01009536e-01 5.18412650e-01 -6.27868235e-01 1.92052513e-01 3.07495266e-01 1.15775377e-01 -9.44945514e-01 7.22561702e-02 7.01970696e-01 5.50035298e-01 -2.29370221e-02 5.62965631e-01 4.97054785e-01 8.58783603e-01 -1.78051312e-02 -5.70743978e-01 4.93687868e-01 -3.31816882e-01 -5.73552847e-01 8.18777204e-01 2.98532695e-01 -3.34766358e-01 -3.43862146e-01 9.17594194e-01 1.61809474e-02 7.57569149e-02 8.17990184e-01 -1.05230260e+00 -5.08181095e-01 9.07606959e-01 -9.89860475e-01 -1.33595437e-01 4.72019315e-01 -1.16673678e-01 1.32260323e+00 -1.24250615e+00 5.63929796e-01 1.16346824e+00 9.46908057e-01 -1.53233705e-03 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-8.63239706e-01 7.36746728e-01 6.89238071e-01 -5.03794670e-01 -5.75931132e-01 6.05726913e-02]	[7.186051368713379, 4.600512504577637]
ea484e2f-dd05-40eb-8fb6-a4496e726b9b	effortless-deep-training-for-traffic-sign	1907.09679	null	https://arxiv.org/abs/1907.09679v1	https://arxiv.org/pdf/1907.09679v1.pdf	Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images	Deep learning has been successfully applied to several problems related to autonomous driving. Often, these solutions rely on large networks that require databases of real image samples of the problem (i.e., real world) for proper training. The acquisition of such real-world data sets is not always possible in the autonomous driving context, and sometimes their annotation is not feasible (e.g., takes too long or is too expensive). Moreover, in many tasks, there is an intrinsic data imbalance that most learning-based methods struggle to cope with. It turns out that traffic sign detection is a problem in which these three issues are seen altogether. In this work, we propose a novel database generation method that requires only (i) arbitrary natural images, i.e., requires no real image from the domain of interest, and (ii) templates of the traffic signs, i.e., templates synthetically created to illustrate the appearance of the category of a traffic sign. The effortlessly generated training database is shown to be effective for the training of a deep detector (such as Faster R-CNN) on German traffic signs, achieving 95.66% of mAP on average. In addition, the proposed method is able to detect traffic signs with an average precision, recall and F1-score of about 94%, 91% and 93%, respectively. The experiments surprisingly show that detectors can be trained with simple data generation methods and without problem domain data for the background, which is in the opposite direction of the common sense for deep learning.	['Thiago Oliveira-Santos', 'Alberto F. de Souza', 'Rodrigo F. Berriel', 'Thiago M. Paixão', 'Claudine Badue', 'Nicu Sebe', 'Lucas Tabelini Torres']	2019-07-23	null	null	null	null	['traffic-sign-detection']	['computer-vision']	[ 2.23901749e-01 1.31004304e-01 1.52277231e-01 -3.08072835e-01 -3.99334699e-01 -2.95741051e-01 6.18002892e-01 -3.13014865e-01 -5.68038881e-01 7.11681843e-01 -6.20842457e-01 -4.84445244e-01 2.29323190e-02 -9.71008718e-01 -9.02087212e-01 -8.04235995e-01 4.09950584e-01 5.02384305e-01 6.06598854e-01 -4.18550432e-01 2.39996955e-01 5.89007378e-01 -2.29627085e+00 -2.37950459e-02 9.15622830e-01 1.25577724e+00 3.53669375e-01 5.32756925e-01 -1.92145064e-01 6.90273404e-01 -9.90897477e-01 -4.97386932e-01 5.23607433e-01 -3.52796108e-01 -2.49485508e-01 2.46490791e-01 8.09074938e-01 -3.58340621e-01 -4.38461512e-01 1.10594845e+00 3.08504850e-01 -5.86281568e-02 5.36630750e-01 -1.47165859e+00 -6.83908984e-02 -2.80577429e-02 -4.82897252e-01 -2.50536110e-02 -2.20633671e-01 6.22295558e-01 6.78279757e-01 -8.77333224e-01 6.45577312e-01 7.93174326e-01 3.46686721e-01 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1eab9c59-1916-48f2-8cd8-66a07d8fd2ad	physics-informed-machine-learning-of-sph-1	2110.13311	null	https://arxiv.org/abs/2110.13311v6	https://arxiv.org/pdf/2110.13311v6.pdf	Physics informed machine learning with smoothed particle hydrodynamics: Hierarchy of reduced Lagrangian models of turbulence	Building efficient, accurate and generalizable reduced order models of developed turbulence remains a major challenge. This manuscript approaches this problem by developing a hierarchy of parameterized reduced Lagrangian models for turbulent flows, and investigates the effects of enforcing physical structure through Smoothed Particle Hydrodynamics (SPH) versus relying on neural networks (NN)s as universal function approximators. Starting from Neural Network (NN) parameterizations of a Lagrangian acceleration operator, this hierarchy of models gradually incorporates a weakly compressible and parameterized SPH framework, which enforces physical symmetries, such as Galilean, rotational and translational invariances. Within this hierarchy, two new parameterized smoothing kernels are developed in order to increase the flexibility of the learn-able SPH simulators. For each model we experiment with different loss functions which are minimized using gradient based optimization, where efficient computations of gradients are obtained by using Automatic Differentiation (AD) and Sensitivity Analysis (SA). Each model within the hierarchy is trained on two data sets associated with weekly compressible Homogeneous Isotropic Turbulence (HIT): (1) a validation set using weakly compressible SPH; and (2) a high fidelity set from Direct Numerical Simulations (DNS). Numerical evidence shows that encoding more SPH structure improves generalizability to different turbulent Mach numbers and time shifts, and that including the novel parameterized smoothing kernels improves the accuracy of SPH at the resolved scales.	['Michael Chertkov', 'Mikhail Stepanov', 'Daniel Livescu', 'Chris Fryer', 'Criston Hyett', 'Yifeng Tian', 'Michael Woodward']	2021-10-25	physics-informed-machine-learning-of-sph	https://openreview.net/forum?id=bidTZROu2y	https://openreview.net/pdf?id=bidTZROu2y	null	['physics-informed-machine-learning']	['graphs']	[-1.86174423e-01 -1.77450314e-01 4.59622085e-01 -1.55928256e-02 -2.94196695e-01 -2.59825110e-01 7.80268490e-01 1.23021729e-01 -4.00577068e-01 9.44051087e-01 3.42250496e-01 -4.87052113e-01 -3.93677324e-01 -8.79586697e-01 -2.47869760e-01 -9.26157832e-01 -6.38268471e-01 6.58439636e-01 3.13144892e-01 -7.01361895e-01 1.93351269e-01 1.14710152e+00 -1.37655091e+00 -1.60185352e-01 8.25424433e-01 8.84977460e-01 -2.97227830e-01 9.25721467e-01 2.30266042e-02 9.30833697e-01 -2.01808512e-01 7.19668046e-02 6.75177991e-01 -4.17342067e-01 -7.83582389e-01 8.26182738e-02 4.29208755e-01 -2.63291448e-01 -3.07290018e-01 7.94901669e-01 5.63113749e-01 1.06187153e+00 5.17767847e-01 -6.76653624e-01 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c2cedd2e-f0dc-4de7-a25b-745496c4c70e	practical-and-asymptotically-exact	2306.17775	null	https://arxiv.org/abs/2306.17775v1	https://arxiv.org/pdf/2306.17775v1.pdf	Practical and Asymptotically Exact Conditional Sampling in Diffusion Models	Diffusion models have been successful on a range of conditional generation tasks including molecular design and text-to-image generation. However, these achievements have primarily depended on task-specific conditional training or error-prone heuristic approximations. Ideally, a conditional generation method should provide exact samples for a broad range of conditional distributions without requiring task-specific training. To this end, we introduce the Twisted Diffusion Sampler, or TDS. TDS is a sequential Monte Carlo (SMC) algorithm that targets the conditional distributions of diffusion models. The main idea is to use twisting, an SMC technique that enjoys good computational efficiency, to incorporate heuristic approximations without compromising asymptotic exactness. We first find in simulation and on MNIST image inpainting and class-conditional generation tasks that TDS provides a computational statistical trade-off, yielding more accurate approximations with many particles but with empirical improvements over heuristics with as few as two particles. We then turn to motif-scaffolding, a core task in protein design, using a TDS extension to Riemannian diffusion models. On benchmark test cases, TDS allows flexible conditioning criteria and often outperforms the state of the art.	['John P. Cunningham', 'David M. Blei', 'Christian A. Naesseth', 'Brian L. Trippe', 'Luhuan Wu']	2023-06-30	null	null	null	null	['image-generation', 'image-inpainting', 'text-to-image-generation', 'protein-design']	['computer-vision', 'computer-vision', 'computer-vision', 'medical']	[ 3.21923465e-01 -1.14180716e-02 3.62789296e-02 5.76127060e-02 -9.57731664e-01 -4.50874686e-01 9.25272584e-01 -2.18880102e-02 -2.33227924e-01 1.17575490e+00 1.30026340e-01 -4.85653877e-01 3.68581899e-02 -6.92353249e-01 -7.91448057e-01 -9.50881720e-01 6.57224879e-02 9.53249574e-01 3.75114471e-01 -8.08979943e-02 3.50822717e-01 4.24285859e-01 -1.25327480e+00 2.66503155e-01 1.10560286e+00 5.09763002e-01 5.12604356e-01 8.52699876e-01 -1.45185189e-02 4.53072697e-01 -5.33143878e-01 -4.81165916e-01 -3.86045501e-03 -7.39693582e-01 -7.25775659e-01 5.53494766e-02 1.15153350e-01 -8.51939693e-02 2.80698359e-01 9.03017282e-01 8.01499009e-01 1.49380907e-01 1.20956004e+00 -6.67343795e-01 -6.94963455e-01 3.84483308e-01 -5.91359377e-01 -8.89706612e-02 2.64579833e-01 4.39235777e-01 8.54568839e-01 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03a22227-41c8-48c9-8d48-407219472316	learning-similarity-among-users-for	2306.03040	null	https://arxiv.org/abs/2306.03040v1	https://arxiv.org/pdf/2306.03040v1.pdf	Learning Similarity among Users for Personalized Session-Based Recommendation from hierarchical structure of User-Session-Item	The task of the session-based recommendation is to predict the next interaction of the user based on the anonymized user's behavior pattern. And personalized version of this system is a promising research field due to its availability to deal with user information. However, there's a problem that the user's preferences and historical sessions were not considered in the typical session-based recommendation since it concentrates only on user-item interaction. In addition, the existing personalized session-based recommendation model has a limited capability in that it only considers the preference of the current user without considering those of similar users. It means there can be the loss of information included within the hierarchical data structure of the user-session-item. To tackle with this problem, we propose USP-SBR(abbr. of User Similarity Powered - Session Based Recommender). To model global historical sessions of users, we propose UserGraph that has two types of nodes - ItemNode and UserNode. We then connect the nodes with three types of edges. The first type of edges connects ItemNode as chronological order, and the second connects ItemNode to UserNode, and the last connects UserNode to ItemNode. With these user embeddings, we propose additional contrastive loss, that makes users with similar intention be close to each other in the vector space. we apply graph neural network on these UserGraph and update nodes. Experimental results on two real-world datasets demonstrate that our method outperforms some state-of-the-art approaches.	['Wooju Kim', 'Haemin Jeong', 'Jisoo Cha']	2023-06-05	null	null	null	null	['session-based-recommendations']	['miscellaneous']	[-2.25611046e-01 -3.98339108e-02 -4.89241302e-01 -3.37552607e-01 2.30222642e-01 -2.91016728e-01 3.27345312e-01 2.72108614e-01 -3.60023648e-01 4.73926961e-01 5.40686131e-01 -2.00032294e-01 -5.51583171e-01 -9.40058529e-01 -2.94157416e-01 -4.75207508e-01 -3.99842262e-01 4.17355508e-01 3.17542940e-01 -5.02927184e-01 2.34489650e-01 3.24385017e-01 -1.31670296e+00 2.57358283e-01 9.10820365e-01 8.71705949e-01 2.05019787e-01 2.56927162e-01 -3.40912342e-01 2.66049981e-01 -2.40583137e-01 -3.58513325e-01 2.19220087e-01 -4.02651012e-01 -7.46163845e-01 -2.12867275e-01 2.25884598e-02 -4.06139016e-01 -7.57668495e-01 8.07197034e-01 4.19203520e-01 6.90363526e-01 5.56705296e-01 -1.37764108e+00 -1.11326325e+00 8.90924096e-01 -4.92065221e-01 3.85388166e-01 6.34599984e-01 -6.09533250e-01 1.22406149e+00 -6.10340118e-01 5.02908885e-01 1.05934823e+00 6.29609048e-01 4.93624449e-01 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9.27161396e-01 -8.05185512e-02 -4.77172285e-01 -5.12172997e-01 -4.15653706e-01]	[10.151515007019043, 5.663521766662598]
4d16ba1c-9380-4400-93a0-f02ab3ce4d44	joint-person-objectness-and-repulsion-for	2006.00155	null	https://arxiv.org/abs/2006.00155v1	https://arxiv.org/pdf/2006.00155v1.pdf	Joint Person Objectness and Repulsion for Person Search	Person search targets to search the probe person from the unconstrainted scene images, which can be treated as the combination of person detection and person matching. However, the existing methods based on the Detection-Matching framework ignore the person objectness and repulsion (OR) which are both beneficial to reduce the effect of distractor images. In this paper, we propose an OR similarity by jointly considering the objectness and repulsion information. Besides the traditional visual similarity term, the OR similarity also contains an objectness term and a repulsion term. The objectness term can reduce the similarity of distractor images that not contain a person and boost the performance of person search by improving the ranking of positive samples. Because the probe person has a different person ID with its \emph{neighbors}, the gallery images having a higher similarity with the \emph{neighbors of probe} should have a lower similarity with the probe person. Based on this repulsion constraint, the repulsion term is proposed to reduce the similarity of distractor images that are not most similar to the probe person. Treating the Faster R-CNN as the person detector, the OR similarity is evaluated on PRW and CUHK-SYSU datasets by the Detection-Matching framework with six description models. The extensive experiments demonstrate that the proposed OR similarity can effectively reduce the similarity of distractor samples and further boost the performance of person search, e.g., improve the mAP from 92.32% to 93.23% for CUHK-SYSY dataset, and from 50.91% to 52.30% for PRW datasets.	['Hantao Yao', 'Changsheng Xu']	2020-05-30	null	null	null	null	['person-search']	['computer-vision']	[-2.46079937e-01 -3.58280241e-01 1.18265815e-01 -3.32065284e-01 -3.34625661e-01 -1.96412146e-01 4.34954137e-01 -1.90572709e-01 -7.59007573e-01 3.75896245e-01 1.71407789e-01 3.73257607e-01 -9.00242552e-02 -8.19217503e-01 -3.57190728e-01 -9.75379825e-01 4.85772103e-01 4.42650855e-01 5.00825584e-01 -1.51163474e-01 -2.99346074e-03 2.14726567e-01 -1.67076945e+00 2.11052373e-01 6.95690393e-01 1.09182966e+00 3.63896072e-01 6.44347817e-02 1.84203386e-01 3.49919826e-01 -9.54423428e-01 -1.82898998e-01 4.42030191e-01 -4.65144664e-01 -2.46084705e-01 1.35679811e-01 5.11602163e-01 -5.12577951e-01 -6.02402568e-01 1.30463588e+00 1.02788150e+00 3.57593566e-01 4.19814527e-01 -1.28773034e+00 -1.00624990e+00 1.75480083e-01 -9.82308269e-01 5.85201919e-01 5.07233322e-01 3.18062693e-01 5.29906988e-01 -1.06453907e+00 1.30239397e-01 1.70935380e+00 2.47917518e-01 6.73471689e-01 -1.03136754e+00 -1.03029990e+00 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4bbe52b2-d480-46fd-a3da-aece9409f2ec	dalaj-a-dataset-for-linguistic-acceptability	2105.06681	null	https://arxiv.org/abs/2105.06681v1	https://arxiv.org/pdf/2105.06681v1.pdf	DaLAJ - a dataset for linguistic acceptability judgments for Swedish: Format, baseline, sharing	We present DaLAJ 1.0, a Dataset for Linguistic Acceptability Judgments for Swedish, comprising 9 596 sentences in its first version; and the initial experiment using it for the binary classification task. DaLAJ is based on the SweLL second language learner data, consisting of essays at different levels of proficiency. To make sure the dataset can be freely available despite the GDPR regulations, we have sentence-scrambled learner essays and removed part of the metadata about learners, keeping for each sentence only information about the mother tongue and the level of the course where the essay has been written. We use the normalized version of learner language as the basis for the DaLAJ sentences, and keep only one error per sentence. We repeat the same sentence for each individual correction tag used in the sentence. For DaLAJ 1.0 we have used four error categories (out of 35 available in SweLL), all connected to lexical or word-building choices. Our baseline results for the binary classification show an accuracy of 58% for DaLAJ 1.0 using BERT embeddings. The dataset is included in the SwedishGlue (Swe. SuperLim) benchmark. Below, we describe the format of the dataset, first experiments, our insights and the motivation for the chosen approach to data sharing.	['Julia Klezl', 'Yousuf Ali Mohammed', 'Elena Volodina']	2021-05-14	null	null	null	null	['linguistic-acceptability']	['natural-language-processing']	[-1.87297210e-01 2.56186724e-01 -1.77973345e-01 -6.18984461e-01 -1.05058777e+00 -8.06636631e-01 5.62256515e-01 9.18307900e-01 -1.21519935e+00 9.19980228e-01 5.85872948e-01 -7.51750827e-01 -1.81454256e-01 -5.14487743e-01 -5.69333136e-01 -3.75421584e-01 6.49485111e-01 4.52315152e-01 3.02164078e-01 -6.08052850e-01 4.34450716e-01 -3.34396958e-02 -1.51498079e+00 4.86084908e-01 9.94655192e-01 7.71704376e-01 2.37443596e-01 8.60311747e-01 -2.65993804e-01 7.96252012e-01 -9.65043426e-01 -1.04407871e+00 -2.70352345e-02 -2.55590081e-01 -9.54953015e-01 -4.05309618e-01 1.07830203e+00 1.42539620e-01 -1.31495550e-01 7.90854335e-01 5.63467443e-01 3.09438765e-01 7.66233325e-01 -8.53549361e-01 -9.49485838e-01 1.22142923e+00 -4.14463021e-02 2.44228274e-01 6.10660017e-01 -1.95874304e-01 1.23636317e+00 -1.09441638e+00 7.24102974e-01 1.10083759e+00 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eb893f6a-58a2-462f-8c08-40d20b9ad2a2	refining-the-responses-of-llms-by-themselves	2305.04039	null	https://arxiv.org/abs/2305.04039v1	https://arxiv.org/pdf/2305.04039v1.pdf	Refining the Responses of LLMs by Themselves	In this paper, we propose a simple yet efficient approach based on prompt engineering that leverages the large language model itself to optimize its answers without relying on auxiliary models. We introduce an iterative self-evaluating optimization mechanism, with the potential for improved output quality as iterations progress, removing the need for manual intervention. The experiment's findings indicate that utilizing our response refinement framework on the GPT-3.5 model yields results that are on par with, or even surpass, those generated by the cutting-edge GPT-4 model. Detailed implementation strategies and illustrative examples are provided to demonstrate the superiority of our proposed solution.	['Tiansheng Xu', 'Tianqiang Yan']	2023-05-06	null	null	null	null	['prompt-engineering']	['natural-language-processing']	[ 1.42615940e-03 3.72330844e-01 9.80309546e-02 -5.35422742e-01 -1.01243317e+00 -7.54302859e-01 2.83384502e-01 3.48316818e-01 -5.28946877e-01 5.39861560e-01 3.99432451e-01 -7.12119579e-01 -2.45097756e-01 -5.22936404e-01 -3.45375001e-01 2.36024172e-03 3.07031512e-01 4.37081426e-01 1.70794234e-01 -5.22277772e-01 6.26061499e-01 2.42002960e-02 -1.56336153e+00 4.73775178e-01 9.84265983e-01 6.94480896e-01 -6.00369321e-03 8.55078340e-01 -2.92734951e-01 9.36763942e-01 -6.43683732e-01 -4.11211312e-01 2.85234332e-01 2.82917153e-02 -8.43144655e-01 -2.82211423e-01 -2.84513906e-02 -3.05576801e-01 2.44721264e-01 5.82340717e-01 5.85722089e-01 2.53557920e-01 5.11913560e-02 -8.83847058e-01 -7.34662771e-01 7.41096318e-01 -1.62109673e-01 -1.48245916e-01 1.07383025e+00 4.89086241e-01 1.14091957e+00 -8.79295051e-01 4.40661043e-01 1.26911914e+00 6.95596159e-01 4.88826662e-01 -1.41372859e+00 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5.73471449e-02 -2.03762390e-02 -1.76685423e-01 -1.55189916e-01 -2.54428536e-01]	[11.327095031738281, 8.207974433898926]
6480d9a0-7f2e-4423-ad18-b6004e5bca72	the-forward-forward-algorithm-as-a-feature	2307.00617	null	https://arxiv.org/abs/2307.00617v1	https://arxiv.org/pdf/2307.00617v1.pdf	The Forward-Forward Algorithm as a feature extractor for skin lesion classification: A preliminary study	Skin cancer, a deadly form of cancer, exhibits a 23\% survival rate in the USA with late diagnosis. Early detection can significantly increase the survival rate, and facilitate timely treatment. Accurate biomedical image classification is vital in medical analysis, aiding clinicians in disease diagnosis and treatment. Deep learning (DL) techniques, such as convolutional neural networks and transformers, have revolutionized clinical decision-making automation. However, computational cost and hardware constraints limit the implementation of state-of-the-art DL architectures. In this work, we explore a new type of neural network that does not need backpropagation (BP), namely the Forward-Forward Algorithm (FFA), for skin lesion classification. While FFA is claimed to use very low-power analog hardware, BP still tends to be superior in terms of classification accuracy. In addition, our experimental results suggest that the combination of FFA and BP can be a better alternative to achieve a more accurate prediction.	['Sidike Paheding', 'Abel Reyes-Angulo']	2023-07-02	null	null	null	null	['skin-lesion-classification', 'classification-1', 'decision-making']	['medical', 'methodology', 'reasoning']	[ 3.27918202e-01 -9.25971344e-02 -4.56181288e-01 -1.72849491e-01 -1.21024348e-01 -2.98330784e-02 2.46160373e-01 3.64472389e-01 -3.77503633e-01 7.49967337e-01 -4.31408316e-01 -6.80455208e-01 -1.45079121e-02 -9.46686089e-01 -1.70842648e-01 -8.32269609e-01 5.63732386e-02 -1.87742874e-01 1.48761034e-01 -8.65731109e-03 -6.89745769e-02 9.22364891e-01 -1.04364884e+00 5.16758502e-01 9.07809734e-01 1.47776651e+00 2.67661083e-02 4.56450313e-01 -2.59909660e-01 1.03220451e+00 -3.44031185e-01 -5.79466164e-01 -1.12636924e-01 -4.16957289e-01 -7.42067099e-01 -4.93569940e-01 -5.46718352e-02 -2.28202105e-01 -4.45279121e-01 1.01046741e+00 7.02575386e-01 -5.12505829e-01 5.62665880e-01 -9.19686317e-01 -2.67393798e-01 2.74173647e-01 -3.09445113e-01 -2.99933031e-02 1.46533057e-01 3.75632048e-02 3.27190429e-01 -5.62342823e-01 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41b944dd-8f97-4f92-8f3e-1856f3de8df5	ted-triple-supervision-decouples-end-to-end	2009.09704	null	https://arxiv.org/abs/2009.09704v3	https://arxiv.org/pdf/2009.09704v3.pdf	"Listen, Understand and Translate": Triple Supervision Decouples End-to-end Speech-to-text Translation	An end-to-end speech-to-text translation (ST) takes audio in a source language and outputs the text in a target language. Existing methods are limited by the amount of parallel corpus. Can we build a system to fully utilize signals in a parallel ST corpus? We are inspired by human understanding system which is composed of auditory perception and cognitive processing. In this paper, we propose Listen-Understand-Translate, (LUT), a unified framework with triple supervision signals to decouple the end-to-end speech-to-text translation task. LUT is able to guide the acoustic encoder to extract as much information from the auditory input. In addition, LUT utilizes a pre-trained BERT model to enforce the upper encoder to produce as much semantic information as possible, without extra data. We perform experiments on a diverse set of speech translation benchmarks, including Librispeech English-French, IWSLT English-German and TED English-Chinese. Our results demonstrate LUT achieves the state-of-the-art performance, outperforming previous methods. The code is available at https://github.com/dqqcasia/st.	['Shuang Xu', 'Rong Ye', 'Lei LI', 'Bo Xu', 'Hao Zhou', 'Qianqian Dong', 'Mingxuan Wang']	2020-09-21	null	null	null	null	['speech-to-text-translation']	['natural-language-processing']	[ 1.90350786e-01 2.46628717e-01 5.39228991e-02 -4.63221520e-01 -1.44905496e+00 -5.80079675e-01 3.09155166e-01 -4.05399829e-01 -1.92077234e-01 3.56927067e-01 5.60776055e-01 -5.29609084e-01 6.85257614e-01 -3.18192959e-01 -8.95362377e-01 -2.90913284e-01 6.06638610e-01 5.97001731e-01 8.06325153e-02 -2.74618953e-01 -2.48592377e-01 -2.23131940e-01 -1.03794014e+00 8.34589839e-01 7.63803184e-01 8.48279893e-01 7.46537089e-01 9.66508746e-01 -3.29010300e-02 7.30848849e-01 -5.59205890e-01 -6.02078974e-01 5.32964319e-02 -8.76654029e-01 -8.25555682e-01 -1.84552923e-01 -1.01946443e-01 -3.04701895e-01 -4.51562732e-01 8.36165607e-01 1.01243126e+00 -1.13135889e-01 4.36311990e-01 -8.63518000e-01 -9.01269495e-01 1.07127655e+00 -2.68456955e-02 1.64812893e-01 5.32734692e-01 2.45420352e-01 1.02190435e+00 -1.49505103e+00 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8d683410-0095-4ef7-839c-c1902fcbeaa0	loftr-detector-free-local-feature-matching	2104.00680	null	https://arxiv.org/abs/2104.00680v1	https://arxiv.org/pdf/2104.00680v1.pdf	LoFTR: Detector-Free Local Feature Matching with Transformers	We present a novel method for local image feature matching. Instead of performing image feature detection, description, and matching sequentially, we propose to first establish pixel-wise dense matches at a coarse level and later refine the good matches at a fine level. In contrast to dense methods that use a cost volume to search correspondences, we use self and cross attention layers in Transformer to obtain feature descriptors that are conditioned on both images. The global receptive field provided by Transformer enables our method to produce dense matches in low-texture areas, where feature detectors usually struggle to produce repeatable interest points. The experiments on indoor and outdoor datasets show that LoFTR outperforms state-of-the-art methods by a large margin. LoFTR also ranks first on two public benchmarks of visual localization among the published methods.	['Xiaowei Zhou', 'Hujun Bao', 'Yuang Wang', 'Zehong Shen', 'Jiaming Sun']	2021-04-01	null	http://openaccess.thecvf.com//content/CVPR2021/html/Sun_LoFTR_Detector-Free_Local_Feature_Matching_With_Transformers_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Sun_LoFTR_Detector-Free_Local_Feature_Matching_With_Transformers_CVPR_2021_paper.pdf	cvpr-2021-1	['image-matching']	['computer-vision']	[-4.44274507e-02 -4.68532443e-01 -2.61470050e-01 -3.44340712e-01 -1.06145346e+00 -3.37356240e-01 6.74177885e-01 2.31300116e-01 -5.25881469e-01 4.06008035e-01 1.24547921e-01 2.84487188e-01 -1.64307877e-01 -9.26427305e-01 -9.19623911e-01 -5.17440677e-01 -1.62885450e-02 2.85281599e-01 6.04028046e-01 6.11484908e-02 4.10798669e-01 8.11209261e-01 -1.80899405e+00 4.29345399e-01 6.21295214e-01 1.37659454e+00 4.03405130e-01 2.80756652e-01 -1.40587976e-02 4.86921042e-01 -1.96078926e-01 -2.53265649e-01 5.45598805e-01 -1.23926237e-01 -9.84925270e-01 2.10402727e-01 1.11807966e+00 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3f2d8941-0694-4b45-bd88-44ded9ee8525	disentangled-person-image-generation	1712.02621	null	http://arxiv.org/abs/1712.02621v4	http://arxiv.org/pdf/1712.02621v4.pdf	Disentangled Person Image Generation	Generating novel, yet realistic, images of persons is a challenging task due to the complex interplay between the different image factors, such as the foreground, background and pose information. In this work, we aim at generating such images based on a novel, two-stage reconstruction pipeline that learns a disentangled representation of the aforementioned image factors and generates novel person images at the same time. First, a multi-branched reconstruction network is proposed to disentangle and encode the three factors into embedding features, which are then combined to re-compose the input image itself. Second, three corresponding mapping functions are learned in an adversarial manner in order to map Gaussian noise to the learned embedding feature space, for each factor respectively. Using the proposed framework, we can manipulate the foreground, background and pose of the input image, and also sample new embedding features to generate such targeted manipulations, that provide more control over the generation process. Experiments on Market-1501 and Deepfashion datasets show that our model does not only generate realistic person images with new foregrounds, backgrounds and poses, but also manipulates the generated factors and interpolates the in-between states. Another set of experiments on Market-1501 shows that our model can also be beneficial for the person re-identification task.	['Luc van Gool', 'Stamatios Georgoulis', 'Qianru Sun', 'Liqian Ma', 'Mario Fritz', 'Bernt Schiele']	2017-12-07	disentangled-person-image-generation-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Ma_Disentangled_Person_Image_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Ma_Disentangled_Person_Image_CVPR_2018_paper.pdf	cvpr-2018-6	['gesture-to-gesture-translation', 'pose-transfer']	['computer-vision', 'computer-vision']	[ 4.26730484e-01 -1.74441449e-02 4.15971369e-01 -1.69349760e-01 -3.72999221e-01 -8.56543958e-01 7.86622703e-01 -5.85231543e-01 -3.66181314e-01 5.99738300e-01 1.35538742e-01 2.38194451e-01 1.68629810e-01 -8.28768015e-01 -9.51280475e-01 -7.90737391e-01 2.21594080e-01 4.83440071e-01 -1.06843352e-01 -1.20680489e-01 -6.49196878e-02 4.81659919e-01 -1.70399070e+00 1.96485713e-01 7.11119771e-01 5.29811859e-01 -4.23159712e-04 1.05815196e+00 2.96532482e-01 3.69427413e-01 -9.62506413e-01 -9.75434005e-01 7.28420377e-01 -3.81884903e-01 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65b1c653-10d6-46db-ae8c-ec32a754ee98	uav-assisted-over-the-air-computation	2101.09856	null	https://arxiv.org/abs/2101.09856v1	https://arxiv.org/pdf/2101.09856v1.pdf	UAV-Assisted Over-the-Air Computation	Over-the-air computation (AirComp) provides a promising way to support ultrafast aggregation of distributed data. However, its performance cannot be guaranteed in long-distance transmission due to the distortion induced by the channel fading and noise. To unleash the full potential of AirComp, this paper proposes to use a low-cost unmanned aerial vehicle (UAV) acting as a mobile base station to assist AirComp systems. Specifically, due to its controllable high-mobility and high-altitude, the UAV can move sufficiently close to the sensors to enable line-of-sight transmission and adaptively adjust all the links' distances, thereby enhancing the signal magnitude alignment and noise suppression. Our goal is to minimize the time-averaging mean-square error for AirComp by jointly optimizing the UAV trajectory, the scaling factor at the UAV, and the transmit power at the sensors, under constraints on the UAV's predetermined locations and flying speed, sensors' average and peak power limits. However, due to the highly coupled optimization variables and time-dependent constraints, the resulting problem is non-convex and challenging. We thus propose an efficient iterative algorithm by applying the block coordinate descent and successive convex optimization techniques. Simulation results verify the convergence of the proposed algorithm and demonstrate the performance gains and robustness of the proposed design compared with benchmarks.	['Wei Chen', 'Ting Wang', 'Yuanming Shi', 'Yong Zhou', 'Min Fu']	2021-01-25	null	null	null	null	['optimize-the-trajectory-of-uav-which-plays-a']	['adversarial']	[ 6.03494011e-02 4.28798869e-02 3.54237594e-02 2.22915187e-01 -2.35457599e-01 -6.05613232e-01 2.78890529e-03 1.60718814e-01 -2.49141380e-01 8.15147698e-01 -3.00026506e-01 -5.21516979e-01 -8.44467640e-01 -9.06604767e-01 -4.62619305e-01 -1.12293243e+00 -8.70690346e-01 -3.18042159e-01 -1.70026273e-01 -2.50441879e-01 -3.71313244e-01 4.59858924e-01 -1.03647518e+00 -7.58495510e-01 1.06805050e+00 1.48215985e+00 2.52925098e-01 6.95236981e-01 8.02020609e-01 2.64756978e-01 -6.96499765e-01 -3.30222733e-02 5.23753822e-01 -2.88301170e-01 6.72454238e-02 2.48911977e-01 -4.05405052e-02 -5.65876663e-01 -5.92557728e-01 9.71973062e-01 6.93298459e-01 1.09857894e-01 8.72640982e-02 -1.42373264e+00 -1.83895454e-01 3.14382851e-01 -5.98568022e-01 1.14390925e-01 9.68427658e-02 -1.00868225e-01 7.59371161e-01 -4.52281833e-01 -1.70922012e-03 5.61801314e-01 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-5.86300492e-01 -6.88436806e-01 -4.19914544e-01 -7.92269111e-01 -7.06148863e-01 7.29280174e-01 -5.88021457e-01 -4.31978256e-01 -4.52910751e-01]	[5.955537796020508, 1.4742523431777954]
f7355383-45b9-4400-8fec-92a0e84bcaf6	improved-speech-enhancement-with-the-wave-u	1811.11307	null	http://arxiv.org/abs/1811.11307v1	http://arxiv.org/pdf/1811.11307v1.pdf	Improved Speech Enhancement with the Wave-U-Net	We study the use of the Wave-U-Net architecture for speech enhancement, a model introduced by Stoller et al for the separation of music vocals and accompaniment. This end-to-end learning method for audio source separation operates directly in the time domain, permitting the integrated modelling of phase information and being able to take large temporal contexts into account. Our experiments show that the proposed method improves several metrics, namely PESQ, CSIG, CBAK, COVL and SSNR, over the state-of-the-art with respect to the speech enhancement task on the Voice Bank corpus (VCTK) dataset. We find that a reduced number of hidden layers is sufficient for speech enhancement in comparison to the original system designed for singing voice separation in music. We see this initial result as an encouraging signal to further explore speech enhancement in the time-domain, both as an end in itself and as a pre-processing step to speech recognition systems.	['Tillman Weyde', 'Craig Macartney']	2018-11-27	null	null	null	null	['audio-source-separation']	['audio']	[ 1.93941951e-01 -4.53771353e-02 2.88858205e-01 -1.03421688e-01 -9.78943169e-01 -5.43054223e-01 4.69231874e-01 4.40668687e-02 -6.12948895e-01 4.04434711e-01 4.32659686e-01 -2.25907043e-01 -4.95899528e-01 -2.08412871e-01 -1.96313009e-01 -8.45457435e-01 -2.47198939e-01 -4.62626517e-02 8.56150240e-02 -3.18670541e-01 -1.62877828e-01 6.21918559e-01 -1.81240988e+00 2.04589576e-01 5.04811823e-01 9.24674392e-01 1.89099237e-01 1.08907104e+00 2.56230831e-01 6.54995441e-01 -6.40211761e-01 -1.23162106e-01 1.72021776e-01 -5.50139427e-01 -5.45162082e-01 4.45148768e-03 3.52619380e-01 -8.11072141e-02 -3.96050096e-01 9.11539018e-01 1.25820220e+00 4.07021165e-01 5.37276387e-01 -7.34179676e-01 -5.41496277e-03 8.86125326e-01 -1.71014801e-01 3.66579175e-01 9.84753519e-02 -3.78285907e-02 1.07660329e+00 -1.04363930e+00 4.16391462e-01 9.71934259e-01 9.55692768e-01 3.90520424e-01 -1.09303784e+00 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5.28176837e-02 8.65266979e-01 -1.23011567e-01 -5.01040578e-01 2.77077734e-01]	[15.20656967163086, 5.774660110473633]
3b8a67aa-5f79-4add-8f35-4d631781cf4a	partnet-a-large-scale-benchmark-for-fine	1812.02713	null	http://arxiv.org/abs/1812.02713v1	http://arxiv.org/pdf/1812.02713v1.pdf	PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding	We present PartNet: a consistent, large-scale dataset of 3D objects annotated with fine-grained, instance-level, and hierarchical 3D part information. Our dataset consists of 573,585 part instances over 26,671 3D models covering 24 object categories. This dataset enables and serves as a catalyst for many tasks such as shape analysis, dynamic 3D scene modeling and simulation, affordance analysis, and others. Using our dataset, we establish three benchmarking tasks for evaluating 3D part recognition: fine-grained semantic segmentation, hierarchical semantic segmentation, and instance segmentation. We benchmark four state-of-the-art 3D deep learning algorithms for fine-grained semantic segmentation and three baseline methods for hierarchical semantic segmentation. We also propose a novel method for part instance segmentation and demonstrate its superior performance over existing methods.	['Angel X. Chang', 'Kaichun Mo', 'Shilin Zhu', 'Li Yi', 'Leonidas J. Guibas', 'Subarna Tripathi', 'Hao Su']	2018-12-06	partnet-a-large-scale-benchmark-for-fine-1	http://openaccess.thecvf.com/content_CVPR_2019/html/Mo_PartNet_A_Large-Scale_Benchmark_for_Fine-Grained_and_Hierarchical_Part-Level_3D_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Mo_PartNet_A_Large-Scale_Benchmark_for_Fine-Grained_and_Hierarchical_Part-Level_3D_CVPR_2019_paper.pdf	cvpr-2019-6	['3d-instance-segmentation-1']	['computer-vision']	[ 1.12511709e-01 1.82609513e-01 -2.40535051e-01 -4.99465823e-01 -6.63001001e-01 -7.27442443e-01 5.29256403e-01 1.15303963e-01 3.42583090e-01 1.62554026e-01 1.82476133e-01 -2.94995233e-02 4.38689925e-02 -7.33531058e-01 -1.11702466e+00 -1.14342511e-01 -1.43496931e-01 1.29493666e+00 6.26291215e-01 1.29755735e-01 2.10708708e-01 1.24987185e+00 -1.73731887e+00 2.10512400e-01 6.17980063e-01 1.41601121e+00 -5.60075864e-02 3.62236410e-01 -6.01524711e-01 2.16847822e-01 -6.10997498e-01 -2.14052618e-01 5.44575870e-01 1.11735389e-01 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4edcb788-8d18-40f8-9b29-2f44fd138a7f	model-based-reinforcement-learning-with-3	2303.14889	null	https://arxiv.org/abs/2303.14889v1	https://arxiv.org/pdf/2303.14889v1.pdf	Model-Based Reinforcement Learning with Isolated Imaginations	World models learn the consequences of actions in vision-based interactive systems. However, in practical scenarios like autonomous driving, noncontrollable dynamics that are independent or sparsely dependent on action signals often exist, making it challenging to learn effective world models. To address this issue, we propose Iso-Dream++, a model-based reinforcement learning approach that has two main contributions. First, we optimize the inverse dynamics to encourage the world model to isolate controllable state transitions from the mixed spatiotemporal variations of the environment. Second, we perform policy optimization based on the decoupled latent imaginations, where we roll out noncontrollable states into the future and adaptively associate them with the current controllable state. This enables long-horizon visuomotor control tasks to benefit from isolating mixed dynamics sources in the wild, such as self-driving cars that can anticipate the movement of other vehicles, thereby avoiding potential risks. On top of our previous work, we further consider the sparse dependencies between controllable and noncontrollable states, address the training collapse problem of state decoupling, and validate our approach in transfer learning setups. Our empirical study demonstrates that Iso-Dream++ outperforms existing reinforcement learning models significantly on CARLA and DeepMind Control.	['Xiaokang Yang', 'Yunbo Wang', 'Xiangming Zhu', 'Minting Pan']	2023-03-27	null	null	null	null	['self-driving-cars']	['computer-vision']	[-1.60592914e-01 2.33405292e-01 -3.36173922e-01 1.78301111e-01 -8.92814100e-02 -6.10046983e-01 8.87808144e-01 -3.87603164e-01 -2.90811419e-01 9.46465373e-01 3.74312609e-01 -1.16787203e-01 -2.17708796e-01 -5.74373424e-01 -9.06308830e-01 -8.64601433e-01 -2.90285945e-01 5.49119413e-01 4.92730588e-02 -5.49691498e-01 -8.30810294e-02 3.88493121e-01 -1.46359396e+00 -2.19477013e-01 9.00134265e-01 4.67912406e-01 4.72500592e-01 7.00014412e-01 3.52881521e-01 1.34855044e+00 -2.74102911e-02 3.35146815e-01 3.55789959e-01 -3.24639857e-01 -3.53892595e-01 1.81281909e-01 1.84927091e-01 -5.61290860e-01 -9.23520207e-01 8.24839175e-01 3.17405760e-01 3.59315068e-01 4.13973987e-01 -1.44250882e+00 -5.14164746e-01 3.72348785e-01 -3.03873569e-01 6.27041049e-03 -2.87678894e-02 1.18611145e+00 6.48585856e-01 -2.86291569e-01 8.00092280e-01 1.46019709e+00 1.22848257e-01 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7cfae8c6-b6db-472a-9ab7-0e8a479cd357	in-sensor-neuromorphic-computing-are-all-you	2212.10881	null	https://arxiv.org/abs/2212.10881v1	https://arxiv.org/pdf/2212.10881v1.pdf	In-Sensor & Neuromorphic Computing are all you need for Energy Efficient Computer Vision	Due to the high activation sparsity and use of accumulates (AC) instead of expensive multiply-and-accumulates (MAC), neuromorphic spiking neural networks (SNNs) have emerged as a promising low-power alternative to traditional DNNs for several computer vision (CV) applications. However, most existing SNNs require multiple time steps for acceptable inference accuracy, hindering real-time deployment and increasing spiking activity and, consequently, energy consumption. Recent works proposed direct encoding that directly feeds the analog pixel values in the first layer of the SNN in order to significantly reduce the number of time steps. Although the overhead for the first layer MACs with direct encoding is negligible for deep SNNs and the CV processing is efficient using SNNs, the data transfer between the image sensors and the downstream processing costs significant bandwidth and may dominate the total energy. To mitigate this concern, we propose an in-sensor computing hardware-software co-design framework for SNNs targeting image recognition tasks. Our approach reduces the bandwidth between sensing and processing by 12-96x and the resulting total energy by 2.32x compared to traditional CV processing, with a 3.8% reduction in accuracy on ImageNet.	['Peter A. Beerel', 'Akhilesh R. Jaiswal', 'Ajey P. Jacob', 'Zihan Yin', 'Joe Mathai', 'Souvik Kundu', 'Md Abdullah-Al Kaiser', 'Zeyu Liu', 'Gourav Datta']	2022-12-21	null	null	null	null	['total-energy']	['miscellaneous']	[ 7.42969751e-01 -1.59284636e-01 3.09928000e-01 -2.08647564e-01 -3.32543366e-02 -4.01307106e-01 3.40624988e-01 4.13839012e-01 -1.07599270e+00 4.89046723e-01 -6.02944732e-01 -2.57093012e-01 3.74264538e-01 -9.34758127e-01 -9.57172930e-01 -6.89779580e-01 3.01477939e-01 -4.43542391e-01 8.07481885e-01 1.97923839e-01 4.31109756e-01 5.52204847e-01 -1.80903220e+00 1.53170332e-01 6.07378066e-01 1.75270569e+00 4.31025356e-01 4.43707287e-01 -1.49492264e-01 6.14259720e-01 -7.95048535e-01 -7.31189102e-02 3.31793338e-01 -2.06107318e-01 3.05901133e-02 -5.94975173e-01 3.37565541e-01 -3.49654824e-01 -4.67684746e-01 1.34827065e+00 6.30976617e-01 -4.53114837e-01 2.18089417e-01 -1.23540163e+00 -3.43936086e-01 8.26914966e-01 -5.41148484e-01 3.77132982e-01 -2.70004302e-01 3.45685452e-01 1.35956213e-01 -6.54221773e-01 1.42667785e-01 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4da6f721-1b19-4b5d-adb4-be15c1c6e3d3	entity-based-semantic-adequacy-for-data-to	null	null	https://aclanthology.org/2021.findings-emnlp.132	https://aclanthology.org/2021.findings-emnlp.132.pdf	Entity-Based Semantic Adequacy for Data-to-Text Generation	While powerful pre-trained language models have improved the fluency of text generation models, semantic adequacy -the ability to generate text that is semantically faithful to the input- remains an unsolved issue. In this paper, we introduce a novel automatic evaluation metric, Entity-Based Semantic Adequacy, which can be used to assess to what extent generation models that verbalise RDF (Resource Description Framework) graphs produce text that contains mentions of the entities occurring in the RDF input. This is important as RDF subject and object entities make up 2/3 of the input. We use our metric to compare 25 models from the WebNLG Shared Tasks and we examine correlation with results from human evaluations of semantic adequacy. We show that while our metric correlates with human evaluation scores, this correlation varies with the specifics of the human evaluation setup. This suggests that in order to measure the entity-based adequacy of generated texts, an automatic metric such as the one proposed here might be more reliable, as less subjective and more focused on correct verbalisation of the input, than human evaluation measures.	['Claire Gardent', 'Albert Gatt', 'Juliette Faille']	null	null	null	null	findings-emnlp-2021-11	['data-to-text-generation']	['natural-language-processing']	[ 9.78264809e-02 1.01376724e+00 9.00712833e-02 -4.28324014e-01 -6.69017613e-01 -5.99466681e-01 9.88760650e-01 6.91433430e-01 -6.15489185e-01 1.13272524e+00 8.65701079e-01 -3.01989108e-01 -1.90252990e-01 -1.21376884e+00 -5.46829224e-01 2.02711880e-01 5.99961340e-01 8.68581951e-01 2.66931057e-01 -5.18752933e-01 3.48822385e-01 4.66072299e-02 -1.53286505e+00 3.87793303e-01 1.19583678e+00 4.92276132e-01 3.28913517e-02 5.45391977e-01 -5.32217622e-01 1.12859416e+00 -9.53625083e-01 -7.16285169e-01 -3.05441886e-01 -8.36189568e-01 -1.38896501e+00 -3.42873067e-01 2.55295843e-01 1.24187827e-01 1.47390395e-01 1.07702661e+00 4.15019393e-01 1.08190536e-01 1.03468263e+00 -1.13706505e+00 -6.88987315e-01 1.14380896e+00 3.61821502e-01 1.05973564e-01 8.92145813e-01 -1.13458388e-01 1.11962044e+00 -6.53104007e-01 1.00683057e+00 1.26451397e+00 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-2.24332318e-01 -4.37806159e-01 -1.15908432e+00 -3.09364736e-01 8.03428054e-01 -2.35152200e-01 -3.21245700e-01 -3.26362640e-01 4.82778400e-02]	[11.526105880737305, 9.025773048400879]
de8989fc-1532-4397-96a4-d50d4341129f	humorhawk-at-semeval-2017-task-6-mixing	null	null	https://aclanthology.org/S17-2010	https://aclanthology.org/S17-2010.pdf	HumorHawk at SemEval-2017 Task 6: Mixing Meaning and Sound for Humor Recognition	This paper describes the winning system for SemEval-2017 Task 6: {\#}HashtagWars: Learning a Sense of Humor. Humor detection has up until now been predominantly addressed using feature-based approaches. Our system utilizes recurrent deep learning methods with dense embeddings to predict humorous tweets from the @midnight show {\#}HashtagWars. In order to include both meaning and sound in the analysis, GloVe embeddings are combined with a novel phonetic representation to serve as input to an LSTM component. The output is combined with a character-based CNN model, and an XGBoost component in an ensemble model which achieves 0.675 accuracy on the evaluation data.	['David Donahue', 'Anna Rumshisky', 'Alexey Romanov']	2017-08-01	null	null	null	semeval-2017-8	['humor-detection']	['natural-language-processing']	[-4.61652607e-01 -2.34780926e-02 1.50704935e-01 -3.99154752e-01 -6.20640218e-01 -1.57290354e-01 6.96035981e-01 2.60419607e-01 -4.84643191e-01 4.78703767e-01 8.03524017e-01 -2.10127383e-01 3.36541653e-01 -9.57167923e-01 -5.38855553e-01 -5.87736905e-01 1.79211169e-01 4.32741731e-01 -1.75345704e-01 -6.80043161e-01 4.28630292e-01 6.90197796e-02 -1.37935901e+00 8.54039073e-01 3.53993684e-01 1.00701833e+00 -2.72114336e-01 6.72236919e-01 -1.37501523e-01 1.51368177e+00 -6.23282552e-01 -6.69388533e-01 -1.77692696e-01 -4.15015280e-01 -8.14583123e-01 -3.89682710e-01 2.46056020e-01 -2.53275752e-01 -8.04459333e-01 6.18728518e-01 7.17372894e-01 5.71915209e-01 4.46499884e-01 -7.39098430e-01 -6.41566813e-01 1.29366672e+00 -1.87443703e-01 1.92030683e-01 2.70226568e-01 1.87253952e-01 1.38023901e+00 -1.18604946e+00 3.66474658e-01 1.02552259e+00 8.57860267e-01 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06dac544-d004-4d10-8a98-6be9640170ec	domain-adaptive-3d-pose-augmentation-for-in	2206.10457	null	https://arxiv.org/abs/2206.10457v2	https://arxiv.org/pdf/2206.10457v2.pdf	Domain Adaptive 3D Pose Augmentation for In-the-wild Human Mesh Recovery	The ability to perceive 3D human bodies from a single image has a multitude of applications ranging from entertainment and robotics to neuroscience and healthcare. A fundamental challenge in human mesh recovery is in collecting the ground truth 3D mesh targets required for training, which requires burdensome motion capturing systems and is often limited to indoor laboratories. As a result, while progress is made on benchmark datasets collected in these restrictive settings, models fail to generalize to real-world "in-the-wild" scenarios due to distribution shifts. We propose Domain Adaptive 3D Pose Augmentation (DAPA), a data augmentation method that enhances the model's generalization ability in in-the-wild scenarios. DAPA combines the strength of methods based on synthetic datasets by getting direct supervision from the synthesized meshes, and domain adaptation methods by using ground truth 2D keypoints from the target dataset. We show quantitatively that finetuning with DAPA effectively improves results on benchmarks 3DPW and AGORA. We further demonstrate the utility of DAPA on a challenging dataset curated from videos of real-world parent-child interaction.	['Serena Yeung', 'Angjoo Kanazawa', 'Kuan-Chieh Wang', 'Zhenzhen Weng']	2022-06-21	null	null	null	null	['human-mesh-recovery']	['computer-vision']	[ 3.96508873e-01 3.01112294e-01 -1.14757277e-01 -3.47199708e-01 -6.77158415e-01 -3.88117969e-01 2.78274387e-01 -1.46478027e-01 -1.61966994e-01 5.69801450e-01 1.87988043e-01 1.90904096e-01 6.52236491e-02 -6.05644226e-01 -1.19612288e+00 -4.03202564e-01 -1.19788878e-01 1.07570016e+00 3.35425675e-01 -4.14103955e-01 -2.65635282e-01 6.41133070e-01 -1.69476664e+00 1.81915089e-01 6.60920084e-01 8.22993040e-01 1.05118945e-01 5.25470257e-01 1.60406619e-01 1.46281675e-01 -3.19240510e-01 -1.54062629e-01 5.77035367e-01 -2.09703624e-01 -6.67522371e-01 3.56227309e-01 9.32775855e-01 -5.16434431e-01 -2.23952591e-01 7.16535509e-01 6.85812056e-01 9.29161236e-02 5.68020225e-01 -1.30998731e+00 -2.58877873e-01 1.03568092e-01 -7.20978081e-01 -1.88775450e-01 8.06194365e-01 1.80944130e-01 4.51869488e-01 -9.48982596e-01 1.10630322e+00 1.36304796e+00 1.00796819e+00 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8a555dd4-83d1-4072-be0f-bb5b385b3701	adaptive-outlier-detection-for-power-mosfets	2201.10126	null	https://arxiv.org/abs/2201.10126v1	https://arxiv.org/pdf/2201.10126v1.pdf	Adaptive Outlier Detection for Power MOSFETs Based on Gaussian Process Regression	Outlier detection of semiconductor devices is important since manufacturing variation is inherently inevitable. In order to properly detect outliers, it is necessary to consider the discrepancy from underlying trend. Conventional methods are insufficient as they cannot track spatial changes of the trend}}. This study proposes an adaptive outlier detection using Gaussian process regression (GPR) with Student-t likelihood, which captures a gradual spatial change of characteristic variation. According to the credible interval of the GPR posterior distribution, the devices having excessively large deviations against the underlying trend are detected. The proposed methodology is validated by the experiments using a commercial SiC wafer and simulation.	['Takashi Sato', 'Michihiro Shintani', 'Kyohei Shimozato']	2022-01-25	null	null	null	null	['gpr', 'gpr']	['computer-vision', 'miscellaneous']	[ 6.99738832e-03 -4.50365692e-01 2.39113927e-01 -2.24967048e-01 -3.93746167e-01 -2.00459227e-01 2.96071708e-01 5.70845425e-01 9.39102024e-02 9.54769313e-01 -2.95238853e-01 -3.31123412e-01 -4.63924646e-01 -6.41390443e-01 -4.96804237e-01 -7.59645224e-01 1.64787218e-01 3.10300082e-01 2.41146386e-01 2.07165465e-01 8.01337898e-01 5.96143723e-01 -1.15764344e+00 -5.71928844e-02 1.10076845e+00 9.58690763e-01 -1.80662483e-01 3.89415145e-01 -5.33257723e-02 1.30916297e-01 -1.10538185e+00 3.25987250e-01 1.46115109e-01 -3.39543492e-01 3.16962451e-01 1.35446573e-02 -1.89207196e-01 -3.38037275e-02 7.39589427e-03 1.35918522e+00 1.79068074e-01 1.19585268e-01 8.31913233e-01 -1.20873630e+00 -5.01399994e-01 2.58168787e-01 -8.80634546e-01 5.74159861e-01 2.16890633e-01 1.13461902e-02 3.16347957e-01 -1.00645220e+00 2.39647523e-01 9.04198527e-01 5.43572307e-01 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fd465743-8b06-4088-8c03-7a499966fd50	anomaly-detection-in-video-sequence-with-1	null	null	http://openaccess.thecvf.com/content_ICCV_2019/html/Nguyen_Anomaly_Detection_in_Video_Sequence_With_Appearance-Motion_Correspondence_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Nguyen_Anomaly_Detection_in_Video_Sequence_With_Appearance-Motion_Correspondence_ICCV_2019_paper.pdf	Anomaly Detection in Video Sequence With Appearance-Motion Correspondence	Anomaly detection in surveillance videos is currently a challenge because of the diversity of possible events. We propose a deep convolutional neural network (CNN) that addresses this problem by learning a correspondence between common object appearances (e.g. pedestrian, background, tree, etc.) and their associated motions. Our model is designed as a combination of a reconstruction network and an image translation model that share the same encoder. The former sub-network determines the most significant structures that appear in video frames and the latter one attempts to associate motion templates to such structures. The training stage is performed using only videos of normal events and the model is then capable to estimate frame-level scores for an unknown input. The experiments on 6 benchmark datasets demonstrate the competitive performance of the proposed approach with respect to state-of-the-art methods.	[' Jean Meunier', 'Trong-Nguyen Nguyen']	2019-10-01	null	null	null	iccv-2019-10	['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos']	['computer-vision', 'methodology']	[ 3.07207763e-01 -2.29866073e-01 2.36715630e-01 -3.11144143e-01 -2.22451523e-01 -2.94673890e-01 7.83568025e-01 -1.09829932e-01 -4.40433443e-01 3.86737436e-01 -1.01664849e-01 -1.31907240e-01 2.62635678e-01 -5.38411021e-01 -1.19124126e+00 -7.61104882e-01 -1.81067988e-01 4.74671014e-02 9.10985589e-01 -5.58873080e-03 1.18185468e-01 5.98576784e-01 -1.59968448e+00 4.73492622e-01 3.69522363e-01 1.15415204e+00 1.05455834e-02 8.34235191e-01 6.14944845e-02 1.23386681e+00 -5.56325674e-01 -6.12844348e-01 4.00844187e-01 -4.16706443e-01 -5.00149250e-01 5.73009431e-01 9.16248500e-01 -6.71472967e-01 -6.23441458e-01 1.35008383e+00 -2.41334196e-02 8.29071030e-02 6.40143692e-01 -1.21600425e+00 -1.01869881e-01 -9.93342996e-02 -5.14788151e-01 7.82274306e-01 3.57855171e-01 1.24211572e-01 5.99570453e-01 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f2fc0b2c-c7b6-48a8-9926-d107b32cfff9	weakly-supervised-actor-action-segmentation	null	null	http://openaccess.thecvf.com/content_cvpr_2017/html/Yan_Weakly_Supervised_Actor-Action_CVPR_2017_paper.html	http://openaccess.thecvf.com/content_cvpr_2017/papers/Yan_Weakly_Supervised_Actor-Action_CVPR_2017_paper.pdf	Weakly Supervised Actor-Action Segmentation via Robust Multi-Task Ranking	Fine-grained activity understanding in videos has attracted considerable recent attention with a shift from action classification to detailed actor and action understanding that provides compelling results for perceptual needs of cutting-edge autonomous systems. However, current methods for detailed understanding of actor and action have significant limitations: they require large amounts of finely labeled data, and they fail to capture any internal relationship among actors and actions. To address these issues, in this paper, we propose a novel, robust multi-task ranking model for weakly supervised actor-action segmentation where only video-level tags are given for training samples. Our model is able to share useful information among different actors and actions while learning a ranking matrix to select representative supervoxels for actors and actions respectively. Final segmentation results are generated by a conditional random field that considers various ranking scores for different video parts. Extensive experimental results on the Actor-Action Dataset (A2D) demonstrate that the proposed approach outperforms the state-of-the-art weakly supervised methods and performs as well as the top-performing fully supervised method.	['Dawen Cai', 'Chenliang Xu', 'Yan Yan', 'Jason J. Corso']	2017-07-01	null	null	null	cvpr-2017-7	['action-understanding']	['computer-vision']	[ 4.76499081e-01 -6.32627532e-02 -7.75635123e-01 -5.68802357e-01 -8.51866722e-01 -5.22755980e-01 8.42258394e-01 -1.11605562e-02 -2.64552802e-01 5.58275044e-01 7.13451684e-01 3.40911120e-01 -7.37729594e-02 -1.69368997e-01 -7.61681914e-01 -6.26474917e-01 -1.30179688e-01 4.95270848e-01 8.31441939e-01 1.62748948e-01 2.63071537e-01 1.35122120e-01 -1.67975736e+00 8.53769243e-01 6.97987556e-01 9.91170347e-01 1.20318487e-01 5.74726880e-01 1.11098327e-01 1.46224630e+00 -3.40025425e-01 -1.92696601e-01 2.87347943e-01 -5.45872152e-01 -1.16673255e+00 7.29860723e-01 6.59672141e-01 -3.79445165e-01 -7.67619014e-02 9.09560323e-01 1.76028237e-01 3.39189708e-01 7.52157152e-01 -1.33271694e+00 -1.32042974e-01 7.36636758e-01 -6.82436407e-01 2.99589396e-01 5.28957665e-01 -5.44221625e-02 1.17012990e+00 -7.29091406e-01 6.69330597e-01 1.36877668e+00 2.41939127e-01 5.54189265e-01 -7.92975724e-01 -3.68994862e-01 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b9cb6376-4e9c-46d4-a890-a72dda4b61f2	signed-graph-neural-networks-a-frequency	2208.07323	null	https://arxiv.org/abs/2208.07323v1	https://arxiv.org/pdf/2208.07323v1.pdf	Signed Graph Neural Networks: A Frequency Perspective	Graph convolutional networks (GCNs) and its variants are designed for unsigned graphs containing only positive links. Many existing GCNs have been derived from the spectral domain analysis of signals lying over (unsigned) graphs and in each convolution layer they perform low-pass filtering of the input features followed by a learnable linear transformation. Their extension to signed graphs with positive as well as negative links imposes multiple issues including computational irregularities and ambiguous frequency interpretation, making the design of computationally efficient low pass filters challenging. In this paper, we address these issues via spectral analysis of signed graphs and propose two different signed graph neural networks, one keeps only low-frequency information and one also retains high-frequency information. We further introduce magnetic signed Laplacian and use its eigendecomposition for spectral analysis of directed signed graphs. We test our methods for node classification and link sign prediction tasks on signed graphs and achieve state-of-the-art performances.	['Yongxin Chen', 'Rahul Singh']	2022-08-15	null	null	null	null	['link-sign-prediction']	['graphs']	[ 5.90976655e-01 5.52253306e-01 -1.65944487e-01 -5.06453991e-01 6.88917339e-02 -6.61354005e-01 4.27884370e-01 1.96248457e-01 -7.46496096e-02 6.59176528e-01 -5.64558953e-02 -4.93295461e-01 -6.56241655e-01 -1.01579988e+00 -7.45402336e-01 -4.60683376e-01 -1.05788708e+00 1.59429997e-01 3.39815140e-01 -3.60942841e-01 -1.61296070e-01 5.34789741e-01 -1.26416719e+00 2.37200961e-01 1.01043749e+00 8.74013782e-01 -5.91259122e-01 9.74858880e-01 5.14869986e-04 6.14576876e-01 -4.02876437e-01 -4.20546025e-01 1.83469534e-01 -4.23448920e-01 -5.37400603e-01 -2.44889885e-01 8.45093012e-01 3.55623782e-01 -6.58540547e-01 1.30276811e+00 5.54862738e-01 -1.94161877e-01 7.44633615e-01 -1.56091797e+00 -1.01036835e+00 1.05789030e+00 -3.60844672e-01 7.28347823e-02 2.65973747e-01 -2.56629497e-01 1.16283286e+00 -5.80017805e-01 7.93503761e-01 9.20881867e-01 1.19555807e+00 1.93263844e-01 -1.47924411e+00 -5.04717708e-01 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5.66554844e-01 6.52492821e-01 -5.70555151e-01 -6.16331518e-01 -9.25399289e-02]	[6.923158645629883, 6.116565704345703]
5b59810a-0ab1-4e46-8858-3a372a700fda	humor-detection-a-transformer-gets-the-last	1909.00252	null	https://arxiv.org/abs/1909.00252v1	https://arxiv.org/pdf/1909.00252v1.pdf	Humor Detection: A Transformer Gets the Last Laugh	Much previous work has been done in attempting to identify humor in text. In this paper we extend that capability by proposing a new task: assessing whether or not a joke is humorous. We present a novel way of approaching this problem by building a model that learns to identify humorous jokes based on ratings gleaned from Reddit pages, consisting of almost 16,000 labeled instances. Using these ratings to determine the level of humor, we then employ a Transformer architecture for its advantages in learning from sentence context. We demonstrate the effectiveness of this approach and show results that are comparable to human performance. We further demonstrate our model's increased capabilities on humor identification problems, such as the previously created datasets for short jokes and puns. These experiments show that this method outperforms all previous work done on these tasks, with an F-measure of 93.1% for the Puns dataset and 98.6% on the Short Jokes dataset.	['Kevin Seppi', 'Orion Weller']	2019-08-31	humor-detection-a-transformer-gets-the-last-1	https://aclanthology.org/D19-1372	https://aclanthology.org/D19-1372.pdf	ijcnlp-2019-11	['humor-detection']	['natural-language-processing']	[-3.10617745e-01 -3.51125863e-03 4.38500680e-02 -2.52885848e-01 -6.43158853e-01 -4.12024021e-01 7.43960798e-01 -9.12581664e-03 -3.52358669e-01 6.66069627e-01 7.99785554e-01 -1.82599574e-01 1.97828725e-01 -5.47593772e-01 -8.47416669e-02 -2.75221586e-01 2.69858122e-01 3.92699093e-01 7.59774223e-02 -6.42668366e-01 8.99647772e-01 1.56726956e-01 -1.04008305e+00 5.89152515e-01 5.72041035e-01 4.94998395e-01 -1.28826022e-01 8.54149342e-01 1.92790665e-02 1.98298728e+00 -9.45104778e-01 -5.71034014e-01 -1.90217465e-01 -8.28696132e-01 -1.31907940e+00 -2.59560402e-02 6.44895494e-01 -4.17492300e-01 -3.23902488e-01 6.61402822e-01 4.14880395e-01 2.60663092e-01 6.59559727e-01 -8.93169999e-01 -8.98266256e-01 9.31693733e-01 -3.85393530e-01 3.97624105e-01 5.63983977e-01 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3fd533e5-b39a-43a8-a4b9-110562131bf0	search-based-regular-expression-inference-on	2305.18575	null	https://arxiv.org/abs/2305.18575v1	https://arxiv.org/pdf/2305.18575v1.pdf	Search-Based Regular Expression Inference on a GPU	Regular expression inference (REI) is a supervised machine learning and program synthesis problem that takes a cost metric for regular expressions, and positive and negative examples of strings as input. It outputs a regular expression that is precise (i.e., accepts all positive and rejects all negative examples), and minimal w.r.t. to the cost metric. We present a novel algorithm for REI over arbitrary alphabets that is enumerative and trades off time for space. Our main algorithmic idea is to implement the search space of regular expressions succinctly as a contiguous matrix of bitvectors. Collectively, the bitvectors represent, as characteristic sequences, all sub-languages of the infix-closure of the union of positive and negative examples. Mathematically, this is a semiring of (a variant of) formal power series. Infix-closure enables bottom-up compositional construction of larger from smaller regular expressions using the operations of our semiring. This minimises data movement and data-dependent branching, hence maximises data-parallelism. In addition, the infix-closure remains unchanged during the search, hence search can be staged: first pre-compute various expensive operations, and then run the compute intensive search process. We provide two C++ implementations, one for general purpose CPUs and one for Nvidia GPUs (using CUDA). We benchmark both on Google Colab Pro: the GPU implementation is on average over 1000x faster than the CPU implementation on the hardest benchmarks.	['Martin Berger', 'Mojtaba Valizadeh']	2023-05-29	null	null	null	null	['program-synthesis']	['computer-code']	[ 6.75157368e-01 -4.18029912e-02 -4.81113166e-01 -1.52363524e-01 -6.00487530e-01 -1.04338789e+00 4.15390074e-01 3.47272277e-01 -5.37465215e-01 7.81286120e-01 -1.97553650e-01 -1.01570165e+00 -1.09336236e-02 -1.22915554e+00 -8.02739024e-01 -6.62273884e-01 -5.23932040e-01 6.30526304e-01 1.48919970e-01 -7.13885799e-02 3.76091152e-01 5.91350973e-01 -2.08868551e+00 5.63470006e-01 3.77877802e-01 1.29534817e+00 -3.24328005e-01 1.08432901e+00 -2.05200151e-01 6.11424327e-01 -6.23090208e-01 -4.64025468e-01 4.35831815e-01 -1.16362095e-01 -1.07482815e+00 -6.63299635e-02 2.53458470e-01 1.71956480e-01 2.47336015e-01 1.12338138e+00 1.12181731e-01 -2.20413327e-01 5.05368531e-01 -1.37238419e+00 2.03257695e-01 4.99954760e-01 -3.96894157e-01 2.40185056e-02 4.79403079e-01 8.85061249e-02 1.44092834e+00 -6.42844200e-01 4.74452287e-01 1.06950247e+00 6.79390013e-01 1.34655714e-01 -1.68804228e+00 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e829b9c0-d743-49cd-8164-15c3e30e455f	multi-task-learning-for-audio-visual-active	null	null	http://research.google.com/ava/2019/Multi_Task_Learning_for_Audio_Visual_Active_Speaker_Detection.pdf	http://research.google.com/ava/2019/Multi_Task_Learning_for_Audio_Visual_Active_Speaker_Detection.pdf	Multi-Task Learning for Audio Visual Active Speaker Detection	This report describes the approach underlying our submission to the active speaker detection task (task B-2) of ActivityNet Challenge 2019. We introduce a new audio-visual model which builds upon a 3D-ResNet18 visual model pretrained for lipreading and a VGG-M acoustic model pretrained for audio-to-video synchronization. The model is trained with two losses in a multi-task learning fashion: a contrastive loss to enforce matching between audio and video features for active speakers, and a regular crossentropy loss to obtain speaker / non-speaker labels. This model obtains 84.0% mAP on the validation set of AVAActiveSpeaker. Experimental results showcase the pretrained embeddings' abilities to transfer across tasks and data formats, as well as the advantage of the proposed multi-task learning strategy.	['Shiguang Shan', 'Shuang Yang', 'Jingyun Xiao', 'Yuanhang Zhang']	2019-06-01	null	null	null	the-activitynet-large-scale-activity-1	['video-synchronization', 'lipreading', 'audio-visual-active-speaker-detection']	['computer-vision', 'computer-vision', 'computer-vision']	[ 2.07267001e-01 3.27196896e-01 -1.09445743e-01 -1.59209639e-01 -1.40364182e+00 -2.96390831e-01 6.41255021e-01 -6.65524080e-02 -6.66638911e-01 2.40382805e-01 5.55228353e-01 1.81478322e-01 2.21156538e-01 1.31227359e-01 -7.23632395e-01 -6.24036431e-01 -3.60912085e-01 1.46589980e-01 4.46723141e-02 2.84287632e-01 5.41463261e-03 2.27644488e-01 -1.65824473e+00 4.27988440e-01 3.01991314e-01 1.27940917e+00 8.48808810e-02 1.10539460e+00 8.56952816e-02 8.06167960e-01 -6.75567925e-01 -2.34742507e-01 -1.55764837e-02 -1.80149507e-02 -7.14802623e-01 7.76744932e-02 8.03189635e-01 -2.09672317e-01 -4.35561329e-01 6.87976897e-01 1.08975518e+00 3.94922465e-01 6.88691795e-01 -1.64533961e+00 -6.17234230e-01 4.15625721e-01 -4.04818594e-01 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5f8cac5d-1f32-44b8-a048-6cab7996c90a	a-disparity-refinement-framework-for-learning	2302.02294	null	https://arxiv.org/abs/2302.02294v1	https://arxiv.org/pdf/2302.02294v1.pdf	A Disparity Refinement Framework for Learning-based Stereo Matching Methods in Cross-domain Setting for Laparoscopic Images	Purpose: Stereo matching methods that enable depth estimation are crucial for visualization enhancement applications in computer-assisted surgery (CAS). Learning-based stereo matching methods are promising to predict accurate results on laparoscopic images. However, they require a large amount of training data, and their performance may be degraded due to domain shifts. Methods: Maintaining robustness and improving the accuracy of learning-based methods are still open problems. To overcome the limitations of learning-based methods, we propose a disparity refinement framework consisting of a local disparity refinement method and a global disparity refinement method to improve the results of learning-based stereo matching methods in a cross-domain setting. Those learning-based stereo matching methods are pre-trained on a large public dataset of natural images and are tested on two datasets of laparoscopic images. Results: Qualitative and quantitative results suggest that our proposed disparity framework can effectively refine disparity maps when they are noise-corrupted on an unseen dataset, without compromising prediction accuracy when the network can generalize well on an unseen dataset. Conclusion: Our proposed disparity refinement framework could work with learning-based methods to achieve robust and accurate disparity prediction. Yet, as a large laparoscopic dataset for training learning-based methods does not exist and the generalization ability of networks remains to be improved, the incorporation of the proposed disparity refinement framework into existing networks will contribute to improving their overall accuracy and robustness associated with depth estimation.	['Cristian A. Linte', 'Richard Simon', 'Zixin Yang']	2023-02-05	null	null	null	null	['stereo-matching-1']	['computer-vision']	[ 3.24843109e-01 2.73951858e-01 -3.13175350e-01 -4.31552589e-01 -5.41729510e-01 -1.15862146e-01 2.38371566e-01 1.13144264e-01 -4.15136725e-01 6.73769653e-01 1.10002682e-01 -3.77057672e-01 -6.47545010e-02 -9.15373206e-01 -6.19081795e-01 -6.19798243e-01 1.49315000e-02 2.40460828e-01 4.08601284e-01 -3.21508288e-01 2.83965290e-01 5.13157070e-01 -1.74016869e+00 5.16747177e-01 1.15736353e+00 1.05871511e+00 3.19918156e-01 4.94204760e-01 -5.61779588e-02 4.98608857e-01 -2.58796215e-01 -9.70083848e-02 7.58565247e-01 -1.62528664e-01 -7.96484828e-01 -4.04256284e-01 7.31538832e-01 -4.84417140e-01 -6.03794932e-01 9.93566394e-01 8.66807759e-01 -3.26129757e-02 4.47404355e-01 -8.92960846e-01 -1.22502148e-01 -4.89985454e-04 -3.75388741e-01 2.14497596e-01 3.78795832e-01 1.87597498e-01 1.99067071e-01 -6.51593029e-01 8.50523889e-01 9.54302967e-01 1.12540257e+00 7.83382714e-01 -1.02941608e+00 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-3.78059745e-01 6.69919372e-01 5.24385154e-01 -6.99310184e-01 -3.09241742e-01 -1.38708293e-01]	[13.926066398620605, -3.0224769115448]
3a5ad07b-6f90-49cc-9e72-85dec895a68d	improving-position-encoding-of-transformers	2305.16642	null	https://arxiv.org/abs/2305.16642v1	https://arxiv.org/pdf/2305.16642v1.pdf	Improving Position Encoding of Transformers for Multivariate Time Series Classification	Transformers have demonstrated outstanding performance in many applications of deep learning. When applied to time series data, transformers require effective position encoding to capture the ordering of the time series data. The efficacy of position encoding in time series analysis is not well-studied and remains controversial, e.g., whether it is better to inject absolute position encoding or relative position encoding, or a combination of them. In order to clarify this, we first review existing absolute and relative position encoding methods when applied in time series classification. We then proposed a new absolute position encoding method dedicated to time series data called time Absolute Position Encoding (tAPE). Our new method incorporates the series length and input embedding dimension in absolute position encoding. Additionally, we propose computationally Efficient implementation of Relative Position Encoding (eRPE) to improve generalisability for time series. We then propose a novel multivariate time series classification (MTSC) model combining tAPE/eRPE and convolution-based input encoding named ConvTran to improve the position and data embedding of time series data. The proposed absolute and relative position encoding methods are simple and efficient. They can be easily integrated into transformer blocks and used for downstream tasks such as forecasting, extrinsic regression, and anomaly detection. Extensive experiments on 32 multivariate time-series datasets show that our model is significantly more accurate than state-of-the-art convolution and transformer-based models. Code and models are open-sourced at \url{https://github.com/Navidfoumani/ConvTran}.	['Mahsa Salehi', 'Geoffrey I. Webb', 'Chang Wei Tan', 'Navid Mohammadi Foumani']	2023-05-26	null	null	null	null	['time-series-classification']	['time-series']	[-2.90155713e-03 -6.98009908e-01 7.99606889e-02 -3.79062533e-01 -3.83892119e-01 -5.73831260e-01 5.01749754e-01 2.93103397e-01 -4.87710953e-01 3.18589300e-01 -1.11387692e-01 -7.68836617e-01 -3.79838377e-01 -8.64007115e-01 -5.84489584e-01 -6.72550499e-01 -7.02127576e-01 2.49517020e-02 -3.03691532e-02 -3.42704296e-01 1.53157830e-01 5.84869385e-01 -1.44855428e+00 2.77097195e-01 7.58332491e-01 1.46179044e+00 -9.25452486e-02 5.28384089e-01 -1.51993215e-01 6.51978314e-01 -6.96471274e-01 -4.33663093e-02 3.62188160e-01 -1.98625520e-01 -5.23551464e-01 -4.95020002e-01 -1.78340957e-01 -3.45231801e-01 -5.16284943e-01 7.82782435e-01 4.71809179e-01 2.24124908e-01 4.57792103e-01 -1.61374545e+00 -5.26895285e-01 6.22579515e-01 -2.85505801e-01 8.30280185e-01 -6.47518337e-02 -2.68873479e-02 8.88992906e-01 -6.51707411e-01 -3.47172953e-02 7.78004646e-01 9.17765975e-01 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868f1fea-c4da-4872-8c9e-550b0c7bf45b	revisiting-the-complexity-analysis-of	2104.08759	null	https://arxiv.org/abs/2104.08759v1	https://arxiv.org/pdf/2104.08759v1.pdf	Revisiting the Complexity Analysis of Conflict-Based Search: New Computational Techniques and Improved Bounds	The problem of Multi-Agent Path Finding (MAPF) calls for finding a set of conflict-free paths for a fleet of agents operating in a given environment. Arguably, the state-of-the-art approach to computing optimal solutions is Conflict-Based Search (CBS). In this work we revisit the complexity analysis of CBS to provide tighter bounds on the algorithm's run-time in the worst-case. Our analysis paves the way to better pinpoint the parameters that govern (in the worst case) the algorithm's computational complexity. Our analysis is based on two complementary approaches: In the first approach we bound the run-time using the size of a Multi-valued Decision Diagram (MDD) -- a layered graph which compactly contains all possible single-agent paths between two given vertices for a specific path length. In the second approach we express the running time by a novel recurrence relation which bounds the algorithm's complexity. We use generating functions-based analysis in order to tightly bound the recurrence. Using these technique we provide several new upper-bounds on CBS's complexity. The results allow us to improve the existing bound on the running time of CBS for many cases. For example, on a set of common benchmarks we improve the upper-bound by a factor of at least $2^{10^{7}}$.	['Oren Salzman', 'Yuval Filmus', 'Ofir Gordon']	2021-04-18	null	null	null	null	['multi-agent-path-finding']	['playing-games']	[ 1.25125647e-01 6.49012253e-02 1.17628379e-02 2.50012893e-03 -4.97420013e-01 -8.33423376e-01 4.44841623e-01 5.29658556e-01 -3.60099286e-01 8.57784927e-01 -3.82313073e-01 -7.53854156e-01 -6.50946617e-01 -1.10610247e+00 -4.53318834e-01 -7.16901660e-01 -7.64618933e-01 6.72286630e-01 9.59411621e-01 -5.57676792e-01 4.43472981e-01 7.93682277e-01 -1.22511506e+00 -1.16669135e-02 5.64091921e-01 8.67618144e-01 -7.73590431e-02 8.67404819e-01 -1.60074800e-01 4.81983364e-01 -6.06433272e-01 -1.15846425e-01 4.62957472e-01 -5.95128655e-01 -1.17514193e+00 3.19520310e-02 -3.66791427e-01 -1.23361975e-01 -4.87686433e-02 9.88194704e-01 -1.19507030e-01 7.22792298e-02 5.16635776e-01 -1.86797595e+00 4.62514907e-01 7.85380065e-01 -7.42105603e-01 3.22869927e-01 4.21970695e-01 -5.62383048e-02 8.63016784e-01 2.75333542e-02 8.55135679e-01 9.49263692e-01 3.90292108e-01 1.32911667e-01 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8b627d5d-69fc-475c-aa54-352a54d2062a	ws-3d-lane-weakly-supervised-3d-lane	2209.11523	null	https://arxiv.org/abs/2209.11523v2	https://arxiv.org/pdf/2209.11523v2.pdf	WS-3D-Lane: Weakly Supervised 3D Lane Detection With 2D Lane Labels	Compared to 2D lanes, real 3D lane data is difficult to collect accurately. In this paper, we propose a novel method for training 3D lanes with only 2D lane labels, called weakly supervised 3D lane detection WS-3D-Lane. By assumptions of constant lane width and equal height on adjacent lanes, we indirectly supervise 3D lane heights in the training. To overcome the problem of the dynamic change of the camera pitch during data collection, a camera pitch self-calibration method is proposed. In anchor representation, we propose a double-layer anchor with a improved non-maximum suppression (NMS) method, which enables the anchor-based method to predict two lane lines that are close. Experiments are conducted on the base of 3D-LaneNet under two supervision methods. Under weakly supervised setting, our WS-3D-Lane outperforms previous 3D-LaneNet: F-score rises to 92.3% on Apollo 3D synthetic dataset, and F1 rises to 74.5% on ONCE-3DLanes. Meanwhile, WS-3D-Lane in purely supervised setting makes more increments and outperforms state-of-the-art. To the best of our knowledge, WS-3D-Lane is the first try of 3D lane detection under weakly supervised setting.	['Jiachen Zhong', 'Jiuhua Zhao', 'Wenbo Ding', 'Jianyong Ai']	2022-09-23	null	null	null	null	['3d-lane-detection', 'lane-detection']	['computer-vision', 'computer-vision']	[-1.08185500e-01 1.30079567e-01 -7.31526256e-01 -5.20021379e-01 -6.01775527e-01 -5.48916817e-01 4.47306007e-01 -2.19808266e-01 -1.95128113e-01 4.08602744e-01 7.90807009e-02 -7.27821946e-01 4.29506212e-01 -5.80145299e-01 -1.03420913e+00 -6.44475818e-01 -1.35156155e-01 3.11976261e-02 8.21027935e-01 -4.09969777e-01 3.60598207e-01 8.27233493e-01 -1.33966577e+00 -2.30462864e-01 8.30053389e-01 6.42872751e-01 8.36134404e-02 6.32506371e-01 -3.06032717e-01 4.05787915e-01 -2.54037172e-01 5.68862334e-02 7.62056947e-01 1.92608479e-02 2.51870275e-01 7.50641286e-01 1.02229857e+00 -4.73449439e-01 -4.73618954e-01 8.48488510e-01 2.35005453e-01 -4.59045656e-02 5.46296239e-01 -1.29831934e+00 -1.88554544e-02 -1.05525590e-02 -1.03470457e+00 7.09443539e-02 8.38767663e-02 4.23835158e-01 5.84737778e-01 -9.41847265e-01 4.80502516e-01 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f4dce6c0-0b8b-4082-9f4b-fd87649335c8	motiongpt-human-motion-as-a-foreign-language	2306.14795	null	https://arxiv.org/abs/2306.14795v1	https://arxiv.org/pdf/2306.14795v1.pdf	MotionGPT: Human Motion as a Foreign Language	Though the advancement of pre-trained large language models unfolds, the exploration of building a unified model for language and other multi-modal data, such as motion, remains challenging and untouched so far. Fortunately, human motion displays a semantic coupling akin to human language, often perceived as a form of body language. By fusing language data with large-scale motion models, motion-language pre-training that can enhance the performance of motion-related tasks becomes feasible. Driven by this insight, we propose MotionGPT, a unified, versatile, and user-friendly motion-language model to handle multiple motion-relevant tasks. Specifically, we employ the discrete vector quantization for human motion and transfer 3D motion into motion tokens, similar to the generation process of word tokens. Building upon this "motion vocabulary", we perform language modeling on both motion and text in a unified manner, treating human motion as a specific language. Moreover, inspired by prompt learning, we pre-train MotionGPT with a mixture of motion-language data and fine-tune it on prompt-based question-and-answer tasks. Extensive experiments demonstrate that MotionGPT achieves state-of-the-art performances on multiple motion tasks including text-driven motion generation, motion captioning, motion prediction, and motion in-between.	['Tao Chen', 'Gang Yu', 'Jingyi Yu', 'Wen Liu', 'Xin Chen', 'Biao Jiang']	2023-06-26	null	null	null	null	['motion-prediction', 'quantization']	['computer-vision', 'methodology']	[-1.69415817e-01 -1.85205787e-01 -6.32845044e-01 -6.41380772e-02 -9.77960229e-01 -4.60308313e-01 7.73403347e-01 -4.42739069e-01 -3.82790416e-01 3.28979820e-01 9.68383789e-01 -2.17775121e-01 4.44650888e-01 -7.52479255e-01 -6.81991637e-01 -6.15958095e-01 2.07174435e-01 2.67004341e-01 3.72468412e-01 -3.23366940e-01 2.09705010e-02 9.91885588e-02 -1.18559241e+00 4.28092688e-01 4.20520693e-01 6.30760074e-01 6.48258388e-01 9.79656994e-01 -3.17744404e-01 1.01182151e+00 -2.75858968e-01 -1.67597402e-02 -1.00687377e-01 -5.27944803e-01 -1.04579413e+00 -5.67182489e-02 3.70060563e-01 -5.18439770e-01 -7.19874024e-01 3.92477363e-01 6.75552309e-01 5.52594125e-01 7.19559133e-01 -1.20825398e+00 -1.03117335e+00 2.90342182e-01 -3.75585675e-01 4.37023491e-02 6.90244496e-01 6.75788224e-01 1.03495121e+00 -1.02357578e+00 1.11374617e+00 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0e78ede9-5945-4079-b675-ed77b47efffb	exploring-the-feasibility-of-chatgpt-for	2303.03836	null	https://arxiv.org/abs/2303.03836v2	https://arxiv.org/pdf/2303.03836v2.pdf	Exploring the Feasibility of ChatGPT for Event Extraction	Event extraction is a fundamental task in natural language processing that involves identifying and extracting information about events mentioned in text. However, it is a challenging task due to the lack of annotated data, which is expensive and time-consuming to obtain. The emergence of large language models (LLMs) such as ChatGPT provides an opportunity to solve language tasks with simple prompts without the need for task-specific datasets and fine-tuning. While ChatGPT has demonstrated impressive results in tasks like machine translation, text summarization, and question answering, it presents challenges when used for complex tasks like event extraction. Unlike other tasks, event extraction requires the model to be provided with a complex set of instructions defining all event types and their schemas. To explore the feasibility of ChatGPT for event extraction and the challenges it poses, we conducted a series of experiments. Our results show that ChatGPT has, on average, only 51.04% of the performance of a task-specific model such as EEQA in long-tail and complex scenarios. Our usability testing experiments indicate that ChatGPT is not robust enough, and continuous refinement of the prompt does not lead to stable performance improvements, which can result in a poor user experience. Besides, ChatGPT is highly sensitive to different prompt styles.	['Ruifeng Xu', 'Changlong Yu', 'Huan Zhao', 'Jun Gao']	2023-03-07	null	null	null	null	['event-extraction']	['natural-language-processing']	[ 9.51997936e-02 4.22240421e-02 1.91554889e-01 -3.33564043e-01 -1.25143671e+00 -8.39897335e-01 6.06397986e-01 7.58453131e-01 -6.68455064e-01 6.78485930e-01 4.37081844e-01 -4.70634550e-01 1.74147874e-01 -4.07198042e-01 -3.51380378e-01 1.09365936e-02 1.03709392e-01 5.33853292e-01 4.04151440e-01 -2.87618190e-01 8.03640783e-02 2.47705355e-02 -1.38685238e+00 5.44472694e-01 9.12420392e-01 6.97069466e-01 2.95615941e-01 7.56667614e-01 -5.68235636e-01 9.40412521e-01 -1.00725830e+00 -3.70986313e-01 -6.40849471e-02 -5.13005376e-01 -1.03043115e+00 -1.10773556e-01 1.89945221e-01 -3.19222659e-01 3.46949436e-02 5.28321266e-01 6.65518045e-01 2.64274240e-01 3.37326139e-01 -1.35409641e+00 -5.60051911e-02 6.83141530e-01 -1.72661677e-01 3.67413163e-01 8.53174865e-01 2.43037030e-01 9.80943441e-01 -6.56390965e-01 6.96660697e-01 1.10040545e+00 6.31150961e-01 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ff309013-8b9b-4fba-a4de-17b4daa71815	bounding-box-tightness-prior-for-weakly	2110.00934	null	https://arxiv.org/abs/2110.00934v1	https://arxiv.org/pdf/2110.00934v1.pdf	Bounding Box Tightness Prior for Weakly Supervised Image Segmentation	This paper presents a weakly supervised image segmentation method that adopts tight bounding box annotations. It proposes generalized multiple instance learning (MIL) and smooth maximum approximation to integrate the bounding box tightness prior into the deep neural network in an end-to-end manner. In generalized MIL, positive bags are defined by parallel crossing lines with a set of different angles, and negative bags are defined as individual pixels outside of any bounding boxes. Two variants of smooth maximum approximation, i.e., $\alpha$-softmax function and $\alpha$-quasimax function, are exploited to conquer the numeral instability introduced by maximum function of bag prediction. The proposed approach was evaluated on two pubic medical datasets using Dice coefficient. The results demonstrate that it outperforms the state-of-the-art methods. The codes are available at \url{https://github.com/wangjuan313/wsis-boundingbox}.	['Bin Xia', 'Juan Wang']	2021-10-03	null	null	null	null	['weakly-supervised-instance-segmentation']	['computer-vision']	[ 1.34551199e-02 5.16787231e-01 -5.23943424e-01 -7.31757522e-01 -1.14479029e+00 -2.93423444e-01 -4.93431762e-02 3.61375451e-01 -5.26396930e-01 8.84753644e-01 -2.65804321e-01 -1.83025241e-01 -1.94335617e-02 -6.15571618e-01 -1.05351877e+00 -7.31712639e-01 -1.74347103e-01 3.38709593e-01 2.51512617e-01 5.68303764e-02 2.07472041e-01 1.65564910e-01 -8.34954083e-01 5.34332693e-01 1.06309497e+00 1.40613723e+00 -1.28692240e-01 1.48589566e-01 -1.76484540e-01 5.37577629e-01 -5.22489071e-01 -4.91566420e-01 5.31628430e-01 -1.40681148e-01 -7.39878893e-01 1.83505367e-03 4.26261127e-01 -5.34140170e-01 -2.81772073e-02 1.05294704e+00 3.48551601e-01 6.39581531e-02 6.06817961e-01 -1.06183565e+00 -5.41832268e-01 5.94220042e-01 -1.09187007e+00 2.71938533e-01 -1.79092020e-01 -6.19956143e-02 1.01384974e+00 -7.53744245e-01 3.31543446e-01 9.30061340e-01 6.71616554e-01 4.25033838e-01 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26fc62a6-91ac-470c-a9ea-d36107eb33d9	bi-directional-training-for-composed-image	2303.16604	null	https://arxiv.org/abs/2303.16604v1	https://arxiv.org/pdf/2303.16604v1.pdf	Bi-directional Training for Composed Image Retrieval via Text Prompt Learning	Composed image retrieval searches for a target image based on a multi-modal user query comprised of a reference image and modification text describing the desired changes. Existing approaches to solving this challenging task learn a mapping from the (reference image, modification text)-pair to an image embedding that is then matched against a large image corpus. One area that has not yet been explored is the reverse direction, which asks the question, what reference image when modified as describe by the text would produce the given target image? In this work we propose a bi-directional training scheme that leverages such reversed queries and can be applied to existing composed image retrieval architectures. To encode the bi-directional query we prepend a learnable token to the modification text that designates the direction of the query and then finetune the parameters of the text embedding module. We make no other changes to the network architecture. Experiments on two standard datasets show that our novel approach achieves improved performance over a baseline BLIP-based model that itself already achieves state-of-the-art performance.	['Stephen Gould', 'Damien Teney', 'Yicong Hong', 'Weixuan Sun', 'Zheyuan Liu']	2023-03-29	null	null	null	null	['composed-image-retrieval']	['computer-vision']	[ 7.43868291e-01 -1.16180256e-01 -3.71208191e-01 -6.14023685e-01 -1.21822631e+00 -8.87221873e-01 1.00738299e+00 -1.02384135e-01 -7.50140786e-01 -4.91112517e-03 2.65974522e-01 -3.29155654e-01 -5.46441600e-02 -4.83400822e-01 -8.34512830e-01 -6.56590879e-01 4.25749034e-01 4.31153059e-01 4.85875160e-01 -1.51056260e-01 4.44357395e-01 3.76629233e-01 -1.47156739e+00 6.05950713e-01 -4.49030213e-02 1.01114655e+00 4.21351552e-01 7.96610296e-01 5.93975596e-02 4.28054869e-01 -3.93748701e-01 -4.33842033e-01 4.81733471e-01 -2.70865738e-01 -9.30230916e-01 1.26357540e-01 1.15028632e+00 -6.71198308e-01 -6.26967371e-01 8.18986714e-01 5.60952365e-01 1.63219765e-01 6.95924878e-01 -1.13517678e+00 -1.08885074e+00 3.99976581e-01 -4.27366018e-01 3.52731645e-01 2.92126596e-01 2.93596476e-01 1.21397460e+00 -9.04049277e-01 1.15048385e+00 1.26291466e+00 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3bbc6f1b-05bc-4471-8ee1-bf7b1ebcae27	vee-bert-accelerating-bert-inference-for	null	null	https://openreview.net/forum?id=3ffY9B-oiAm	https://openreview.net/pdf?id=3ffY9B-oiAm	VEE-BERT: Accelerating BERT Inference for Named Entity Recognition via Vote Early Exiting	Named entity recognition (NER) is of great importance for a wide range of tasks, such as medical health record understanding, document analysis, dialogue understanding. BERT and its variants are the most performing models for NER. However, these models are notorious for being large and slow during inference. Thus their usage in the industry is limited. Pilot experiments exhibit that in the NER task, BERT suffers from the severe over-thinking problem, thus motivating BERT to exit early at intermediate layers. Thus, in this work, we propose a novel method, \underline{V}ote \underline{E}arly \underline{E}xiting BERT (VEE-BERT), for improving the early exiting of BERT on NER tasks. To be able to deal with complex NER tasks with nested entities, we adopt the Biaffine NER model \citep{yu-etal-2020-named}, which converts a sequence labeling task to the table filling task. VEE-BERT makeS early exiting decisions by comparing the predictions of the current layer with those of the previous layers. Experiments on six benchmark NER tasks demonstrate that our method is effective in accelerating the BERT Biaffine model's inference speed with less performance loss compared to the baseline early exiting method.	['Anonymous']	2022-01-16	null	null	null	acl-arr-january-2022-1	['dialogue-understanding']	['natural-language-processing']	[-1.10785581e-01 4.70544547e-01 1.18157350e-01 -4.12251085e-01 -4.50166643e-01 -4.74090487e-01 2.86905408e-01 3.28441024e-01 -9.49388385e-01 9.38934267e-01 1.78202599e-01 -6.43251181e-01 4.33389582e-02 -8.44368756e-01 -5.29419065e-01 -3.00435275e-02 8.76797587e-02 7.48710155e-01 3.77276123e-01 -4.20315713e-01 -4.61742729e-02 5.42599738e-01 -9.25723493e-01 5.75780511e-01 1.05317366e+00 7.80311167e-01 2.21112624e-01 7.33480155e-01 -5.15787125e-01 1.05157959e+00 -7.09866583e-01 -8.98683548e-01 1.62154553e-04 -2.14752436e-01 -1.10938418e+00 -7.28271723e-01 -1.97360128e-01 -2.03174457e-01 -1.91239923e-01 8.86829555e-01 5.95989943e-01 4.23686832e-01 5.38751066e-01 -9.66534793e-01 -1.62182152e-01 1.09391725e+00 -1.02899827e-01 4.66090530e-01 2.63274521e-01 -3.13313574e-01 1.08708632e+00 -9.87635612e-01 7.43505180e-01 1.12761152e+00 1.12447631e+00 8.13411236e-01 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0fc664e6-5868-464c-829e-d324a1ac27cf	aspos-assamese-part-of-speech-tagger-using	2212.07043	null	https://arxiv.org/abs/2212.07043v1	https://arxiv.org/pdf/2212.07043v1.pdf	AsPOS: Assamese Part of Speech Tagger using Deep Learning Approach	Part of Speech (POS) tagging is crucial to Natural Language Processing (NLP). It is a well-studied topic in several resource-rich languages. However, the development of computational linguistic resources is still in its infancy despite the existence of numerous languages that are historically and literary rich. Assamese, an Indian scheduled language, spoken by more than 25 million people, falls under this category. In this paper, we present a Deep Learning (DL)-based POS tagger for Assamese. The development process is divided into two stages. In the first phase, several pre-trained word embeddings are employed to train several tagging models. This allows us to evaluate the performance of the word embeddings in the POS tagging task. The top-performing model from the first phase is employed to annotate another set of new sentences. In the second phase, the model is trained further using the fresh dataset. Finally, we attain a tagging accuracy of 86.52% in F1 score. The model may serve as a baseline for further study on DL-based Assamese POS tagging.	['Priyankoo Sarmah', 'Sukumar Nandi', 'Dhrubajyoti Pathak']	2022-12-14	null	null	null	null	['part-of-speech-tagging']	['natural-language-processing']	[-1.00688569e-01 -1.02414517e-02 -2.88442373e-01 -3.42723250e-01 -9.65345979e-01 -6.28341377e-01 5.82926333e-01 4.64582413e-01 -8.26098025e-01 8.67233455e-01 3.69132191e-01 -3.09446365e-01 4.82734889e-01 -6.37533605e-01 -2.17984915e-01 -5.90522885e-01 -9.87967551e-02 5.91202319e-01 5.72339475e-01 -1.95525140e-01 2.67505109e-01 6.23625040e-01 -9.37191367e-01 1.30173430e-01 7.56145477e-01 6.49651170e-01 4.88147855e-01 3.44599783e-01 -7.16914117e-01 5.51254272e-01 -6.31881595e-01 -7.47351348e-01 -1.15841292e-01 -1.87714070e-01 -9.25412595e-01 -1.78905740e-01 -2.02371925e-02 2.95725405e-01 1.60868373e-02 1.18721867e+00 6.11158133e-01 1.29780978e-01 3.80582839e-01 -6.91153049e-01 -7.52563298e-01 9.51396048e-01 -2.24506021e-01 4.17743593e-01 1.52337074e-01 -4.27718908e-01 1.26429629e+00 -1.32541645e+00 6.08137846e-01 1.00587511e+00 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272d2214-186b-4fd0-a3e2-837ec5c90ff3	a-new-perspective-on-building-efficient-and	2304.04757	null	https://arxiv.org/abs/2304.04757v1	https://arxiv.org/pdf/2304.04757v1.pdf	A new perspective on building efficient and expressive 3D equivariant graph neural networks	Geometric deep learning enables the encoding of physical symmetries in modeling 3D objects. Despite rapid progress in encoding 3D symmetries into Graph Neural Networks (GNNs), a comprehensive evaluation of the expressiveness of these networks through a local-to-global analysis lacks today. In this paper, we propose a local hierarchy of 3D isomorphism to evaluate the expressive power of equivariant GNNs and investigate the process of representing global geometric information from local patches. Our work leads to two crucial modules for designing expressive and efficient geometric GNNs; namely local substructure encoding (LSE) and frame transition encoding (FTE). To demonstrate the applicability of our theory, we propose LEFTNet which effectively implements these modules and achieves state-of-the-art performance on both scalar-valued and vector-valued molecular property prediction tasks. We further point out the design space for future developments of equivariant graph neural networks. Our codes are available at \url{https://github.com/yuanqidu/LeftNet}.	['Zhi-Ming Ma', 'Carla Gomes', 'Shuiwang Ji', 'Guifeng Wang', 'Dieqiao Feng', 'Limei Wang', 'Yuanqi Du', 'Weitao Du']	2023-04-07	null	null	null	null	['molecular-property-prediction']	['miscellaneous']	[ 1.56279102e-01 8.98858681e-02 -2.82962322e-01 -3.42384189e-01 -3.24874669e-01 -6.45448565e-01 7.32684314e-01 5.15233278e-02 1.48009613e-01 5.78272581e-01 2.78026104e-01 -6.77793503e-01 -3.98725092e-01 -1.06302226e+00 -9.87767220e-01 -7.44431734e-01 -4.80260134e-01 3.37255090e-01 -7.81426206e-02 -4.10191059e-01 6.39353842e-02 1.04836452e+00 -1.04916251e+00 1.79598093e-01 6.25269353e-01 1.04334545e+00 -1.82345659e-01 6.92916632e-01 1.40104994e-01 9.92692053e-01 5.07954992e-02 -3.05788904e-01 5.44951081e-01 -5.23993850e-01 -1.03119373e+00 -2.78352320e-01 6.87842667e-01 -4.02246326e-01 -7.78079808e-01 8.76565814e-01 6.42403305e-01 1.41851366e-01 6.62078798e-01 -1.08752096e+00 -9.37012970e-01 4.51601267e-01 -1.07579092e-02 2.21746862e-01 1.65113539e-01 3.08734864e-01 1.42045069e+00 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377807a4-f893-4b0a-88f1-aec5039f4d52	qmagface-simple-and-accurate-quality-aware	2111.13475	null	https://arxiv.org/abs/2111.13475v3	https://arxiv.org/pdf/2111.13475v3.pdf	QMagFace: Simple and Accurate Quality-Aware Face Recognition	Face recognition systems have to deal with large variabilities (such as different poses, illuminations, and expressions) that might lead to incorrect matching decisions. These variabilities can be measured in terms of face image quality which is defined over the utility of a sample for recognition. Previous works on face recognition either do not employ this valuable information or make use of non-inherently fit quality estimates. In this work, we propose a simple and effective face recognition solution (QMagFace) that combines a quality-aware comparison score with a recognition model based on a magnitude-aware angular margin loss. The proposed approach includes model-specific face image qualities in the comparison process to enhance the recognition performance under unconstrained circumstances. Exploiting the linearity between the qualities and their comparison scores induced by the utilized loss, our quality-aware comparison function is simple and highly generalizable. The experiments conducted on several face recognition databases and benchmarks demonstrate that the introduced quality-awareness leads to consistent improvements in the recognition performance. Moreover, the proposed QMagFace approach performs especially well under challenging circumstances, such as cross-pose, cross-age, or cross-quality. Consequently, it leads to state-of-the-art performances on several face recognition benchmarks, such as 98.50% on AgeDB, 83.95% on XQLFQ, and 98.74% on CFP-FP. The code for QMagFace is publicly available	['Arjan Kuijper', 'Kiran Raja', 'Florian Kirchbuchner', 'Naser Damer', 'Marco Huber', 'Malte Ihlefeld', 'Philipp Terhörst']	2021-11-26	null	null	null	null	['face-image-quality']	['computer-vision']	[ 1.03953265e-01 -3.22377026e-01 -9.06028375e-02 -7.79639065e-01 -7.88005829e-01 -1.99361354e-01 5.33220649e-01 -3.90251756e-01 -1.82824925e-01 4.59928125e-01 -2.18034297e-01 1.69094428e-01 -3.99221778e-01 -6.58617854e-01 -6.62697017e-01 -8.80776882e-01 8.62837434e-02 1.08326674e-01 -3.10586244e-01 8.99155217e-04 1.80596143e-01 7.87392378e-01 -1.86662579e+00 1.35249775e-02 9.16557491e-01 1.54642940e+00 -3.06313187e-01 1.07169427e-01 1.12765469e-01 -4.50652055e-02 -6.82378173e-01 -8.19920897e-01 4.47390229e-01 -1.98752373e-01 -2.42175579e-01 1.02700628e-01 8.20205986e-01 -1.00008637e-01 -7.41560832e-02 1.11956513e+00 7.49423265e-01 -1.45545945e-01 6.00291610e-01 -1.33669269e+00 -4.42682534e-01 1.23079342e-03 -6.56657577e-01 -1.02913994e-02 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c8acf130-93ec-4bb4-8340-a06bd19647cb	sokoto-coventry-fingerprint-dataset	1807.10609	null	http://arxiv.org/abs/1807.10609v1	http://arxiv.org/pdf/1807.10609v1.pdf	Sokoto Coventry Fingerprint Dataset	This paper presents the Sokoto Coventry Fingerprint Dataset (SOCOFing), a biometric fingerprint database designed for academic research purposes. SOCOFing is made up of 6,000 fingerprint images from 600 African subjects. SOCOFing contains unique attributes such as labels for gender, hand and finger name as well as synthetically altered versions with three different levels of alteration for obliteration, central rotation, and z-cut. The dataset is freely available for noncommercial research purposes at: https://www.kaggle.com/ruizgara/socofing	['Ariel Ruiz-Garcia', 'Yahaya Isah Shehu', 'Vasile Palade', 'Anne James']	2018-07-24	null	null	null	null	['gender-prediction']	['computer-vision']	[ 1.14560224e-01 -1.91576723e-02 -5.41884720e-01 -4.86461252e-01 9.80971530e-02 -9.68745172e-01 4.78799373e-01 -5.55642648e-03 -2.81156093e-01 8.48974764e-01 2.81325784e-02 -6.25331163e-01 -1.07854456e-01 -7.92136431e-01 -5.75573385e-01 -2.18002483e-01 -1.00517027e-01 3.92893106e-01 -2.41331100e-01 5.26738390e-02 6.34336531e-01 9.43891764e-01 -1.19569623e+00 -5.89156174e-04 6.23248816e-01 7.36469030e-01 -2.81573504e-01 8.19490552e-01 5.26786745e-01 -1.19517297e-02 -4.29420978e-01 -9.69100296e-01 4.75728095e-01 1.30742222e-01 -5.86894751e-01 -6.70236707e-01 9.66645300e-01 -8.04144800e-01 -2.46083304e-01 8.67288828e-01 5.92915893e-01 -4.70246255e-01 5.24555206e-01 -1.31434321e+00 -7.91517079e-01 3.37336749e-01 -5.44458508e-01 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9.18112621e-02 4.26063873e-02 1.11377120e+00 1.98905706e-01 3.50883007e-01 -2.61888623e-01 5.54875970e-01 -6.25922263e-01 -7.45828375e-02 -1.02857697e+00 -1.00363719e+00 1.39460951e-01 3.14808518e-01 -8.03502738e-01 1.08064376e-01 -6.97133467e-02]	[13.000273704528809, 1.0132246017456055]
10ac37ef-86ec-4f67-b439-87d98b8ab95f	using-clinical-narratives-and-structured-data	1806.04818	null	http://arxiv.org/abs/1806.04818v2	http://arxiv.org/pdf/1806.04818v2.pdf	Using Clinical Narratives and Structured Data to Identify Distant Recurrences in Breast Cancer	Accurately identifying distant recurrences in breast cancer from the Electronic Health Records (EHR) is important for both clinical care and secondary analysis. Although multiple applications have been developed for computational phenotyping in breast cancer, distant recurrence identification still relies heavily on manual chart review. In this study, we aim to develop a model that identifies distant recurrences in breast cancer using clinical narratives and structured data from EHR. We apply MetaMap to extract features from clinical narratives and also retrieve structured clinical data from EHR. Using these features, we train a support vector machine model to identify distant recurrences in breast cancer patients. We train the model using 1,396 double-annotated subjects and validate the model using 599 double-annotated subjects. In addition, we validate the model on a set of 4,904 single-annotated subjects as a generalization test. We obtained a high area under curve (AUC) score of 0.92 (SD=0.01) in the cross-validation using the training dataset, then obtained AUC scores of 0.95 and 0.93 in the held-out test and generalization test using 599 and 4,904 samples respectively. Our model can accurately and efficiently identify distant recurrences in breast cancer by combining features extracted from unstructured clinical narratives and structured clinical data.	['Xiaoyu Li', 'Yuan Luo', 'Susan Clare', 'Zexian Zeng', 'Ankita Roy', 'Seema Khan', 'Sasa Espino']	2018-06-13	null	null	null	null	['computational-phenotyping']	['medical']	[ 1.13685936e-01 2.26500630e-01 -6.46440566e-01 -4.73082960e-01 -1.47251236e+00 -4.18212205e-01 1.49850518e-01 1.03446722e+00 -3.21965516e-01 1.00670338e+00 4.82669294e-01 -4.16270554e-01 -4.57504570e-01 -7.05769360e-01 -1.69929743e-01 -4.74520415e-01 -1.64615139e-01 3.92845720e-01 -2.78258204e-01 3.99503440e-01 -2.04412490e-01 4.22144532e-01 -8.93726051e-01 8.44766557e-01 1.00550365e+00 9.83819842e-01 -1.95496082e-01 8.68943274e-01 2.49092266e-01 1.25645828e+00 -5.18798828e-01 -4.17374849e-01 -2.62023866e-01 -5.14763474e-01 -7.80961335e-01 -1.55357569e-01 -3.14802617e-01 1.82562997e-03 -1.47956282e-01 5.23465514e-01 4.81157601e-01 -6.24448776e-01 6.57274008e-01 -8.55215251e-01 -5.23266017e-01 4.81786609e-01 -2.53072619e-01 2.08958834e-02 6.31785035e-01 -1.29877374e-01 8.95760596e-01 -6.49168432e-01 1.10413504e+00 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06340fee-2d0e-4018-aac0-e37c6e33a34a	bingham-policy-parameterization-for-3d	2202.03957	null	https://arxiv.org/abs/2202.03957v1	https://arxiv.org/pdf/2202.03957v1.pdf	Bingham Policy Parameterization for 3D Rotations in Reinforcement Learning	We propose a new policy parameterization for representing 3D rotations during reinforcement learning. Today in the continuous control reinforcement learning literature, many stochastic policy parameterizations are Gaussian. We argue that universally applying a Gaussian policy parameterization is not always desirable for all environments. One such case in particular where this is true are tasks that involve predicting a 3D rotation output, either in isolation, or coupled with translation as part of a full 6D pose output. Our proposed Bingham Policy Parameterization (BPP) models the Bingham distribution and allows for better rotation (quaternion) prediction over a Gaussian policy parameterization in a range of reinforcement learning tasks. We evaluate BPP on the rotation Wahba problem task, as well as a set of vision-based next-best pose robot manipulation tasks from RLBench. We hope that this paper encourages more research into developing other policy parameterization that are more suited for particular environments, rather than always assuming Gaussian.	['Pieter Abbeel', 'Stephen James']	2022-02-08	null	null	null	null	['robot-manipulation']	['robots']	[-1.13797098e-01 8.34931433e-02 -5.21017015e-01 -1.33913651e-01 -5.85973859e-01 -6.05356455e-01 7.46891379e-01 -7.78707340e-02 -9.08751428e-01 1.08702302e+00 5.50863817e-02 -5.11067092e-01 -1.27554998e-01 -4.32891369e-01 -7.72931278e-01 -9.95975792e-01 -7.12305456e-02 8.81415904e-01 6.15932792e-03 -5.91279030e-01 5.03918648e-01 8.52245271e-01 -1.22658563e+00 -4.81970102e-01 6.53942406e-01 5.05265474e-01 4.83724624e-01 8.75239193e-01 4.13511306e-01 4.99373794e-01 -5.28294265e-01 1.70322746e-01 4.40382957e-01 -1.77089572e-01 -7.43012547e-01 5.28447367e-02 1.79498628e-01 -4.07494724e-01 -7.45759159e-02 9.23405886e-01 5.49029350e-01 5.82698584e-01 9.39066052e-01 -1.21377265e+00 -2.83189327e-01 4.98346478e-01 -5.03215969e-01 3.70355025e-02 3.35304797e-01 4.85245883e-01 8.65749478e-01 -6.29862174e-02 6.28657103e-01 1.63510442e+00 3.42168987e-01 5.02994359e-01 -1.38670444e+00 -4.02340531e-01 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418f8685-5ece-45de-a05a-4e0386a9c9f5	comprehensive-punctuation-restoration-for	null	null	https://aclanthology.org/2021.findings-emnlp.393	https://aclanthology.org/2021.findings-emnlp.393.pdf	Comprehensive Punctuation Restoration for English and Polish	Punctuation restoration is a fundamental requirement for the readability of text derived from Automatic Speech Recognition (ASR) systems. Most contemporary solutions are limited to predicting only a few of the most frequently occurring marks, such as periods, commas, and question marks - and only one per word. However, in written language, we deal with a much larger number of punctuation characters (such as parentheses, hyphens, etc.), and their combinations (like parenthesis followed by a dot). Such comprehensive punctuation cannot always be unambiguously reduced to a basic set of the most frequently occurring marks. In this work, we evaluate several methods in the comprehensive punctuation reconstruction task. We conduct experiments on parallel corpora of two different languages, English and Polish - languages with a relatively simple and complex morphology, respectively. We also investigate the influence of building a model on comprehensive punctuation on the quality of the basic punctuation restoration task	['Tomasz Walkowiak', 'Michał Pogoda']	null	null	null	null	findings-emnlp-2021-11	['punctuation-restoration']	['natural-language-processing']	[ 2.90880322e-01 -2.37025976e-01 1.97393566e-01 -2.27121934e-01 -6.94391251e-01 -6.68639958e-01 5.59285104e-01 6.06038153e-01 -6.81260526e-01 1.01106811e+00 1.13602929e-01 -6.99986160e-01 7.88331963e-03 -3.33628923e-01 -6.06741250e-01 -4.59918588e-01 3.22318524e-01 2.64426649e-01 3.23869318e-01 -3.74011517e-01 3.17753047e-01 5.67834258e-01 -1.47510636e+00 1.57738790e-01 1.11477888e+00 4.40254986e-01 7.17597008e-01 3.98985416e-01 -6.47670805e-01 4.72579807e-01 -1.09655750e+00 -4.31790322e-01 -1.95239753e-01 -4.11005676e-01 -6.92961991e-01 4.74374950e-01 1.32445037e-01 1.44384518e-01 -1.09783977e-01 1.22973633e+00 1.39466926e-01 5.56238554e-03 7.18849242e-01 -5.65122485e-01 -4.40272540e-01 1.00812328e+00 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2931fba5-795a-4370-983d-be78419c6dca	counterfactual-multi-agent-policy-gradients	1705.08926	null	http://arxiv.org/abs/1705.08926v2	http://arxiv.org/pdf/1705.08926v2.pdf	Counterfactual Multi-Agent Policy Gradients	Cooperative multi-agent systems can be naturally used to model many real world problems, such as network packet routing and the coordination of autonomous vehicles. There is a great need for new reinforcement learning methods that can efficiently learn decentralised policies for such systems. To this end, we propose a new multi-agent actor-critic method called counterfactual multi-agent (COMA) policy gradients. COMA uses a centralised critic to estimate the Q-function and decentralised actors to optimise the agents' policies. In addition, to address the challenges of multi-agent credit assignment, it uses a counterfactual baseline that marginalises out a single agent's action, while keeping the other agents' actions fixed. COMA also uses a critic representation that allows the counterfactual baseline to be computed efficiently in a single forward pass. We evaluate COMA in the testbed of StarCraft unit micromanagement, using a decentralised variant with significant partial observability. COMA significantly improves average performance over other multi-agent actor-critic methods in this setting, and the best performing agents are competitive with state-of-the-art centralised controllers that get access to the full state.	['Nantas Nardelli', 'Gregory Farquhar', 'Triantafyllos Afouras', 'Shimon Whiteson', 'Jakob Foerster']	2017-05-24	null	null	null	null	['smac-1']	['playing-games']	[-6.11301422e-01 5.47675133e-01 -2.83495188e-01 4.72441539e-02 -6.22017503e-01 -3.43690902e-01 9.13738906e-01 8.63530114e-02 -8.70365560e-01 1.30848312e+00 -1.29277676e-01 -2.66934156e-01 -1.58427641e-01 -5.10222793e-01 -7.04408169e-01 -9.09289718e-01 -5.59720218e-01 1.02809191e+00 3.96074384e-01 -5.84314525e-01 -1.00723095e-01 4.72411454e-01 -1.25970018e+00 -3.47242653e-01 6.24720812e-01 6.16084754e-01 8.07937756e-02 1.14852226e+00 5.81068337e-01 1.37761343e+00 -9.08721149e-01 -6.91837296e-02 3.95396233e-01 -3.28631043e-01 -5.33583879e-01 1.47404566e-01 -8.01815093e-02 -6.16265714e-01 -9.10141468e-02 7.57728755e-01 7.33548343e-01 3.78083766e-01 5.14851868e-01 -2.07177305e+00 2.09653348e-01 5.16779721e-01 -6.14484429e-01 1.33223161e-01 -2.13296741e-01 7.79104173e-01 8.83747160e-01 1.11048043e-01 3.60717893e-01 1.73469925e+00 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84b7a42d-07a7-49e4-a648-edd0952523a9	ceu-net-ensemble-semantic-segmentation-of	2203.04873	null	https://arxiv.org/abs/2203.04873v2	https://arxiv.org/pdf/2203.04873v2.pdf	CEU-Net: Ensemble Semantic Segmentation of Hyperspectral Images Using Clustering	Most semantic segmentation approaches of Hyperspectral images (HSIs) use and require preprocessing steps in the form of patching to accurately classify diversified land cover in remotely sensed images. These approaches use patching to incorporate the rich neighborhood information in images and exploit the simplicity and segmentability of the most common HSI datasets. In contrast, most landmasses in the world consist of overlapping and diffused classes, making neighborhood information weaker than what is seen in common HSI datasets. To combat this issue and generalize the segmentation models to more complex and diverse HSI datasets, in this work, we propose our novel flagship model: Clustering Ensemble U-Net (CEU-Net). CEU-Net uses the ensemble method to combine spectral information extracted from convolutional neural network (CNN) training on a cluster of landscape pixels. Our CEU-Net model outperforms existing state-of-the-art HSI semantic segmentation methods and gets competitive performance with and without patching when compared to baseline models. We highlight CEU-Net's high performance across Botswana, KSC, and Salinas datasets compared to HybridSN and AeroRIT methods.	['Salimeh Yasaei Sekeh', 'Nicholas Soucy']	2022-03-09	null	null	null	null	['clustering-ensemble']	['graphs']	[ 6.86290383e-01 -2.10894838e-01 -1.82942197e-01 -5.37085414e-01 -5.01767159e-01 -9.43727374e-01 2.51267403e-01 -1.02610409e-01 -2.72881985e-01 7.45237708e-01 -1.09977305e-01 -7.43104160e-01 -3.62187654e-01 -1.35442340e+00 -5.22162795e-01 -8.24630320e-01 -2.91968375e-01 3.19946855e-01 3.90911192e-01 -5.42391300e-01 -1.05465747e-01 6.72668338e-01 -1.78445005e+00 2.94009060e-01 1.51256526e+00 1.05439782e+00 4.03007507e-01 4.01728213e-01 -1.15298770e-01 1.97733551e-01 -2.73339719e-01 4.32038873e-01 9.22156274e-01 -5.03995299e-01 -8.62134397e-01 1.87799662e-01 9.00802791e-01 -3.54278982e-01 -1.03469141e-01 1.41205502e+00 2.79726148e-01 4.04780477e-01 6.98953032e-01 -1.00165594e+00 -2.98605710e-01 7.92690516e-01 -9.56252396e-01 5.33794202e-02 -4.11381871e-01 -9.48492903e-03 9.79925692e-01 -3.99162084e-01 4.04829681e-01 8.77478540e-01 1.21694756e+00 -1.33993961e-02 -1.28010941e+00 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-1.06560969e+00 -1.22640121e+00 -4.35910709e-02 3.78319860e-01 -5.37621200e-01 -4.21531141e-01 -3.11081056e-02]	[9.438397407531738, -1.4531651735305786]
631a8dc8-e8b1-4585-a160-e13f170cea5a	how-informative-is-the-approximation-error	2305.05318	null	https://arxiv.org/abs/2305.05318v1	https://arxiv.org/pdf/2305.05318v1.pdf	How Informative is the Approximation Error from Tensor Decomposition for Neural Network Compression?	Tensor decompositions have been successfully applied to compress neural networks. The compression algorithms using tensor decompositions commonly minimize the approximation error on the weights. Recent work assumes the approximation error on the weights is a proxy for the performance of the model to compress multiple layers and fine-tune the compressed model. Surprisingly, little research has systematically evaluated which approximation errors can be used to make choices regarding the layer, tensor decomposition method, and level of compression. To close this gap, we perform an experimental study to test if this assumption holds across different layers and types of decompositions, and what the effect of fine-tuning is. We include the approximation error on the features resulting from a compressed layer in our analysis to test if this provides a better proxy, as it explicitly takes the data into account. We find the approximation error on the weights has a positive correlation with the performance error, before as well as after fine-tuning. Basing the approximation error on the features does not improve the correlation significantly. While scaling the approximation error commonly is used to account for the different sizes of layers, the average correlation across layers is smaller than across all choices (i.e. layers, decompositions, and level of compression) before fine-tuning. When calculating the correlation across the different decompositions, the average rank correlation is larger than across all choices. This means multiple decompositions can be considered for compression and the approximation error can be used to choose between them.	['Julian F. P. Kooij', 'Kim Batselier', 'Jetze T. Schuurmans']	2023-05-09	null	null	null	null	['neural-network-compression', 'neural-network-compression']	['methodology', 'miscellaneous']	[-2.34237358e-01 -1.82179660e-01 -1.49404883e-01 -3.26883942e-01 -9.50597599e-02 -3.76546115e-01 4.50558484e-01 4.16806430e-01 -8.09648275e-01 1.81060761e-01 5.02580762e-01 -4.43256974e-01 -5.29322445e-01 -8.20132554e-01 -5.88834107e-01 -7.20790386e-01 -1.70205459e-01 3.04712445e-01 4.05010790e-01 -2.43411660e-02 3.15973550e-01 4.78842318e-01 -1.68403804e+00 5.30635893e-01 6.24658823e-01 1.08564830e+00 2.01559916e-01 3.85915577e-01 -2.51650274e-01 4.48192716e-01 -6.73459649e-01 -7.59382188e-01 5.60987830e-01 -2.29557484e-01 -8.65369260e-01 3.87150981e-02 4.03168619e-01 -4.65718120e-01 -6.06255829e-02 1.04833853e+00 1.92147031e-01 -5.74658364e-02 4.54654187e-01 -8.85343432e-01 -1.19035751e-01 1.25710833e+00 -4.84197319e-01 4.23595756e-01 -5.10311276e-02 9.54514593e-02 1.19898677e+00 -3.02169234e-01 5.39265990e-01 1.30524004e+00 8.66416037e-01 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75f8b814-696e-4cff-a0d8-68299a23e520	convolutional-sequence-generation-for	null	null	http://yjxiong.me/papers/iccv19csgn.pdf	http://www.dahualin.org/publications/dhl19_csgn.pdf	Convolutional Sequence Generation for Skeleton-Based Action Synthesis	In this work, we aim to generate long actions represented as sequences of skeletons. The generated sequences must demonstrate continuous, meaningful human actions, while maintaining coherence among body parts. Instead of generating skeletons sequentially following an autoregressive model, we propose a framework that generates the entire sequence altogether by ransforming from a sequence of latent vectors sampled from a Gaussian process (GP). This framework, named Convolutional Sequence Generation Network (CSGN), jointly models structures in temporal and spatial dimensions. It captures the temporal structure at multiple scales through the GP prior and the temporal convolutions; and establishes the spatial connection between the latent vectors and the skeleton graphs via a novel graph refining scheme. It is noteworthy that CSGN allows bidirectional transforms between the latent and the observed spaces, thus enabling semantic manipulation of the action sequences in various forms. We conducted empirical studies on multiple datasets, including a set of high-quality dancing sequences collected by us. The results show that our framework can produce long action sequences that are coherent across time steps and among body parts.	['Huahan Yan', 'Zhizhong Li', 'Yuanjun Xiong', 'Sijie Yan']	2019-10-27	convolutional-sequence-generation-for-1	http://openaccess.thecvf.com/content_ICCV_2019/html/Yan_Convolutional_Sequence_Generation_for_Skeleton-Based_Action_Synthesis_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Yan_Convolutional_Sequence_Generation_for_Skeleton-Based_Action_Synthesis_ICCV_2019_paper.pdf	iccv-2019-2019-10	['human-action-generation']	['computer-vision']	[ 5.10778427e-01 2.18042359e-01 9.44743231e-02 -1.48335528e-02 -3.78905147e-01 -4.27456528e-01 8.19370508e-01 -6.45453990e-01 1.02168582e-01 5.19406974e-01 6.67750597e-01 3.08761299e-01 -1.95757911e-01 -1.00219059e+00 -7.67366469e-01 -5.89492798e-01 -2.59142131e-01 3.65835994e-01 2.07828879e-01 -1.04170069e-01 3.06993276e-02 4.78622794e-01 -1.57129836e+00 2.58801967e-01 6.67545438e-01 5.42966068e-01 2.28876457e-01 9.88178134e-01 4.19846065e-02 9.84345257e-01 -5.60474932e-01 -4.84570920e-01 2.36630425e-01 -7.36168623e-01 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88631c41-fefe-4c7b-9a8b-283eb8ce89f3	cloud-detection-through-wavelet-transforms-in	2007.13678	null	https://arxiv.org/abs/2007.13678v1	https://arxiv.org/pdf/2007.13678v1.pdf	Cloud Detection through Wavelet Transforms in Machine Learning and Deep Learning	Cloud detection is a specialized application of image recognition and object detection using remotely sensed data. The task presents a number of challenges, including analyzing images obtained in visible, infrared and multi-spectral frequencies, usually without ground truth data for comparison. Moreover, machine learning and deep learning (MLDL) algorithms applied to this task are required to be computationally efficient, as they are typically deployed in low-power devices and called to operate in real-time. This paper explains Wavelet Transform (WT) theory, comparing it to more widely used image and signal processing transforms, and explores the use of WT as a powerful signal compressor and feature extractor for MLDL classifiers.	['Philippe Reiter']	2020-07-10	null	null	null	null	['cloud-detection']	['computer-vision']	[ 7.81538129e-01 -9.20005798e-01 -1.07904963e-01 2.00189445e-02 -6.17719650e-01 -3.82411778e-01 3.98678660e-01 3.95979136e-02 -4.87498909e-01 3.55022937e-01 -5.65252125e-01 -4.26853240e-01 -2.67042935e-01 -1.06407535e+00 -1.12848155e-01 -1.09986758e+00 -3.66022855e-01 -1.15421742e-01 -1.97914526e-01 5.18913753e-02 -2.50725038e-02 9.32771385e-01 -1.81715512e+00 3.25067341e-01 6.56445861e-01 1.63499069e+00 5.06494105e-01 9.42663968e-01 -7.95391276e-02 6.71346068e-01 -6.82364643e-01 -9.46883298e-03 4.18888509e-01 -4.71686535e-02 -2.70502746e-01 3.36459935e-01 5.01092613e-01 -3.69358003e-01 -2.09942609e-01 1.21816790e+00 8.09768558e-01 -1.04939193e-02 5.39294302e-01 -1.11718118e+00 -6.40651047e-01 -6.84652245e-03 -5.83929718e-01 7.86323071e-01 9.89132747e-02 7.01270476e-02 6.65449977e-01 -1.17945254e+00 9.02605504e-02 7.99902618e-01 7.40402102e-01 -2.72451378e-02 -1.01395702e+00 -5.06246924e-01 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40756929-e78d-4c26-a418-75c88ec35fb6	bag-of-words-forced-decoding-for-cross	null	null	https://aclanthology.info/papers/N15-1123/n15-1123	https://www.aclweb.org/anthology/N15-1123	Bag-of-Words Forced Decoding for Cross-Lingual Information Retrieval	null	['Felix Hieber', 'Stefan Riezler']	2015-05-01	null	null	null	hlt-2015-5	['cross-lingual-information-retrieval']	['natural-language-processing']	[-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01 -8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01 -5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01 -2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01 -7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302e-01 8.47113907e-01 6.29996777e-01 -1.62287033e+00 5.77558100e-01 6.81926727e-01 5.49597681e-01 5.66466339e-02 1.04480565e+00 6.13576770e-02 1.59847176e+00 -6.05450153e-01 -4.56729174e-01 1.43710867e-01 -1.88022718e-01 -5.35770595e-01 -3.36825520e-01 7.91536197e-02 -5.88986814e-01 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c39cc154-b01f-4f84-a213-18e1165c5848	figments-and-misalignments-a-framework-for	2304.14133	null	https://arxiv.org/abs/2304.14133v1	https://arxiv.org/pdf/2304.14133v1.pdf	Figments and Misalignments: A Framework for Fine-grained Crossmodal Misinformation Detection	Multimedia content has become ubiquitous on social media platforms, leading to the rise of multimodal misinformation and the urgent need for effective strategies to detect and prevent its spread. This study focuses on CrossModal Misinformation (CMM) where image-caption pairs work together to spread falsehoods. We contrast CMM with Asymmetric Multimodal Misinformation (AMM), where one dominant modality propagates falsehoods while other modalities have little or no influence. We show that AMM adds noise to the training and evaluation process while exacerbating the unimodal bias, where text-only or image-only detectors can seemingly outperform their multimodal counterparts on an inherently multimodal task. To address this issue, we collect and curate FIGMENTS, a robust evaluation benchmark for CMM, which consists of real world cases of misinformation, excludes AMM and utilizes modality balancing to successfully alleviate unimodal bias. FIGMENTS also provides a first step towards fine-grained CMM detection by including three classes: truthful, out-of-context, and miscaptioned image-caption pairs. Furthermore, we introduce a method for generating realistic synthetic training data that maintains crossmodal relations between legitimate images and false human-written captions that we term Crossmodal HArd Synthetic MisAlignment (CHASMA). We conduct extensive comparative study using a Transformer-based architecture. Our results show that incorporating CHASMA in conjunction with other generated datasets consistently improved the overall performance on FIGMENTS in both binary (+6.26%) and multiclass settings (+15.8%).We release our code at: https://github.com/stevejpapad/figments-and-misalignments	['Panagiotis C. Petrantonakis', 'Symeon Papadopoulos', 'Christos Koutlis', 'Stefanos-Iordanis Papadopoulos']	2023-04-27	null	null	null	null	['misinformation']	['miscellaneous']	[ 3.89696985e-01 -1.86623819e-02 4.41449732e-02 -2.86662906e-01 -1.34685814e+00 -9.59838688e-01 1.17540109e+00 -6.55172393e-02 -3.50327075e-01 7.18258560e-01 1.63002089e-01 -4.63432312e-01 4.05820191e-01 -3.20873529e-01 -1.02809489e+00 -5.05039930e-01 2.79096544e-01 3.74677241e-01 9.08881351e-02 -3.69047552e-01 1.51524603e-01 -3.91265983e-03 -1.29630136e+00 1.00654101e+00 8.88894856e-01 7.64441788e-01 -1.73341125e-01 7.08391070e-01 -5.01590734e-03 6.51820421e-01 -8.23484004e-01 -1.26398480e+00 6.71714917e-02 -3.56539845e-01 -6.59586847e-01 1.02522366e-01 9.67065811e-01 -2.97617644e-01 -3.37643266e-01 1.24547780e+00 5.70432246e-01 -3.08898598e-01 8.35993886e-01 -1.71470881e+00 -5.57146490e-01 6.10655725e-01 -8.57150793e-01 1.41602457e-01 4.98449713e-01 5.53878069e-01 5.62662125e-01 -1.27346969e+00 5.71677148e-01 1.70939326e+00 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32c5db28-5c82-4204-b345-08baa64db9ab	augmented-2d-tan-a-two-stage-approach-for	2106.10634	null	https://arxiv.org/abs/2106.10634v2	https://arxiv.org/pdf/2106.10634v2.pdf	Augmented 2D-TAN: A Two-stage Approach for Human-centric Spatio-Temporal Video Grounding	We propose an effective two-stage approach to tackle the problem of language-based Human-centric Spatio-Temporal Video Grounding (HC-STVG) task. In the first stage, we propose an Augmented 2D Temporal Adjacent Network (Augmented 2D-TAN) to temporally ground the target moment corresponding to the given description. Primarily, we improve the original 2D-TAN from two aspects: First, a temporal context-aware Bi-LSTM Aggregation Module is developed to aggregate clip-level representations, replacing the original max-pooling. Second, we propose to employ Random Concatenation Augmentation (RCA) mechanism during the training phase. In the second stage, we use pretrained MDETR model to generate per-frame bounding boxes via language query, and design a set of hand-crafted rules to select the best matching bounding box outputted by MDETR for each frame within the grounded moment.	['Wei-Shi Zheng', 'Xiang Li', 'Jian-Fang Hu', 'Zihang Lin', 'Chaolei Tan']	2021-06-20	null	null	null	null	['video-grounding', 'spatio-temporal-video-grounding']	['computer-vision', 'computer-vision']	[ 1.75852507e-01 -5.09785227e-02 -6.77099302e-02 -3.33708882e-01 -1.14708662e+00 -2.12946013e-01 6.40303314e-01 -7.32061267e-03 -5.94094872e-01 4.47316885e-01 5.33518672e-01 -1.16815306e-01 2.42359608e-01 -8.87316167e-01 -9.27599907e-01 -4.90190178e-01 -1.06853440e-01 1.21688619e-01 6.37504876e-01 -2.35997498e-01 3.14317793e-02 4.73103046e-01 -1.32282233e+00 8.16233695e-01 5.68368912e-01 1.19250810e+00 2.54188627e-01 6.79773629e-01 -1.63719490e-01 1.13936162e+00 -5.83320022e-01 -2.92377859e-01 1.24398328e-01 -7.63832808e-01 -6.29406273e-01 1.41553894e-01 2.42414549e-01 -4.71387118e-01 -6.40577435e-01 7.68477380e-01 4.17108744e-01 7.72798598e-01 3.79109144e-01 -1.15030909e+00 -4.13035214e-01 7.61035919e-01 -6.25471771e-01 2.37605780e-01 5.52102387e-01 1.43255264e-01 8.39781642e-01 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c6f17473-3472-485f-a674-4f741f4ab497	an-in-router-identification-scheme-for	2104.13013	null	https://arxiv.org/abs/2104.13013v1	https://arxiv.org/pdf/2104.13013v1.pdf	An In-router Identification Scheme for Selective Discard of Video Packets	High quality (HQ) video services occupy large portions of the total bandwidth and are among the main causes of congestion at network bottlenecks. Since video is resilient to data loss, throwing away less important video packets can ease network congestion with minimal damage to video quality and free up bandwidth for other data flows. Frame type is one of the features that can be used to determine the importance of video packets, but this information is stored in the packet payload. Due to limited processing power of devices in high throughput/speed networks, data encryption and user credibility issues, it is costly for the network to find the frame type of each packet. Therefore, a fast and reliable standalone method to recognize video packet types at network level is desired. This paper proposes a method to model the structure of live video streams in a network node which results in determining the frame type of each packet. It enables the network nodes to mark and if need be to discard less important video packets ahead of congestion, and therefore preserve video quality and free up bandwidth for more important packet types. The method does not need to read the IP layer payload and uses only the packet header data for decisions. Experimental results indicate while dropping packets under packet type prediction degrades video quality with respect to its true type by 0.5-3 dB, it has 7-20 dB improvement over when packets are dropped randomly.	['Mohammad Ghanbari', 'Mohammad Ghasempour', 'Ashkan Moharrami']	2021-04-27	null	null	null	null	['type-prediction']	['computer-code']	[-6.28826320e-02 -5.36086969e-02 -8.30011010e-01 -3.15994442e-01 6.47522509e-02 -4.29676563e-01 -4.36104953e-01 2.93376863e-01 -4.38032210e-01 9.44989324e-01 -1.91775233e-01 -5.08575499e-01 1.85189351e-01 -9.28139210e-01 -2.05369949e-01 -6.90095127e-01 -5.48227012e-01 -1.58826217e-01 9.48736906e-01 6.06176145e-02 6.51746333e-01 6.51236475e-01 -1.45376599e+00 3.06466669e-01 4.61034536e-01 1.32746124e+00 2.31878534e-01 8.20865571e-01 -4.17820126e-01 1.23510325e+00 -1.07623613e+00 -2.76604027e-01 2.85630494e-01 -1.73422411e-01 -7.54269898e-01 1.39457121e-01 -1.56902522e-02 -1.11621070e+00 -3.71618718e-01 1.04129732e+00 2.13237673e-01 -1.59265220e-01 2.08686441e-01 -1.68654966e+00 1.09175496e-01 6.17651761e-01 -4.91215587e-01 8.68475020e-01 3.97305667e-01 -6.16583936e-02 8.12874854e-01 -2.70262152e-01 5.62300801e-01 1.19318354e+00 2.62573808e-01 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b0836cb9-ceba-49ec-b52f-58a355ef699a	ner-mqmrc-formulating-named-entity	2205.05904	null	https://arxiv.org/abs/2205.05904v1	https://arxiv.org/pdf/2205.05904v1.pdf	NER-MQMRC: Formulating Named Entity Recognition as Multi Question Machine Reading Comprehension	NER has been traditionally formulated as a sequence labeling task. However, there has been recent trend in posing NER as a machine reading comprehension task (Wang et al., 2020; Mengge et al., 2020), where entity name (or other information) is considered as a question, text as the context and entity value in text as answer snippet. These works consider MRC based on a single question (entity) at a time. We propose posing NER as a multi-question MRC task, where multiple questions (one question per entity) are considered at the same time for a single text. We propose a novel BERT-based multi-question MRC (NER-MQMRC) architecture for this formulation. NER-MQMRC architecture considers all entities as input to BERT for learning token embeddings with self-attention and leverages BERT-based entity representation for further improving these token embeddings for NER task. Evaluation on three NER datasets show that our proposed architecture leads to average 2.5 times faster training and 2.3 times faster inference as compared to NER-SQMRC framework based models by considering all entities together in a single pass. Further, we show that our model performance does not degrade compared to single-question based MRC (NER-SQMRC) (Devlin et al., 2019) leading to F1 gain of +0.41%, +0.32% and +0.27% for AE-Pub, Ecommerce5PT and Twitter datasets respectively. We propose this architecture primarily to solve large scale e-commerce attribute (or entity) extraction from unstructured text of a magnitude of 50k+ attributes to be extracted on a scalable production environment with high performance and optimised training and inference runtimes.	['Promod Yenigalla', 'Kartik Mehta', 'Avi Jain', 'Anubhav Shrimal']	2022-05-12	null	https://aclanthology.org/2022.naacl-industry.26	https://aclanthology.org/2022.naacl-industry.26.pdf	naacl-acl-2022-7	['machine-reading-comprehension']	['natural-language-processing']	[-1.80199355e-01 2.60262489e-01 2.03838602e-01 -3.98476958e-01 -1.22259104e+00 -8.84791791e-01 6.47076964e-01 8.98678124e-01 -1.27208960e+00 8.54537904e-01 3.67777735e-01 -6.08600497e-01 -3.01972121e-01 -1.02224028e+00 -7.37117589e-01 -1.10511012e-01 2.29802772e-01 5.38674712e-01 1.14064828e-01 -3.74467909e-01 2.07295358e-01 4.05139402e-02 -1.16939533e+00 5.04507236e-02 9.92676139e-01 9.49864388e-01 9.01415125e-02 1.25182271e+00 -3.38834405e-01 8.49051178e-01 -7.03984439e-01 -9.75853205e-01 5.37085980e-02 2.44691044e-01 -1.36667454e+00 -7.13737667e-01 6.67069733e-01 -9.85301957e-02 -3.16702724e-01 7.39366412e-01 7.65341580e-01 3.72913480e-01 6.15933180e-01 -9.59295750e-01 -1.11097848e+00 7.22110152e-01 -2.06604049e-01 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aa3a6720-4700-4299-993b-f30f54139244	cnn-based-fast-source-device-identification	2001.11847	null	https://arxiv.org/abs/2001.11847v3	https://arxiv.org/pdf/2001.11847v3.pdf	CNN-based fast source device identification	Source identification is an important topic in image forensics, since it allows to trace back the origin of an image. This represents a precious information to claim intellectual property but also to reveal the authors of illicit materials. In this paper we address the problem of device identification based on sensor noise and propose a fast and accurate solution using convolutional neural networks (CNNs). Specifically, we propose a 2-channel-based CNN that learns a way of comparing camera fingerprint and image noise at patch level. The proposed solution turns out to be much faster than the conventional approach and to ensure an increased accuracy. This makes the approach particularly suitable in scenarios where large databases of images are analyzed, like over social networks. In this vein, since images uploaded on social media usually undergo at least two compression stages, we include investigations on double JPEG compressed images, always reporting higher accuracy than standard approaches.	['Luisa Verdoliva', 'Davide Cozzolino', 'Paolo Bestagini', 'Stefano Tubaro', 'Sara Mandelli']	2020-01-31	null	null	null	null	['image-forensics']	['computer-vision']	[ 4.50683922e-01 -1.33539334e-01 -7.46250823e-02 -3.79249640e-02 -7.18088329e-01 -7.73950756e-01 5.06239891e-01 4.04487401e-01 -6.26687169e-01 5.82520068e-01 -2.89782345e-01 -5.31565726e-01 -7.76049420e-02 -1.03247058e+00 -1.00723135e+00 -6.36401534e-01 7.03208372e-02 8.60481337e-02 2.72198915e-02 1.58401847e-01 5.70117414e-01 8.18738222e-01 -1.43665302e+00 1.44844085e-01 2.88295746e-01 1.35198390e+00 -4.02395278e-02 6.71949148e-01 -6.02857210e-02 9.69288766e-01 -7.37410367e-01 -9.76581395e-01 2.65691847e-01 -9.81794819e-02 -8.00099790e-01 -6.06204718e-02 6.45723760e-01 -7.71134019e-01 -2.35914618e-01 1.43128622e+00 3.93959075e-01 -5.12620032e-01 3.06520730e-01 -1.14865899e+00 -4.59377855e-01 6.94733083e-01 -4.46565866e-01 3.39399636e-01 4.30653244e-01 -1.24630131e-01 4.47347879e-01 -2.93543994e-01 6.64332449e-01 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d3afb3df-291c-42e7-8374-ffc6efcd0e8b	rtmpose-real-time-multi-person-pose	2303.07399	null	https://arxiv.org/abs/2303.07399v2	https://arxiv.org/pdf/2303.07399v2.pdf	RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose	Recent studies on 2D pose estimation have achieved excellent performance on public benchmarks, yet its application in the industrial community still suffers from heavy model parameters and high latency. In order to bridge this gap, we empirically explore key factors in pose estimation including paradigm, model architecture, training strategy, and deployment, and present a high-performance real-time multi-person pose estimation framework, RTMPose, based on MMPose. Our RTMPose-m achieves 75.8% AP on COCO with 90+ FPS on an Intel i7-11700 CPU and 430+ FPS on an NVIDIA GTX 1660 Ti GPU, and RTMPose-l achieves 67.0% AP on COCO-WholeBody with 130+ FPS. To further evaluate RTMPose's capability in critical real-time applications, we also report the performance after deploying on the mobile device. Our RTMPose-s achieves 72.2% AP on COCO with 70+ FPS on a Snapdragon 865 chip, outperforming existing open-source libraries. Code and models are released at https://github.com/open-mmlab/mmpose/tree/1.x/projects/rtmpose.	['Kai Chen', 'Yining Li', 'Chengqi Lyu', 'Rui Han', 'Ningsheng Ma', 'Li Zhang', 'Peng Lu', 'Tao Jiang']	2023-03-13	null	null	null	null	['2d-human-pose-estimation', 'multi-person-pose-estimation']	['computer-vision', 'computer-vision']	[-5.01473486e-01 -3.02287459e-01 -6.71198666e-02 -2.31997281e-01 -8.66634667e-01 -2.74170786e-01 -2.30273351e-01 -5.51875353e-01 -3.69497716e-01 2.59549230e-01 -1.42245561e-01 1.09003279e-02 3.47584784e-01 -5.14145195e-01 -6.52565360e-01 -2.01509118e-01 -1.49594709e-01 8.07393909e-01 1.06009200e-01 -3.88867669e-02 -1.67203426e-01 3.04736227e-01 -1.34567881e+00 1.25816450e-01 3.77833277e-01 1.24883795e+00 -5.90804107e-02 1.00506413e+00 7.40448415e-01 3.46792728e-01 -7.36334980e-01 -6.99990749e-01 3.58071357e-01 4.03343469e-01 -4.18215930e-01 -2.54653305e-01 8.00931036e-01 -4.83502895e-01 -4.67316329e-01 5.44411898e-01 1.11197734e+00 -3.68411511e-01 1.73906222e-01 -1.60480893e+00 3.13351512e-01 8.83576646e-03 -8.47196221e-01 -2.88989693e-02 6.99404359e-01 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0ff9a9cd-d0fa-4f01-bc44-974e29f16b0e	bridge-the-gap-between-language-models-and	2302.09302	null	https://arxiv.org/abs/2302.09302v1	https://arxiv.org/pdf/2302.09302v1.pdf	Bridge the Gap between Language models and Tabular Understanding	Table pretrain-then-finetune paradigm has been proposed and employed at a rapid pace after the success of pre-training in the natural language domain. Despite the promising findings in tabular pre-trained language models (TPLMs), there is an input gap between pre-training and fine-tuning phases. For instance, TPLMs jointly pre-trained with table and text input could be effective for tasks also with table-text joint input like table question answering, but it may fail for tasks with only tables or text as input such as table retrieval. To this end, we propose UTP, an approach that dynamically supports three types of multi-modal inputs: table-text, table, and text. Specifically, UTP is pre-trained with two strategies: (1) We first utilize a universal mask language modeling objective on each kind of input, enforcing the model to adapt various inputs. (2) We then present Cross-Modal Contrastive Regularization (CMCR), which utilizes contrastive learning to encourage the consistency between table-text cross-modality representations via unsupervised instance-wise training signals during pre-training. By these means, the resulting model not only bridges the input gap between pre-training and fine-tuning but also advances in the alignment of table and text. Extensive results show UTP achieves superior results on uni-modal input tasks (e.g., table retrieval) and cross-modal input tasks (e.g., table question answering).	['Jia Li', 'Daxin Jiang', 'Jianhui Chang', 'Chenyu You', 'Jian Pei', 'Ming Gong', 'Linjun Shou', 'Nuo Chen']	2023-02-16	null	null	null	null	['table-retrieval']	['natural-language-processing']	[ 4.23967600e-01 6.23353794e-02 -3.66678149e-01 -4.70469803e-01 -1.38706493e+00 -8.51973951e-01 8.40430796e-01 3.44378561e-01 -4.73193645e-01 4.70296800e-01 3.59044373e-01 -5.39756775e-01 -2.10518464e-01 -7.33017802e-01 -9.24240351e-01 -2.43436426e-01 5.39156973e-01 8.51613343e-01 9.63084474e-02 -5.60934067e-01 -5.30828498e-02 2.13258937e-01 -1.49957430e+00 1.15750027e+00 1.10402179e+00 1.00492883e+00 8.63641798e-02 4.46633101e-01 -7.54228294e-01 9.23540354e-01 -4.31012064e-01 -5.99176407e-01 9.95068923e-02 -4.34841275e-01 -8.94444585e-01 3.11852191e-02 6.11096144e-01 6.94024637e-02 -2.92848170e-01 6.64928436e-01 2.99246103e-01 3.73938978e-01 6.67370141e-01 -9.99675035e-01 -9.50801075e-01 1.10573304e+00 -4.14134681e-01 2.75038425e-02 2.87059307e-01 1.61365390e-01 1.38623536e+00 -1.16151977e+00 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b9d42cf9-cf33-4160-bd0a-54fce0277bc4	point-cloud-recognition-with-position-to	2210.02030	null	https://arxiv.org/abs/2210.02030v1	https://arxiv.org/pdf/2210.02030v1.pdf	Point Cloud Recognition with Position-to-Structure Attention Transformers	In this paper, we present Position-to-Structure Attention Transformers (PS-Former), a Transformer-based algorithm for 3D point cloud recognition. PS-Former deals with the challenge in 3D point cloud representation where points are not positioned in a fixed grid structure and have limited feature description (only 3D coordinates ($x, y, z$) for scattered points). Existing Transformer-based architectures in this domain often require a pre-specified feature engineering step to extract point features. Here, we introduce two new aspects in PS-Former: 1) a learnable condensation layer that performs point downsampling and feature extraction; and 2) a Position-to-Structure Attention mechanism that recursively enriches the structural information with the position attention branch. Compared with the competing methods, while being generic with less heuristics feature designs, PS-Former demonstrates competitive experimental results on three 3D point cloud tasks including classification, part segmentation, and scene segmentation.	['Zhuowen Tu', 'James Hou', 'Zheng Ding']	2022-10-05	null	null	null	null	['scene-segmentation']	['computer-vision']	[ 2.54897445e-01 2.59602278e-01 6.65374249e-02 -2.66654879e-01 -1.00053012e+00 -5.42163610e-01 4.35467541e-01 3.11560869e-01 7.21967518e-02 -3.53294536e-02 -3.60599726e-01 -3.54876101e-01 -1.01085506e-01 -8.06060135e-01 -1.09614480e+00 -4.50603992e-01 -8.86244774e-02 9.82156992e-01 3.32749844e-01 -1.95982143e-01 5.90918124e-01 1.40317166e+00 -1.74541318e+00 1.59601435e-01 7.57378459e-01 1.28936851e+00 2.48807713e-01 4.95124698e-01 -4.64490265e-01 1.48153812e-01 -4.56973761e-01 -1.44458592e-01 6.25129759e-01 2.15136200e-01 -8.10570300e-01 4.20938641e-01 7.32420266e-01 -4.69102599e-02 -3.38637829e-02 9.50823367e-01 3.00649047e-01 -4.04525287e-02 8.32435727e-01 -1.35737312e+00 -3.94159347e-01 7.29731396e-02 -7.46381521e-01 1.72539651e-02 3.05554301e-01 -5.82129508e-02 9.41351533e-01 -1.45057547e+00 5.09880126e-01 1.29897273e+00 9.62779999e-01 2.65134543e-01 -9.34833169e-01 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5ac1a457-9475-4ca0-a5e1-08d9a88cd9b3	multi-modal-trip-hazard-affordance-detection	1706.06718	null	http://arxiv.org/abs/1706.06718v1	http://arxiv.org/pdf/1706.06718v1.pdf	Multi-Modal Trip Hazard Affordance Detection On Construction Sites	Trip hazards are a significant contributor to accidents on construction and manufacturing sites, where over a third of Australian workplace injuries occur [1]. Current safety inspections are labour intensive and limited by human fallibility,making automation of trip hazard detection appealing from both a safety and economic perspective. Trip hazards present an interesting challenge to modern learning techniques because they are defined as much by affordance as by object type; for example wires on a table are not a trip hazard, but can be if lying on the ground. To address these challenges, we conduct a comprehensive investigation into the performance characteristics of 11 different colour and depth fusion approaches, including 4 fusion and one non fusion approach; using colour and two types of depth images. Trained and tested on over 600 labelled trip hazards over 4 floors and 2000m$\mathrm{^{2}}$ in an active construction site,this approach was able to differentiate between identical objects in different physical configurations (see Figure 1). Outperforming a colour-only detector, our multi-modal trip detector fuses colour and depth information to achieve a 4% absolute improvement in F1-score. These investigative results and the extensive publicly available dataset moves us one step closer to assistive or fully automated safety inspection systems on construction sites.	['Niko Sünderhauf', 'Sean McMahon', 'Michael Milford', 'Ben Upcroft']	2017-06-21	null	null	null	null	['affordance-detection']	['computer-vision']	[ 6.50123239e-01 1.52784765e-01 3.67679536e-01 -1.04584351e-01 -1.31469321e+00 -3.28118265e-01 3.47869277e-01 4.77535009e-01 -3.33617181e-01 4.27534193e-01 1.35533333e-01 -5.73151469e-01 -6.33899570e-01 -7.74712384e-01 -5.13353467e-01 -8.34980190e-01 -9.72002968e-02 2.87507772e-01 4.74625468e-01 -4.09164727e-01 2.75438190e-01 5.72453022e-01 -1.80557144e+00 6.03315115e-01 1.81969702e-01 1.00902581e+00 3.18231881e-01 5.76257110e-01 5.09763002e-01 6.64082587e-01 -7.84745395e-01 -3.83077592e-01 3.08529556e-01 -6.54493719e-02 -9.14467633e-01 9.39909890e-02 5.49795210e-01 -1.25059932e-01 -1.39672056e-01 3.34683150e-01 7.62125909e-01 1.84992567e-01 9.42930579e-01 -1.17367864e+00 -2.44614393e-01 1.60588641e-02 -5.86519599e-01 3.02255899e-01 5.89795053e-01 3.90926480e-01 8.74287188e-01 -5.18836796e-01 6.75621927e-02 9.84503031e-01 9.77092922e-01 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cf926252-c32f-422a-8d50-611c20b2e418	learn-to-cluster-faces-with-better-subgraphs	2304.10831	null	https://arxiv.org/abs/2304.10831v1	https://arxiv.org/pdf/2304.10831v1.pdf	Learn to Cluster Faces with Better Subgraphs	Face clustering can provide pseudo-labels to the massive unlabeled face data and improve the performance of different face recognition models. The existing clustering methods generally aggregate the features within subgraphs that are often implemented based on a uniform threshold or a learned cutoff position. This may reduce the recall of subgraphs and hence degrade the clustering performance. This work proposed an efficient neighborhood-aware subgraph adjustment method that can significantly reduce the noise and improve the recall of the subgraphs, and hence can drive the distant nodes to converge towards the same centers. More specifically, the proposed method consists of two components, i.e. face embeddings enhancement using the embeddings from neighbors, and enclosed subgraph construction of node pairs for structural information extraction. The embeddings are combined to predict the linkage probabilities for all node pairs to replace the cosine similarities to produce new subgraphs that can be further used for aggregation of GCNs or other clustering methods. The proposed method is validated through extensive experiments against a range of clustering solutions using three benchmark datasets and numerical results confirm that it outperforms the SOTA solutions in terms of generalization capability.	['Qiang Yang', 'Xinjia Chen', 'Fan Deng', 'Guanqun Hou', 'Di Jiang', 'Yuan Cao']	2023-04-21	null	null	null	null	['face-recognition', 'face-clustering']	['computer-vision', 'computer-vision']	[-5.65404482e-02 1.04903253e-02 -5.86673990e-02 -6.44892812e-01 -2.60078460e-01 -3.80041569e-01 4.98247296e-01 6.31577075e-02 1.04051962e-01 4.02093649e-01 3.01409364e-01 2.29882523e-01 -4.40818608e-01 -8.87560487e-01 -2.82560796e-01 -1.23947215e+00 4.08269018e-02 3.01800817e-01 2.23761827e-01 2.44358122e-01 3.47806364e-01 6.60917401e-01 -1.83262312e+00 1.22973053e-02 9.39110219e-01 9.48916495e-01 -1.27790377e-01 -1.85414776e-01 -4.92116511e-01 3.26880366e-01 -2.94313073e-01 -2.33427867e-01 2.44755954e-01 -1.60142124e-01 -3.37629914e-01 2.85842508e-01 9.97676775e-02 1.28043694e-02 -2.27000490e-01 1.09851551e+00 4.54170376e-01 1.65833771e-01 7.85811424e-01 -1.38024616e+00 -7.50719845e-01 6.17971122e-01 -9.90881145e-01 6.59381449e-02 7.49945492e-02 -1.94704935e-01 5.84990799e-01 -9.61039364e-01 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df2d56b1-a605-4ceb-be4b-c2d39f8bc201	a-clinically-motivated-self-supervised	2207.04812	null	https://arxiv.org/abs/2207.04812v1	https://arxiv.org/pdf/2207.04812v1.pdf	A clinically motivated self-supervised approach for content-based image retrieval of CT liver images	Deep learning-based approaches for content-based image retrieval (CBIR) of CT liver images is an active field of research, but suffers from some critical limitations. First, they are heavily reliant on labeled data, which can be challenging and costly to acquire. Second, they lack transparency and explainability, which limits the trustworthiness of deep CBIR systems. We address these limitations by (1) proposing a self-supervised learning framework that incorporates domain-knowledge into the training procedure and (2) providing the first representation learning explainability analysis in the context of CBIR of CT liver images. Results demonstrate improved performance compared to the standard self-supervised approach across several metrics, as well as improved generalisation across datasets. Further, we conduct the first representation learning explainability analysis in the context of CBIR, which reveals new insights into the feature extraction process. Lastly, we perform a case study with cross-examination CBIR that demonstrates the usability of our proposed framework. We believe that our proposed framework could play a vital role in creating trustworthy deep CBIR systems that can successfully take advantage of unlabeled data.	['Robert Jenssen', 'Michael Christian Kampffmeyer', 'Karl Øyvind Mikalsen', 'Keyur Radiya', 'Eirik Agnalt Østmo', 'Kristoffer Knutsen Wickstrøm']	2022-07-11	null	null	null	null	['content-based-image-retrieval']	['computer-vision']	[-3.14695798e-02 2.30867401e-01 -2.00186536e-01 -4.90667552e-01 -1.00663698e+00 -4.31147486e-01 4.80895966e-01 5.23117483e-01 -3.33003044e-01 3.65246922e-01 4.58649099e-01 -5.93033433e-01 -7.48608410e-01 -5.55226386e-01 -3.67703348e-01 -7.31650770e-01 -1.32567585e-01 3.45628113e-01 -1.98792562e-01 -6.14904203e-02 4.90562022e-02 4.65668947e-01 -1.09157455e+00 5.45402229e-01 6.23553216e-01 1.34038281e+00 -2.06555858e-01 2.79326707e-01 1.04166254e-01 1.40048599e+00 -4.16028738e-01 -4.77355361e-01 2.96664029e-01 -3.63286406e-01 -1.26786780e+00 6.40508235e-02 2.37562984e-01 -7.14414716e-01 -5.30556440e-01 8.59837115e-01 5.01901925e-01 -1.54053524e-01 6.99873686e-01 -1.24616945e+00 -1.21056116e+00 6.30601823e-01 -2.22448900e-01 2.28425264e-01 1.32917047e-01 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1.02351224e+00 2.39503816e-01 4.86795574e-01 6.19722068e-01 -2.35215425e-01 -5.76859832e-01 -7.98750341e-01 -3.81462276e-01 8.47226560e-01 4.04757082e-01 -5.06153703e-01 -3.36187571e-01 5.58712669e-02]	[14.486734390258789, -1.6486941576004028]
eed94ad7-d407-42d5-ac93-6931c6b9edd5	superpixel-image-classification-with-graph	2002.05544	null	https://arxiv.org/abs/2002.05544v2	https://arxiv.org/pdf/2002.05544v2.pdf	Superpixel Image Classification with Graph Attention Networks	This paper presents a methodology for image classification using Graph Neural Network (GNN) models. We transform the input images into region adjacency graphs (RAGs), in which regions are superpixels and edges connect neighboring superpixels. Our experiments suggest that Graph Attention Networks (GATs), which combine graph convolutions with self-attention mechanisms, outperforms other GNN models. Although raw image classifiers perform better than GATs due to information loss during the RAG generation, our methodology opens an interesting avenue of research on deep learning beyond rectangular-gridded images, such as 360-degree field of view panoramas. Traditional convolutional kernels of current state-of-the-art methods cannot handle panoramas, whereas the adapted superpixel algorithms and the resulting region adjacency graphs can naturally feed a GNN, without topology issues.	['Luís C. Lamb', 'Cláudio R. Jung', 'Thiago L. T. da Silveira', 'Pedro H. C. Avelar', 'Anderson R. Tavares']	2020-02-13	null	null	null	null	['superpixel-image-classification']	['computer-vision']	[ 3.29862624e-01 4.14180398e-01 -2.96444833e-01 -2.08734199e-01 6.27055317e-02 -5.91021240e-01 6.26373470e-01 -8.31481963e-02 6.85250503e-04 4.20907736e-01 1.27481762e-02 -7.60444760e-01 -1.12315290e-01 -1.42509401e+00 -1.10178053e+00 -5.74209869e-01 -3.62074763e-01 2.17412993e-01 4.39301342e-01 -9.43020210e-02 1.30790547e-01 7.93620706e-01 -1.07219732e+00 2.42520928e-01 7.73764551e-01 8.73893738e-01 -8.54914114e-02 1.11713767e+00 -1.66236281e-01 1.11110485e+00 -2.45490849e-01 -3.60060722e-01 4.74806935e-01 -3.17689091e-01 -7.77651846e-01 2.19057918e-01 1.49698460e+00 -3.41425329e-01 -8.37297142e-01 1.26792622e+00 -5.97969517e-02 1.87955245e-01 6.52619064e-01 -1.29990351e+00 -1.41687334e+00 8.04052711e-01 -6.32106721e-01 2.84084082e-01 -1.21872745e-01 6.52036294e-02 1.14243281e+00 -6.36742890e-01 6.99191511e-01 1.36659729e+00 9.47182775e-01 1.72144026e-01 -1.29789102e+00 -3.43016654e-01 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c60e001f-89de-4052-9f1b-437cdb70c682	learning-universal-policies-via-text-guided	2302.00111	null	https://arxiv.org/abs/2302.00111v2	https://arxiv.org/pdf/2302.00111v2.pdf	Learning Universal Policies via Text-Guided Video Generation	A goal of artificial intelligence is to construct an agent that can solve a wide variety of tasks. Recent progress in text-guided image synthesis has yielded models with an impressive ability to generate complex novel images, exhibiting combinatorial generalization across domains. Motivated by this success, we investigate whether such tools can be used to construct more general-purpose agents. Specifically, we cast the sequential decision making problem as a text-conditioned video generation problem, where, given a text-encoded specification of a desired goal, a planner synthesizes a set of future frames depicting its planned actions in the future, after which control actions are extracted from the generated video. By leveraging text as the underlying goal specification, we are able to naturally and combinatorially generalize to novel goals. The proposed policy-as-video formulation can further represent environments with different state and action spaces in a unified space of images, which, for example, enables learning and generalization across a variety of robot manipulation tasks. Finally, by leveraging pretrained language embeddings and widely available videos from the internet, the approach enables knowledge transfer through predicting highly realistic video plans for real robots.	['Joshua B. Tenenbaum', 'Yilun Du', 'Pieter Abbeel', 'Dale Schuurmans', 'Ofir Nachum', 'Hanjun Dai', 'Bo Dai', 'Mengjiao Yang']	2023-01-31	null	null	null	null	['video-generation', 'robot-manipulation']	['computer-vision', 'robots']	[ 6.54991865e-01 4.38324839e-01 -1.05000123e-01 -2.06972748e-01 -4.89190429e-01 -5.43075204e-01 1.08641493e+00 -1.11437790e-01 -3.53945673e-01 9.22463775e-01 4.18992400e-01 7.57838935e-02 -1.06171571e-01 -8.03694546e-01 -1.17548323e+00 -7.04621196e-01 -1.83489338e-01 4.59415734e-01 -9.44948941e-02 -2.24628016e-01 2.38966078e-01 4.53134388e-01 -1.76087821e+00 2.22011819e-01 5.37021220e-01 8.79829466e-01 8.35570216e-01 6.83966458e-01 1.48190036e-01 1.07354009e+00 -3.14650387e-01 -1.43425509e-01 3.12827259e-01 -4.61374253e-01 -8.66825223e-01 7.06170321e-01 2.61278301e-01 -4.20679212e-01 -6.30300462e-01 9.33600783e-01 -2.20998544e-02 5.40309787e-01 6.18794382e-01 -1.51954615e+00 -9.03351009e-01 6.20023310e-01 4.19071838e-02 -3.42341900e-01 7.13562727e-01 7.15041280e-01 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