paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 contras... | ['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 -6.54888153... | [10.904122352600098, -3.19826340675354] |
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... | ['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 -0.0... | [4.108067512512207, 2.5313918590545654] |
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 int... | ['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
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-1.59806117e-01 -7.73780227e-01 -4.27237689e-01 -8.94838035e-01
-2.22650431e-02 5.80078185e-01 2.27432296e-01 7.82305524... | [14.416749954223633, -2.6473865509033203] |
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, o... | ['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
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5.97388029e-01 6.97633564e-01 7.11509049e-01 -4.68338251... | [11.704413414001465, 8.787125587463379] |
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... | ['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 -2.96037108... | [10.312397003173828, 3.0609676837921143] |
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 fr... | ["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 -5.69678806... | [9.949773788452148, 2.066671371459961] |
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,... | ['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
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3.62201542e-01 4.10817742e-01 4.29108232e-01 -3.09205115... | [10.787711143493652, 1.7245361804962158] |
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 matchin... | ['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 -2.59398431... | [9.193623542785645, -2.815704345703125] |
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 unsupervi... | ['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... | 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 -2.99421877... | [7.610289096832275, 8.192411422729492] |
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 cl... | ['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 1.00863606... | [11.838162422180176, -2.410230875015259] |
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 addres... | ['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 -4.56084788... | [10.858824729919434, 6.290560245513916] |
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 ... | ['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 -3.99732709... | [14.932299613952637, 5.997016906738281] |
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... | ['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 -2.31316969... | [9.024341583251953, 7.699723243713379] |
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 writi... | ['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 2.35963147... | [10.079096794128418, -2.048078775405884] |
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 impo... | ['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
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9.21464115e-02 -6.49544835e-01 -7.33651340e-01 -8.35923910e-01
2.78266370e-01 -4.22362387e-01 7.99928367e-01 -3.93675953... | [10.87353801727295, -1.3224146366119385] |
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 dif... | ['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 -5.17594278... | [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 a... | ['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 -3.58176559... | [4.523309230804443, 0.9217168092727661] |
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 reco... | ['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 -1.98529050... | [13.300140380859375, 5.099424839019775] |
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 t... | ['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 -6.58730090... | [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.... | ['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 -3.19966882... | [6.79782247543335, 2.9034512042999268] |
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 d... | ['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 -4.90878522... | [10.262532234191895, 2.3373639583587646] |
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 ... | ['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 -7.40103424... | [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 co... | ['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', 'Ko... | 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
-1.43028900e-01 -3.61466147e-02 -6.16367124e-02 -3.55209231... | [14.084664344787598, 5.887567043304443] |
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 localizat... | ['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 2.67532974... | [13.47535514831543, 0.1996879130601883] |
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 de... | ['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 1.43530279... | [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 th... | ['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 -6.56426132... | [9.531344413757324, 3.1573846340179443] |
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-st... | ['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 1.91036314... | [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). Typ... | ['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 -4.20492217... | [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 simpl... | ['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 2.92250991... | [5.994422912597656, 6.705770015716553] |
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 on... | ['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 2.01430112... | [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 environm... | ['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 -7.71406591... | [9.69228744506836, 7.228153228759766] |
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 add... | ['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 -5.98341107... | [3.8484015464782715, 1.4174107313156128] |
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 ... | ['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 -5.16925156... | [14.655917167663574, 0.8570389151573181] |
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 procedu... | ['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 2.08370592... | [7.82854700088501, 1.6355620622634888] |
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 ... | ['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 -4.71446589... | [8.450397491455078, -3.086303234100342] |
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 d... | ['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 7.75442243... | [5.893338203430176, 6.24467134475708] |
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 ... | ['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 2.40202099... | [10.286691665649414, 1.546873688697815] |
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 relati... | ['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 -5.99156559... | [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 inte... | ['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 1.92052513... | [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 auto... | ['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 -4.18550432... | [8.001172065734863, -0.8689755797386169] |
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 Sm... | ['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 -7.01361895... | [6.512576580047607, 3.4191911220550537] |
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 pro... | ['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 -8.08979943... | [4.956787586212158, 5.437209129333496] |
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... | ['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 -5.02927184... | [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 red... | ['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 -1.51163474... | [14.784210205078125, 0.830872118473053] |
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. ... | ['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 -6.08052850... | [10.864001274108887, 10.396400451660156] |
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... | ['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 -5.22277772... | [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. ... | ['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 -8.65731109... | [15.334518432617188, -2.8375802040100098] |
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... | ['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 -2.74618953... | [14.487900733947754, 7.129994869232178] |
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 volu... | ['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 6.11484908... | [7.976312637329102, -1.9702038764953613] |
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
disentangle... | ['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 -1.20680489... | [11.980929374694824, -0.8113272786140442] |
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 us... | ['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 -2.50441879... | [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... | ['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 -3.18670541... | [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 ... | ['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 1.29755735... | [8.06144905090332, -3.2508127689361572] |
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... | ['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 -5.49691498... | [4.339493274688721, 1.4385608434677124] |
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 multip... | ['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 1.97923839... | [8.260927200317383, 2.5000152587890625] |
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 ca... | ['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 -5.18752933... | [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 @midnig... | ['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 -6.80043161... | [8.87075424194336, 10.999714851379395] |
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 cap... | ['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 -4.14103955... | [7.221089839935303, -1.2790919542312622] |
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 p... | ['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 2.07165465... | [7.236245155334473, 2.7825067043304443] |
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 mo... | [' 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 -5.58873080... | [7.911880970001221, 1.3766623735427856] |
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 ac... | ['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 1.62748948... | [8.401788711547852, 0.5541182160377502] |
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 followe... | ['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 -3.60942841... | [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 f... | ['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 -6.42668366... | [8.881365776062012, 11.0570707321167] |
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 ... | ['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 -7.13885799... | [8.292802810668945, 7.2214837074279785] |
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 synchronizatio... | ['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 2.84287632... | [14.397024154663086, 5.139214515686035] |
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 the... | ['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 -3.21508288... | [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 cont... | ['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 -3.42704296... | [7.062349319458008, 2.9396963119506836] |
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 t... | ['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 -5.57676792... | [4.98619270324707, 1.940748691558838] |
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... | ['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 -4.09969777... | [7.996078014373779, -1.6962213516235352] |
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 ... | ['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 -3.23366940... | [7.310318470001221, -0.16960465908050537] |
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) s... | ['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 -2.87618190... | [10.292695999145508, 8.885078430175781] |
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, ... | ['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 5.68303764... | [9.648865699768066, 0.28439566493034363] |
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 i... | ['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 -1.51056260... | [10.88422966003418, 1.3206249475479126] |
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... | ['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 -4.20315713... | [9.75764274597168, 9.497977256774902] |
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. Assames... | ['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 -1.95525140... | [10.017110824584961, 9.800996780395508] |
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 l... | ['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
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-3.98725092e-01 -1.06302226e+00 -9.87767220e-01 -7.44431734e-01
-4.80260134e-01 3.37255090e-01 -7.81426206e-02 -4.10191059... | [6.828405380249023, 6.079482555389404] |
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 fa... | ['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 8.99155217... | [13.07817268371582, 0.6562681198120117] |
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 synt... | ['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 5.26738390... | [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 ma... | ['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 3.99503440... | [8.392370223999023, 8.524347305297852] |
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 fo... | ['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 -5.91279030... | [4.2661519050598145, 1.7837622165679932] |
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,... | ['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 -3.74011517... | [14.164745330810547, 7.140047550201416] |
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 ... | ['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 -5.84314525... | [3.7800562381744385, 2.0237011909484863] |
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 an... | ['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 -5.42391300... | [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 l... | ['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 -2.43411660... | [8.567523002624512, 3.3172144889831543] |
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 ge... | ['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 -1.04170069... | [7.340619087219238, -0.1300220787525177] |
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 learni... | ['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 5.18913753... | [9.77625846862793, -1.4568290710449219] |
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 -6.52004302... | [-1.5392024517059326, 15.86921501159668] |
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 contr... | ['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 -3.69047552... | [11.223494529724121, 1.2195216417312622] |
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. Prim... | ['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 -2.35997498... | [10.227200508117676, 0.6344988942146301] |
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 o... | ['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 6.06176145... | [11.053439140319824, -1.7179031372070312] |
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 an... | ['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 -3.74467909... | [9.6571683883667, 9.473301887512207] |
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 n... | ['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 1.58401847... | [12.38770866394043, 1.005350112915039] |
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 architec... | ['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 -3.88867669... | [7.145882606506348, -0.7728545069694519] |
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 joi... | ['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 -5.60934067... | [11.04353141784668, 8.419761657714844] |
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 co... | ['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 -1.95982143... | [7.932945728302002, -3.471970558166504] |
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 eco... | ['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 -4.09164727... | [7.411587238311768, 1.4519522190093994] |
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 ... | ['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 2.44358122... | [13.446097373962402, 1.042874813079834] |
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... | ['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 -6.14904203... | [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 g... | ['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 -9.43020210... | [9.674510955810547, 1.0625784397125244] |
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 investig... | ['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 -2.24628016... | [4.559652805328369, 0.7866344451904297] |
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