Split (2)

train · 44.6k rows

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e70b11d0-d598-4e56-8060-d4b504595603	otov2-automatic-generic-user-friendly	2303.06862	null	https://arxiv.org/abs/2303.06862v2	https://arxiv.org/pdf/2303.06862v2.pdf	OTOV2: Automatic, Generic, User-Friendly	The existing model compression methods via structured pruning typically require complicated multi-stage procedures. Each individual stage necessitates numerous engineering efforts and domain-knowledge from the end-users which prevent their wider applications onto broader scenarios. We propose the second generation of Only-Train-Once (OTOv2), which first automatically trains and compresses a general DNN only once from scratch to produce a more compact model with competitive performance without fine-tuning. OTOv2 is automatic and pluggable into various deep learning applications, and requires almost minimal engineering efforts from the users. Methodologically, OTOv2 proposes two major improvements: (i) Autonomy: automatically exploits the dependency of general DNNs, partitions the trainable variables into Zero-Invariant Groups (ZIGs), and constructs the compressed model; and (ii) Dual Half-Space Projected Gradient (DHSPG): a novel optimizer to more reliably solve structured-sparsity problems. Numerically, we demonstrate the generality and autonomy of OTOv2 on a variety of model architectures such as VGG, ResNet, CARN, ConvNeXt, DenseNet and StackedUnets, the majority of which cannot be handled by other methods without extensive handcrafting efforts. Together with benchmark datasets including CIFAR10/100, DIV2K, Fashion-MNIST, SVNH and ImageNet, its effectiveness is validated by performing competitively or even better than the state-of-the-arts. The source code is available at https://github.com/tianyic/only_train_once.	['Ilya Zharkov', 'Zhihui Zhu', 'Tianyu Ding', 'Luming Liang', 'Tianyi Chen']	2023-03-13	null	null	null	null	['model-compression']	['methodology']	[ 4.45064940e-02 1.62891775e-01 -2.61542648e-01 -5.19616544e-01 -5.72265744e-01 -3.07080686e-01 3.58881205e-01 -3.75934631e-01 -6.57834053e-01 8.63379896e-01 -2.46305093e-01 -4.90363926e-01 -2.61603057e-01 -5.77871561e-01 -9.27591383e-01 -6.48971677e-01 8.88481215e-02 6.44247651e-01 -1.35551423e-01 -8.29062462e-02 -1.03673615e-01 4.13504452e-01 -1.36787248e+00 3.30473296e-02 8.47669780e-01 1.34279263e+00 7.11133182e-01 3.51757169e-01 8.63258317e-02 7.55568624e-01 5.45767136e-02 -6.58580065e-01 7.00092793e-01 -8.11609179e-02 -6.97370470e-01 -7.25057498e-02 6.78378224e-01 -4.54352707e-01 -5.84975600e-01 1.18853045e+00 4.83093083e-01 8.88683200e-02 2.01068103e-01 -1.02755260e+00 -3.86922717e-01 7.68312275e-01 -4.35539186e-01 2.59862393e-01 -5.83528340e-01 3.09192210e-01 1.10311890e+00 -9.81555283e-01 6.04806185e-01 1.09199798e+00 8.36298347e-01 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ce730813-7965-4cf1-8db9-db1cb2475eb9	deep-neural-networks-for-blind-image-quality	2109.12161	null	https://arxiv.org/abs/2109.12161v1	https://arxiv.org/pdf/2109.12161v1.pdf	Deep Neural Networks for Blind Image Quality Assessment: Addressing the Data Challenge	The enormous space and diversity of natural images is usually represented by a few small-scale human-rated image quality assessment (IQA) datasets. This casts great challenges to deep neural network (DNN) based blind IQA (BIQA), which requires large-scale training data that is representative of the natural image distribution. It is extremely difficult to create human-rated IQA datasets composed of millions of images due to constraints of subjective testing. While a number of efforts have focused on design innovations to enhance the performance of DNN based BIQA, attempts to address the scarcity of labeled IQA data remain surprisingly missing. To address this data challenge, we construct so far the largest IQA database, namely Waterloo Exploration-II, which contains 3,570 pristine reference and around 3.45 million singly and multiply distorted images. Since subjective testing for such a large dataset is nearly impossible, we develop a novel mechanism that synthetically assigns perceptual quality labels to the distorted images. We construct a DNN-based BIQA model called EONSS, train it on Waterloo Exploration-II, and test it on nine subject-rated IQA datasets, without any retraining or fine-tuning. The results show that with a straightforward DNN architecture, EONSS is able to outperform the very state-of-the-art in BIQA, both in terms of quality prediction performance and execution speed. This study strongly supports the view that the quantity and quality of meaningfully annotated training data, rather than a sophisticated network architecture or training strategy, is the dominating factor that determines the performance of DNN-based BIQA models. (Note: Since this is an ongoing project, the final versions of Waterloo Exploration-II database, quality annotations, and EONSS, will be made publicly available in the future when it culminates.)	['Zhou Wang', 'Zhongling Wang', 'ShahRukh Athar']	2021-09-24	null	null	null	null	['blind-image-quality-assessment']	['computer-vision']	[ 1.63023211e-02 -3.27037036e-01 2.14690790e-01 -5.35386622e-01 -1.05418849e+00 -4.96852785e-01 4.35943156e-01 -4.66098964e-01 -4.80967551e-01 7.16070175e-01 4.76962805e-01 -3.75725269e-01 -3.47352922e-01 -7.05328465e-01 -5.57595909e-01 -5.35693526e-01 -2.20738854e-02 5.28980911e-01 5.47356121e-02 -3.63843977e-01 1.71326086e-01 6.15007102e-01 -1.75754261e+00 2.79871970e-01 1.07540739e+00 1.19691789e+00 7.60600567e-02 9.19767261e-01 1.49729982e-01 9.05624092e-01 -9.78084683e-01 -8.34630609e-01 6.60740137e-01 -5.38604021e-01 -8.39182019e-01 1.13348633e-01 8.77080083e-01 -6.75251663e-01 -5.74515045e-01 1.14827216e+00 1.04876888e+00 1.52841061e-01 4.82435346e-01 -1.29196537e+00 -1.05413413e+00 7.13539794e-02 -8.38873759e-02 3.85415137e-01 2.60275781e-01 6.91985786e-01 1.07360518e+00 -9.56797302e-01 4.33822840e-01 1.12866819e+00 6.30816400e-01 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b0bef628-dc6b-422d-a64b-f46b6f557824	np-match-towards-a-new-probabilistic-model	2301.13569	null	https://arxiv.org/abs/2301.13569v2	https://arxiv.org/pdf/2301.13569v2.pdf	NP-Match: Towards a New Probabilistic Model for Semi-Supervised Learning	Semi-supervised learning (SSL) has been widely explored in recent years, and it is an effective way of leveraging unlabeled data to reduce the reliance on labeled data. In this work, we adjust neural processes (NPs) to the semi-supervised image classification task, resulting in a new method named NP-Match. NP-Match is suited to this task for two reasons. Firstly, NP-Match implicitly compares data points when making predictions, and as a result, the prediction of each unlabeled data point is affected by the labeled data points that are similar to it, which improves the quality of pseudo-labels. Secondly, NP-Match is able to estimate uncertainty that can be used as a tool for selecting unlabeled samples with reliable pseudo-labels. Compared with uncertainty-based SSL methods implemented with Monte-Carlo (MC) dropout, NP-Match estimates uncertainty with much less computational overhead, which can save time at both the training and the testing phases. We conducted extensive experiments on five public datasets under three semi-supervised image classification settings, namely, the standard semi-supervised image classification, the imbalanced semi-supervised image classification, and the multi-label semi-supervised image classification, and NP-Match outperforms state-of-the-art (SOTA) approaches or achieves competitive results on them, which shows the effectiveness of NP-Match and its potential for SSL. The codes are at https://github.com/Jianf-Wang/NP-Match	['Thomas Lukasiewicz', 'Xiaolin Hu', 'JianFeng Wang']	2023-01-31	null	null	null	null	['semi-supervised-image-classification']	['computer-vision']	[ 1.85538590e-01 1.15730062e-01 -6.49389386e-01 -8.88335347e-01 -1.13451588e+00 -1.67126700e-01 3.48649949e-01 4.55859393e-01 -5.96955240e-01 7.94696271e-01 -1.01358078e-01 7.11672846e-03 9.65650603e-02 -6.86122239e-01 -8.51445436e-01 -8.82177293e-01 3.83575529e-01 7.89283454e-01 9.07733068e-02 5.76824427e-01 1.24596067e-01 2.23205522e-01 -1.48703790e+00 3.05394828e-01 1.02113497e+00 1.21923470e+00 -1.87763512e-01 1.79511029e-02 -1.92197695e-01 7.48626113e-01 -3.07902426e-01 -5.19313455e-01 7.67173544e-02 -4.52289134e-01 -6.19271338e-01 1.76096037e-01 2.73052245e-01 -2.14886516e-02 -2.31391913e-03 1.24915802e+00 4.24450755e-01 -8.56883004e-02 8.70612144e-01 -1.45016026e+00 -5.48432171e-01 7.16740668e-01 -8.37953091e-01 -1.64522693e-01 -1.73582345e-01 1.76703796e-01 8.48543346e-01 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e80301be-2ce2-4638-943c-875a106f992e	color-learning-for-image-compression	2306.17460	null	https://arxiv.org/abs/2306.17460v1	https://arxiv.org/pdf/2306.17460v1.pdf	Color Learning for Image Compression	Deep learning based image compression has gained a lot of momentum in recent times. To enable a method that is suitable for image compression and subsequently extended to video compression, we propose a novel deep learning model architecture, where the task of image compression is divided into two sub-tasks, learning structural information from luminance channel and color from chrominance channels. The model has two separate branches to process the luminance and chrominance components. The color difference metric CIEDE2000 is employed in the loss function to optimize the model for color fidelity. We demonstrate the benefits of our approach and compare the performance to other codecs. Additionally, the visualization and analysis of latent channel impulse response is performed.	['Siegfried Fößel', 'Heiko Sparenberg', 'Thomas Richter', 'Srivatsa Prativadibhayankaram']	2023-06-30	null	null	null	null	['video-compression', 'image-compression']	['computer-vision', 'computer-vision']	[ 3.01963210e-01 -5.10330498e-01 -1.59739047e-01 -3.98150116e-01 -7.90358424e-01 2.04200149e-01 5.97345352e-01 -9.12003666e-02 -4.40541595e-01 5.34629464e-01 2.74760634e-01 -1.99081078e-01 1.10091552e-01 -7.99237430e-01 -6.71103537e-01 -7.55952358e-01 -3.49617332e-01 -1.28810599e-01 -2.10407928e-01 9.55440551e-02 3.33407432e-01 3.66945505e-01 -1.62406778e+00 8.89087021e-01 5.11265576e-01 1.35356414e+00 3.35866630e-01 1.07745552e+00 1.62897229e-01 1.23590374e+00 -4.72223550e-01 -4.11890417e-01 3.69471580e-01 -6.71376526e-01 -8.12060595e-01 3.80527750e-02 2.64376372e-01 -7.26820111e-01 -8.11583579e-01 1.11872113e+00 5.15300214e-01 -1.68856804e-03 7.52134800e-01 -1.01582611e+00 -7.76789606e-01 1.89973608e-01 -5.86419344e-01 -2.45579332e-02 1.02095298e-01 4.63769883e-02 9.67360079e-01 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6a71f4b7-c539-41ed-8adb-6a70adc2367f	learning-with-an-evolving-class-ontology	2210.04993	null	https://arxiv.org/abs/2210.04993v4	https://arxiv.org/pdf/2210.04993v4.pdf	Continual Learning with Evolving Class Ontologies	Lifelong learners must recognize concept vocabularies that evolve over time. A common yet underexplored scenario is learning with class labels that continually refine/expand old classes. For example, humans learn to recognize ${\tt dog}$ before dog breeds. In practical settings, dataset $\textit{versioning}$ often introduces refinement to ontologies, such as autonomous vehicle benchmarks that refine a previous ${\tt vehicle}$ class into ${\tt school-bus}$ as autonomous operations expand to new cities. This paper formalizes a protocol for studying the problem of $\textit{Learning with Evolving Class Ontology}$ (LECO). LECO requires learning classifiers in distinct time periods (TPs); each TP introduces a new ontology of "fine" labels that refines old ontologies of "coarse" labels (e.g., dog breeds that refine the previous ${\tt dog}$). LECO explores such questions as whether to annotate new data or relabel the old, how to leverage coarse labels, and whether to finetune the previous TP's model or train from scratch. To answer these questions, we leverage insights from related problems such as class-incremental learning. We validate them under the LECO protocol through the lens of image classification (CIFAR and iNaturalist) and semantic segmentation (Mapillary). Our experiments lead to surprising conclusions; while the current status quo is to relabel existing datasets with new ontologies (such as COCO-to-LVIS or Mapillary1.2-to-2.0), LECO demonstrates that a far better strategy is to annotate $\textit{new}$ data with the new ontology. However, this produces an aggregate dataset with inconsistent old-vs-new labels, complicating learning. To address this challenge, we adopt methods from semi-supervised and partial-label learning. Such strategies can surprisingly be made near-optimal, approaching an "oracle" that learns on the aggregate dataset exhaustively labeled with the newest ontology.	['Shu Kong', 'Deva Ramanan', 'Yu-Xiong Wang', 'Deepak Pathak', 'Zhiqiu Lin']	2022-10-10	null	null	null	null	['partial-label-learning']	['methodology']	[ 1.72841564e-01 4.25406426e-01 -5.23713827e-01 -6.51736259e-01 -8.09405982e-01 -1.07923639e+00 4.63778377e-01 2.04264119e-01 -6.14907146e-01 9.03105676e-01 -4.82555091e-01 -5.09060085e-01 -3.60323846e-01 -9.21249807e-01 -1.08504748e+00 -5.53669751e-01 -3.05556536e-01 1.03644252e+00 2.90637702e-01 -2.11706713e-01 -1.91952720e-01 1.51338130e-01 -2.15140247e+00 1.39077336e-01 7.83797741e-01 1.12510931e+00 -8.69357362e-02 4.54973489e-01 -2.84827113e-01 9.29550827e-01 -4.82340217e-01 -4.57600474e-01 6.54327273e-01 2.52885800e-02 -1.19561088e+00 -3.97002604e-03 7.47274220e-01 6.29347861e-02 -1.02362353e-02 1.14840066e+00 -2.05745578e-01 -4.07219753e-02 5.77635705e-01 -1.77354395e+00 -5.22446215e-01 9.38410163e-01 -1.40456885e-01 6.73736781e-02 -3.92312706e-02 3.44761342e-01 1.14617741e+00 -2.87963420e-01 9.26675797e-01 1.02204466e+00 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ec7fa89a-e5b4-4b04-a5cb-2c568018bcbb	tuda-reproducibility-reprogen-replicability	null	null	https://aclanthology.org/2021.inlg-1.32	https://aclanthology.org/2021.inlg-1.32.pdf	TUDA-Reproducibility @ ReproGen: Replicability of Human Evaluation of Text-to-Text and Concept-to-Text Generation	This paper describes our contribution to the Shared Task ReproGen by Belz et al. (2021), which investigates the reproducibility of human evaluations in the context of Natural Language Generation. We selected the paper “Generation of Company descriptions using concept-to-text and text-to-text deep models: data set collection and systems evaluation” (Qader et al., 2018) and aimed to replicate, as closely to the original as possible, the human evaluation and the subsequent comparison between the human judgements and the automatic evaluation metrics. Here, we first outline the text generation task of the paper of Qader et al. (2018). Then, we document how we approached our replication of the paper’s human evaluation. We also discuss the difficulties we encountered and which information was missing. Our replication has medium to strong correlation (0.66 Spearman overall) with the original results of Qader et al. (2018), but due to the missing information about how Qader et al. (2018) compared the human judgements with the metric scores, we have refrained from reproducing this comparison.	['Steffen Eger', 'Yanran Chen', 'Christian Richter']	null	null	null	null	inlg-acl-2021-8	['concept-to-text-generation', 'paper-generation']	['natural-language-processing', 'natural-language-processing']	[ 2.84301937e-02 6.23086095e-01 1.99232116e-01 -1.59750402e-01 -9.56465364e-01 -9.70615506e-01 1.38002789e+00 4.10443932e-01 -5.23316860e-01 9.18009996e-01 5.44517457e-01 -4.01971787e-01 -2.79364198e-01 -6.86232090e-01 -4.14941341e-01 -1.13953263e-01 3.55927676e-01 6.58048749e-01 -1.14212520e-01 -3.41400683e-01 7.59356081e-01 -2.31608246e-02 -1.63290477e+00 6.23792887e-01 8.15056980e-01 8.10369015e-01 9.79911089e-02 9.18068469e-01 -7.54250735e-02 8.92240942e-01 -9.22212243e-01 -8.23033690e-01 5.32460772e-02 -3.70914906e-01 -1.22806084e+00 -1.18473612e-01 5.16867280e-01 -1.62889197e-01 6.30181730e-02 7.31604815e-01 7.03633010e-01 1.21010013e-01 9.53464746e-01 -1.27135575e+00 -1.17412782e+00 8.74864757e-01 -1.24247402e-01 -1.04560494e-01 4.62097585e-01 2.63038456e-01 1.02401435e+00 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94fb7768-1ddc-474e-b523-4d4dced6385f	exploring-data-augmentation-for-code	2302.03499	null	https://arxiv.org/abs/2302.03499v1	https://arxiv.org/pdf/2302.03499v1.pdf	Exploring Data Augmentation for Code Generation Tasks	Advances in natural language processing, such as transfer learning from pre-trained language models, have impacted how models are trained for programming language tasks too. Previous research primarily explored code pre-training and expanded it through multi-modality and multi-tasking, yet the data for downstream tasks remain modest in size. Focusing on data utilization for downstream tasks, we propose and adapt augmentation methods that yield consistent improvements in code translation and summarization by up to 6.9% and 7.5% respectively. Further analysis suggests that our methods work orthogonally and show benefits in output code style and numeric consistency. We also discuss test data imperfections.	['Gerasimos Lampouras', 'Pinzhen Chen']	2023-02-05	null	null	null	null	['code-translation', 'program-synthesis']	['computer-code', 'computer-code']	[ 1.27610371e-01 7.51283839e-02 -5.82417905e-01 -6.48594558e-01 -1.32039285e+00 -7.93984652e-01 3.78649801e-01 6.13312542e-01 -4.22092974e-01 4.62851077e-01 4.54515159e-01 -8.83738816e-01 4.99950260e-01 -4.46214020e-01 -1.01141882e+00 2.27927506e-01 -9.08550397e-02 2.42814943e-01 -3.22072536e-01 -4.47975695e-01 5.87130427e-01 -2.78051496e-01 -9.86155629e-01 9.35741127e-01 1.16614318e+00 1.23476952e-01 -3.10742017e-03 9.96894300e-01 -5.58936775e-01 9.38120961e-01 -6.27328992e-01 -4.72941011e-01 6.58688620e-02 -1.20715372e-01 -1.10752070e+00 -4.51454073e-01 5.78120768e-01 -4.22452748e-01 9.50476897e-05 8.47603977e-01 4.05105114e-01 -1.86334714e-01 3.30817342e-01 -1.21564364e+00 -1.33753204e+00 1.20205283e+00 -6.68327689e-01 1.36733010e-01 4.89066988e-01 1.62351012e-01 1.03913963e+00 -9.92474556e-01 5.52162766e-01 9.25449550e-01 1.16258442e+00 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79ef6974-a0a5-4806-bc3e-de3e7f75c192	accurate-shape-and-phase-averaging-of-time	2109.00978	null	https://arxiv.org/abs/2109.00978v1	https://arxiv.org/pdf/2109.00978v1.pdf	Accurate shape and phase averaging of time series through Dynamic Time Warping	We propose a novel time series averaging method based on Dynamic Time Warping (DTW). In contrast to previous methods, our algorithm preserves durational information and the distinctive durational features of the sequences due to a simple conversion of the output of DTW into a time sequence and an innovative iterative averaging process. We show that it accurately estimates the ground truth mean sequences and mean temporal location of landmarks in synthetic and real-world datasets and outperforms state-of-the-art methods.	['Kristian Nymoen', 'George Sioros']	2021-09-02	null	null	null	null	['time-series-averaging']	['time-series']	[ 3.08876455e-01 -6.68315947e-01 9.55494419e-02 -1.34292334e-01 -7.93562770e-01 -1.02133703e+00 8.51102591e-01 -1.05056338e-01 -4.25071239e-01 8.92886460e-01 1.80537179e-01 1.00985572e-01 -4.39157903e-01 -4.87416118e-01 -4.01081353e-01 -8.20718646e-01 -9.42892373e-01 5.93170449e-02 4.72978443e-01 -2.62068063e-01 4.87318367e-01 7.18205392e-01 -1.18331742e+00 -1.77383959e-01 6.15376651e-01 1.09716582e+00 -2.91293353e-01 9.03078318e-01 2.00186059e-01 4.15899783e-01 -6.42068744e-01 1.61636591e-01 4.35840636e-01 -6.67385817e-01 -3.70515674e-01 -1.81213573e-01 2.45544806e-01 -6.72428161e-02 -7.29487896e-01 7.46713519e-01 4.93318975e-01 6.14292324e-01 4.68210548e-01 -1.25725520e+00 -4.94221509e-01 9.21619460e-02 -7.93263435e-01 5.53775489e-01 6.39005244e-01 4.59858440e-02 6.75512731e-01 -5.83755851e-01 1.02305961e+00 8.23666990e-01 1.34054506e+00 9.77685973e-02 -1.44970298e+00 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-7.41409481e-01 5.28336287e-01 7.96640098e-01 -7.41477609e-01 -6.23955131e-01 3.64102304e-01]	[7.328769207000732, 3.3391127586364746]
b31b8315-cfe5-452f-b0a1-2f65d20cc20d	scalable-logo-recognition-in-real-world	null	null	https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/deliver/index/docId/61111/file/61111.pdf	https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/deliver/index/docId/61111/file/61111.pdf	Scalable logo recognition in real-world images	In this paper we propose a highly effective and scalable framework for recognizing logos in images. At the core of our approach lays a method for encoding and indexing the relative spatial layout of local features detected in the logo images. Based on the analysis of the local features and the composition of basic spatial structures, such as edges and triangles, we can derive a quantized representation of the regions in the logos and minimize the false positive detections. Furthermore, we propose a cascaded index for scalable multi-class recognition of logos. For the evaluation of our system, we have constructed and released a logo recognition benchmark which consists of manually labeled logo images, complemented with non-logo images, all posted on Flickr. The dataset consists of a training, validation, and test set with 32 logo-classes. We thoroughly evaluate our system with this benchmark and show that our approach effectively recognizes different logo classes with high precision.	['Roelof van Zwol', 'Rainer Lienhart', 'Lluis Garcia Pueyo', 'Stefan Romberg']	2011-04-17	null	null	null	acm-international-conference-on-multimedia-4	['logo-recognition']	['computer-vision']	[ 4.31117058e-01 -6.00126207e-01 -2.45120466e-01 -2.12603077e-01 -6.19436324e-01 -9.40363050e-01 4.72790211e-01 3.39913547e-01 3.04446872e-02 -8.15451890e-02 8.53040218e-02 4.54204332e-04 -3.91686529e-01 -8.64468038e-01 -6.57956600e-01 -5.41732132e-01 -3.67810816e-01 3.93396974e-01 6.05075240e-01 2.35706680e-02 7.17507780e-01 8.80949616e-01 -2.02365112e+00 6.16879344e-01 4.40473884e-01 1.48135662e+00 -5.36027253e-02 8.72002184e-01 2.68132269e-01 7.95635641e-01 -6.15933776e-01 2.89214045e-01 5.04033029e-01 -2.48873040e-01 -6.72215044e-01 4.24746096e-01 1.20896399e+00 -1.53612867e-01 -4.75804150e-01 9.08923209e-01 3.36871654e-01 7.44408593e-02 8.94091785e-01 -1.01783037e+00 -4.19855952e-01 -2.64671911e-02 -5.67355692e-01 4.43969250e-01 5.01316249e-01 -2.38168016e-01 1.31513548e+00 -1.19567835e+00 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d6dad688-be00-43ed-bec0-2b9924df2f23	natural-language-rationales-with-full-stack	2010.07526	null	https://arxiv.org/abs/2010.07526v1	https://arxiv.org/pdf/2010.07526v1.pdf	Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs	Natural language rationales could provide intuitive, higher-level explanations that are easily understandable by humans, complementing the more broadly studied lower-level explanations based on gradients or attention weights. We present the first study focused on generating natural language rationales across several complex visual reasoning tasks: visual commonsense reasoning, visual-textual entailment, and visual question answering. The key challenge of accurate rationalization is comprehensive image understanding at all levels: not just their explicit content at the pixel level, but their contextual contents at the semantic and pragmatic levels. We present Rationale^VT Transformer, an integrated model that learns to generate free-text rationales by combining pretrained language models with object recognition, grounded visual semantic frames, and visual commonsense graphs. Our experiments show that the base pretrained language model benefits from visual adaptation and that free-text rationalization is a promising research direction to complement model interpretability for complex visual-textual reasoning tasks.	['Yejin Choi', 'Noah A. Smith', 'Ronan Le Bras', 'Jae Sung Park', 'Chandra Bhagavatula', 'Ana Marasović']	2020-10-15	null	https://aclanthology.org/2020.findings-emnlp.253	https://aclanthology.org/2020.findings-emnlp.253.pdf	findings-of-the-association-for-computational	['visual-commonsense-reasoning']	['reasoning']	[ 4.55547035e-01 7.67828882e-01 -2.55808294e-01 -5.10690451e-01 -3.15929145e-01 -5.26757956e-01 7.91137516e-01 2.27463812e-01 -2.45587211e-02 4.49634522e-01 8.57631207e-01 -7.96877980e-01 1.27566695e-01 -6.69958770e-01 -8.43962729e-01 -2.09685147e-01 5.24513543e-01 4.16524082e-01 1.90310672e-01 -4.43709314e-01 5.08227885e-01 2.95170665e-01 -1.44947326e+00 1.15346241e+00 7.56335437e-01 7.00247943e-01 1.54645666e-01 6.80268228e-01 -4.18834150e-01 1.61672390e+00 -2.78685629e-01 -6.93461239e-01 -1.80953771e-01 -5.21672785e-01 -8.23126793e-01 -5.09054847e-02 8.23977411e-01 -4.23107564e-01 -6.23335205e-02 1.08875823e+00 -6.62545636e-02 -2.69220062e-02 8.99305165e-01 -1.55164480e+00 -1.95696759e+00 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dc8f8370-8284-4693-a81b-c1ebd8cbefa1	design-of-human-machine-interface-through	2207.03112	null	https://arxiv.org/abs/2207.03112v3	https://arxiv.org/pdf/2207.03112v3.pdf	Deep learning based Hand gesture recognition system and design of a Human-Machine Interface	In this work, a real-time hand gesture recognition system-based human-computer interface (HCI) is presented. The system consists of six stages: (1) hand detection, (2) gesture segmentation, (3) use of five pre-trained convolutional neural network models (CNN) and vision transformer (ViT), (4) building an interactive human-machine interface (HMI), (5) development of a gesture-controlled virtual mouse, (6) use of Kalman filter to estimate the hand position, based on that the smoothness of the motion of pointer is improved. In our work, five pre-trained CNN (VGG16, VGG19, ResNet50, ResNet101, and Inception-V1) models and ViT have been employed to classify hand gesture images. Two multi-class datasets (one public and one custom) have been used to validate the models. Considering the model's performances, it is observed that Inception-V1 has significantly shown a better classification performance compared to the other four CNN models and ViT in terms of accuracy, precision, recall, and F-score values. We have also expanded this system to control some desktop applications (such as VLC player, audio player, file management, playing 2D Super-Mario-Bros game, etc.) with different customized gesture commands in real-time scenarios. The average speed of this system has reached 25 fps (frames per second), which meets the requirements for the real-time scenario. Performance of the proposed gesture control system obtained the average response time in milisecond for each control which makes it suitable for real-time. This model (prototype) will benefit physically disabled people interacting with desktops.	['Ratnakar Dash', 'Tapas Kumar Mishra', 'Abir Sen']	2022-07-07	null	null	null	null	['hand-gesture-recognition', 'hand-detection', 'hand-gesture-recognition-1', 'gesture-recognition']	['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision']	[ 1.19503982e-01 -3.84060025e-01 1.33627698e-01 -2.64625065e-02 -6.59630597e-02 -3.05525184e-01 4.59862351e-01 -2.85684556e-01 -9.44963276e-01 4.32123214e-01 -5.41960262e-03 -5.67175806e-01 -2.36196622e-01 -5.39660871e-01 -8.17114860e-02 -5.76973021e-01 -9.94743034e-02 4.28614318e-01 7.13162184e-01 -1.05930254e-01 5.05592108e-01 9.31447804e-01 -1.74598157e+00 2.02125221e-01 2.20886827e-01 7.50694513e-01 4.35298026e-01 1.36180305e+00 -6.62590861e-02 3.50850821e-01 -5.69728136e-01 4.18392211e-01 2.26010501e-01 -1.01935245e-01 -7.02629149e-01 -2.69651979e-01 -3.30290571e-02 -7.69625545e-01 -3.62914443e-01 5.38735747e-01 1.02148569e+00 3.11105162e-01 6.11257136e-01 -1.20969772e+00 7.17755258e-02 5.85532933e-02 -2.90685207e-01 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22b202f1-5924-492d-8696-33c7f63a6c43	styletalker-one-shot-style-based-audio-driven	2208.10922	null	https://arxiv.org/abs/2208.10922v1	https://arxiv.org/pdf/2208.10922v1.pdf	StyleTalker: One-shot Style-based Audio-driven Talking Head Video Generation	We propose StyleTalker, a novel audio-driven talking head generation model that can synthesize a video of a talking person from a single reference image with accurately audio-synced lip shapes, realistic head poses, and eye blinks. Specifically, by leveraging a pretrained image generator and an image encoder, we estimate the latent codes of the talking head video that faithfully reflects the given audio. This is made possible with several newly devised components: 1) A contrastive lip-sync discriminator for accurate lip synchronization, 2) A conditional sequential variational autoencoder that learns the latent motion space disentangled from the lip movements, such that we can independently manipulate the motions and lip movements while preserving the identity. 3) An auto-regressive prior augmented with normalizing flow to learn a complex audio-to-motion multi-modal latent space. Equipped with these components, StyleTalker can generate talking head videos not only in a motion-controllable way when another motion source video is given but also in a completely audio-driven manner by inferring realistic motions from the input audio. Through extensive experiments and user studies, we show that our model is able to synthesize talking head videos with impressive perceptual quality which are accurately lip-synced with the input audios, largely outperforming state-of-the-art baselines.	['Sung Ju Hwang', 'Minyoung Song', 'Dongchan Min']	2022-08-23	null	null	null	null	['talking-head-generation', 'video-generation']	['computer-vision', 'computer-vision']	[ 9.21847671e-02 1.67563140e-01 -2.22285956e-01 -9.96667519e-02 -1.14691436e+00 -5.51236987e-01 6.49241388e-01 -8.62295151e-01 8.80118236e-02 5.57626426e-01 8.47974837e-01 3.99763554e-01 5.41591585e-01 -2.82720506e-01 -1.06379116e+00 -8.83066416e-01 3.82787257e-01 3.34167540e-01 -3.20000589e-01 1.19383633e-01 -1.32188173e-02 1.37860090e-01 -2.03572845e+00 3.58628601e-01 5.78799546e-01 9.46576178e-01 2.27291688e-01 1.27506936e+00 1.94197968e-01 1.03054702e+00 -4.95033622e-01 -3.40621263e-01 2.38813888e-02 -6.32238805e-01 -5.41984797e-01 6.77521229e-01 7.54079938e-01 -5.84025919e-01 -6.92680299e-01 7.86951721e-01 6.04367018e-01 1.04681335e-01 6.12947583e-01 -1.39658785e+00 -5.69836795e-01 4.63820279e-01 -2.59855121e-01 -4.45332170e-01 9.04606342e-01 7.79628158e-01 9.07478631e-01 -8.61271143e-01 8.25424910e-01 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01fa4b1e-75af-40fc-92a8-855c20d4fb10	semi-supervised-graph-based-genre	null	null	https://aclanthology.org/W14-3706	https://aclanthology.org/W14-3706.pdf	Semi-supervised Graph-based Genre Classification for Web Pages	null	['Serge Sharoff', 'Katja Markert', 'Noushin Rezapour Asheghi']	2014-10-01	null	null	null	ws-2014-10	['genre-classification']	['computer-vision']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 -8.59008506e-02 -2.09758401e-01 9.22211111e-01 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01631c7a-d0f5-42f8-bcc4-c00ff022281b	a-differentiable-self-disambiguated-sense	null	null	https://openreview.net/forum?id=Hyls7h05FQ	https://openreview.net/pdf?id=Hyls7h05FQ	A Differentiable Self-disambiguated Sense Embedding Model via Scaled Gumbel Softmax	We present a differentiable multi-prototype word representation model that disentangles senses of polysemous words and produces meaningful sense-specific embeddings without external resources. It jointly learns how to disambiguate senses given local context and how to represent senses using hard attention. Unlike previous multi-prototype models, our model approximates discrete sense selection in a differentiable manner via a modified Gumbel softmax. We also propose a novel human evaluation task that quantitatively measures (1) how meaningful the learned sense groups are to humans and (2) how well the model is able to disambiguate senses given a context sentence. Our model outperforms competing approaches on both human evaluations and multiple word similarity tasks.	['Jordan Boyd-Graber', 'Leah Findlater', 'Mohit Iyyer', 'Fenfei Guo']	2018-09-27	null	null	null	null	['hard-attention', 'word-similarity']	['methodology', 'natural-language-processing']	[ 2.51451045e-01 5.96211106e-02 -1.30050704e-01 -6.51743591e-01 -8.56342554e-01 -7.66475379e-01 7.77448416e-01 8.27162445e-01 -1.19729531e+00 6.44236505e-01 5.16313672e-01 -1.68404102e-01 -5.80462813e-03 -8.56510639e-01 -3.72700542e-02 -3.33906651e-01 1.66760504e-01 5.80151141e-01 -1.36875585e-01 -5.58536470e-01 3.18159789e-01 -1.99415702e-02 -1.31046402e+00 -1.33750886e-01 1.09131527e+00 7.25295782e-01 3.14497709e-01 6.31127059e-01 -2.41110936e-01 1.20197453e-01 -9.17584300e-01 -3.84673834e-01 5.68665862e-02 -1.25522465e-01 -7.95986533e-01 -4.11130428e-01 7.62184083e-01 2.59236425e-01 -8.25539753e-02 1.36748350e+00 5.17091274e-01 7.65975714e-01 9.19051647e-01 -7.66962707e-01 -1.69389212e+00 6.65793002e-01 -1.38450101e-01 6.21912718e-01 5.61859906e-01 1.13071151e-01 1.52351499e+00 -9.08223629e-01 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8a50d75a-1be0-448e-ad8f-c5573b63f5a3	fast-privacy-preserving-text-classification	2101.07365	null	https://arxiv.org/abs/2101.07365v2	https://arxiv.org/pdf/2101.07365v2.pdf	Fast Privacy-Preserving Text Classification based on Secure Multiparty Computation	We propose a privacy-preserving Naive Bayes classifier and apply it to the problem of private text classification. In this setting, a party (Alice) holds a text message, while another party (Bob) holds a classifier. At the end of the protocol, Alice will only learn the result of the classifier applied to her text input and Bob learns nothing. Our solution is based on Secure Multiparty Computation (SMC). Our Rust implementation provides a fast and secure solution for the classification of unstructured text. Applying our solution to the case of spam detection (the solution is generic, and can be used in any other scenario in which the Naive Bayes classifier can be employed), we can classify an SMS as spam or ham in less than 340ms in the case where the dictionary size of Bob's model includes all words (n = 5200) and Alice's SMS has at most m = 160 unigrams. In the case with n = 369 and m = 8 (the average of a spam SMS in the database), our solution takes only 21ms.	['Diego F. Aranha', 'Anderson C. A. Nascimento', 'Rafael Dowsley', 'Davis Railsback', 'Amanda Resende']	2021-01-18	null	null	null	null	['spam-detection']	['natural-language-processing']	[ 3.20384979e-01 1.62926257e-01 1.37506947e-02 -5.02004743e-01 -7.66363204e-01 -1.16073370e+00 7.19354570e-01 4.59258914e-01 -9.73093092e-01 5.19379139e-01 -1.24236673e-01 -8.53689432e-01 2.62526095e-01 -1.10206318e+00 -4.96350139e-01 -1.04399490e+00 1.48967043e-01 5.78433156e-01 3.84962678e-01 -2.57645011e-01 2.31865689e-01 4.71921206e-01 -1.20411003e+00 5.73823988e-01 1.32741839e-01 1.16606069e+00 -4.50437695e-01 1.04468238e+00 -1.03958234e-01 5.86806238e-01 -5.31846464e-01 -1.15958583e+00 7.21189260e-01 -1.89452872e-01 -1.15217280e+00 -3.27846587e-01 1.76610291e-01 -5.65360904e-01 -6.43158674e-01 1.02302384e+00 4.54782993e-01 -9.75199789e-02 4.43215698e-01 -1.35938632e+00 4.49285686e-01 8.10663581e-01 -1.38470292e-01 -2.08284795e-01 2.16690361e-01 -8.10530484e-02 9.02144909e-01 -1.40029624e-01 5.89124560e-01 9.51915860e-01 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1adf31d2-5b9e-439b-9f5d-ad181e9cdb04	q-map-a-convolutional-approach-for-goal	1810.02927	null	https://arxiv.org/abs/1810.02927v2	https://arxiv.org/pdf/1810.02927v2.pdf	Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks	Being able to reach any desired location in the environment can be a valuable asset for an agent. Learning a policy to navigate between all pairs of states individually is often not feasible. An all-goals updating algorithm uses each transition to learn Q-values towards all goals simultaneously and off-policy. However the expensive numerous updates in parallel limited the approach to small tabular cases so far. To tackle this problem we propose to use convolutional network architectures to generate Q-values and updates for a large number of goals at once. We demonstrate the accuracy and generalization qualities of the proposed method on randomly generated mazes and Sokoban puzzles. In the case of on-screen goal coordinates the resulting mapping from frames to distance-maps directly informs the agent about which places are reachable and in how many steps. As an example of application we show that replacing the random actions in epsilon-greedy exploration by several actions towards feasible goals generates better exploratory trajectories on Montezuma's Revenge and Super Mario All-Stars games.	['Petar Kormushev', 'Vitaly Levdik', 'Fabio Pardo']	2018-10-06	q-map-a-convolutional-approach-for-goal-1	https://openreview.net/forum?id=rye7XnRqFm	https://openreview.net/pdf?id=rye7XnRqFm	iclr-2019-5	['montezumas-revenge', 'snes-games']	['playing-games', 'playing-games']	[-1.52226090e-01 1.90611169e-01 1.80412844e-01 -1.28942639e-01 -7.83847511e-01 -9.86970663e-01 5.55401802e-01 1.63474813e-01 -7.29623020e-01 1.39052606e+00 -6.69034645e-02 -4.75617915e-01 -6.17990911e-01 -1.17650247e+00 -7.79290676e-01 -8.59189689e-01 -6.51820660e-01 9.96420920e-01 3.35949630e-01 -6.28140390e-01 5.80362976e-01 6.79507732e-01 -1.60661292e+00 9.69888493e-02 5.98619878e-01 6.98296428e-01 5.24384320e-01 1.05111372e+00 -2.95535172e-03 7.38512337e-01 -4.95609254e-01 2.31878087e-03 5.65208912e-01 -4.59881783e-01 -8.75340223e-01 -1.54514700e-01 -1.06562063e-01 -3.23094696e-01 -6.68036044e-02 1.05875993e+00 4.58454311e-01 6.64531827e-01 3.27483863e-01 -1.24345279e+00 -1.15120813e-01 6.57209158e-01 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9187558c-6507-443d-b5f0-9028542baa2e	how-to-solve-few-shot-abusive-content	2305.14081	null	https://arxiv.org/abs/2305.14081v1	https://arxiv.org/pdf/2305.14081v1.pdf	How to Solve Few-Shot Abusive Content Detection Using the Data We Actually Have	Due to the broad range of social media platforms and their user groups, the requirements of abusive language detection systems are varied and ever-changing. Already a large set of annotated corpora with different properties and label sets were created, such as hate or misogyny detection, but the form and targets of abusive speech are constantly changing. Since, the annotation of new corpora is expensive, in this work we leverage datasets we already have, covering a wide range of tasks related to abusive language detection, in order to build models cheaply for a new target label set and/or language, using only a few training examples of the target domain. We propose a two-step approach: first we train our model in a multitask fashion. We then carry out few-shot adaptation to the target requirements. Our experiments show that by leveraging already existing datasets and only a few-shots of the target task the performance of models can be improved not only monolingually but across languages as well. Our analysis also shows that our models acquire a general understanding of abusive language, since they improve the prediction of labels which are present only in the target dataset. We also analyze the trade-off between specializing the already existing datasets to a given target setup for best performance and its negative effects on model adaptability.	['Alexander Fraser', 'Viktor Hangya']	2023-05-23	null	null	null	null	['abusive-language']	['natural-language-processing']	[ 1.17520709e-02 -1.15448296e-01 -1.76345259e-01 -3.66744548e-01 -7.01452553e-01 -8.14334214e-01 6.34799719e-01 8.66829082e-02 -6.40647233e-01 6.43449068e-01 1.07211113e-01 8.58207047e-03 2.69174606e-01 -3.30882937e-01 -3.43434960e-01 -3.05706412e-01 1.23045668e-01 5.80287814e-01 6.41538978e-01 -5.70805132e-01 7.95595068e-03 3.91517252e-01 -1.32324612e+00 2.95459479e-01 1.02062297e+00 5.28771996e-01 4.49948832e-02 5.77215552e-01 -1.55458897e-01 1.07968688e+00 -8.55366051e-01 -7.94510007e-01 2.03654349e-01 -4.44974542e-01 -9.17719305e-01 2.35158056e-01 4.92941082e-01 -3.11836153e-01 -1.72515586e-01 1.13938916e+00 5.85944653e-01 1.02609769e-01 4.74732876e-01 -1.02258122e+00 -6.36269510e-01 9.04598713e-01 -4.81566280e-01 6.78795695e-01 2.88824886e-01 3.46828401e-01 8.91105950e-01 -7.70702779e-01 8.46019208e-01 1.26940131e+00 6.59015656e-01 8.51657689e-01 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76de10a5-ed59-4ef7-92f0-149e23fe8e58	a-two-branch-neural-network-for-non	2104.08902	null	https://arxiv.org/abs/2104.08902v1	https://arxiv.org/pdf/2104.08902v1.pdf	A Two-branch Neural Network for Non-homogeneous Dehazing via Ensemble Learning	Recently, there has been rapid and significant progress on image dehazing. Many deep learning based methods have shown their superb performance in handling homogeneous dehazing problems. However, we observe that even if a carefully designed convolutional neural network (CNN) can perform well on large-scaled dehazing benchmarks, the network usually fails on the non-homogeneous dehazing datasets introduced by NTIRE challenges. The reasons are mainly in two folds. Firstly, due to its non-homogeneous nature, the non-uniformly distributed haze is harder to be removed than the homogeneous haze. Secondly, the research challenge only provides limited data (there are only 25 training pairs in NH-Haze 2021 dataset). Thus, learning the mapping from the domain of hazy images to that of clear ones based on very limited data is extremely hard. To this end, we propose a simple but effective approach for non-homogeneous dehazing via ensemble learning. To be specific, we introduce a two-branch neural network to separately deal with the aforementioned problems and then map their distinct features by a learnable fusion tail. We show extensive experimental results to illustrate the effectiveness of our proposed method.	['Keyan Wang', 'Xiyao Wang', 'Jun Chen', 'Minghan Fu', 'Huan Liu', 'Yankun Yu']	2021-04-18	null	null	null	null	['image-dehazing']	['computer-vision']	[ 9.92346182e-02 -3.29986453e-01 2.23802373e-01 -2.69809544e-01 -7.42425144e-01 -1.14422716e-01 4.40236598e-01 -2.00044006e-01 -2.30433553e-01 8.33409548e-01 8.74426737e-02 -7.18904436e-02 -1.97851807e-01 -9.51697230e-01 -7.05101371e-01 -1.28212941e+00 5.95244579e-02 -6.00046366e-02 3.03613216e-01 -6.09516919e-01 4.30485373e-03 1.75281137e-01 -2.00716472e+00 2.16137186e-01 1.39016485e+00 1.08735943e+00 1.94345728e-01 5.88462949e-01 2.92791277e-01 9.62185264e-01 -8.29321742e-01 -1.46032885e-01 5.43683529e-01 -5.26076019e-01 -4.22885269e-01 5.64856688e-03 9.44542527e-01 -6.38834953e-01 -4.58925247e-01 1.18608725e+00 6.49836183e-01 2.54933894e-01 7.96008527e-01 -1.35366702e+00 -8.70765567e-01 1.96469963e-01 -6.10145509e-01 2.96999127e-01 -4.00575012e-01 -5.19940034e-02 7.38995790e-01 -8.18080127e-01 1.29260033e-01 8.11915278e-01 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-4.77194458e-01 -9.79039311e-01 -8.55817318e-01 4.37569618e-01 9.31550086e-01 -7.16834784e-01 -8.57061625e-01 -7.08755180e-02]	[10.940751075744629, -3.093118906021118]
d57a6946-618d-4074-8633-b0d79a15308e	self-sustaining-multiple-access-with	null	null	http://www.mosaic-lab.org/uploads/papers/1bcf6de2-74be-41a5-9431-1f169c0ed8af.pdf	http://www.mosaic-lab.org/uploads/papers/1bcf6de2-74be-41a5-9431-1f169c0ed8af.pdf	Self-Sustaining Multiple Access with Continual Deep Reinforcement Learning for Dynamic Metaverse Applications	The Metaverse is a new paradigm that aims to create a virtual environment consisting of numerous worlds, each of which will offer a different set of services. To deal with such a dynamic and complex scenario, considering the stringent quality of service requirements aimed at the 6th generation of communication systems (6G), one potential approach is to adopt self-sustaining strategies, which can be realized by employing Adaptive Artificial Intelligence (Adaptive AI) where models are continually re-trained with new data and conditions. One aspect of self-sustainability is the management of multiple access to the frequency spectrum. Although several innovative methods have been proposed to address this challenge, mostly using Deep Reinforcement Learning (DRL), the problem of adapting agents to a non-stationary environment has not yet been precisely addressed. This paper fills in the gap in the current literature by investigating the problem of multiple access in multi-channel environments to maximize the throughput of the intelligent agent when the number of active User Equipments (UEs) may fluctuate over time. To solve the problem, a Double Deep Q-Learning (DDQL) technique empowered by Continual Learning (CL) is proposed to overcome the non-stationary situation, while the environment is unknown. Numerical simulations demonstrate that, compared to other well-known methods, the CL-DDQL algorithm achieves significantly higher throughputs with a considerably shorter convergence time in highly dynamic scenarios.	['Richard Li', 'Tarik Taleb', 'Masoud Shokrnezhad', 'Hamidreza Mazandarani']	2023-06-26	null	null	null	ieee-metacom-kyoto-2023-6	['continual-learning', 'q-learning']	['methodology', 'methodology']	[-2.07274139e-01 -2.13532612e-01 -2.87729353e-01 3.53894889e-01 -1.31129861e-01 -1.96986854e-01 1.39843047e-01 -5.96670806e-02 -3.11956912e-01 1.34910548e+00 -3.69753957e-01 -4.49817955e-01 -7.21931577e-01 -1.03411222e+00 -2.94528782e-01 -1.13945341e+00 -4.32372898e-01 4.95199651e-01 -1.24834239e-01 -6.30232871e-01 1.11860655e-01 4.33148891e-01 -1.49294198e+00 -5.64800858e-01 1.12957895e+00 1.30795336e+00 4.63321865e-01 6.96104884e-01 -2.09188253e-01 3.99978489e-01 -9.31495607e-01 -1.65250793e-01 1.89347833e-01 -5.70110917e-01 -4.42650169e-01 2.19766483e-01 -7.68383861e-01 -9.96324420e-02 -1.13587208e-01 8.16987276e-01 9.51781929e-01 2.25990608e-01 2.13957787e-01 -1.39976907e+00 -3.10479879e-01 2.85655618e-01 -5.31338334e-01 5.95325887e-01 1.87880605e-01 9.69894454e-02 6.58037066e-01 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dc0461c7-6e77-4a4b-8c8f-278f8e6190a7	e2e-load-end-to-end-long-form-online-action	2306.07703	null	https://arxiv.org/abs/2306.07703v1	https://arxiv.org/pdf/2306.07703v1.pdf	E2E-LOAD: End-to-End Long-form Online Action Detection	Recently, there has been a growing trend toward feature-based approaches for Online Action Detection (OAD). However, these approaches have limitations due to their fixed backbone design, which ignores the potential capability of a trainable backbone. In this paper, we propose the first end-to-end OAD model, termed E2E-LOAD, designed to address the major challenge of OAD, namely, long-term understanding and efficient online reasoning. Specifically, our proposed approach adopts an initial spatial model that is shared by all frames and maintains a long sequence cache for inference at a low computational cost. We also advocate an asymmetric spatial-temporal model for long-form and short-form modeling effectively. Furthermore, we propose a novel and efficient inference mechanism that accelerates heavy spatial-temporal exploration. Extensive ablation studies and experiments demonstrate the effectiveness and efficiency of our proposed method. Notably, we achieve 17.3 (+12.6) FPS for end-to-end OAD with 72.4%~(+1.2%), 90.3%~(+0.7%), and 48.1%~(+26.0%) mAP on THMOUS14, TVSeries, and HDD, respectively, which is 3x faster than previous approaches. The source code will be made publicly available.	['Lin Ma', 'Wei zhang', 'Bairui Wang', 'Weixin Luo', 'Shuqiang Cao']	2023-06-13	null	null	null	null	['action-detection', 'online-action-detection']	['computer-vision', 'computer-vision']	[-1.33892447e-01 -9.01113749e-02 -4.47860569e-01 -3.19382757e-01 -8.01821530e-01 -4.01542336e-01 2.65775681e-01 -1.55195221e-01 -5.47406495e-01 5.41153848e-01 3.55697751e-01 -3.07296306e-01 -9.19109657e-02 -6.21324778e-01 -6.85639679e-01 -1.58618540e-01 -3.01763505e-01 1.18650131e-01 7.38611817e-01 1.44282673e-02 1.89951763e-01 3.62177044e-01 -1.55517888e+00 2.37973958e-01 8.15658152e-01 1.23638761e+00 4.38499242e-01 7.63128936e-01 -4.18083109e-02 1.03504717e+00 -3.48877519e-01 -2.26559177e-01 3.34526747e-01 -8.79025981e-02 -7.72519588e-01 2.30110921e-02 4.77264613e-01 -1.05955601e+00 -8.39165211e-01 7.87716150e-01 6.52830780e-01 1.57124475e-01 -1.42250266e-02 -1.18956292e+00 -1.07945941e-01 2.63046265e-01 -8.74935150e-01 3.35588723e-01 5.07472336e-01 4.83501077e-01 8.44811678e-01 -6.98412478e-01 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49dedb4c-c577-4fe7-bc1d-ebb07941b9e0	heimdal-highly-efficient-method-for-detection	2210.15425	null	https://arxiv.org/abs/2210.15425v1	https://arxiv.org/pdf/2210.15425v1.pdf	HEiMDaL: Highly Efficient Method for Detection and Localization of wake-words	Streaming keyword spotting is a widely used solution for activating voice assistants. Deep Neural Networks with Hidden Markov Model (DNN-HMM) based methods have proven to be efficient and widely adopted in this space, primarily because of the ability to detect and identify the start and end of the wake-up word at low compute cost. However, such hybrid systems suffer from loss metric mismatch when the DNN and HMM are trained independently. Sequence discriminative training cannot fully mitigate the loss-metric mismatch due to the inherent Markovian style of the operation. We propose an low footprint CNN model, called HEiMDaL, to detect and localize keywords in streaming conditions. We introduce an alignment-based classification loss to detect the occurrence of the keyword along with an offset loss to predict the start of the keyword. HEiMDaL shows 73% reduction in detection metrics along with equivalent localization accuracy and with the same memory footprint as existing DNN-HMM style models for a given wake-word.	['Devang Naik', 'Priyanka Padmanabhan', 'Minsik Cho', 'Mohammad Samragh Razlighi', 'Arnav Kundu']	2022-10-26	null	null	null	null	['keyword-spotting']	['speech']	[ 2.17923030e-01 -3.14614326e-01 -2.32559443e-01 -2.77663976e-01 -8.83487046e-01 -3.46224904e-01 2.82666713e-01 5.59883602e-02 -7.07185388e-01 2.61052459e-01 2.46977970e-01 -4.42919999e-01 2.09493369e-01 -9.93712768e-02 -4.67442602e-01 -5.40926099e-01 -7.58644268e-02 3.49380732e-01 3.98204893e-01 3.02447110e-01 1.13001943e-01 5.10125577e-01 -1.65961909e+00 2.32029915e-01 2.89033890e-01 1.06292307e+00 9.56826866e-01 1.26533771e+00 -2.58154869e-01 7.99331248e-01 -8.68549049e-01 1.91337451e-01 7.23537952e-02 -3.46417189e-01 -7.07923770e-01 -3.89385760e-01 6.27374828e-01 -6.61496818e-01 -7.81705320e-01 6.22223556e-01 1.13059783e+00 2.15573192e-01 -8.03037286e-02 -1.14777672e+00 1.74419701e-01 6.48882568e-01 -1.61663696e-01 4.36872184e-01 2.61937797e-01 9.14459229e-02 1.25945997e+00 -9.36825633e-01 3.37889135e-01 1.13449335e+00 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3f02bb6b-3e5f-4596-90e8-673701f64112	evaluation-of-generalizability-of-neural	2004.07313	null	https://arxiv.org/abs/2004.07313v2	https://arxiv.org/pdf/2004.07313v2.pdf	Evaluation of Generalizability of Neural Program Analyzers under Semantic-Preserving Transformations	The abundance of publicly available source code repositories, in conjunction with the advances in neural networks, has enabled data-driven approaches to program analysis. These approaches, called neural program analyzers, use neural networks to extract patterns in the programs for tasks ranging from development productivity to program reasoning. Despite the growing popularity of neural program analyzers, the extent to which their results are generalizable is unknown. In this paper, we perform a large-scale evaluation of the generalizability of two popular neural program analyzers using seven semantically-equivalent transformations of programs. Our results caution that in many cases the neural program analyzers fail to generalize well, sometimes to programs with negligible textual differences. The results provide the initial stepping stones for quantifying robustness in neural program analyzers.	['Md Rafiqul Islam Rabin', 'Mohammad Amin Alipour']	2020-04-15	null	null	null	null	['method-name-prediction']	['natural-language-processing']	[ 3.21274810e-02 -2.53080446e-02 -4.31132585e-01 -4.22129661e-01 -2.95138896e-01 -8.41213167e-01 2.99632519e-01 4.15910721e-01 -7.85711110e-02 1.00692213e-01 3.60402018e-01 -9.99219298e-01 6.00787140e-02 -1.01495433e+00 -7.98039436e-01 7.87897632e-02 -5.15762158e-02 -1.93828851e-01 1.17546603e-01 -4.10872012e-01 4.63960052e-01 3.47206861e-01 -1.66452646e+00 5.18464744e-01 8.05247009e-01 3.37664127e-01 -4.04434413e-01 8.67887020e-01 -3.44494879e-01 1.19284236e+00 -3.85283291e-01 -6.72462583e-01 6.73693120e-02 -1.52172133e-01 -7.98247576e-01 -6.69919789e-01 4.35742259e-01 -3.55091214e-01 -3.74457777e-01 1.53958428e+00 8.35567042e-02 -5.91303371e-02 4.02360350e-01 -1.40451193e+00 -1.09196615e+00 1.24109149e+00 -5.26540697e-01 3.57985377e-01 4.61965829e-01 3.58789533e-01 1.32113230e+00 -7.22022951e-01 5.33590496e-01 1.31765127e+00 1.19135642e+00 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8662ac7d-bef9-4d97-b827-921a2f0d3a7d	declarative-sequential-pattern-mining-of-care	1707.08342	null	http://arxiv.org/abs/1707.08342v1	http://arxiv.org/pdf/1707.08342v1.pdf	Declarative Sequential Pattern Mining of Care Pathways	Sequential pattern mining algorithms are widely used to explore care pathways database, but they generate a deluge of patterns, mostly redundant or useless. Clinicians need tools to express complex mining queries in order to generate less but more significant patterns. These algorithms are not versatile enough to answer complex clinician queries. This article proposes to apply a declarative pattern mining approach based on Answer Set Programming paradigm. It is exemplified by a pharmaco-epidemiological study investigating the possible association between hospitalization for seizure and antiepileptic drug switch from a french medico-administrative database.	['Yann Dauxais', 'André Happe', 'Thomas Guyet']	2017-07-26	null	null	null	null	['sequential-pattern-mining']	['natural-language-processing']	[ 1.08426079e-01 1.50309965e-01 -2.64003277e-01 -4.37660575e-01 -9.19781532e-03 -4.34196532e-01 -3.20824693e-05 8.95561755e-01 -2.13043392e-01 9.64854836e-01 1.51906312e-01 -7.28799284e-01 -9.49697852e-01 -1.05825174e+00 -6.20338619e-02 -1.43095404e-01 -3.18923503e-01 8.77894759e-01 1.79441944e-01 -1.11080207e-01 1.83827266e-01 6.69584572e-01 -1.85469282e+00 1.11130106e+00 9.03119028e-01 7.51161754e-01 1.01899132e-01 3.03013444e-01 -2.47185081e-01 9.15909529e-01 -5.23132861e-01 -1.22125797e-01 9.23609659e-02 -7.05722511e-01 -6.86431468e-01 -1.85414806e-01 -6.84970438e-01 3.53247821e-01 3.40156764e-01 7.28392363e-01 3.47619504e-01 -3.20830166e-01 4.74581540e-01 -1.05839014e+00 1.44830450e-01 6.30454063e-01 6.39150292e-02 3.49070460e-01 1.05463171e+00 2.03788519e-01 6.80625379e-01 -6.40427172e-01 8.22498441e-01 9.19819415e-01 4.79716480e-01 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c2d735a3-fd3f-4dc6-b6c2-46f8a66a37b7	multi-modal-recurrent-fusion-for-indoor	2203.00510	null	https://arxiv.org/abs/2203.00510v2	https://arxiv.org/pdf/2203.00510v2.pdf	Multi-Modal Recurrent Fusion for Indoor Localization	This paper considers indoor localization using multi-modal wireless signals including Wi-Fi, inertial measurement unit (IMU), and ultra-wideband (UWB). By formulating the localization as a multi-modal sequence regression problem, a multi-stream recurrent fusion method is proposed to combine the current hidden state of each modality in the context of recurrent neural networks while accounting for the modality uncertainty which is directly learned from its own immediate past states. The proposed method was evaluated on the large-scale SPAWC2021 multi-modal localization dataset and compared with a wide range of baseline methods including the trilateration method, traditional fingerprinting methods, and convolution network-based methods.	['Toshiaki Koike-Akino', 'Wang', 'Pu', 'Philip V. Orlik', 'Jianyuan Yu']	2022-02-19	null	null	null	null	['indoor-localization']	['computer-vision']	[ 3.02080750e-01 -5.18763363e-01 -2.51435280e-01 -4.32547301e-01 -1.34767675e+00 -2.56730586e-01 5.06200016e-01 -3.46415102e-01 -5.68768799e-01 1.11495292e+00 5.17990351e-01 -3.95259202e-01 -3.12091708e-01 -6.59834683e-01 -8.64678323e-01 -5.58469474e-01 -2.69396394e-01 -2.06200063e-01 -3.12325686e-01 1.60724893e-01 -1.46053791e-01 2.01856524e-01 -1.21337426e+00 -2.22948790e-02 3.44358504e-01 1.70284259e+00 2.35050935e-02 9.52643037e-01 2.33360767e-01 1.15506351e+00 -7.25067019e-01 3.53392735e-02 -1.17361590e-01 -2.49146428e-02 -4.15873349e-01 -9.58201289e-01 4.65708971e-01 -1.88523442e-01 -6.76521778e-01 6.79687381e-01 8.64424765e-01 4.56397682e-01 1.71366960e-01 -9.88300264e-01 -1.74479604e-01 7.88645446e-01 -2.81905502e-01 2.69366801e-01 6.62278116e-01 -6.41102314e-01 6.39566600e-01 -6.43123567e-01 2.92043447e-01 1.05211651e+00 1.47044063e+00 4.09633666e-02 -1.02245331e+00 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-3.84995013e-01 -3.36945504e-01 8.85293901e-01 -6.90157592e-01 -1.84965000e-01 -3.90016139e-01]	[6.422280788421631, 0.8850423097610474]
fa6754a0-f186-42f3-bed4-07709ba2e958	boils-bayesian-optimisation-for-logic	2111.06178	null	https://arxiv.org/abs/2111.06178v1	https://arxiv.org/pdf/2111.06178v1.pdf	BOiLS: Bayesian Optimisation for Logic Synthesis	Optimising the quality-of-results (QoR) of circuits during logic synthesis is a formidable challenge necessitating the exploration of exponentially sized search spaces. While expert-designed operations aid in uncovering effective sequences, the increase in complexity of logic circuits favours automated procedures. Inspired by the successes of machine learning, researchers adapted deep learning and reinforcement learning to logic synthesis applications. However successful, those techniques suffer from high sample complexities preventing widespread adoption. To enable efficient and scalable solutions, we propose BOiLS, the first algorithm adapting modern Bayesian optimisation to navigate the space of synthesis operations. BOiLS requires no human intervention and effectively trades-off exploration versus exploitation through novel Gaussian process kernels and trust-region constrained acquisitions. In a set of experiments on EPFL benchmarks, we demonstrate BOiLS's superior performance compared to state-of-the-art in terms of both sample efficiency and QoR values.	['Haitham Bou Ammar', 'Jun Wang', 'Xingchen Wan', 'Rasul Tutunov', 'Cedric Malherbe', 'Antoine Grosnit']	2021-11-11	null	null	null	null	['bayesian-optimisation']	['methodology']	[ 2.42429897e-01 -3.98511179e-02 -4.81442451e-01 -1.88773409e-01 -8.94682050e-01 -8.23195517e-01 5.14413714e-01 4.97943722e-02 -3.38966548e-01 7.58719683e-01 -2.93393970e-01 -7.07098186e-01 -5.37585735e-01 -6.62667871e-01 -5.31051159e-01 -5.90130627e-01 -9.29135382e-02 5.95336080e-01 6.28957301e-02 1.13951325e-01 6.83802724e-01 5.93134999e-01 -1.11197758e+00 4.58619930e-02 7.28418887e-01 1.22590077e+00 -1.50851622e-01 7.04281867e-01 3.16549577e-02 6.23676419e-01 -3.80381525e-01 -6.62670434e-01 3.39919597e-01 -3.44297796e-01 -5.21864474e-01 -2.02727452e-01 -3.10279317e-02 -1.75198622e-03 -2.23156348e-01 1.17265654e+00 6.59499168e-01 -4.67945114e-02 5.66603541e-01 -1.21148801e+00 -3.67797017e-01 7.57286906e-01 -2.57575005e-01 1.90519765e-01 2.43954528e-02 5.03555954e-01 1.31953573e+00 -7.80398607e-01 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4.99138623e-01 -8.03395569e-01 -1.23974538e+00 -4.00933176e-01 -3.71869296e-01 -6.20452881e-01 3.62798065e-01 -5.79024374e-01 -1.31501555e-01 -2.33810514e-01]	[6.149320125579834, 3.6325907707214355]
abb54730-58b9-45dd-afac-b2abb2573c9c	transformer-based-multi-aspect-multi	2205.03432	null	https://arxiv.org/abs/2205.03432v1	https://arxiv.org/pdf/2205.03432v1.pdf	Transformer-Based Multi-Aspect Multi-Granularity Non-Native English Speaker Pronunciation Assessment	Automatic pronunciation assessment is an important technology to help self-directed language learners. While pronunciation quality has multiple aspects including accuracy, fluency, completeness, and prosody, previous efforts typically only model one aspect (e.g., accuracy) at one granularity (e.g., at the phoneme-level). In this work, we explore modeling multi-aspect pronunciation assessment at multiple granularities. Specifically, we train a Goodness Of Pronunciation feature-based Transformer (GOPT) with multi-task learning. Experiments show that GOPT achieves the best results on speechocean762 with a public automatic speech recognition (ASR) acoustic model trained on Librispeech.	['James Glass', 'Peng Chang', 'Iek-Heng Chu', 'Ziyi Chen', 'Yuan Gong']	2022-05-06	null	null	null	null	['phone-level-pronunciation-scoring', 'word-level-pronunciation-scoring', 'utterance-level-pronounciation-scoring']	['speech', 'speech', 'speech']	[-4.43369955e-01 -4.15860772e-01 -4.48680133e-01 -4.89561230e-01 -1.58363497e+00 -6.85146809e-01 3.38281870e-01 1.70792714e-01 -4.32736546e-01 4.51208889e-01 6.54995441e-01 -6.54213965e-01 -6.78022206e-02 -5.77702761e-01 -4.28987771e-01 -2.09088504e-01 5.98529100e-01 4.56046790e-01 -5.83096333e-02 -4.74108696e-01 8.45712349e-02 1.50681302e-01 -1.44951093e+00 4.17377114e-01 1.36168373e+00 8.46290529e-01 6.42908812e-02 9.55170631e-01 -4.33895826e-01 8.54642510e-01 -8.00935268e-01 -5.66890180e-01 -3.54763925e-01 -1.04213245e-01 -7.69146144e-01 -3.18200320e-01 7.20399618e-01 -4.93293911e-01 7.45092258e-02 1.19800937e+00 5.94651043e-01 3.97767685e-02 5.53360403e-01 -1.06941247e+00 -8.14349711e-01 1.00628352e+00 2.26985052e-01 1.52266309e-01 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756f7780-13ce-4a2d-ba55-0d6dfd515135	deep-kinematics-analysis-for-monocular-3d	null	null	http://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Deep_Kinematics_Analysis_for_Monocular_3D_Human_Pose_Estimation_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_Deep_Kinematics_Analysis_for_Monocular_3D_Human_Pose_Estimation_CVPR_2020_paper.pdf	Deep Kinematics Analysis for Monocular 3D Human Pose Estimation	For monocular 3D pose estimation conditioned on 2D detection, noisy/unreliable input is a key obstacle in this task. Simple structure constraints attempting to tackle this problem, e.g., symmetry loss and joint angle limit, could only provide marginal improvements and are commonly treated as auxiliary losses in previous researches. Thus it still remains challenging about how to effectively utilize the power of human prior knowledge for this task. In this paper, we propose to address above issue in a systematic view. Firstly, we show that optimizing the kinematics structure of noisy 2D inputs is critical to obtain accurate 3D estimations. Secondly, based on corrected 2D joints, we further explicitly decompose articulated motion with human topology, which leads to more compact 3D static structure easier for estimation. Finally, temporal refinement emphasizing the validity of 3D dynamic structure is naturally developed to pursue more accurate result. Above three steps are seamlessly integrated into deep neural models, which form a deep kinematics analysis pipeline concurrently considering the static/dynamic structure of 2D inputs and 3D outputs. Extensive experiments show that proposed framework achieves state-of-the-art performance on two widely used 3D human action datasets. Meanwhile, targeted ablation study shows that each former step is critical for the latter one to obtain promising results.	[' Wenjun Zhang', ' Xiaokang Yang', ' Jiancheng Yang', ' Bingbing Ni', ' Zhenbo Yu', 'Jingwei Xu']	2020-06-01	null	null	null	cvpr-2020-6	['monocular-3d-human-pose-estimation']	['computer-vision']	[-1.20817386e-02 6.05669320e-02 -2.53637075e-01 -1.07605472e-01 -6.02907717e-01 -2.77706951e-01 4.51315403e-01 -4.20545280e-01 -5.10885596e-01 7.09292889e-01 2.36311391e-01 1.89360082e-02 -1.26619488e-01 -3.03522885e-01 -7.70681441e-01 -5.86409569e-01 2.20891684e-02 2.52537549e-01 2.73061246e-01 -2.17720538e-01 1.49720952e-01 5.85354030e-01 -1.39964819e+00 -1.92560822e-01 8.11808348e-01 9.22796428e-01 2.45639563e-01 3.79289448e-01 1.23415641e-01 5.93666553e-01 -4.55975831e-01 -3.51688623e-01 5.24861217e-01 -2.69482344e-01 -6.93279147e-01 1.81730926e-01 3.00001174e-01 -6.78378463e-01 -3.72983545e-01 1.03848362e+00 7.01359868e-01 7.71583095e-02 5.89766741e-01 -1.13458860e+00 -1.78753644e-01 2.99726874e-01 -6.81893945e-01 -1.38388425e-01 5.35148263e-01 4.01324958e-01 9.80783641e-01 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67889440-5a52-4fc0-ba41-44010735e781	semantic-random-walk-for-graph-representation	2305.06531	null	https://arxiv.org/abs/2305.06531v1	https://arxiv.org/pdf/2305.06531v1.pdf	Semantic Random Walk for Graph Representation Learning in Attributed Graphs	In this study, we focus on the graph representation learning (a.k.a. network embedding) in attributed graphs. Different from existing embedding methods that treat the incorporation of graph structure and semantic as the simple combination of two optimization objectives, we propose a novel semantic graph representation (SGR) method to formulate the joint optimization of the two heterogeneous sources into a common high-order proximity based framework. Concretely, we first construct an auxiliary weighted graph, where the complex homogeneous and heterogeneous relations among nodes and attributes in the original graph are comprehensively encoded. Conventional embedding methods that consider high-order topology proximities can then be easily applied to the newly constructed graph to learn the representations of both node and attribute while capturing the nonlinear high-order intrinsic correlation inside or among graph structure and semantic. The learned attribute embeddings can also effectively support some semantic-oriented inference tasks (e.g., semantic community detection), helping to reveal the graph's deep semantic. The effectiveness of SGR is further verified on a series of real graphs, where it achieves impressive performance over other baselines.	['Meng Qin']	2023-05-11	null	null	null	null	['community-detection', 'network-embedding']	['graphs', 'methodology']	[-2.65738852e-02 4.41872686e-01 -1.83111534e-01 -4.28858817e-01 -2.49975130e-01 -4.58046466e-01 6.85250700e-01 5.76017022e-01 -1.04262561e-01 2.52906024e-01 5.11133671e-01 -1.36309922e-01 -4.37133759e-01 -1.20801866e+00 -3.65935504e-01 -7.59952068e-01 -1.63549870e-01 4.52828020e-01 7.39514902e-02 -2.41411731e-01 2.87524462e-02 2.93051034e-01 -1.14447999e+00 -3.24027240e-01 8.72481465e-01 7.45991945e-01 1.07257627e-01 3.21530849e-01 -3.31910431e-01 7.43898153e-01 -3.45024943e-01 -3.66063565e-01 -1.97219938e-01 -1.14485845e-01 -7.52806246e-01 1.77747592e-01 1.64500877e-01 6.26607984e-02 -8.70888233e-01 1.37225115e+00 2.67471910e-01 1.50503725e-01 6.83065951e-01 -1.50332975e+00 -1.39333713e+00 5.45136690e-01 -5.41980445e-01 -4.40238183e-03 3.40306640e-01 -2.60061592e-01 1.77132952e+00 -8.38047326e-01 6.18076384e-01 1.50526917e+00 4.28141505e-01 1.15232646e-01 -1.42202902e+00 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c4c77e7d-2727-43cc-a69c-b124a9a0a33e	leaningtower-lt-edi-acl2022-when-hope-and	null	null	https://aclanthology.org/2022.ltedi-1.46	https://aclanthology.org/2022.ltedi-1.46.pdf	LeaningTower@LT-EDI-ACL2022: When Hope and Hate Collide	The 2022 edition of LT-EDI proposed two tasks in various languages. Task Hope Speech Detection required models for the automatic identification of hopeful comments for equality, diversity, and inclusion. Task Homophobia/Transphobia Detection focused on the identification of homophobic and transphobic comments. We targeted both tasks in English by using reinforced BERT-based approaches. Our core strategy aimed at exploiting the data available for each given task to augment the amount of supervised instances in the other. On the basis of an active learning process, we trained a model on the dataset for Task i and applied it to the dataset for Task j to iteratively integrate new silver data for Task i. Our official submissions to the shared task obtained a macro-averaged F_1 score of 0.53 for Hope Speech and 0.46 for Homo/Transphobia, placing our team in the third and fourth positions out of 11 and 12 participating teams respectively.	['Alberto Barrón-Cedeño', 'Katerina Korre', 'Marta Marchiori Manerba', 'Arianna Muti']	null	null	null	null	ltedi-acl-2022-5	['hope-speech-detection']	['natural-language-processing']	[-8.09171200e-02 7.85383523e-01 -3.92668813e-01 -2.90751427e-01 -1.24671602e+00 -4.19001609e-01 5.47932565e-01 6.98888600e-01 -6.69049978e-01 6.49297178e-01 9.85434353e-01 -3.05278122e-01 -2.33411655e-01 -2.20078483e-01 8.22851211e-02 -3.85736227e-01 4.25094873e-01 5.41853905e-01 -4.42254394e-01 -1.59549758e-01 4.77006912e-01 1.50988430e-01 -1.15031910e+00 6.51392579e-01 1.05600536e+00 6.22028589e-01 -1.07147157e-01 6.55240893e-01 -1.88837484e-01 1.06199896e+00 -3.63117069e-01 -1.05759823e+00 -1.37354031e-01 -2.53121853e-01 -1.00929034e+00 -6.64424011e-03 3.16967934e-01 -2.11394265e-01 -6.65240362e-02 7.89565384e-01 6.53830588e-01 1.94152474e-01 4.94821697e-01 -1.02568805e+00 -5.48330605e-01 1.09752202e+00 -7.52552897e-02 -5.76289371e-03 4.47807550e-01 1.53456226e-01 1.16479301e+00 -1.26255620e+00 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31ba979c-c966-4e34-94ba-2e8d5c9b362e	uw-stanford-system-description-for-aesw-2016	null	null	https://aclanthology.org/W16-0511	https://aclanthology.org/W16-0511.pdf	UW-Stanford System Description for AESW 2016 Shared Task on Grammatical Error Detection	null	['Michael Goodman', 'Woodley Packard', 'Dan Flickinger']	2016-06-01	null	null	null	ws-2016-6	['grammatical-error-detection']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 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e1c7ece4-687b-475a-9274-3c838d247ae4	learning-to-encode-evolutionary-knowledge-for	2004.09974	null	https://arxiv.org/abs/2004.09974v1	https://arxiv.org/pdf/2004.09974v1.pdf	Learning to Encode Evolutionary Knowledge for Automatic Commenting Long Novels	Static knowledge graph has been incorporated extensively into sequence-to-sequence framework for text generation. While effectively representing structured context, static knowledge graph failed to represent knowledge evolution, which is required in modeling dynamic events. In this paper, an automatic commenting task is proposed for long novels, which involves understanding context of more than tens of thousands of words. To model the dynamic storyline, especially the transitions of the characters and their relations, Evolutionary Knowledge Graph(EKG) is proposed and learned within a multi-task framework. Given a specific passage to comment, sequential modeling is used to incorporate historical and future embedding for context representation. Further, a graph-to-sequence model is designed to utilize the EKG for comment generation. Extensive experimental results show that our EKG-based method is superior to several strong baselines on both automatic and human evaluations.	['Jie zhou', 'Cheng Niu', 'Canxiang Yan', 'Jianhao Yan', 'Yangyin Xu']	2020-04-21	null	null	null	null	['graph-to-sequence', 'comment-generation']	['natural-language-processing', 'natural-language-processing']	[ 1.54520899e-01 1.48841128e-01 -2.37494186e-01 -5.02663143e-02 -6.19159520e-01 -7.04161525e-01 9.95078743e-01 4.87017781e-01 -1.32878408e-01 1.16049695e+00 1.09173834e+00 -2.82649189e-01 3.56637686e-01 -7.60645330e-01 -6.43758535e-01 -2.57973462e-01 3.37072425e-02 1.29582942e-01 3.27761590e-01 -5.01720250e-01 4.43093985e-01 -2.09221289e-01 -9.02379572e-01 5.70437014e-01 8.42493653e-01 7.94661343e-02 3.31035227e-01 1.21461976e+00 -4.62861329e-01 9.48329508e-01 -6.93610549e-01 -6.73891187e-01 -2.82639831e-01 -9.58297491e-01 -1.00458312e+00 -4.79122847e-02 4.28996563e-01 -1.19556949e-01 -5.10883510e-01 6.55870795e-01 6.33204281e-01 5.96626103e-01 9.20354605e-01 -9.50163722e-01 -1.27063680e+00 1.27130139e+00 -4.29560930e-01 5.10744154e-01 7.17184067e-01 7.31125623e-02 1.31018198e+00 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6b444a19-0216-4437-8403-151db9f78e95	advancing-linguistic-features-and-insights-by	null	null	https://aclanthology.org/C16-1071	https://aclanthology.org/C16-1071.pdf	Advancing Linguistic Features and Insights by Label-informed Feature Grouping: An Exploration in the Context of Native Language Identification	We propose a hierarchical clustering approach designed to group linguistic features for supervised machine learning that is inspired by variationist linguistics. The method makes it possible to abstract away from the individual feature occurrences by grouping features together that behave alike with respect to the target class, thus providing a new, more general perspective on the data. On the one hand, it reduces data sparsity, leading to quantitative performance gains. On the other, it supports the formation and evaluation of hypotheses about individual choices of linguistic structures. We explore the method using features based on verb subcategorization information and evaluate the approach in the context of the Native Language Identification (NLI) task.	['Serhiy Bykh', 'Detmar Meurers']	2016-12-01	advancing-linguistic-features-and-insights-by-1	https://aclanthology.org/C16-1071	https://aclanthology.org/C16-1071.pdf	coling-2016-12	['native-language-identification']	['natural-language-processing']	[-1.18064687e-01 1.56201869e-01 -5.25248468e-01 -7.97053158e-01 -4.25134063e-01 -6.08183622e-01 7.03228891e-01 7.88636684e-01 -6.50773525e-01 3.10303360e-01 7.08370268e-01 -3.15949261e-01 -5.13469756e-01 -6.62533224e-01 -1.23260379e-01 -6.19454980e-01 1.93440840e-02 6.37501538e-01 -6.29247874e-02 -1.04452103e-01 2.73847610e-01 5.15634120e-01 -1.74925566e+00 4.90357101e-01 8.88454139e-01 8.66774082e-01 1.80217654e-01 -2.51586586e-01 -2.81108737e-01 7.47940183e-01 -2.29459032e-01 -2.20148951e-01 7.07788616e-02 -2.44253442e-01 -9.50724661e-01 2.75919229e-01 2.28361413e-01 4.75151658e-01 1.67654306e-01 1.06348968e+00 -4.74375300e-02 1.91683114e-01 8.90423477e-01 -8.08480442e-01 -6.48954988e-01 1.07037795e+00 -3.73349518e-01 6.53433427e-02 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d8e5bbab-dd02-4282-9667-466cc6b12044	explain-edit-and-understand-rethinking-user	2112.09669	null	https://arxiv.org/abs/2112.09669v2	https://arxiv.org/pdf/2112.09669v2.pdf	Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations	In attempts to "explain" predictions of machine learning models, researchers have proposed hundreds of techniques for attributing predictions to features that are deemed important. While these attributions are often claimed to hold the potential to improve human "understanding" of the models, surprisingly little work explicitly evaluates progress towards this aspiration. In this paper, we conduct a crowdsourcing study, where participants interact with deception detection models that have been trained to distinguish between genuine and fake hotel reviews. They are challenged both to simulate the model on fresh reviews, and to edit reviews with the goal of lowering the probability of the originally predicted class. Successful manipulations would lead to an adversarial example. During the training (but not the test) phase, input spans are highlighted to communicate salience. Through our evaluation, we observe that for a linear bag-of-words model, participants with access to the feature coefficients during training are able to cause a larger reduction in model confidence in the testing phase when compared to the no-explanation control. For the BERT-based classifier, popular local explanations do not improve their ability to reduce the model confidence over the no-explanation case. Remarkably, when the explanation for the BERT model is given by the (global) attributions of a linear model trained to imitate the BERT model, people can effectively manipulate the model.	['Graham Neubig', 'Zachary C. Lipton', 'William W. Cohen', 'Norman Sadeh', 'Danish Pruthi', 'Siddhant Arora']	2021-12-17	null	null	null	null	['deception-detection']	['miscellaneous']	[ 3.99000585e-01 7.93743730e-01 4.75282408e-02 -5.16876459e-01 -4.58100826e-01 -6.60198748e-01 7.99687147e-01 2.61275470e-01 -3.44023973e-01 5.71906447e-01 6.30810931e-02 -5.15706182e-01 5.07266223e-01 -4.97834861e-01 -7.57696688e-01 -4.56202000e-01 5.47645390e-01 3.48244339e-01 -2.17255697e-01 -3.43289495e-01 7.41016626e-01 2.25773931e-01 -1.35647285e+00 5.32315850e-01 7.32387900e-01 3.65148991e-01 -3.16351712e-01 6.63969517e-01 3.12716663e-01 7.74328589e-01 -1.07863510e+00 -1.12316823e+00 3.58032852e-01 -5.72975636e-01 -3.90132815e-01 4.51166406e-02 6.89105392e-01 -4.84445393e-01 -2.53095524e-03 1.07959378e+00 1.98923811e-01 2.57477537e-02 6.64949000e-01 -1.59171951e+00 -1.17942858e+00 6.43005550e-01 -1.37316987e-01 8.73415694e-02 4.25361574e-01 5.90930820e-01 1.09047878e+00 -7.90191233e-01 3.31740528e-01 1.25586128e+00 6.70224488e-01 9.43219125e-01 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9780dc8f-6a5a-459c-807f-d0034a9b4204	towards-end-to-end-semi-supervised-table	2305.02769	null	https://arxiv.org/abs/2305.02769v2	https://arxiv.org/pdf/2305.02769v2.pdf	Towards End-to-End Semi-Supervised Table Detection with Deformable Transformer	Table detection is the task of classifying and localizing table objects within document images. With the recent development in deep learning methods, we observe remarkable success in table detection. However, a significant amount of labeled data is required to train these models effectively. Many semi-supervised approaches are introduced to mitigate the need for a substantial amount of label data. These approaches use CNN-based detectors that rely on anchor proposals and post-processing stages such as NMS. To tackle these limitations, this paper presents a novel end-to-end semi-supervised table detection method that employs the deformable transformer for detecting table objects. We evaluate our semi-supervised method on PubLayNet, DocBank, ICADR-19 and TableBank datasets, and it achieves superior performance compared to previous methods. It outperforms the fully supervised method (Deformable transformer) by +3.4 points on 10\% labels of TableBank-both dataset and the previous CNN-based semi-supervised approach (Soft Teacher) by +1.8 points on 10\% labels of PubLayNet dataset. We hope this work opens new possibilities towards semi-supervised and unsupervised table detection methods.	['Muhammad Zeshan Afzal', 'Marcus Liwicki', 'Didier Stricker', 'Khurram Azeem Hashmi', 'Tahira Shehzadi']	2023-05-04	null	null	null	null	['table-detection']	['miscellaneous']	[ 7.10889697e-02 5.17340779e-01 -2.66158849e-01 -5.22454441e-01 -1.45426917e+00 -8.03978801e-01 7.31051981e-01 4.31813359e-01 -6.06249094e-01 6.63104594e-01 3.74063253e-02 1.22906044e-01 4.53066379e-01 -7.38540649e-01 -7.48548031e-01 -4.63639200e-01 1.57216609e-01 1.01861775e+00 5.93837082e-01 -2.40964904e-01 1.04457788e-01 2.60440290e-01 -1.11446774e+00 7.74007499e-01 5.28523505e-01 1.30353951e+00 1.32300094e-01 6.03033721e-01 -3.36988479e-01 9.38060939e-01 -8.07123840e-01 -6.39586866e-01 2.62996763e-01 -3.47908616e-01 -7.05826581e-01 2.52769768e-01 6.96902692e-01 -2.30164424e-01 -2.73156971e-01 1.12247705e+00 6.00306809e-01 -6.43938780e-02 5.44853806e-01 -9.05438125e-01 -5.04095912e-01 1.10722804e+00 -4.90270376e-01 2.82487541e-01 9.49144438e-02 -4.44413543e-01 1.12053430e+00 -1.03912640e+00 8.58813047e-01 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-5.47173023e-01 -5.56433260e-01 -1.23226714e+00 -6.11231208e-01 1.46331325e-01 4.41623628e-02 -6.54605329e-01 -2.62314975e-01 3.55173498e-01]	[11.691697120666504, 3.018949508666992]
99f0ff20-8dee-4684-a1ea-6c544ea9a984	adversarial-advantage-actor-critic-model-for	1710.11277	null	http://arxiv.org/abs/1710.11277v2	http://arxiv.org/pdf/1710.11277v2.pdf	Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning	This paper presents a new method --- adversarial advantage actor-critic (Adversarial A2C), which significantly improves the efficiency of dialogue policy learning in task-completion dialogue systems. Inspired by generative adversarial networks (GAN), we train a discriminator to differentiate responses/actions generated by dialogue agents from responses/actions by experts. Then, we incorporate the discriminator as another critic into the advantage actor-critic (A2C) framework, to encourage the dialogue agent to explore state-action within the regions where the agent takes actions similar to those of the experts. Experimental results in a movie-ticket booking domain show that the proposed Adversarial A2C can accelerate policy exploration efficiently.	['Kam-Fai Wong', 'Yun-Nung Chen', 'Jingjing Liu', 'Jianfeng Gao', 'Xiujun Li', 'Baolin Peng']	2017-10-31	null	null	null	null	['task-completion-dialogue-policy-learning']	['natural-language-processing']	[ 2.28642419e-01 7.13989794e-01 -2.36752406e-02 -3.88595223e-01 -8.38166893e-01 -8.55600417e-01 9.56077874e-01 -5.97694635e-01 -4.48921472e-01 1.20843959e+00 6.51759446e-01 -4.22669202e-01 4.12602782e-01 -5.63109457e-01 -3.35781276e-01 -7.53044248e-01 1.61856890e-01 9.13970113e-01 -5.04829846e-02 -7.96822309e-01 1.44357339e-01 2.16530457e-01 -4.20824766e-01 4.03581530e-01 9.57516611e-01 5.97292781e-01 -4.49836403e-02 1.05003071e+00 -4.16234851e-01 1.44101334e+00 -1.12751901e+00 -3.04619730e-01 3.73674691e-01 -9.50814664e-01 -1.01389432e+00 -2.15503480e-02 -2.59904355e-01 -8.30321729e-01 -7.22916603e-01 9.50818002e-01 6.16350353e-01 6.43023252e-01 7.00828552e-01 -1.16982484e+00 -6.10703349e-01 7.04064190e-01 -1.06477395e-01 -1.25131039e-02 5.80751479e-01 7.41491437e-01 5.51026046e-01 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ae2a805d-b03e-437c-8406-eb53b15736e9	pointly-supervised-instance-segmentation	2104.06404	null	https://arxiv.org/abs/2104.06404v2	https://arxiv.org/pdf/2104.06404v2.pdf	Pointly-Supervised Instance Segmentation	We propose an embarrassingly simple point annotation scheme to collect weak supervision for instance segmentation. In addition to bounding boxes, we collect binary labels for a set of points uniformly sampled inside each bounding box. We show that the existing instance segmentation models developed for full mask supervision can be seamlessly trained with point-based supervision collected via our scheme. Remarkably, Mask R-CNN trained on COCO, PASCAL VOC, Cityscapes, and LVIS with only 10 annotated random points per object achieves 94%--98% of its fully-supervised performance, setting a strong baseline for weakly-supervised instance segmentation. The new point annotation scheme is approximately 5 times faster than annotating full object masks, making high-quality instance segmentation more accessible in practice. Inspired by the point-based annotation form, we propose a modification to PointRend instance segmentation module. For each object, the new architecture, called Implicit PointRend, generates parameters for a function that makes the final point-level mask prediction. Implicit PointRend is more straightforward and uses a single point-level mask loss. Our experiments show that the new module is more suitable for the point-based supervision.	['Alexander Kirillov', 'Omkar Parkhi', 'Bowen Cheng']	2021-04-13	null	http://openaccess.thecvf.com//content/CVPR2022/html/Cheng_Pointly-Supervised_Instance_Segmentation_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Cheng_Pointly-Supervised_Instance_Segmentation_CVPR_2022_paper.pdf	cvpr-2022-1	['weakly-supervised-instance-segmentation']	['computer-vision']	[ 1.64081797e-01 6.69931293e-01 -5.01093566e-01 -6.62991941e-01 -1.28877676e+00 -6.79120600e-01 4.73516881e-01 1.04024082e-01 -4.50879723e-01 4.39461291e-01 -4.91111964e-01 -1.82635516e-01 3.70535314e-01 -7.21850634e-01 -1.38055813e+00 -5.15122354e-01 8.76509920e-02 8.92720103e-01 9.07358468e-01 -6.65645674e-02 5.87344989e-02 6.40926778e-01 -1.34239745e+00 1.70518219e-01 9.74757612e-01 1.07193863e+00 2.03231111e-01 5.71270108e-01 -2.07196757e-01 1.19741097e-01 -7.40380883e-01 -6.64298892e-01 7.55465984e-01 6.88811541e-02 -1.12309754e+00 3.93579930e-01 9.42624688e-01 -3.19340140e-01 1.16182797e-01 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670d9685-2ddc-41f7-a8b5-291ffffbf4a7	deep-learning-is-a-good-steganalysis-tool	1511.04855	null	http://arxiv.org/abs/1511.04855v2	http://arxiv.org/pdf/1511.04855v2.pdf	Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover source-mismatch	Since the BOSS competition, in 2010, most steganalysis approaches use a learning methodology involving two steps: feature extraction, such as the Rich Models (RM), for the image representation, and use of the Ensemble Classifier (EC) for the learning step. In 2015, Qian et al. have shown that the use of a deep learning approach that jointly learns and computes the features, is very promising for the steganalysis. In this paper, we follow-up the study of Qian et al., and show that, due to intrinsic joint minimization, the results obtained from a Convolutional Neural Network (CNN) or a Fully Connected Neural Network (FNN), if well parameterized, surpass the conventional use of a RM with an EC. First, numerous experiments were conducted in order to find the best " shape " of the CNN. Second, experiments were carried out in the clairvoyant scenario in order to compare the CNN and FNN to an RM with an EC. The results show more than 16% reduction in the classification error with our CNN or FNN. Third, experiments were also performed in a cover-source mismatch setting. The results show that the CNN and FNN are naturally robust to the mismatch problem. In Addition to the experiments, we provide discussions on the internal mechanisms of a CNN, and weave links with some previously stated ideas, in order to understand the impressive results we obtained.	['Pasquet Jérôme', 'Marc Chaumont', 'Lionel Pibre', 'Dino Ienco']	2015-11-16	null	null	null	null	['steganalysis']	['computer-vision']	[ 4.54111934e-01 9.26681757e-02 6.47429079e-02 2.54243255e-01 -4.69276547e-01 -3.44349951e-01 8.69221926e-01 -4.08550471e-01 -3.52666259e-01 5.91273248e-01 -1.91180855e-01 -4.77075964e-01 9.81714949e-02 -7.69195139e-01 -9.06682074e-01 -1.16715431e+00 -3.62852603e-01 -3.39936465e-02 1.60729319e-01 -4.65732127e-01 3.02183270e-01 4.21132714e-01 -1.63035202e+00 3.64229649e-01 4.61483419e-01 1.15316176e+00 -5.56479134e-02 8.65836442e-01 4.06755835e-01 8.63260508e-01 -7.32791960e-01 -4.77733612e-01 5.54206848e-01 -4.94079262e-01 -6.59189105e-01 -1.15101188e-01 7.86979944e-02 -2.17542291e-01 -3.25967520e-01 1.08086193e+00 3.41170043e-01 -2.89710671e-01 5.30204356e-01 -1.06134057e+00 -1.04425177e-01 6.68316782e-01 -2.64317870e-01 -5.26066162e-02 -2.25176960e-02 3.05725664e-01 7.86674917e-01 -5.99284172e-01 5.13245821e-01 1.01695883e+00 6.51471734e-01 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4c04f321-f402-4024-8d87-47c0f6b021b5	augmenting-rule-based-dns-censorship	2302.02031	null	https://arxiv.org/abs/2302.02031v2	https://arxiv.org/pdf/2302.02031v2.pdf	Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning	The proliferation of global censorship has led to the development of a plethora of measurement platforms to monitor and expose it. Censorship of the domain name system (DNS) is a key mechanism used across different countries. It is currently detected by applying heuristics to samples of DNS queries and responses (probes) for specific destinations. These heuristics, however, are both platform-specific and have been found to be brittle when censors change their blocking behavior, necessitating a more reliable automated process for detecting censorship. In this paper, we explore how machine learning (ML) models can (1) help streamline the detection process, (2) improve the potential of using large-scale datasets for censorship detection, and (3) discover new censorship instances and blocking signatures missed by existing heuristic methods. Our study shows that supervised models, trained using expert-derived labels on instances of known anomalies and possible censorship, can learn the detection heuristics employed by different measurement platforms. More crucially, we find that unsupervised models, trained solely on uncensored instances, can identify new instances and variations of censorship missed by existing heuristics. Moreover, both methods demonstrate the capability to uncover a substantial number of new DNS blocking signatures, i.e., injected fake IP addresses overlooked by existing heuristics. These results are underpinned by an important methodological finding: comparing the outputs of models trained using the same probes but with labels arising from independent processes allows us to more reliably detect cases of censorship in the absence of ground-truth labels of censorship.	['Jacob Brown', 'Vinod Yegneswaran', 'Prateek Mittal', 'Nick Feamster', 'Nguyen Phong Hoang', 'Arjun Nitin Bhagoji', 'Van Tran', 'Xi Jiang']	2023-02-03	null	null	null	null	['blocking']	['natural-language-processing']	[-3.38810235e-02 -2.29640692e-01 -2.81498760e-01 -1.94294229e-01 -1.02188766e+00 -1.43521059e+00 9.41432774e-01 1.82504028e-01 1.04173414e-01 7.26241827e-01 -2.40361542e-02 -9.57474351e-01 -3.51159275e-01 -6.36450291e-01 -5.48618972e-01 -4.94890153e-01 -1.11591205e-01 6.81262255e-01 4.68612045e-01 4.51631248e-01 3.94130111e-01 1.18070054e+00 -1.02603698e+00 2.16488376e-01 5.18736184e-01 6.40244603e-01 -7.55958736e-01 5.48010826e-01 -2.24254280e-01 8.60565960e-01 -9.70242500e-01 -3.26936036e-01 2.91498035e-01 -2.93112785e-01 -5.72042167e-01 1.70843020e-01 5.59255064e-01 -4.54652578e-01 -5.89029789e-01 9.59943652e-01 1.01482913e-01 -3.67844164e-01 3.30149233e-01 -1.37387204e+00 -2.78865099e-01 4.73010063e-01 -2.59673417e-01 6.26064718e-01 2.91135997e-01 2.52369463e-01 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8732a0e4-e14a-474e-b160-92426eb18068	l_p-norm-constrained-coding-with-frank-wolfe	1802.10252	null	https://arxiv.org/abs/1802.10252v4	https://arxiv.org/pdf/1802.10252v4.pdf	Frank-Wolfe Network: An Interpretable Deep Structure for Non-Sparse Coding	The problem of $L_p$-norm constrained coding is to convert signal into code that lies inside an $L_p$-ball and most faithfully reconstructs the signal. Previous works under the name of sparse coding considered the cases of $L_0$ and $L_1$ norms. The cases with $p>1$ values, i.e. non-sparse coding studied in this paper, remain a difficulty. We propose an interpretable deep structure namely Frank-Wolfe Network (F-W Net), whose architecture is inspired by unrolling and truncating the Frank-Wolfe algorithm for solving an $L_p$-norm constrained problem with $p\geq 1$. We show that the Frank-Wolfe solver for the $L_p$-norm constraint leads to a novel closed-form nonlinear unit, which is parameterized by $p$ and termed $pool_p$. The $pool_p$ unit links the conventional pooling, activation, and normalization operations, making F-W Net distinct from existing deep networks either heuristically designed or converted from projected gradient descent algorithms. We further show that the hyper-parameter $p$ can be made learnable instead of pre-chosen in F-W Net, which gracefully solves the non-sparse coding problem even with unknown $p$. We evaluate the performance of F-W Net on an extensive range of simulations as well as the task of handwritten digit recognition, where F-W Net exhibits strong learning capability. We then propose a convolutional version of F-W Net, and apply the convolutional F-W Net into image denoising and super-resolution tasks, where F-W Net all demonstrates impressive effectiveness, flexibility, and robustness.	['Zheng-Jun Zha', 'Dong Liu', 'Ke Sun', 'Zhangyang Wang', 'Runsheng Liu']	2018-02-28	null	null	null	null	['handwritten-digit-recognition']	['computer-vision']	[ 2.28692174e-01 3.30846369e-01 2.98381802e-02 -3.37830812e-01 -7.41564929e-01 -1.02335125e-01 -7.77858123e-02 -6.56344771e-01 -2.26055890e-01 6.37111902e-01 3.64978075e-01 -6.52860105e-02 -3.17125320e-01 -8.23889017e-01 -9.92308795e-01 -7.76618600e-01 -5.62988877e-01 -3.08505535e-01 -1.81134909e-01 -3.23275000e-01 1.92376554e-01 4.04315114e-01 -1.14882791e+00 2.46020436e-01 7.73252666e-01 1.31548512e+00 1.73067823e-01 4.11898613e-01 -5.21269217e-02 7.84560382e-01 -2.44070888e-01 -2.57981360e-01 7.75756836e-01 -6.45845830e-01 -4.62524146e-01 1.06582440e-01 5.66734552e-01 -3.12754333e-01 -6.96109891e-01 1.34484148e+00 3.90534818e-01 2.72740543e-01 3.04789722e-01 -8.75176907e-01 -1.00135875e+00 6.06116593e-01 -7.60675251e-01 2.01968655e-01 5.23825325e-02 8.09000805e-02 1.01200485e+00 -1.27304125e+00 6.23310089e-01 1.20290458e+00 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-7.03023970e-01 -7.80029476e-01 -9.95039284e-01 2.98952401e-01 5.65130770e-01 -3.48932564e-01 -5.22975445e-01 2.08317488e-01]	[11.490449905395508, -2.1038644313812256]
59c66db6-c497-4410-bf73-b4aad10a6cfc	diacritics-restoration-using-bert-with	2105.11408	null	https://arxiv.org/abs/2105.11408v1	https://arxiv.org/pdf/2105.11408v1.pdf	Diacritics Restoration using BERT with Analysis on Czech language	We propose a new architecture for diacritics restoration based on contextualized embeddings, namely BERT, and we evaluate it on 12 languages with diacritics. Furthermore, we conduct a detailed error analysis on Czech, a morphologically rich language with a high level of diacritization. Notably, we manually annotate all mispredictions, showing that roughly 44% of them are actually not errors, but either plausible variants (19%), or the system corrections of erroneous data (25%). Finally, we categorize the real errors in detail. We release the code at https://github.com/ufal/bert-diacritics-restoration.	['Jana Straková', 'Milan Straka', 'Jakub Náplava']	2021-05-24	null	null	null	null	['irish-text-diacritization', 'croatian-text-diacritization', 'french-text-diacritization', 'romanian-text-diacritization', 'turkish-text-diacritization', 'hungarian-text-diacritization', 'slovak-text-diacritization', 'spanish-text-diacritization', 'latvian-text-diacritization', 'czech-text-diacritization', 'vietnamese-text-diacritization']	['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[-3.92482311e-01 4.07441407e-01 7.16865063e-02 -2.86333591e-01 -7.45819330e-01 -5.93308032e-01 6.71185613e-01 6.99524701e-01 -7.86268115e-01 7.23728657e-01 6.41712129e-01 -6.21684670e-01 4.36208427e-01 -6.59620345e-01 -7.76250184e-01 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cccacbc1-b140-4cfe-a363-0dc200afcb47	hashcc-lightweight-method-to-improve-the	2305.04296	null	https://arxiv.org/abs/2305.04296v1	https://arxiv.org/pdf/2305.04296v1.pdf	HashCC: Lightweight Method to Improve the Quality of the Camera-less NeRF Scene Generation	Neural Radiance Fields has become a prominent method of scene generation via view synthesis. A critical requirement for the original algorithm to learn meaningful scene representation is camera pose information for each image in a data set. Current approaches try to circumnavigate this assumption with moderate success, by learning approximate camera positions alongside learning neural representations of a scene. This requires complicated camera models, causing a long and complicated training process, or results in a lack of texture and sharp details in rendered scenes. In this work we introduce Hash Color Correction (HashCC) -- a lightweight method for improving Neural Radiance Fields rendered image quality, applicable also in situations where camera positions for a given set of images are unknown.	['Jan Olszewski']	2023-05-07	null	null	null	null	['scene-generation']	['computer-vision']	[ 5.55571616e-01 -1.89845547e-01 2.52805054e-01 -5.29028893e-01 -5.91232479e-01 -8.36049736e-01 7.38854349e-01 -9.03258771e-02 -3.43973309e-01 8.14013958e-01 1.92416161e-01 -3.28702718e-01 2.26564743e-02 -9.14479911e-01 -9.77987647e-01 -6.57445490e-01 1.36243433e-01 1.46851212e-01 1.36010334e-01 -3.84877622e-01 5.90243816e-01 8.46493185e-01 -1.65345025e+00 1.26781061e-01 5.63492119e-01 5.17557621e-01 3.66172731e-01 1.21421933e+00 4.71137185e-03 1.10931849e+00 -7.00604439e-01 -4.39348340e-01 6.72336698e-01 -4.58984703e-01 -4.61078852e-01 3.56338352e-01 1.01912761e+00 -5.21676838e-01 -4.24969941e-01 1.15708160e+00 3.29167426e-01 3.67607594e-01 5.75391114e-01 -9.46080685e-01 -2.36743242e-01 6.68924302e-02 -5.70845246e-01 6.16323538e-02 2.46751711e-01 -1.02948975e-02 7.08974123e-01 -6.67492390e-01 6.24668121e-01 8.39928567e-01 7.47922301e-01 5.60477555e-01 -1.27576041e+00 -2.37272203e-01 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5126ed7e-e057-49b4-ab36-342b36bcf563	xtreme-up-a-user-centric-scarce-data	2305.11938	null	https://arxiv.org/abs/2305.11938v2	https://arxiv.org/pdf/2305.11938v2.pdf	XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages	Data scarcity is a crucial issue for the development of highly multilingual NLP systems. Yet for many under-represented languages (ULs) -- languages for which NLP re-search is particularly far behind in meeting user needs -- it is feasible to annotate small amounts of data. Motivated by this, we propose XTREME-UP, a benchmark defined by: its focus on the scarce-data scenario rather than zero-shot; its focus on user-centric tasks -- tasks with broad adoption by speakers of high-resource languages; and its focus on under-represented languages where this scarce-data scenario tends to be most realistic. XTREME-UP evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks that are of general utility. We create new datasets for OCR, autocomplete, semantic parsing, and transliteration, and build on and refine existing datasets for other tasks. XTREME-UP provides methodology for evaluating many modeling scenarios including text-only, multi-modal (vision, audio, and text),supervised parameter tuning, and in-context learning. We evaluate commonly used models on the benchmark. We release all code and scripts to train and evaluate models	['Partha Talukdar', 'Dmitry Panteleev', 'Melvin Johnson', 'Reeve Ingle', 'Dan Garrette', 'Colin Cherry', 'Isaac Caswell', 'Vera Axelrod', 'David I. Adelani', 'Connie Tao', 'Bidisha Samanta', 'Brian Roark', 'Dana L. Dickinson', 'Christo Kirov', 'Anna Katanova', 'Nitish Gupta', 'John Wieting', 'Xinyi Wang', 'Jean-Michel A. Sarr', 'Parker Riley', 'Shruti Rijhwani', 'Massimo Nicosia', 'Min Ma', 'Mihir Kale', 'Alexander Gutkin', 'Jonathan H. Clark', 'Sebastian Ruder']	2023-05-19	null	null	null	null	['optical-character-recognition', 'transliteration', 'semantic-parsing', 'multilingual-nlp']	['computer-vision', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[-3.64740826e-02 5.29397614e-02 -2.47627079e-01 -3.20156664e-01 -1.54244673e+00 -8.49636853e-01 6.32286131e-01 1.60680823e-02 -6.86803043e-01 5.77289045e-01 5.35091162e-01 -5.01768768e-01 1.18585594e-01 -1.45412475e-01 -5.25712252e-01 2.15123799e-02 3.97892326e-01 1.13167214e+00 -2.94500232e-01 -4.78410125e-01 -2.51329005e-01 3.80972415e-01 -1.25738156e+00 5.34725904e-01 9.43810344e-01 6.10931754e-01 6.83833301e-01 6.54337347e-01 -4.92938310e-01 6.45764232e-01 -5.19072831e-01 -5.28925478e-01 9.32160169e-02 1.36491537e-01 -8.15507948e-01 -2.16833621e-01 7.71762192e-01 1.79784596e-01 -8.13934132e-02 6.77254260e-01 9.48087096e-01 -3.98324355e-02 5.46787143e-01 -9.92111921e-01 -9.53260124e-01 9.42152381e-01 -1.76879510e-01 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bc1622fe-2c51-4fce-88af-59696bc4b5fa	r2-mlp-round-roll-mlp-for-multi-view-3d	2211.11085	null	https://arxiv.org/abs/2211.11085v1	https://arxiv.org/pdf/2211.11085v1.pdf	R2-MLP: Round-Roll MLP for Multi-View 3D Object Recognition	Recently, vision architectures based exclusively on multi-layer perceptrons (MLPs) have gained much attention in the computer vision community. MLP-like models achieve competitive performance on a single 2D image classification with less inductive bias without hand-crafted convolution layers. In this work, we explore the effectiveness of MLP-based architecture for the view-based 3D object recognition task. We present an MLP-based architecture termed as Round-Roll MLP (R$^2$-MLP). It extends the spatial-shift MLP backbone by considering the communications between patches from different views. R$^2$-MLP rolls part of the channels along the view dimension and promotes information exchange between neighboring views. We benchmark MLP results on ModelNet10 and ModelNet40 datasets with ablations in various aspects. The experimental results show that, with a conceptually simple structure, our R$^2$-MLP achieves competitive performance compared with existing state-of-the-art methods.	['Ping Li', 'Tan Yu', 'Shuo Chen']	2022-11-20	null	null	null	null	['3d-object-recognition']	['computer-vision']	[ 1.68343484e-01 2.20623791e-01 -6.61199838e-02 -5.24948895e-01 -1.57886326e-01 -1.94647402e-01 7.13389754e-01 -3.22278947e-01 -4.21801299e-01 2.04475150e-01 -1.14798453e-02 -4.88363802e-01 2.01329112e-01 -7.57976055e-01 -1.04005516e+00 -7.48291612e-01 -1.83442920e-01 -2.51919515e-02 3.34583193e-01 1.93494588e-01 2.49357343e-01 8.11886847e-01 -1.42794037e+00 8.62518370e-01 -1.36203263e-02 1.43820429e+00 2.39949033e-01 5.39108098e-01 -1.70076583e-02 1.01763952e+00 -1.77922055e-01 -2.44617537e-01 4.33151633e-01 2.79023081e-01 -7.77541339e-01 4.30376530e-02 7.15587437e-01 -3.55762780e-01 -4.31254625e-01 7.07904220e-01 6.27959728e-01 -3.48242790e-01 5.48308432e-01 -8.34750295e-01 -8.70601594e-01 5.41382313e-01 -7.32945085e-01 2.79287905e-01 5.20436727e-02 2.03140005e-01 9.54485655e-01 -1.37902021e+00 4.36126292e-01 1.34193218e+00 9.41865206e-01 4.01256561e-01 -1.39057529e+00 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-4.39568698e-01 -2.56168365e-01 5.86000085e-01 -5.16944110e-01 -1.34498850e-01 9.08866152e-02]	[8.24619197845459, -3.5789997577667236]
9df8d5a6-161f-42a2-94dd-0e907a33eb9a	detecting-malicious-pdf-using-cnn-1	2007.12729	null	https://arxiv.org/abs/2007.12729v2	https://arxiv.org/pdf/2007.12729v2.pdf	Detecting malicious PDF using CNN	Malicious PDF files represent one of the biggest threats to computer security. To detect them, significant research has been done using handwritten signatures or machine learning based on manual feature extraction. Those approaches are both time-consuming, require significant prior knowledge and the list of features has to be updated with each newly discovered vulnerability. In this work, we propose a novel algorithm that uses an ensemble of Convolutional Neural Network (CNN) on the byte level of the file, without any handcrafted features. We show, using a data set of 90000 files downloadable online, that our approach maintains a high detection rate (94%) of PDF malware and even detects new malicious files, still undetected by most antiviruses. Using automatically generated features from our CNN network, and applying a clustering algorithm, we also obtain high similarity between the antiviruses' labels and the resulting clusters.	['Yishay Mansour', 'Raphael Fettaya']	2020-07-24	detecting-malicious-pdf-using-cnn	https://openreview.net/forum?id=SJeW-A4tDS	https://openreview.net/pdf?id=SJeW-A4tDS	iclr-2020-1	['computer-security']	['miscellaneous']	[-7.61151910e-02 -4.09160465e-01 -1.25639707e-01 -1.15802966e-01 -2.24539205e-01 -1.20628607e+00 6.81251585e-01 5.89043736e-01 -3.01521987e-01 5.03068864e-01 -3.70250553e-01 -4.88013476e-01 -1.84800848e-01 -9.70779479e-01 -6.68456674e-01 -4.73887861e-01 -4.17364597e-01 5.60695946e-01 6.55111551e-01 -1.37170106e-01 7.96195149e-01 7.80121386e-01 -1.40155756e+00 3.48303080e-01 6.49938643e-01 9.66659963e-01 -2.05135480e-01 1.00144160e+00 -2.12169275e-01 7.83008754e-01 -1.03651476e+00 -4.93420959e-01 4.87519175e-01 1.71899982e-02 -6.46962345e-01 -1.18414335e-01 4.68084633e-01 -4.90653485e-01 -6.06763780e-01 1.27206779e+00 -3.16105112e-02 -4.09926862e-01 7.04487264e-01 -1.27055061e+00 -6.80521488e-01 6.05956495e-01 -4.02696431e-01 4.57715482e-01 1.06598139e-01 1.25596255e-01 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414d86fa-1412-46a3-baae-f24e6504ee2b	deep-homography-estimation-for-dynamic-scenes	2004.02132	null	https://arxiv.org/abs/2004.02132v1	https://arxiv.org/pdf/2004.02132v1.pdf	Deep Homography Estimation for Dynamic Scenes	Homography estimation is an important step in many computer vision problems. Recently, deep neural network methods have shown to be favorable for this problem when compared to traditional methods. However, these new methods do not consider dynamic content in input images. They train neural networks with only image pairs that can be perfectly aligned using homographies. This paper investigates and discusses how to design and train a deep neural network that handles dynamic scenes. We first collect a large video dataset with dynamic content. We then develop a multi-scale neural network and show that when properly trained using our new dataset, this neural network can already handle dynamic scenes to some extent. To estimate a homography of a dynamic scene in a more principled way, we need to identify the dynamic content. Since dynamic content detection and homography estimation are two tightly coupled tasks, we follow the multi-task learning principles and augment our multi-scale network such that it jointly estimates the dynamics masks and homographies. Our experiments show that our method can robustly estimate homography for challenging scenarios with dynamic scenes, blur artifacts, or lack of textures.	['Aseem Agarwala', 'Feng Liu', 'Shu Zhang', 'Hoang Le']	2020-04-05	deep-homography-estimation-for-dynamic-scenes-1	http://openaccess.thecvf.com/content_CVPR_2020/html/Le_Deep_Homography_Estimation_for_Dynamic_Scenes_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Le_Deep_Homography_Estimation_for_Dynamic_Scenes_CVPR_2020_paper.pdf	cvpr-2020-6	['homography-estimation']	['computer-vision']	[ 2.78520435e-01 -5.40713668e-01 8.89474973e-02 -1.60443723e-01 -2.29501292e-01 -5.38845003e-01 5.20839453e-01 -8.92879903e-01 -1.84837237e-01 4.05508250e-01 2.57328391e-01 2.32324108e-01 -4.37978562e-03 -5.32478929e-01 -1.15578592e+00 -7.63718367e-01 -1.52159398e-02 3.70204836e-01 2.77138114e-01 -1.34905338e-01 2.25093082e-01 5.13528764e-01 -1.34956181e+00 1.70851558e-01 5.54449320e-01 6.91982329e-01 3.81636828e-01 9.62885261e-01 4.04731721e-01 7.77727902e-01 -4.17677671e-01 -1.63008064e-01 6.90089762e-01 -3.81160229e-01 -7.20827997e-01 4.48069453e-01 1.16389287e+00 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ee9df783-574f-4864-ac93-5511aecce45f	motion-capture-dataset-for-practical-use-of	2306.08861	null	https://arxiv.org/abs/2306.08861v2	https://arxiv.org/pdf/2306.08861v2.pdf	Motion Capture Dataset for Practical Use of AI-based Motion Editing and Stylization	In this work, we proposed a new style-diverse dataset for the domain of motion style transfer. The motion dataset uses an industrial-standard human bone structure and thus is industry-ready to be plugged into 3D characters for many projects. We claim the challenges in motion style transfer and encourage future work in this domain by releasing the proposed motion dataset both to the public and the market. We conduct a comprehensive study on motion style transfer in the experiment using the state-of-the-art method, and the results show the proposed dataset's validity for the motion style transfer task.	['Masafumi Takahashi', 'Sentaro Yojima', 'Keito Inoue', 'Chen-Chieh Liao', 'Makito Kobayashi']	2023-06-15	null	null	null	null	['motion-style-transfer', 'style-transfer']	['computer-code', 'computer-vision']	[ 1.28046736e-01 -2.01214448e-01 -3.00770968e-01 -1.88919991e-01 -3.53232950e-01 -4.76011336e-01 6.30333364e-01 -1.03811812e+00 -4.74249393e-01 8.28726709e-01 4.65797782e-01 -7.67160654e-02 4.99283820e-01 -7.88788497e-01 -5.32081366e-01 -5.71796298e-01 3.01064551e-01 5.15253425e-01 9.03625548e-01 -4.68902469e-01 2.67805159e-01 3.88681561e-01 -1.17261338e+00 4.54414815e-01 2.79263318e-01 6.86984479e-01 1.86680347e-01 6.63129270e-01 -1.58118159e-01 5.96225083e-01 -6.02562189e-01 -6.08710885e-01 4.99557227e-01 -9.05875027e-01 -1.12233186e+00 -1.87012002e-01 5.43954194e-01 -5.38518786e-01 -6.40583932e-01 7.51463473e-01 9.62900221e-01 1.49602517e-01 9.51742530e-01 -1.40851033e+00 -9.39451575e-01 6.06096201e-02 -4.96155530e-01 1.11877486e-01 3.70152175e-01 3.24386030e-01 5.77931583e-01 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c0faa938-cfb2-487e-abc4-4c982efc6b79	interactive-video-object-segmentation-using	2007.08139	null	https://arxiv.org/abs/2007.08139v1	https://arxiv.org/pdf/2007.08139v1.pdf	Interactive Video Object Segmentation Using Global and Local Transfer Modules	An interactive video object segmentation algorithm, which takes scribble annotations on query objects as input, is proposed in this paper. We develop a deep neural network, which consists of the annotation network (A-Net) and the transfer network (T-Net). First, given user scribbles on a frame, A-Net yields a segmentation result based on the encoder-decoder architecture. Second, T-Net transfers the segmentation result bidirectionally to the other frames, by employing the global and local transfer modules. The global transfer module conveys the segmentation information in an annotated frame to a target frame, while the local transfer module propagates the segmentation information in a temporally adjacent frame to the target frame. By applying A-Net and T-Net alternately, a user can obtain desired segmentation results with minimal efforts. We train the entire network in two stages, by emulating user scribbles and employing an auxiliary loss. Experimental results demonstrate that the proposed interactive video object segmentation algorithm outperforms the state-of-the-art conventional algorithms. Codes and models are available at https://github.com/yuk6heo/IVOS-ATNet.	['Chang-Su Kim', 'Yuk Heo', 'Yeong Jun Koh']	2020-07-16	null	https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2729_ECCV_2020_paper.php	https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123620290.pdf	eccv-2020-8	['interactive-video-object-segmentation']	['computer-vision']	[ 3.28395516e-01 1.73464179e-01 -1.96316838e-01 -4.50201243e-01 -7.01285660e-01 -5.25927961e-01 -1.08221799e-01 -2.15267107e-01 -4.29916054e-01 5.17665327e-01 -2.75906265e-01 -8.25496390e-02 3.39571714e-01 -7.56323397e-01 -9.73157167e-01 -6.73491120e-01 8.43443051e-02 3.90428513e-01 9.55785275e-01 2.25631341e-01 6.54216185e-02 2.41000533e-01 -9.90970612e-01 3.77993435e-01 5.86758673e-01 1.32675111e+00 4.14814681e-01 7.64630258e-01 -1.49792075e-01 1.07885456e+00 -3.50266546e-01 -4.35477287e-01 2.93087512e-01 -5.60365677e-01 -1.20470798e+00 2.50931948e-01 3.11800838e-01 -9.81389284e-01 -6.03726149e-01 1.09108901e+00 1.56302214e-01 1.78127944e-01 2.46722102e-01 -1.28124058e+00 -4.91806477e-01 6.60074532e-01 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fe942e87-206a-48f5-a41f-f3070a7d346e	direction-of-arrival-estimation-of-sound	2203.16940	null	https://arxiv.org/abs/2203.16940v2	https://arxiv.org/pdf/2203.16940v2.pdf	Direction of Arrival Estimation of Sound Sources Using Icosahedral CNNs	In this paper, we present a new model for Direction of Arrival (DOA) estimation of sound sources based on an Icosahedral Convolutional Neural Network (CNN) applied over SRP-PHAT power maps computed from the signals received by a microphone array. This icosahedral CNN is equivariant to the 60 rotational symmetries of the icosahedron, which represent a good approximation of the continuous space of spherical rotations, and can be implemented using standard 2D convolutional layers, having a lower computational cost than most of the spherical CNNs. In addition, instead of using fully connected layers after the icosahedral convolutions, we propose a new soft-argmax function that can be seen as a differentiable version of the argmax function and allows us to solve the DOA estimation as a regression problem interpreting the output of the convolutional layers as a probability distribution. We prove that using models that fit the equivariances of the problem allows us to outperform other state-of-the-art models with a lower computational cost and more robustness, obtaining root mean square localization errors lower than 10{\deg} even in scenarios with a reverberation time $T_{60}$ of 1.5 s.	['Jose R. Beltran', 'Antonio Miguel', 'David Diaz-Guerra']	2022-03-31	null	null	null	null	['direction-of-arrival-estimation']	['audio']	[-2.65416712e-01 2.20297098e-01 7.31393993e-01 -6.39274269e-02 -6.46172106e-01 -7.70715237e-01 4.28281188e-01 -2.26017267e-01 -4.59881842e-01 3.25659424e-01 2.11836979e-01 -3.49875748e-01 -2.16417074e-01 -9.14891720e-01 -1.03878891e+00 -9.19243872e-01 -1.99589536e-01 2.22896740e-01 8.18287432e-02 -3.66902314e-02 -1.74836114e-01 8.37551951e-01 -1.42743945e+00 -2.80967113e-02 3.43275726e-01 1.33603513e+00 -1.05587997e-01 8.74098897e-01 4.17830527e-01 6.07981801e-01 -9.54773247e-01 -2.39214927e-01 2.85601407e-01 -7.28067663e-03 -3.79628897e-01 -4.63754326e-01 6.80172801e-01 -1.01301968e-01 -6.92468464e-01 1.05949306e+00 8.18034530e-01 2.74083078e-01 5.09556413e-01 -8.85507286e-01 -5.53508103e-01 4.43199068e-01 1.68102801e-01 1.85214400e-01 2.69244164e-01 -4.09583986e-01 8.56686890e-01 -1.02787292e+00 1.70202449e-01 9.17320371e-01 9.38000262e-01 1.50503442e-01 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b9f0febe-2f05-49af-bfe4-28264b39185b	transformer-based-multi-instance-learning-for	2303.14999	null	https://arxiv.org/abs/2303.14999v1	https://arxiv.org/pdf/2303.14999v1.pdf	Transformer-based Multi-Instance Learning for Weakly Supervised Object Detection	Weakly Supervised Object Detection (WSOD) enables the training of object detection models using only image-level annotations. State-of-the-art WSOD detectors commonly rely on multi-instance learning (MIL) as the backbone of their detectors and assume that the bounding box proposals of an image are independent of each other. However, since such approaches only utilize the highest score proposal and discard the potentially useful information from other proposals, their independent MIL backbone often limits models to salient parts of an object or causes them to detect only one object per class. To solve the above problems, we propose a novel backbone for WSOD based on our tailored Vision Transformer named Weakly Supervised Transformer Detection Network (WSTDN). Our algorithm is not only the first to demonstrate that self-attention modules that consider inter-instance relationships are effective backbones for WSOD, but also we introduce a novel bounding box mining method (BBM) integrated with a memory transfer refinement (MTR) procedure to utilize the instance dependencies for facilitating instance refinements. Experimental results on PASCAL VOC2007 and VOC2012 benchmarks demonstrate the effectiveness of our proposed WSTDN and modified instance refinement modules.	['Min-Ling Zhang', 'Weijia Zhang', 'Zhaofei Wang']	2023-03-27	null	null	null	null	['weakly-supervised-object-detection']	['computer-vision']	[ 2.95239091e-01 5.52549958e-01 -4.34515744e-01 -3.02940160e-01 -7.90386796e-01 -1.94853887e-01 6.45885706e-01 1.06413074e-01 -3.74391347e-01 3.68572474e-01 -9.63905305e-02 -7.35518038e-02 -1.05265966e-02 -8.75077665e-01 -1.09088278e+00 -5.98673701e-01 1.33518547e-01 5.32712102e-01 1.17418373e+00 -1.42102510e-01 9.56933722e-02 3.31420302e-01 -1.76396716e+00 6.65723324e-01 9.21786308e-01 1.24735653e+00 7.30650544e-01 2.89546579e-01 -3.41144115e-01 9.96239185e-01 -4.79538620e-01 -3.76888961e-01 4.24350679e-01 1.35671375e-02 -8.74313474e-01 3.05300236e-01 7.69253194e-01 -3.31845254e-01 -1.00458443e-01 1.02480245e+00 2.89195925e-01 -1.68286160e-01 5.65338314e-01 -1.26539338e+00 -5.40131748e-01 7.74530649e-01 -7.60607898e-01 2.82245547e-01 -2.92303294e-01 6.95273727e-02 1.17153323e+00 -1.35751009e+00 6.40649438e-01 1.08422506e+00 6.25785410e-01 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4.80492413e-01 3.57100517e-01 -8.86892438e-01 -9.03101042e-02 2.98278574e-02]	[9.308374404907227, 1.0792121887207031]
54038440-128a-460b-86fc-29a6dcd58b5a	3d-point-cloud-segmentation-using-gis	2108.06306	null	https://arxiv.org/abs/2108.06306v1	https://arxiv.org/pdf/2108.06306v1.pdf	3D point cloud segmentation using GIS	In this paper we propose an approach to perform semantic segmentation of 3D point cloud data by importing the geographic information from a 2D GIS layer (OpenStreetMap). The proposed automatic procedure identifies meaningful units such as buildings and adjusts their locations to achieve best fit between the GIS polygonal perimeters and the point cloud. Our processing pipeline is presented and illustrated by segmenting point cloud data of Trinity College Dublin (Ireland) campus constructed from optical imagery collected by a drone.	['Rozenn Dahyot', 'Vladimir Krylov', 'Chao-Jung Liu']	2021-08-13	null	null	null	null	['point-cloud-segmentation']	['computer-vision']	[ 3.61528397e-02 1.75624415e-01 5.68312705e-01 -4.36442465e-01 -1.85469642e-01 -8.76413345e-01 4.79910553e-01 6.15417480e-01 -4.54078823e-01 4.19156015e-01 -2.51403958e-01 -5.81456721e-01 -2.62136847e-01 -1.33984780e+00 -4.40356195e-01 -2.76595063e-04 -3.43513608e-01 8.21435452e-01 5.34995794e-01 -2.86645383e-01 6.51030958e-01 1.20729947e+00 -1.56043911e+00 -2.42159054e-01 1.07325566e+00 9.20083821e-01 4.27939534e-01 7.08584607e-01 -3.19528282e-01 -6.30961776e-01 -3.26764375e-01 2.12455899e-01 8.78615737e-01 4.01256740e-01 -7.13014424e-01 3.47274631e-01 3.34402531e-01 -2.78430939e-01 2.78436959e-01 1.01651144e+00 8.16783588e-03 1.37705952e-01 5.75309277e-01 -1.28563333e+00 9.47424844e-02 -9.42836255e-02 -5.68218291e-01 1.48505092e-01 3.58775079e-01 -1.45785481e-01 5.87905288e-01 -7.76861429e-01 5.10436714e-01 1.23040891e+00 8.09368372e-01 -5.62849522e-01 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b7825da3-aa12-4802-b534-134f261f617d	one-shot-coresets-the-case-of-k-clustering	1711.09649	null	http://arxiv.org/abs/1711.09649v3	http://arxiv.org/pdf/1711.09649v3.pdf	One-Shot Coresets: The Case of k-Clustering	Scaling clustering algorithms to massive data sets is a challenging task. Recently, several successful approaches based on data summarization methods, such as coresets and sketches, were proposed. While these techniques provide provably good and small summaries, they are inherently problem dependent - the practitioner has to commit to a fixed clustering objective before even exploring the data. However, can one construct small data summaries for a wide range of clustering problems simultaneously? In this work, we affirmatively answer this question by proposing an efficient algorithm that constructs such one-shot summaries for k-clustering problems while retaining strong theoretical guarantees.	['Olivier Bachem', 'Silvio Lattanzi', 'Mario Lucic']	2017-11-27	null	null	null	null	['data-summarization']	['miscellaneous']	[ 7.06119165e-02 8.37968066e-02 -3.00191820e-01 -8.38838071e-02 -1.11437333e+00 -5.59297681e-01 3.27715844e-01 7.49792516e-01 -9.56314877e-02 6.77987635e-01 4.63763416e-01 -5.79080312e-03 -8.09841275e-01 -6.42520010e-01 -2.62729555e-01 -7.64709353e-01 -3.10788333e-01 9.31948364e-01 4.17483717e-01 2.12057784e-01 8.63850296e-01 2.79957145e-01 -1.71580005e+00 5.07903984e-03 1.11473846e+00 5.20198703e-01 6.91970512e-02 8.10869336e-01 -4.45289344e-01 4.89234477e-01 -7.39778221e-01 -2.31223762e-01 2.31887564e-01 -3.18971813e-01 -8.06298018e-01 5.60474634e-01 2.13994816e-01 -1.50739640e-01 -7.45272487e-02 8.59148920e-01 4.34656322e-01 2.99534738e-01 6.23578787e-01 -1.55173242e+00 -3.79337311e-01 9.21690047e-01 -8.72415006e-01 1.56761203e-02 4.56012428e-01 -2.61352450e-01 1.08169425e+00 -7.19190776e-01 6.42355204e-01 1.11614883e+00 5.08368433e-01 3.63467336e-01 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c7373b60-d586-43d9-8e54-8b02a549dc5b	vddb-a-comprehensive-resource-and-machine	2209.13521	null	https://arxiv.org/abs/2209.13521v1	https://arxiv.org/pdf/2209.13521v1.pdf	VDDB: a comprehensive resource and machine learning platform for antiviral drug discovery	Virus infection is one of the major diseases that seriously threaten human health. To meet the growing demand for mining and sharing data resources related to antiviral drugs and to accelerate the design and discovery of new antiviral drugs, we presented an open-access antiviral drug resource and machine learning platform (VDDB), which, to the best of our knowledge, is the first comprehensive dedicated resource for experimentally verified potential drugs/molecules based on manually curated data. Currently, VDDB highlights 848 clinical vaccines, 199 clinical antibodies, as well as over 710,000 small molecules targeting 39 medically important viruses including SARS-CoV-2. Furthermore, VDDB stores approximately 3 million records of pharmacological data for these collected potential antiviral drugs/molecules, involving 314 cell infection-based phenotypic and 234 target-based genotypic assays. Based on these annotated pharmacological data, VDDB allows users to browse, search and download reliable information about these collects for various viruses of interest. In particular, VDDB also integrates 57 cell infection- and 117 target-based associated high-accuracy machine learning models to support various antivirals identification-related tasks, such as compound activity prediction, virtual screening, drug repositioning and target fishing. VDDB is freely accessible at http://vddb.idruglab.cn.	['Ling Wang', 'Hanxuan Cai', 'Duancheng Zhao', 'Jingxing Wu', 'Yihao Chen', 'Shunming Tao']	2022-09-17	null	null	null	null	['activity-prediction', 'activity-prediction']	['computer-vision', 'time-series']	[ 2.01546624e-01 -6.65524065e-01 -8.66562426e-01 2.62241602e-01 -6.70323372e-01 -7.39703476e-01 2.94140011e-01 7.17715204e-01 -1.27268612e-01 1.55076456e+00 -1.80874988e-01 -5.65651178e-01 -3.99347320e-02 -5.10650396e-01 -5.28542161e-01 -1.02448177e+00 -1.57971859e-01 8.53883028e-01 -2.03751817e-01 -1.22797973e-02 4.22763139e-01 9.26975131e-01 -9.64582086e-01 2.31903970e-01 1.21909356e+00 6.25519395e-01 3.09038907e-01 3.09600294e-01 1.50763616e-02 1.05752200e-01 -6.05683744e-01 1.69347022e-02 -3.28498632e-01 -4.66164291e-01 -4.55342978e-01 -6.86966360e-01 -1.44091472e-01 -2.08409280e-01 1.22843228e-01 7.25931823e-01 7.74617493e-01 -3.89598548e-01 7.93237388e-01 -1.01662612e+00 -8.47365558e-01 -3.02339375e-01 -1.08379886e-01 1.41622841e-01 5.44715047e-01 5.43194890e-01 7.52317250e-01 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3008b2d9-0c48-4121-9e50-249f3123037e	a-task-oriented-dialogue-architecture-via	null	null	https://aclanthology.org/2021.sigdial-1.46	https://aclanthology.org/2021.sigdial-1.46.pdf	A Task-Oriented Dialogue Architecture via Transformer Neural Language Models and Symbolic Injection	Recently, transformer language models have been applied to build both task- and non-task-oriented dialogue systems. Although transformers perform well on most of the NLP tasks, they perform poorly on context retrieval and symbolic reasoning. Our work aims to address this limitation by embedding the model in an operational loop that blends both natural language generation and symbolic injection. We evaluated our system on the multi-domain DSTC8 data set and reported joint goal accuracy of 75.8% (ranked among the first half positions), intent accuracy of 97.4% (which is higher than the reported literature), and a 15% improvement for success rate compared to a baseline with no symbolic injection. These promising results suggest that transformer language models can not only generate proper system responses but also symbolic representations that can further be used to enhance the overall quality of the dialogue management as well as serving as scaffolding for complex conversational reasoning.	['Anthony Tomasic', 'Aaron Steinfeld', 'John Zimmerman', 'Antian Wang', 'Oscar J. Romero']	null	null	null	null	sigdial-acl-2021-7	['dialogue-management']	['natural-language-processing']	[ 3.03240657e-01 7.50628650e-01 -9.74323973e-03 -3.93341899e-01 -9.10741389e-01 -6.58183515e-01 1.17033923e+00 1.37610063e-01 -9.56223905e-02 9.46245015e-01 7.05232859e-01 -5.18279850e-01 2.00489193e-01 -6.79264963e-01 -1.82065353e-01 -1.08178936e-01 2.25851730e-01 8.83035123e-01 3.25614631e-01 -9.12449360e-01 3.74501169e-01 -2.91084517e-02 -1.25978446e+00 9.30612922e-01 1.25370479e+00 7.02058315e-01 -1.09595202e-01 7.42146075e-01 -5.98951757e-01 1.53485417e+00 -1.02162194e+00 -5.14436901e-01 -3.89453292e-01 -5.44139743e-01 -1.30317879e+00 -3.00285220e-01 7.18195438e-02 -4.02443349e-01 4.14974615e-02 5.00375628e-01 4.47806120e-01 8.97354931e-02 5.23897827e-01 -1.12594521e+00 -4.57143098e-01 1.03786659e+00 1.09070219e-01 -1.74694285e-01 1.01469600e+00 3.05259615e-01 1.08810067e+00 -5.95990002e-01 6.01802289e-01 1.47891319e+00 5.63014984e-01 9.53171849e-01 -1.26260173e+00 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1.63455784e-01 2.21006513e-01 1.28293941e-02 -4.90211576e-01 -9.05026644e-02 -2.35603988e-01]	[12.685951232910156, 8.122532844543457]
60cb8cd0-b21d-4d1d-8d5c-b10e4b6c4915	urban-stylegan-learning-to-generate-and	2305.09602	null	https://arxiv.org/abs/2305.09602v1	https://arxiv.org/pdf/2305.09602v1.pdf	Urban-StyleGAN: Learning to Generate and Manipulate Images of Urban Scenes	A promise of Generative Adversarial Networks (GANs) is to provide cheap photorealistic data for training and validating AI models in autonomous driving. Despite their huge success, their performance on complex images featuring multiple objects is understudied. While some frameworks produce high-quality street scenes with little to no control over the image content, others offer more control at the expense of high-quality generation. A common limitation of both approaches is the use of global latent codes for the whole image, which hinders the learning of independent object distributions. Motivated by SemanticStyleGAN (SSG), a recent work on latent space disentanglement in human face generation, we propose a novel framework, Urban-StyleGAN, for urban scene generation and manipulation. We find that a straightforward application of SSG leads to poor results because urban scenes are more complex than human faces. To provide a more compact yet disentangled latent representation, we develop a class grouping strategy wherein individual classes are grouped into super-classes. Moreover, we employ an unsupervised latent exploration algorithm in the $\mathcal{S}$-space of the generator and show that it is more efficient than the conventional $\mathcal{W}^{+}$-space in controlling the image content. Results on the Cityscapes and Mapillary datasets show the proposed approach achieves significantly more controllability and improved image quality than previous approaches on urban scenes and is on par with general-purpose non-controllable generative models (like StyleGAN2) in terms of quality.	['Bin Yang', 'Karim Guirguis', 'Daniel Cremers', 'Tarun Yenamandra', 'Youssef Farag', 'George Eskandar']	2023-05-16	null	null	null	null	['scene-generation', 'face-generation']	['computer-vision', 'computer-vision']	[ 3.83134097e-01 4.52060610e-01 8.71324688e-02 -2.41234556e-01 -6.66499674e-01 -6.25612140e-01 1.00017297e+00 -7.48263001e-01 -7.16593638e-02 8.70370507e-01 -9.93840583e-03 -2.34622270e-01 -5.34718335e-02 -1.02684629e+00 -7.95969069e-01 -1.06415427e+00 2.44547054e-01 4.38641667e-01 -1.91602111e-01 -3.53297204e-01 -1.90066025e-01 4.16440874e-01 -1.77551425e+00 -7.66661093e-02 1.00078940e+00 6.99798167e-01 1.06326215e-01 4.79703277e-01 2.90168285e-01 7.59577036e-01 -5.67769825e-01 -6.14081502e-01 6.36401474e-01 -8.12689960e-01 -2.40815669e-01 2.94876844e-01 5.55134892e-01 -1.76004812e-01 -4.50697690e-01 1.11915433e+00 4.83475417e-01 -9.31127816e-02 8.07051420e-01 -1.67570126e+00 -7.78067648e-01 3.02562028e-01 -3.84284317e-01 -2.98924744e-01 -3.90573958e-04 5.61200082e-01 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56a7116d-e89c-4d91-81ae-0c2b354859de	pretrained-speech-encoders-and-efficient-fine	null	null	https://aclanthology.org/2022.iwslt-1.23	https://aclanthology.org/2022.iwslt-1.23.pdf	Pretrained Speech Encoders and Efficient Fine-tuning Methods for Speech Translation: UPC at IWSLT 2022	This paper describes the submissions of the UPC Machine Translation group to the IWSLT 2022 Offline Speech Translation and Speech-to-Speech Translation tracks. The offline task involves translating English speech to German, Japanese and Chinese text. Our Speech Translation systems are trained end-to-end and are based on large pretrained speech and text models. We use an efficient fine-tuning technique that trains only specific layers of our system, and explore the use of adapter modules for the non-trainable layers. We further investigate the suitability of different speech encoders (wav2vec 2.0, HuBERT) for our models and the impact of knowledge distillation from the Machine Translation model that we use for the decoder (mBART). For segmenting the IWSLT test sets we fine-tune a pretrained audio segmentation model and achieve improvements of 5 BLEU compared to the given segmentation. Our best single model uses HuBERT and parallel adapters and achieves 29.42 BLEU at English-German MuST-C tst-COMMON and 26.77 at IWSLT 2020 test. By ensembling many models, we further increase translation quality to 30.83 BLEU and 27.78 accordingly. Furthermore, our submission for English-Japanese achieves 15.85 and English-Chinese obtains 25.63 BLEU on the MuST-C tst-COMMON sets. Finally, we extend our system to perform English-German Speech-to-Speech Translation with a pretrained Text-to-Speech model.	['Marta R. Costa-jussà', 'José Fonollosa', 'Carlos Escolano', 'Gerard I. Gállego', 'Ioannis Tsiamas']	null	null	null	null	iwslt-acl-2022-5	['speech-to-speech-translation']	['speech']	[ 1.42774105e-01 2.41102010e-01 -7.71593899e-02 -3.75346214e-01 -1.71227610e+00 -7.73225963e-01 5.22231042e-01 -4.52627957e-01 -5.72814226e-01 5.83221614e-01 3.58393341e-01 -1.10540557e+00 5.93506873e-01 -3.49651098e-01 -9.01054323e-01 -2.93263733e-01 3.25289965e-01 1.07437932e+00 9.55211893e-02 -3.80079597e-01 -4.88584697e-01 -2.56644581e-02 -6.05770111e-01 6.83008432e-01 7.89324760e-01 6.42253995e-01 2.75992811e-01 1.25920618e+00 -5.49107231e-02 2.75459915e-01 -8.16303253e-01 -6.32864892e-01 3.22984427e-01 -7.65831172e-01 -1.16441715e+00 -1.44058540e-01 2.12517723e-01 -2.04864040e-01 -3.63183439e-01 7.06534326e-01 8.00441146e-01 -3.11705060e-02 4.23349500e-01 -7.36279130e-01 -6.43509686e-01 1.21147537e+00 -8.62750784e-02 1.29859671e-01 3.01567823e-01 1.96470127e-01 8.68599772e-01 -9.89363432e-01 5.51972985e-01 1.24490166e+00 4.48816806e-01 8.26492608e-01 -1.04651916e+00 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3f9aae1c-ec3c-48b0-8ab0-a0cd7376377b	robust-pedestrian-attribute-recognition-using	2110.08708	null	https://arxiv.org/abs/2110.08708v4	https://arxiv.org/pdf/2110.08708v4.pdf	Robust Pedestrian Attribute Recognition Using Group Sparsity for Occlusion Videos	Occlusion processing is a key issue in pedestrian attribute recognition (PAR). Nevertheless, several existing video-based PAR methods have not yet considered occlusion handling in depth. In this paper, we formulate finding non-occluded frames as sparsity-based temporal attention of a crowded video. In this manner, a model is guided not to pay attention to the occluded frame. However, temporal sparsity cannot include a correlation between attributes when occlusion occurs. For example, "boots" and "shoe color" cannot be recognized when the foot is invisible. To solve the uncorrelated attention issue, we also propose a novel group sparsity-based temporal attention module. Group sparsity is applied across attention weights in correlated attributes. Thus, attention weights in a group are forced to pay attention to the same frames. Experimental results showed that the proposed method achieved a higher F1-score than the state-of-the-art methods on two video-based PAR datasets.	['Jungchan Cho', 'Kimin Yun', 'Geonu Lee']	2021-10-17	null	null	null	null	['pedestrian-attribute-recognition', 'occlusion-handling']	['computer-vision', 'computer-vision']	[ 1.85820207e-01 -4.20742035e-01 -1.34277359e-01 -5.23710489e-01 -2.68468559e-01 1.02148484e-02 9.84121040e-02 6.52909726e-02 -4.01001126e-01 8.22479248e-01 2.92668223e-01 1.87229514e-01 1.08303554e-01 -6.70633495e-01 -7.94026196e-01 -7.52892733e-01 1.06741115e-01 -5.94832338e-02 4.60682452e-01 1.04222983e-01 1.93520993e-01 2.12845847e-01 -1.78374183e+00 3.94616574e-01 9.32968140e-01 1.00435305e+00 4.10678595e-01 2.11892143e-01 -3.15587632e-02 7.79226840e-01 -5.75821817e-01 -3.69732678e-01 9.75850970e-02 -3.26866746e-01 -2.56485999e-01 6.54441178e-01 9.27739203e-01 -4.86365467e-01 -4.01717305e-01 1.05268562e+00 4.41043019e-01 4.63850558e-01 3.45097512e-01 -1.34219551e+00 -8.07821989e-01 1.49784371e-01 -8.97638679e-01 7.03819811e-01 6.80712461e-01 1.99101612e-01 8.40584338e-01 -9.56355214e-01 4.60857272e-01 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a5ed7dcb-7145-40ec-9377-8437c61c8ddb	sdf-3dgan-a-3d-object-generative-method-based	2303.06821	null	https://arxiv.org/abs/2303.06821v1	https://arxiv.org/pdf/2303.06821v1.pdf	SDF-3DGAN: A 3D Object Generative Method Based on Implicit Signed Distance Function	In this paper, we develop a new method, termed SDF-3DGAN, for 3D object generation and 3D-Aware image synthesis tasks, which introduce implicit Signed Distance Function (SDF) as the 3D object representation method in the generative field. We apply SDF for higher quality representation of 3D object in space and design a new SDF neural renderer, which has higher efficiency and higher accuracy. To train only on 2D images, we first generate the objects, which are represented by SDF, from Gaussian distribution. Then we render them to 2D images and use them to apply GAN training method together with 2D images in the dataset. In the new rendering method, we relieve all the potential of SDF mathematical property to alleviate computation pressure in the previous SDF neural renderer. In specific, our new SDF neural renderer can solve the problem of sampling ambiguity when the number of sampling point is not enough, \ie use the less points to finish higher quality sampling task in the rendering pipeline. And in this rendering pipeline, we can locate the surface easily. Therefore, we apply normal loss on it to control the smoothness of generated object surface, which can make our method enjoy the much higher generation quality. Quantitative and qualitative experiments conducted on public benchmarks demonstrate favorable performance against the state-of-the-art methods in 3D object generation task and 3D-Aware image synthesis task. Our codes will be released at https://github.com/lutao2021/SDF-3DGAN.	['Libo Zhang', 'Ruyi Ji', 'Lutao Jiang']	2023-03-13	null	null	null	null	['3d-aware-image-synthesis']	['computer-vision']	[ 1.65039033e-01 1.56746134e-01 2.06999645e-01 -8.97454098e-02 -6.16022289e-01 -3.70427608e-01 5.37205935e-01 -6.50778294e-01 1.23734437e-01 6.38867795e-01 2.38864832e-02 -1.13083899e-01 3.53667170e-01 -1.20978963e+00 -8.03911269e-01 -7.17395961e-01 3.20549548e-01 3.77565384e-01 2.71499842e-01 -2.54062675e-02 1.85931787e-01 6.74023092e-01 -1.50842714e+00 1.40731424e-01 1.06090188e+00 1.22168207e+00 4.36783522e-01 4.54663515e-01 -5.31123459e-01 3.77042532e-01 -7.20964134e-01 -1.62595659e-01 5.10887265e-01 -5.67305923e-01 -5.30284584e-01 1.78747162e-01 2.12606147e-01 -6.75303936e-01 -1.15366593e-01 9.60296214e-01 8.65137219e-01 7.92181641e-02 9.38414156e-01 -1.20094526e+00 -7.97875047e-01 1.38862776e-02 -8.82840633e-01 -2.88080305e-01 2.69480437e-01 3.36628675e-01 3.76988173e-01 -1.06414390e+00 7.25973487e-01 1.40727079e+00 4.94637698e-01 7.99752116e-01 -1.09055364e+00 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2.28707552e-01 2.06740215e-01 -7.04290092e-01 -2.70559996e-01 4.29179594e-02]	[9.068685531616211, -3.493950128555298]
16f33f69-d76a-4211-90a6-af2c19cfa63a	semanticac-semantics-assisted-framework-for	2302.05940	null	https://arxiv.org/abs/2302.05940v1	https://arxiv.org/pdf/2302.05940v1.pdf	SemanticAC: Semantics-Assisted Framework for Audio Classification	In this paper, we propose SemanticAC, a semantics-assisted framework for Audio Classification to better leverage the semantic information. Unlike conventional audio classification methods that treat class labels as discrete vectors, we employ a language model to extract abundant semantics from labels and optimize the semantic consistency between audio signals and their labels. We verify that simple textual information from labels and advanced pretraining models enable more abundant semantic supervision for better performance. Specifically, we design a text encoder to capture the semantic information from the text extension of labels. Then we map the audio signals to align with the semantics of corresponding class labels via an audio encoder and a similarity calculation module so as to enforce the semantic consistency. Extensive experiments on two audio datasets, ESC-50 and US8K demonstrate that our proposed method consistently outperforms the compared audio classification methods.	['Xiu Li', 'Ran Liao', 'Hantao Zhou', 'Shuyan Li', 'Yue Ma', 'Yicheng Xiao']	2023-02-12	null	null	null	null	['audio-classification']	['audio']	[ 4.09624726e-01 -5.73362783e-02 -3.74080718e-01 -9.90354776e-01 -8.02539945e-01 -7.65228629e-01 3.07685465e-01 1.59298882e-01 -1.62888572e-01 2.62949288e-01 6.12266243e-01 1.71674520e-01 -1.42173350e-01 -3.41082811e-01 -4.14576113e-01 -2.53287226e-01 -1.70611471e-01 1.74641997e-01 -9.45869088e-03 5.76932654e-02 -5.45478472e-03 -2.87128299e-01 -1.79461932e+00 6.44640684e-01 4.79096115e-01 1.56809878e+00 -7.08779506e-03 4.22458708e-01 -2.23274752e-01 9.10367429e-01 -4.21006382e-01 -1.01450980e-01 7.80541301e-02 -4.05928195e-01 -9.58386719e-01 1.45038009e-01 4.32217270e-01 -3.73131372e-02 -4.36977893e-01 1.09587109e+00 4.89661038e-01 3.00041616e-01 6.15630329e-01 -1.49081314e+00 -4.95755941e-01 9.82228220e-01 -8.22534189e-02 -1.20369121e-01 6.85424507e-01 -2.96832234e-01 1.62880731e+00 -8.30700278e-01 3.48442465e-01 1.26204515e+00 7.39017129e-01 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-9.98749614e-01 6.58900857e-01 2.15663090e-01 -8.18204343e-01 -5.29880285e-01 4.50151980e-01]	[15.287185668945312, 5.12149715423584]
be812e88-addb-416b-a04b-5cf91698cd36	a2-rl-aesthetics-aware-reinforcement-learning	1709.04595	null	http://arxiv.org/abs/1709.04595v3	http://arxiv.org/pdf/1709.04595v3.pdf	A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping	Image cropping aims at improving the aesthetic quality of images by adjusting their composition. Most weakly supervised cropping methods (without bounding box supervision) rely on the sliding window mechanism. The sliding window mechanism requires fixed aspect ratios and limits the cropping region with arbitrary size. Moreover, the sliding window method usually produces tens of thousands of windows on the input image which is very time-consuming. Motivated by these challenges, we firstly formulate the aesthetic image cropping as a sequential decision-making process and propose a weakly supervised Aesthetics Aware Reinforcement Learning (A2-RL) framework to address this problem. Particularly, the proposed method develops an aesthetics aware reward function which especially benefits image cropping. Similar to human's decision making, we use a comprehensive state representation including both the current observation and the historical experience. We train the agent using the actor-critic architecture in an end-to-end manner. The agent is evaluated on several popular unseen cropping datasets. Experiment results show that our method achieves the state-of-the-art performance with much fewer candidate windows and much less time compared with previous weakly supervised methods.	['Junge Zhang', 'Huikai Wu', 'Debang Li', 'Kaiqi Huang']	2017-09-14	a2-rl-aesthetics-aware-reinforcement-learning-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Li_A2-RL_Aesthetics_Aware_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_A2-RL_Aesthetics_Aware_CVPR_2018_paper.pdf	cvpr-2018-6	['image-cropping']	['computer-vision']	[ 2.76505470e-01 2.66385227e-01 -3.31585228e-01 -1.96925789e-01 -9.05951798e-01 -2.77353793e-01 2.79051751e-01 -5.14933579e-02 -3.63876998e-01 3.96726638e-01 -9.55058113e-02 -1.55656517e-01 3.07543874e-01 -7.29592323e-01 -8.11188400e-01 -8.62347007e-01 1.03136115e-01 5.57963699e-02 3.80074345e-02 -2.33840629e-01 2.00072423e-01 -1.99204832e-01 -1.33543730e+00 9.55576301e-02 1.02249074e+00 1.22399855e+00 5.61901033e-01 5.97873807e-01 9.62624103e-02 5.49660206e-01 -4.90072817e-01 -3.33966881e-01 4.49530929e-01 -3.78793776e-01 -5.00835180e-01 6.44911110e-01 5.62550724e-02 -3.66714507e-01 2.60737985e-01 1.02172422e+00 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8de3fd0e-23ca-45e9-9a60-b2a17e77210b	coswara-a-website-application-enabling-covid	2206.05053	null	https://arxiv.org/abs/2206.05053v1	https://arxiv.org/pdf/2206.05053v1.pdf	Coswara: A website application enabling COVID-19 screening by analysing respiratory sound samples and health symptoms	The COVID-19 pandemic has accelerated research on design of alternative, quick and effective COVID-19 diagnosis approaches. In this paper, we describe the Coswara tool, a website application designed to enable COVID-19 detection by analysing respiratory sound samples and health symptoms. A user using this service can log into a website using any device connected to the internet, provide there current health symptom information and record few sound sampled corresponding to breathing, cough, and speech. Within a minute of analysis of this information on a cloud server the website tool will output a COVID-19 probability score to the user. As the COVID-19 pandemic continues to demand massive and scalable population level testing, we hypothesize that the proposed tool provides a potential solution towards this.	['Murali Alagesan', 'Sadhana Gonuguntla', 'Suhail K K', 'Sahiti Nori', 'Chandrakiran C', 'Sriram Ganapathy', 'Pravin Mote', 'Srikanth Raj Chetupalli', 'Neeraj Kumar Sharma', 'Debottam Dutta', 'Debarpan Bhattacharya']	2022-06-09	null	null	null	null	['covid-19-detection']	['medical']	[ 1.07227024e-02 -4.86210167e-01 1.64675415e-01 3.17901999e-01 -5.24735153e-01 -8.97031128e-01 3.79618444e-02 6.52466595e-01 -2.69084543e-01 4.37053084e-01 2.33539660e-02 -5.71289837e-01 -2.12198287e-01 -7.93755770e-01 7.32740462e-02 -3.47976804e-01 3.39800864e-02 9.16889727e-01 3.76040816e-01 1.41359851e-01 -1.54480964e-01 5.67190588e-01 -1.17768598e+00 5.94682693e-02 4.01100665e-01 5.31950772e-01 4.88248467e-01 1.48172724e+00 2.05796793e-01 1.54298946e-01 -8.96870494e-01 1.12988085e-01 3.93913202e-02 -2.39038959e-01 -4.60432768e-01 -9.29559886e-01 -4.06943142e-01 -6.86828315e-01 5.09442091e-01 5.25042534e-01 9.75133538e-01 -2.54942149e-01 5.92138231e-01 -1.22140765e+00 1.46889821e-01 -6.60369322e-02 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514e6ffa-a5a3-46a3-8356-c99e4e459728	pyramid-scene-parsing-network	1612.01105	null	http://arxiv.org/abs/1612.01105v2	http://arxiv.org/pdf/1612.01105v2.pdf	Pyramid Scene Parsing Network	Scene parsing is challenging for unrestricted open vocabulary and diverse scenes. In this paper, we exploit the capability of global context information by different-region-based context aggregation through our pyramid pooling module together with the proposed pyramid scene parsing network (PSPNet). Our global prior representation is effective to produce good quality results on the scene parsing task, while PSPNet provides a superior framework for pixel-level prediction tasks. The proposed approach achieves state-of-the-art performance on various datasets. It came first in ImageNet scene parsing challenge 2016, PASCAL VOC 2012 benchmark and Cityscapes benchmark. A single PSPNet yields new record of mIoU accuracy 85.4% on PASCAL VOC 2012 and accuracy 80.2% on Cityscapes.	['Jiaya Jia', 'Xiaogang Wang', 'Xiaojuan Qi', 'Jianping Shi', 'Hengshuang Zhao']	2016-12-04	pyramid-scene-parsing-network-1	http://openaccess.thecvf.com/content_cvpr_2017/html/Zhao_Pyramid_Scene_Parsing_CVPR_2017_paper.html	http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhao_Pyramid_Scene_Parsing_CVPR_2017_paper.pdf	cvpr-2017-7	['thermal-image-segmentation', 'dichotomous-image-segmentation']	['computer-vision', 'computer-vision']	[ 4.61511374e-01 7.31610730e-02 -1.46068521e-02 -6.48341119e-01 -1.11063182e+00 -5.29315472e-01 4.51078862e-01 7.96112418e-02 -5.73742211e-01 5.74074924e-01 3.27446729e-01 -2.29671285e-01 4.07079637e-01 -1.08969939e+00 -9.45776761e-01 -5.91997623e-01 3.07472758e-02 -2.27023974e-01 6.66466773e-01 -2.03016236e-01 5.30728288e-02 3.50668043e-01 -1.46784329e+00 6.82579160e-01 7.91753888e-01 1.25562000e+00 7.08800673e-01 9.49912727e-01 -4.38325316e-01 1.07537830e+00 -4.40127611e-01 -4.92276341e-01 3.98417860e-01 1.45333037e-01 -8.96053314e-01 -3.76083627e-02 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c79da213-f63f-465a-a0ea-023d95c6b91f	grubert-a-gru-based-method-to-fuse-bert	null	null	https://aclanthology.org/2020.aacl-srw.19	https://aclanthology.org/2020.aacl-srw.19.pdf	GRUBERT: A GRU-Based Method to Fuse BERT Hidden Layers for Twitter Sentiment Analysis	In this work, we introduce a GRU-based architecture called GRUBERT that learns to map the different BERT hidden layers to fused embeddings with the aim of achieving high accuracy on the Twitter sentiment analysis task. Tweets are known for their highly diverse language, and by exploiting different linguistic information present across BERT hidden layers, we can capture the full extent of this language at the embedding level. Our method can be easily adapted to other embeddings capturing different linguistic information. We show that our method outperforms well-known heuristics of using BERT (e.g. using only the last layer) and other embeddings such as ELMo. We observe potential label noise resulting from the data acquisition process and employ early stopping as well as a voting classifier to overcome it.	['Zuowen Wang', 'Pouya Pourjafar', 'Matthias Matti', 'Leo Horne']	2020-12-01	null	null	null	asian-chapter-of-the-association-for	['twitter-sentiment-analysis']	['natural-language-processing']	[-4.26667273e-01 1.53588563e-01 -3.81772786e-01 -4.62499231e-01 -6.04307055e-01 -7.47534335e-01 6.92012906e-01 6.16706729e-01 -7.93986857e-01 3.48923653e-01 5.29084504e-01 -1.13986686e-01 1.19349524e-01 -1.02411914e+00 -5.16484141e-01 -4.77625340e-01 -1.59333929e-01 7.00363159e-01 1.66134015e-01 -7.38685071e-01 -1.60193518e-01 2.78040439e-01 -1.28053403e+00 4.11494881e-01 3.71181935e-01 9.73432720e-01 -2.09566951e-01 7.77230978e-01 -3.51930887e-01 9.72841620e-01 -5.69775224e-01 -7.74451673e-01 3.06974888e-01 1.83298796e-01 -7.79747248e-01 -2.55527765e-01 6.15577810e-02 -1.29579768e-01 -9.46631357e-02 8.10572863e-01 3.08720976e-01 2.00927556e-01 6.80383861e-01 -1.22901130e+00 -5.25185108e-01 1.00241768e+00 -3.35889459e-01 9.12810788e-02 1.27581239e-01 -2.84126014e-01 1.58392215e+00 -9.87355709e-01 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9690ff55-2c21-4cc1-a3f5-9bcbf6dd663e	could-you-guess-an-interesting-movie-from-the	1704.02199	null	http://arxiv.org/abs/1704.02199v1	http://arxiv.org/pdf/1704.02199v1.pdf	Could you guess an interesting movie from the posters?: An evaluation of vision-based features on movie poster database	In this paper, we aim to estimate the Winner of world-wide film festival from the exhibited movie poster. The task is an extremely challenging because the estimation must be done with only an exhibited movie poster, without any film ratings and box-office takings. In order to tackle this problem, we have created a new database which is consist of all movie posters included in the four biggest film festivals. The movie poster database (MPDB) contains historic movies over 80 years which are nominated a movie award at each year. We apply a couple of feature types, namely hand-craft, mid-level and deep feature to extract various information from a movie poster. Our experiments showed suggestive knowledge, for example, the Academy award estimation can be better rate with a color feature and a facial emotion feature generally performs good rate on the MPDB. The paper may suggest a possibility of modeling human taste for a movie recommendation.	['Kazushige Okayasu', 'Yuta Matsuzaki', 'Akio Nakamura', 'Ryousuke Takasawa', 'Naomichi Kobayashi', 'Takaaki Imanari', 'Hirokatsu Kataoka', 'Yoshihiro Kanehara']	2017-04-07	null	null	null	null	['movie-recommendation']	['miscellaneous']	[-5.07125497e-01 -3.58285457e-01 -6.63013682e-02 -9.30997193e-01 -8.41004074e-01 -6.51867568e-01 2.21598491e-01 1.68768033e-01 -4.82258648e-01 4.67751145e-01 1.23985589e-01 6.42051280e-01 -1.94888428e-01 -7.77844369e-01 -7.30041683e-01 -3.97998035e-01 -1.33187860e-01 2.13939145e-01 -5.00783250e-02 -3.88655543e-01 5.31360865e-01 4.34624195e-01 -1.38965201e+00 1.07174098e+00 2.83305123e-02 1.83051753e+00 2.51407832e-01 7.62070000e-01 1.09164327e-01 8.78537416e-01 -8.75084341e-01 -1.09944785e+00 4.60705340e-01 -1.33242220e-01 -4.11972165e-01 4.32436883e-01 6.63184464e-01 -6.31372809e-01 -2.16849238e-01 9.82333958e-01 3.88251811e-01 3.28411490e-01 6.67310357e-01 -1.20623076e+00 -7.14607775e-01 6.44217908e-01 -4.65207726e-01 2.97850490e-01 5.63144028e-01 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6f3ce690-d45f-4974-ae30-94e342914681	predicate-representations-and-polysemy-in	null	null	https://aclanthology.org/2021.iwcs-1.6	https://aclanthology.org/2021.iwcs-1.6.pdf	Predicate Representations and Polysemy in VerbNet Semantic Parsing	Despite recent advances in semantic role labeling propelled by pre-trained text encoders like BERT, performance lags behind when applied to predicates observed infrequently during training or to sentences in new domains. In this work, we investigate how role labeling performance on low-frequency predicates and out-of-domain data can be further improved by using VerbNet, a verb lexicon that groups verbs into hierarchical classes based on shared syntactic and semantic behavior and defines semantic representations describing relations between arguments. We find that VerbNet classes provide an effective level of abstraction, improving generalization on low-frequency predicates by allowing them to learn from the training examples of other predicates belonging to the same class. We also find that joint training of VerbNet role labeling and predicate disambiguation of VerbNet classes for polysemous verbs leads to improvements in both tasks, naturally supporting the extraction of VerbNet’s semantic representations.	['Martha Palmer', 'James Gung']	null	null	null	null	iwcs-acl-2021-6	['semantic-role-labeling']	['natural-language-processing']	[ 5.86907983e-01 6.00090146e-01 -7.15522647e-01 -6.78560019e-01 -3.82986367e-01 -9.54150259e-01 6.47167504e-01 1.03092432e+00 -5.40500224e-01 1.00812387e+00 8.16157281e-01 -1.17317848e-01 -4.67008352e-01 -9.94634330e-01 -4.35846031e-01 -2.34741881e-01 -2.18795776e-01 7.51032770e-01 5.56613028e-01 -7.45534778e-01 -2.02244252e-01 2.34674156e-01 -1.72265863e+00 7.42088795e-01 3.89983773e-01 5.68380654e-01 2.41118282e-01 1.45192578e-01 -3.38269413e-01 1.12394333e+00 -6.53034627e-01 -2.95862973e-01 -1.23751454e-01 -3.54355484e-01 -1.29267514e+00 -1.56809852e-01 2.91126166e-02 1.90208301e-01 -2.43873466e-02 7.39115596e-01 1.96793124e-01 3.69756073e-01 4.34805214e-01 -9.79818285e-01 -5.07824004e-01 1.10468173e+00 1.56122863e-01 5.72781503e-01 7.89939404e-01 -3.21824640e-01 2.04795265e+00 -3.17915231e-01 1.09119821e+00 1.57783401e+00 5.40396452e-01 7.95061529e-01 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-9.09567595e-01 -5.73359907e-01 1.16943038e+00 2.31431007e-01 -1.02050114e+00 -2.59645373e-01 -1.78323537e-02]	[10.254110336303711, 9.311917304992676]
75ae923a-7f11-440a-888c-99f80f5fde43	end-to-end-audio-visual-speech-recognition	2102.06657	null	https://arxiv.org/abs/2102.06657v1	https://arxiv.org/pdf/2102.06657v1.pdf	End-to-end Audio-visual Speech Recognition with Conformers	In this work, we present a hybrid CTC/Attention model based on a ResNet-18 and Convolution-augmented transformer (Conformer), that can be trained in an end-to-end manner. In particular, the audio and visual encoders learn to extract features directly from raw pixels and audio waveforms, respectively, which are then fed to conformers and then fusion takes place via a Multi-Layer Perceptron (MLP). The model learns to recognise characters using a combination of CTC and an attention mechanism. We show that end-to-end training, instead of using pre-computed visual features which is common in the literature, the use of a conformer, instead of a recurrent network, and the use of a transformer-based language model, significantly improve the performance of our model. We present results on the largest publicly available datasets for sentence-level speech recognition, Lip Reading Sentences 2 (LRS2) and Lip Reading Sentences 3 (LRS3), respectively. The results show that our proposed models raise the state-of-the-art performance by a large margin in audio-only, visual-only, and audio-visual experiments.	['Maja Pantic', 'Stavros Petridis', 'Pingchuan Ma']	2021-02-12	null	null	null	null	['lipreading', 'audio-visual-speech-recognition']	['computer-vision', 'speech']	[ 5.93688071e-01 6.02904521e-02 2.06729304e-02 -1.69537038e-01 -1.41052234e+00 -3.00643414e-01 8.62315953e-01 -4.13503731e-03 -4.39801365e-01 3.80145550e-01 4.45321918e-01 -5.46143115e-01 5.68019211e-01 -1.80419102e-01 -9.77318466e-01 -7.00210869e-01 3.29342842e-01 -1.13481253e-01 2.01846093e-01 8.92632231e-02 5.31434417e-02 1.75661117e-01 -1.81054914e+00 7.00236142e-01 5.88227332e-01 1.43443692e+00 3.63985866e-01 1.21875620e+00 7.20795244e-02 1.02543652e+00 -3.48577648e-01 -4.39400911e-01 -1.01622775e-01 -4.03491795e-01 -6.08810246e-01 1.14932097e-01 5.60933232e-01 -1.39350921e-01 -2.84913987e-01 7.32419133e-01 1.06316137e+00 5.80682233e-03 7.58907139e-01 -9.75475907e-01 -6.76286638e-01 3.33459288e-01 -4.89308238e-01 -9.86704528e-02 5.41721642e-01 3.83243740e-01 1.08311176e+00 -1.13085234e+00 3.64699066e-01 1.40209854e+00 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62960bc8-958f-4969-b05e-1fea347413c5	when-3d-aided-2d-face-recognition-meets-deep	1709.06532	null	http://arxiv.org/abs/1709.06532v1	http://arxiv.org/pdf/1709.06532v1.pdf	When 3D-Aided 2D Face Recognition Meets Deep Learning: An extended UR2D for Pose-Invariant Face Recognition	Most of the face recognition works focus on specific modules or demonstrate a research idea. This paper presents a pose-invariant 3D-aided 2D face recognition system (UR2D) that is robust to pose variations as large as 90? by leveraging deep learning technology. The architecture and the interface of UR2D are described, and each module is introduced in detail. Extensive experiments are conducted on the UHDB31 and IJB-A, demonstrating that UR2D outperforms existing 2D face recognition systems such as VGG-Face, FaceNet, and a commercial off-the-shelf software (COTS) by at least 9% on the UHDB31 dataset and 3% on the IJB-A dataset on average in face identification tasks. UR2D also achieves state-of-the-art performance of 85% on the IJB-A dataset by comparing the Rank-1 accuracy score from template matching. It fills a gap by providing a 3D-aided 2D face recognition system that has compatible results with 2D face recognition systems using deep learning techniques.	['Ioannis A. Kakadiaris', 'Pengfei Dou', 'Xiang Xu', 'Ha A. Le']	2017-09-19	null	null	null	null	['robust-face-recognition']	['computer-vision']	[-5.43508351e-01 5.25477119e-02 1.10126793e-01 -8.30824733e-01 -4.54973549e-01 -1.75951779e-01 4.34715956e-01 -9.52139914e-01 -3.46361212e-02 9.23158526e-02 -4.25725162e-01 -3.10410500e-01 -1.20677061e-01 -5.36187410e-01 -4.57457960e-01 -5.57826757e-01 -2.31425315e-01 6.48832738e-01 -3.25487971e-01 -2.62658864e-01 2.67646112e-03 1.51860023e+00 -1.79177928e+00 2.32365951e-02 -2.83768833e-01 1.66110587e+00 -4.80316311e-01 8.25776607e-02 1.53114395e-02 -1.15447879e-01 -6.32767022e-01 -6.15284383e-01 8.01324189e-01 1.38277337e-01 -4.80845928e-01 -7.55435508e-03 1.53756440e+00 -6.03262126e-01 -7.21024752e-01 5.61138332e-01 1.41934454e+00 -4.16463315e-01 6.11444712e-01 -1.45741117e+00 -4.52061087e-01 -3.66476417e-01 -5.66591322e-01 1.33206770e-01 6.17627144e-01 -4.59364206e-02 -1.12704538e-01 -1.43496537e+00 3.71877789e-01 1.79623806e+00 1.31698871e+00 1.17504525e+00 -1.00910234e+00 -1.11916745e+00 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6.09907769e-02 1.19916625e-01 -1.02217937e+00 -3.37030351e-01 -3.86888891e-01]	[13.334014892578125, 0.6341338157653809]
e8b6b6d2-8798-40da-8768-d733a86154bc	efficient-match-pair-retrieval-for-large	2307.04520	null	https://arxiv.org/abs/2307.04520v1	https://arxiv.org/pdf/2307.04520v1.pdf	Efficient Match Pair Retrieval for Large-scale UAV Images via Graph Indexed Global Descriptor	SfM (Structure from Motion) has been extensively used for UAV (Unmanned Aerial Vehicle) image orientation. Its efficiency is directly influenced by feature matching. Although image retrieval has been extensively used for match pair selection, high computational costs are consumed due to a large number of local features and the large size of the used codebook. Thus, this paper proposes an efficient match pair retrieval method and implements an integrated workflow for parallel SfM reconstruction. First, an individual codebook is trained online by considering the redundancy of UAV images and local features, which avoids the ambiguity of training codebooks from other datasets. Second, local features of each image are aggregated into a single high-dimension global descriptor through the VLAD (Vector of Locally Aggregated Descriptors) aggregation by using the trained codebook, which remarkably reduces the number of features and the burden of nearest neighbor searching in image indexing. Third, the global descriptors are indexed via the HNSW (Hierarchical Navigable Small World) based graph structure for the nearest neighbor searching. Match pairs are then retrieved by using an adaptive threshold selection strategy and utilized to create a view graph for divide-and-conquer based parallel SfM reconstruction. Finally, the performance of the proposed solution has been verified using three large-scale UAV datasets. The test results demonstrate that the proposed solution accelerates match pair retrieval with a speedup ratio ranging from 36 to 108 and improves the efficiency of SfM reconstruction with competitive accuracy in both relative and absolute orientation.	['Lizhe Wang', 'Lelin Li', 'Bingxuan Guo', 'Wanshou Jiang', 'Qingquan Li', 'Yichen Ma', 'San Jiang']	2023-07-10	null	null	null	null	['image-retrieval', 'retrieval']	['computer-vision', 'methodology']	[-3.28520383e-03 -8.35069180e-01 -2.22582564e-01 -1.11013226e-01 -5.13044953e-01 -8.13173831e-01 5.77424347e-01 5.36715388e-01 -3.67608696e-01 1.52981892e-01 -2.43348688e-01 7.99446274e-03 -7.66921282e-01 -1.15457571e+00 -3.33716363e-01 -6.08286500e-01 -3.02299917e-01 4.57120389e-01 4.90598977e-01 -3.55846971e-01 4.29799795e-01 8.79242599e-01 -2.09311008e+00 -1.86941594e-01 6.38534546e-01 1.38773632e+00 6.20439410e-01 3.64782780e-01 7.35568851e-02 2.64241874e-01 -5.71553051e-01 -1.29940817e-02 7.81162739e-01 -9.48841870e-02 -7.54428685e-01 1.80332527e-01 6.39605045e-01 -6.34838343e-01 -3.46112132e-01 8.84500384e-01 5.50359845e-01 4.48824942e-01 3.38120818e-01 -1.30414712e+00 5.43923723e-03 -9.06628594e-02 -5.33214033e-01 1.17017984e-01 6.11458123e-01 -1.14165388e-01 8.48822892e-01 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d8ae2a7b-e0e6-4f7e-a2ae-378034ec6e0a	idan-image-difference-attention-network-for	2208.08292	null	https://arxiv.org/abs/2208.08292v1	https://arxiv.org/pdf/2208.08292v1.pdf	IDAN: Image Difference Attention Network for Change Detection	Remote sensing image change detection is of great importance in disaster assessment and urban planning. The mainstream method is to use encoder-decoder models to detect the change region of two input images. Since the change content of remote sensing images has the characteristics of wide scale range and variety, it is necessary to improve the detection accuracy of the network by increasing the attention mechanism, which commonly includes: Squeeze-and-Excitation block, Non-local and Convolutional Block Attention Module, among others. These methods consider the importance of different location features between channels or within channels, but fail to perceive the differences between input images. In this paper, we propose a novel image difference attention network (IDAN). In the image preprocessing stage, we use a pre-training model to extract the feature differences between two input images to obtain the feature difference map (FD-map), and Canny for edge detection to obtain the edge difference map (ED-map). In the image feature extracting stage, the FD-map and ED-map are input to the feature difference attention module and edge compensation module, respectively, to optimize the features extracted by IDAN. Finally, the change detection result is obtained through the feature difference operation. IDAN comprehensively considers the differences in regional and edge features of images and thus optimizes the extracted image features. The experimental results demonstrate that the F1-score of IDAN improves 1.62% and 1.98% compared to the baseline model on WHU dataset and LEVIR-CD dataset, respectively.	['Xueyun Chen', 'Qichen Ding', 'Zican Hu', 'Hongkun Liu']	2022-08-17	null	null	null	null	['edge-detection']	['computer-vision']	[ 2.90246010e-01 -6.98709369e-01 3.26271802e-01 -3.71342540e-01 -3.68137449e-01 -1.11453444e-01 4.17816371e-01 -7.77152330e-02 -7.32610524e-01 3.38965982e-01 2.30268970e-01 -1.49912789e-01 -1.92112550e-02 -1.08354926e+00 -3.78961444e-01 -8.06675315e-01 -8.90545994e-02 -5.45135081e-01 2.17929095e-01 -4.22847211e-01 2.49296755e-01 6.49418950e-01 -1.43105233e+00 2.61646211e-01 8.50705624e-01 1.11428666e+00 4.76812631e-01 6.64268911e-01 -2.49928460e-02 4.29602772e-01 -3.19291830e-01 3.72776270e-01 4.21703696e-01 -5.68571448e-01 -4.16472197e-01 3.84234823e-02 3.10138389e-02 -8.16423774e-01 -4.57995534e-01 1.44123149e+00 9.49690998e-01 2.39360809e-01 5.76867461e-01 -8.45941305e-01 -1.09530783e+00 3.67303818e-01 -8.67143393e-01 1.14367187e+00 -9.75579098e-02 2.24428445e-01 6.41757131e-01 -1.20665765e+00 3.12585622e-01 1.20317531e+00 4.43481952e-01 1.32820010e-01 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c3c52e8a-5969-4354-ad52-cfe034845815	perceiver-actor-a-multi-task-transformer-for	2209.05451	null	https://arxiv.org/abs/2209.05451v2	https://arxiv.org/pdf/2209.05451v2.pdf	Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation	Transformers have revolutionized vision and natural language processing with their ability to scale with large datasets. But in robotic manipulation, data is both limited and expensive. Can manipulation still benefit from Transformers with the right problem formulation? We investigate this question with PerAct, a language-conditioned behavior-cloning agent for multi-task 6-DoF manipulation. PerAct encodes language goals and RGB-D voxel observations with a Perceiver Transformer, and outputs discretized actions by ``detecting the next best voxel action''. Unlike frameworks that operate on 2D images, the voxelized 3D observation and action space provides a strong structural prior for efficiently learning 6-DoF actions. With this formulation, we train a single multi-task Transformer for 18 RLBench tasks (with 249 variations) and 7 real-world tasks (with 18 variations) from just a few demonstrations per task. Our results show that PerAct significantly outperforms unstructured image-to-action agents and 3D ConvNet baselines for a wide range of tabletop tasks.	['Dieter Fox', 'Lucas Manuelli', 'Mohit Shridhar']	2022-09-12	null	null	null	null	['robot-manipulation']	['robots']	[ 4.57616672e-02 1.85120739e-02 -1.16147660e-01 -2.30673775e-01 -7.72538364e-01 -7.94686615e-01 6.37189209e-01 -2.90681034e-01 -5.72371960e-01 4.93770480e-01 4.11448061e-01 -1.01490095e-01 -8.85447673e-03 -4.80070621e-01 -1.11658013e+00 -6.05905414e-01 -2.56106168e-01 8.46562207e-01 1.36764899e-01 -2.80998498e-01 1.93638131e-01 4.86835480e-01 -1.45721090e+00 5.04788876e-01 4.35526073e-01 1.13810015e+00 7.13818252e-01 9.48239088e-01 1.50906682e-01 1.17758882e+00 -5.24465084e-01 3.60268891e-01 7.99862444e-01 -3.52759510e-02 -7.38313556e-01 2.43933260e-01 6.06632710e-01 -9.05410886e-01 -7.02396214e-01 6.85442269e-01 4.82823998e-01 4.93385255e-01 4.55620408e-01 -1.43496442e+00 -6.96257591e-01 6.54396713e-01 -3.46129864e-01 -6.97133392e-02 5.30750096e-01 1.15619278e+00 9.18107867e-01 -5.65597653e-01 9.51604307e-01 1.84684086e+00 1.91445366e-01 5.40235817e-01 -1.10789359e+00 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-3.67501199e-01 4.34557855e-01 6.14051700e-01 -1.03564298e+00 -4.26565148e-02 -1.10634185e-01]	[4.741800308227539, 0.4744839370250702]
ecb18496-42d1-4e45-8892-3a6bdb7a4aa0	very-low-resolution-iris-recognition-via	2210.09765	null	https://arxiv.org/abs/2210.09765v1	https://arxiv.org/pdf/2210.09765v1.pdf	Very Low-Resolution Iris Recognition Via Eigen-Patch Super-Resolution and Matcher Fusion	Current research in iris recognition is moving towards enabling more relaxed acquisition conditions. This has effects on the quality of acquired images, with low resolution being a predominant issue. Here, we evaluate a super-resolution algorithm used to reconstruct iris images based on Eigen-transformation of local image patches. Each patch is reconstructed separately, allowing better quality of enhanced images by preserving local information. Contrast enhancement is used to improve the reconstruction quality, while matcher fusion has been adopted to improve iris recognition performance. We validate the system using a database of 1,872 near-infrared iris images. The presented approach is superior to bilinear or bicubic interpolation, especially at lower resolutions, and the fusion of the two systems pushes the EER to below 5% for down-sampling factors up to a image size of only 13x13.	['Josef Bigun', 'Reuben A. Farrugia', 'Fernando Alonso-Fernandez']	2022-10-18	null	null	null	null	['iris-recognition']	['computer-vision']	[ 5.56107461e-01 -2.35540614e-01 -3.99370119e-02 -2.77275592e-01 -8.21200609e-01 -1.38159797e-01 2.93790191e-01 -2.05709096e-02 -5.89478254e-01 8.21685076e-01 3.31055611e-01 5.03410138e-02 -4.66709077e-01 -6.11102879e-01 -2.84541041e-01 -9.73210335e-01 1.93751156e-01 -7.01098070e-02 7.65159773e-03 4.40686047e-02 4.79804993e-01 6.21307909e-01 -1.86706400e+00 4.15254235e-01 1.28430736e+00 6.55480623e-01 -1.43701211e-01 6.47634029e-01 1.84436888e-01 4.26433831e-01 -4.49016064e-01 -3.93723965e-01 4.03130770e-01 -4.90777820e-01 -6.77227497e-01 9.60726440e-02 8.96338403e-01 -4.11843032e-01 1.46583676e-01 1.29905999e+00 5.94993293e-01 3.09926458e-02 4.04259056e-01 -1.28536552e-01 -4.46831912e-01 9.34865400e-02 -7.67523885e-01 2.08639368e-01 4.17681098e-01 1.08465515e-01 3.51125240e-01 -7.05866158e-01 4.51794922e-01 8.25986683e-01 5.82588077e-01 2.42539689e-01 -1.78593612e+00 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-9.53854084e-01 2.33591840e-01 6.91657543e-01 -2.77157158e-01 -4.22733873e-01 1.24750540e-01]	[3.7485086917877197, -3.629601001739502]
96750618-a43b-4857-8f34-61e02f53fc92	automl-meets-time-series-regression-design	2107.13186	null	https://arxiv.org/abs/2107.13186v1	https://arxiv.org/pdf/2107.13186v1.pdf	AutoML Meets Time Series Regression Design and Analysis of the AutoSeries Challenge	Analyzing better time series with limited human effort is of interest to academia and industry. Driven by business scenarios, we organized the first Automated Time Series Regression challenge (AutoSeries) for the WSDM Cup 2020. We present its design, analysis, and post-hoc experiments. The code submission requirement precluded participants from any manual intervention, testing automated machine learning capabilities of solutions, across many datasets, under hardware and time limitations. We prepared 10 datasets from diverse application domains (sales, power consumption, air quality, traffic, and parking), featuring missing data, mixed continuous and categorical variables, and various sampling rates. Each dataset was split into a training and a test sequence (which was streamed, allowing models to continuously adapt). The setting of time series regression, differs from classical forecasting in that covariates at the present time are known. Great strides were made by participants to tackle this AutoSeries problem, as demonstrated by the jump in performance from the sample submission, and post-hoc comparisons with AutoGluon. Simple yet effective methods were used, based on feature engineering, LightGBM, and random search hyper-parameter tuning, addressing all aspects of the challenge. Our post-hoc analyses revealed that providing additional time did not yield significant improvements. The winners' code was open-sourced https://www.4paradigm.com/competition/autoseries2020.	['Isabelle Guyon', 'Wei-Wei Tu', 'Zhen Xu']	2021-07-28	null	null	null	null	['time-series-regression']	['time-series']	[-2.99602062e-01 -3.79266053e-01 -3.14220339e-01 -3.71295452e-01 -6.89573348e-01 -8.03269207e-01 6.40154660e-01 -3.18065137e-02 -8.98504257e-02 6.05842948e-01 -1.27129629e-01 -7.06592202e-01 -5.44577599e-01 -4.26293612e-01 -4.85476792e-01 -5.81248879e-01 -7.94074237e-01 4.70481753e-01 -3.09374154e-01 -4.68695641e-01 2.58477807e-01 3.80574018e-01 -1.58464932e+00 2.25492015e-01 6.72419965e-01 1.27939749e+00 -1.95105579e-02 5.65658987e-01 4.54671904e-02 7.31186450e-01 -6.99054420e-01 -2.22787391e-02 7.47299433e-01 -5.70808984e-02 -3.54364514e-01 -1.81037784e-01 1.92121938e-02 1.78609435e-02 -1.16909996e-01 2.85653085e-01 4.35156971e-01 1.68099850e-01 1.38473138e-01 -2.04861784e+00 -2.90189087e-01 5.15838146e-01 -5.45493543e-01 3.98822308e-01 1.60940260e-01 4.70224887e-01 9.79580104e-01 -5.37099063e-01 4.54852581e-01 8.72215927e-01 9.50202405e-01 1.19381003e-01 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2.20287383e-01 3.91969234e-01 -4.36825484e-01 -6.86061919e-01 5.38512133e-02]	[7.123617649078369, 3.105365514755249]
d3572178-0669-409b-b178-6a058c60130b	wavelet-diffusion-models-are-fast-and	2211.16152	null	https://arxiv.org/abs/2211.16152v2	https://arxiv.org/pdf/2211.16152v2.pdf	Wavelet Diffusion Models are fast and scalable Image Generators	Diffusion models are rising as a powerful solution for high-fidelity image generation, which exceeds GANs in quality in many circumstances. However, their slow training and inference speed is a huge bottleneck, blocking them from being used in real-time applications. A recent DiffusionGAN method significantly decreases the models' running time by reducing the number of sampling steps from thousands to several, but their speeds still largely lag behind the GAN counterparts. This paper aims to reduce the speed gap by proposing a novel wavelet-based diffusion scheme. We extract low-and-high frequency components from both image and feature levels via wavelet decomposition and adaptively handle these components for faster processing while maintaining good generation quality. Furthermore, we propose to use a reconstruction term, which effectively boosts the model training convergence. Experimental results on CelebA-HQ, CIFAR-10, LSUN-Church, and STL-10 datasets prove our solution is a stepping-stone to offering real-time and high-fidelity diffusion models. Our code and pre-trained checkpoints are available at \url{https://github.com/VinAIResearch/WaveDiff.git}.	['Anh Tran', 'Quan Dao', 'Hao Phung']	2022-11-29	null	http://openaccess.thecvf.com//content/CVPR2023/html/Phung_Wavelet_Diffusion_Models_Are_Fast_and_Scalable_Image_Generators_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Phung_Wavelet_Diffusion_Models_Are_Fast_and_Scalable_Image_Generators_CVPR_2023_paper.pdf	cvpr-2023-1	['blocking']	['natural-language-processing']	[ 2.20792629e-02 -8.04163218e-02 -2.76492566e-01 -8.04515108e-02 -1.18848515e+00 -2.46429473e-01 6.20943367e-01 -4.10720497e-01 -2.80860633e-01 7.83291698e-01 1.53263956e-01 -2.31255233e-01 1.46729246e-01 -9.91604805e-01 -6.13916278e-01 -9.86799419e-01 1.28251567e-01 2.78201967e-01 5.29724844e-02 -2.03754798e-01 3.80523168e-02 7.45628923e-02 -1.31029260e+00 2.09501177e-01 1.03821623e+00 9.34993088e-01 3.26680928e-01 8.53267670e-01 7.08765537e-02 9.67321754e-01 -5.07024050e-01 -2.34117880e-01 3.11693072e-01 -9.16175306e-01 -4.24020380e-01 -2.65290122e-02 2.38826439e-01 -6.09937072e-01 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79b95059-12ed-469c-8149-909028b7b317	towards-accurate-post-training-quantization	2303.14341	null	https://arxiv.org/abs/2303.14341v1	https://arxiv.org/pdf/2303.14341v1.pdf	Towards Accurate Post-Training Quantization for Vision Transformer	Vision transformer emerges as a potential architecture for vision tasks. However, the intense computation and non-negligible delay hinder its application in the real world. As a widespread model compression technique, existing post-training quantization methods still cause severe performance drops. We find the main reasons lie in (1) the existing calibration metric is inaccurate in measuring the quantization influence for extremely low-bit representation, and (2) the existing quantization paradigm is unfriendly to the power-law distribution of Softmax. Based on these observations, we propose a novel Accurate Post-training Quantization framework for Vision Transformer, namely APQ-ViT. We first present a unified Bottom-elimination Blockwise Calibration scheme to optimize the calibration metric to perceive the overall quantization disturbance in a blockwise manner and prioritize the crucial quantization errors that influence more on the final output. Then, we design a Matthew-effect Preserving Quantization for Softmax to maintain the power-law character and keep the function of the attention mechanism. Comprehensive experiments on large-scale classification and detection datasets demonstrate that our APQ-ViT surpasses the existing post-training quantization methods by convincing margins, especially in lower bit-width settings (e.g., averagely up to 5.17% improvement for classification and 24.43% for detection on W4A4). We also highlight that APQ-ViT enjoys versatility and works well on diverse transformer variants.	['Xianglong Liu', 'Xiaolin Wei', 'Junjie Liu', 'Zhenhua Chai', 'Qinghua Yan', 'Haotong Qin', 'Yifu Ding']	2023-03-25	null	null	null	null	['model-compression']	['methodology']	[ 3.63745540e-01 -2.84069896e-01 -3.40992212e-01 -1.94710851e-01 -7.41764903e-01 -2.56226838e-01 4.72349375e-01 -4.21242937e-02 -4.56229061e-01 2.62971342e-01 1.55455858e-01 -5.09966135e-01 1.48751382e-02 -6.19475603e-01 -7.19363868e-01 -8.64348948e-01 3.75285804e-01 -1.31609872e-01 4.10608470e-01 -1.40529320e-01 3.00602943e-01 1.49305120e-01 -1.52167320e+00 7.39542320e-02 9.80982900e-01 1.50588977e+00 4.90184069e-01 6.20071709e-01 1.48718059e-01 7.12703288e-01 -7.79909909e-01 -7.03934371e-01 4.45527673e-01 -3.26614261e-01 -3.27095509e-01 -8.62613916e-02 7.04083622e-01 -5.73830724e-01 -7.31677711e-01 1.52245438e+00 7.33738184e-01 -4.03440088e-01 4.88375634e-01 -1.16320038e+00 -1.02940440e+00 6.19740903e-01 -6.61234677e-01 3.77861530e-01 -2.81440437e-01 5.09855032e-01 1.09289587e+00 -8.52687299e-01 -5.62307313e-02 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068a7014-6219-47be-aa0d-37826fcc84e3	a-closer-look-at-rehearsal-free-continual	2203.17269	null	https://arxiv.org/abs/2203.17269v2	https://arxiv.org/pdf/2203.17269v2.pdf	A Closer Look at Rehearsal-Free Continual Learning	Continual learning is a setting where machine learning models learn novel concepts from continuously shifting training data, while simultaneously avoiding degradation of knowledge on previously seen classes which may disappear from the training data for extended periods of time (a phenomenon known as the catastrophic forgetting problem). Current approaches for continual learning of a single expanding task (aka class-incremental continual learning) require extensive rehearsal of previously seen data to avoid this degradation of knowledge. Unfortunately, rehearsal comes at a cost to memory, and it may also violate data-privacy. Instead, we explore combining knowledge distillation and parameter regularization in new ways to achieve strong continual learning performance without rehearsal. Specifically, we take a deep dive into common continual learning techniques: prediction distillation, feature distillation, L2 parameter regularization, and EWC parameter regularization. We first disprove the common assumption that parameter regularization techniques fail for rehearsal-free continual learning of a single, expanding task. Next, we explore how to leverage knowledge from a pre-trained model in rehearsal-free continual learning and find that vanilla L2 parameter regularization outperforms EWC parameter regularization and feature distillation. Finally, we explore the recently popular ImageNet-R benchmark, and show that L2 parameter regularization implemented in self-attention blocks of a ViT transformer outperforms recent popular prompting for continual learning methods.	['Zsolt Kira', 'Shaunak Halbe', 'Yen-Chang Hsu', 'Junjiao Tian', 'James Seale Smith']	2022-03-31	null	null	null	null	['l2-regularization', 'novel-concepts']	['methodology', 'reasoning']	[ 3.58448744e-01 2.05201551e-01 -1.06127061e-01 -3.32566470e-01 -6.33745909e-01 -4.68465358e-01 6.05658829e-01 2.48439461e-01 -9.64873016e-01 1.09184945e+00 1.94781825e-01 -4.14305359e-01 -1.61716744e-01 -4.77708012e-01 -1.18918431e+00 -7.15772092e-01 -4.23852690e-02 2.84510285e-01 1.59337461e-01 9.15987492e-02 6.64715394e-02 4.55778867e-01 -1.65389574e+00 2.85275757e-01 5.68375349e-01 8.27918887e-01 2.78175268e-02 4.21609163e-01 5.52206161e-03 1.03668320e+00 -4.25174981e-01 -2.72369802e-01 5.40791452e-01 -2.37978995e-01 -9.69878316e-01 9.28587541e-02 6.91343009e-01 -2.19974831e-01 -3.75846326e-01 6.65642798e-01 2.55944163e-01 7.26181924e-01 5.15755355e-01 -1.36086202e+00 -1.20392466e+00 4.93225634e-01 -8.08884323e-01 3.88247043e-01 -9.84345376e-02 3.13621044e-01 6.58829629e-01 -1.13824427e+00 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7459b253-7b68-4117-b271-08005a3d3ca4	accurate-data-efficient-unconstrained-text	1812.11894	null	http://arxiv.org/abs/1812.11894v1	http://arxiv.org/pdf/1812.11894v1.pdf	Accurate, Data-Efficient, Unconstrained Text Recognition with Convolutional Neural Networks	Unconstrained text recognition is an important computer vision task, featuring a wide variety of different sub-tasks, each with its own set of challenges. One of the biggest promises of deep neural networks has been the convergence and automation of feature extractors from input raw signals, allowing for the highest possible performance with minimum required domain knowledge. To this end, we propose a data-efficient, end-to-end neural network model for generic, unconstrained text recognition. In our proposed architecture we strive for simplicity and efficiency without sacrificing recognition accuracy. Our proposed architecture is a fully convolutional network without any recurrent connections trained with the CTC loss function. Thus it operates on arbitrary input sizes and produces strings of arbitrary length in a very efficient and parallelizable manner. We show the generality and superiority of our proposed text recognition architecture by achieving state of the art results on seven public benchmark datasets, covering a wide spectrum of text recognition tasks, namely: Handwriting Recognition, CAPTCHA recognition, OCR, License Plate Recognition, and Scene Text Recognition. Our proposed architecture has won the ICFHR2018 Competition on Automated Text Recognition on a READ Dataset.	['Khaled F. Hussain', 'Usama S. Mohammed', 'Mohamed Yousef']	2018-12-31	null	null	null	null	['license-plate-recognition']	['computer-vision']	[ 7.74569094e-01 -4.57513571e-01 1.36738867e-01 -5.23955345e-01 -5.35466611e-01 -7.15682983e-01 8.60374093e-01 -4.09138799e-01 -6.13923311e-01 4.03362870e-01 -1.06247433e-01 -4.38488036e-01 7.43888468e-02 -3.91110480e-01 -8.12156200e-01 -6.73649371e-01 5.81329226e-01 4.84833777e-01 -5.22905588e-02 3.22216898e-02 5.48268020e-01 6.66258097e-01 -1.36452043e+00 3.02745134e-01 7.55965292e-01 1.28024089e+00 1.34384692e-01 1.02565336e+00 1.91004016e-02 9.04191792e-01 -5.11819541e-01 -8.66437912e-01 1.57139778e-01 -1.69832662e-01 -6.04738057e-01 4.12652105e-01 5.84069192e-01 -3.87140006e-01 -7.67416716e-01 7.65734792e-01 6.42932236e-01 -1.14420861e-01 8.34001839e-01 -6.68233871e-01 -8.13642144e-01 5.06298006e-01 -3.47240418e-01 -7.07218237e-03 -9.41205099e-02 1.53864101e-01 1.07544374e+00 -1.05626547e+00 3.60196888e-01 7.10223317e-01 6.40980303e-01 5.43699145e-01 -1.01061308e+00 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2.50628024e-01 5.31500220e-01 2.01195455e-03 -1.12288520e-01 3.09703112e-01]	[11.962005615234375, 2.340162515640259]
7f733aba-9c1d-4fff-9099-b1153d47109d	dgecn-a-depth-guided-edge-convolutional	2204.09983	null	https://arxiv.org/abs/2204.09983v1	https://arxiv.org/pdf/2204.09983v1.pdf	DGECN: A Depth-Guided Edge Convolutional Network for End-to-End 6D Pose Estimation	Monocular 6D pose estimation is a fundamental task in computer vision. Existing works often adopt a two-stage pipeline by establishing correspondences and utilizing a RANSAC algorithm to calculate 6 degrees-of-freedom (6DoF) pose. Recent works try to integrate differentiable RANSAC algorithms to achieve an end-to-end 6D pose estimation. However, most of them hardly consider the geometric features in 3D space, and ignore the topology cues when performing differentiable RANSAC algorithms. To this end, we proposed a Depth-Guided Edge Convolutional Network (DGECN) for 6D pose estimation task. We have made efforts from the following three aspects: 1) We take advantages ofestimated depth information to guide both the correspondences-extraction process and the cascaded differentiable RANSAC algorithm with geometric information. 2)We leverage the uncertainty ofthe estimated depth map to improve accuracy and robustness ofthe output 6D pose. 3) We propose a differentiable Perspective-n-Point(PnP) algorithm via edge convolution to explore the topology relations between 2D-3D correspondences. Experiments demonstrate that our proposed network outperforms current works on both effectiveness and efficiency.	['Chunxia Xiao', 'Shengjie Zheng', 'Wenxiao Zhang', 'Yanping Fu', 'Fei Luo', 'Tuo Cao']	2022-04-21	null	http://openaccess.thecvf.com//content/CVPR2022/html/Cao_DGECN_A_Depth-Guided_Edge_Convolutional_Network_for_End-to-End_6D_Pose_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Cao_DGECN_A_Depth-Guided_Edge_Convolutional_Network_for_End-to-End_6D_Pose_CVPR_2022_paper.pdf	cvpr-2022-1	['6d-pose-estimation-1']	['computer-vision']	[-3.34757745e-01 -3.37171227e-01 -5.03649861e-02 -5.35021603e-01 -5.03532648e-01 -7.44027138e-01 6.69085026e-01 -3.91813338e-01 -3.69012177e-01 2.54580766e-01 3.12804848e-01 -2.35511377e-01 -1.66094214e-01 -6.92743599e-01 -7.38624036e-01 -3.04856181e-01 2.51548171e-01 5.53863943e-01 5.11286519e-02 -5.84452599e-02 6.28695250e-01 1.07641506e+00 -1.19589138e+00 -3.89651358e-01 9.26683009e-01 1.09015238e+00 -2.50057966e-01 5.57544827e-01 1.21958233e-01 2.24737331e-01 8.10980126e-02 -3.16422701e-01 6.54946089e-01 -7.39129931e-02 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844bbf7b-390e-4b07-92a7-2119cf5ef0f8	online-multi-object-tracking-and-segmentation	2009.00100	null	https://arxiv.org/abs/2009.00100v2	https://arxiv.org/pdf/2009.00100v2.pdf	Online Multi-Object Tracking and Segmentation with GMPHD Filter and Mask-based Affinity Fusion	In this paper, we propose a highly practical fully online multi-object tracking and segmentation (MOTS) method that uses instance segmentation results as an input. The proposed method is based on the Gaussian mixture probability hypothesis density (GMPHD) filter, a hierarchical data association (HDA), and a mask-based affinity fusion (MAF) model to achieve high-performance online tracking. The HDA consists of two associations: segment-to-track and track-to-track associations. One affinity, for position and motion, is computed by using the GMPHD filter, and the other affinity, for appearance is computed by using the responses from a single object tracker such as a kernalized correlation filter. These two affinities are simply fused by using a score-level fusion method such as min-max normalization referred to as MAF. In addition, to reduce the number of false positive segments, we adopt mask IoU-based merging (mask merging). The proposed MOTS framework with the key modules: HDA, MAF, and mask merging, is easily extensible to simultaneously track multiple types of objects with CPU only execution in parallel processing. In addition, the developed framework only requires simple parameter tuning unlike many existing MOTS methods that need intensive hyperparameter optimization. In the experiments on the two popular MOTS datasets, the key modules show some improvements. For instance, ID-switch decreases by more than half compared to a baseline method in the training sets. In conclusion, our tracker achieves state-of-the-art MOTS performance in the test sets.	['Witold Pedrycz', 'Seong-Whan Lee', 'Kwangjin Yoon', 'Young-chul Yoon', 'Young-min Song', 'Moongu Jeon']	2020-08-31	null	null	null	null	['online-multi-object-tracking', 'multi-object-tracking-and-segmentation']	['computer-vision', 'computer-vision']	[-5.29979169e-02 -4.25873697e-01 1.03769009e-03 -2.74944931e-01 -8.22641909e-01 -4.29857284e-01 3.92706007e-01 2.96458304e-01 -6.55626178e-01 5.46513140e-01 -4.95683044e-01 2.47159861e-02 -6.21109735e-03 -6.26991451e-01 -7.39635885e-01 -1.04098654e+00 2.51234382e-01 8.27009261e-01 1.29978645e+00 2.70549357e-01 7.80321285e-02 5.36991954e-01 -1.84682345e+00 -1.32651567e-01 9.25472379e-01 1.19227266e+00 4.52881426e-01 6.52293563e-01 -3.26052457e-01 2.39859506e-01 -5.03522992e-01 -2.38921836e-01 3.47426832e-01 -9.08213332e-02 -2.35365674e-01 -6.31217510e-02 5.65884590e-01 -1.42315626e-01 1.36525020e-01 1.15404594e+00 6.43103480e-01 2.00526699e-01 6.14222527e-01 -1.22292471e+00 1.75940380e-01 3.27568978e-01 -9.92969155e-01 3.81908029e-01 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b25899fc-fc6f-4fa2-bd03-817ebffd905a	simulation-based-bayesian-inference-for	2303.05873	null	https://arxiv.org/abs/2303.05873v1	https://arxiv.org/pdf/2303.05873v1.pdf	Simulation-based Bayesian inference for robotic grasping	General robotic grippers are challenging to control because of their rich nonsmooth contact dynamics and the many sources of uncertainties due to the environment or sensor noise. In this work, we demonstrate how to compute 6-DoF grasp poses using simulation-based Bayesian inference through the full stochastic forward simulation of the robot in its environment while robustly accounting for many of the uncertainties in the system. A Riemannian manifold optimization procedure preserving the nonlinearity of the rotation space is used to compute the maximum a posteriori grasp pose. Simulation and physical benchmarks show the promising high success rate of the approach.	['Gilles Louppe', 'Olivier Brüls', 'Norman Marlier']	2023-03-10	null	null	null	null	['robotic-grasping']	['robots']	[-2.24688888e-01 -8.08424875e-02 8.17869529e-02 -1.95776045e-01 -6.46344125e-01 -4.49729592e-01 3.36875319e-01 -2.28374645e-01 -3.05587888e-01 6.77718997e-01 -2.70688593e-01 1.59034491e-01 -7.53414512e-01 -3.83064866e-01 -9.92437899e-01 -8.97602737e-01 -3.45077306e-01 1.07528138e+00 7.06603155e-02 -2.77844816e-01 3.57348680e-01 7.75138259e-01 -9.67581630e-01 -7.53447711e-01 5.89570701e-01 5.87084174e-01 7.81327367e-01 5.45088172e-01 5.31455517e-01 1.67701185e-01 1.14444774e-02 7.96458796e-02 3.78292203e-01 3.17497075e-01 -3.09478879e-01 7.12214559e-02 -1.47365719e-01 -5.61853409e-01 -7.02874482e-01 1.23224211e+00 2.73603886e-01 5.57246745e-01 7.31336236e-01 -9.14739907e-01 -7.55445734e-02 4.78759110e-01 -2.28288993e-01 -5.44785380e-01 4.53669310e-01 2.77854323e-01 6.48235977e-01 -1.07822275e+00 8.27128410e-01 1.37230253e+00 3.72347832e-01 3.10342222e-01 -1.04188383e+00 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2742aed6-adb5-47b6-b1b4-075ba2c48dcb	discourse-based-argument-segmentation-and	null	null	https://aclanthology.org/2021.isa-1.5	https://aclanthology.org/2021.isa-1.5.pdf	Discourse-based Argument Segmentation and Annotation	The paper presents a discourse-based approach to the analysis of argumentative texts departing from the assumption that the coherence of a text should capture argumentation structure as well and, therefore, existing discourse analysis tools can be successfully applied for argument segmentation and annotation tasks. We tested the widely used Penn Discourse Tree Bank full parser (Lin et al., 2010) and the state-of-the-art neural network NeuralEDUSeg (Wang et al., 2018) and XLNet (Yang et al., 2019) models on the two-stage discourse segmentation and discourse relation recognition. The two-stage approach outperformed the PDTB parser by broad margin, i.e. the best achieved F1 scores of 21.2 % for PDTB parser vs 66.37% for NeuralEDUSeg and XLNet models. Neural network models were fine-tuned and evaluated on the argumentative corpus showing a promising accuracy of 60.22%. The complete argument structures were reconstructed for further argumentation mining tasks. The reference Dagstuhl argumentative corpus containing 2,222 elementary discourse unit pairs annotated with the top-level and fine-grained PDTB relations will be released to the research community.	['Dietrich Klakow', 'Marius Mosbach', 'Volha Petukhova', 'Ekaterina Saveleva']	null	null	null	null	acl-isa-iwcs-2021-6	['discourse-segmentation']	['natural-language-processing']	[ 3.02981675e-01 1.33471000e+00 -3.05405974e-01 -2.98812032e-01 -8.07270169e-01 -6.81233943e-01 9.39134598e-01 7.98386991e-01 -6.12212658e-01 1.11094093e+00 5.34810364e-01 -9.45583701e-01 -3.71717960e-01 -8.13682258e-01 -6.26479745e-01 -3.09491247e-01 -2.20137299e-03 8.76510382e-01 2.32348815e-01 -6.58763707e-01 5.14694870e-01 -4.13319431e-02 -1.34186280e+00 9.20676351e-01 1.07563245e+00 8.14025581e-01 8.80417414e-03 9.82477725e-01 -4.34857696e-01 1.26494491e+00 -1.09539747e+00 -5.89412987e-01 -5.11243761e-01 -3.13728809e-01 -1.66460824e+00 -5.58422267e-01 2.40320861e-01 -1.77652195e-01 7.76934698e-02 6.49127603e-01 2.94355273e-01 -7.10382387e-02 4.98430729e-01 -6.47223175e-01 -4.34924036e-01 1.71343839e+00 -2.37729564e-01 4.04950589e-01 3.54031056e-01 -4.73939180e-01 1.29824197e+00 -5.69753706e-01 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1.08280933e+00 -2.04521492e-01 -5.93602002e-01 -2.84430623e-01 -1.58629632e+00 2.51618207e-01 1.97352633e-01 6.46586716e-02 -4.65009511e-01 1.61836565e-01 -1.73254684e-02]	[10.716875076293945, 9.447065353393555]
b7f3e15d-778a-4d45-80a2-7c40b5ace8fb	towards-discriminative-and-transferable-one	2210.05783	null	https://arxiv.org/abs/2210.05783v1	https://arxiv.org/pdf/2210.05783v1.pdf	Towards Discriminative and Transferable One-Stage Few-Shot Object Detectors	Recent object detection models require large amounts of annotated data for training a new classes of objects. Few-shot object detection (FSOD) aims to address this problem by learning novel classes given only a few samples. While competitive results have been achieved using two-stage FSOD detectors, typically one-stage FSODs underperform compared to them. We make the observation that the large gap in performance between two-stage and one-stage FSODs are mainly due to their weak discriminability, which is explained by a small post-fusion receptive field and a small number of foreground samples in the loss function. To address these limitations, we propose the Few-shot RetinaNet (FSRN) that consists of: a multi-way support training strategy to augment the number of foreground samples for dense meta-detectors, an early multi-level feature fusion providing a wide receptive field that covers the whole anchor area and two augmentation techniques on query and source images to enhance transferability. Extensive experiments show that the proposed approach addresses the limitations and boosts both discriminability and transferability. FSRN is almost two times faster than two-stage FSODs while remaining competitive in accuracy, and it outperforms the state-of-the-art of one-stage meta-detectors and also some two-stage FSODs on the MS-COCO and PASCAL VOC benchmarks.	['Juergen Beyerer', 'Bin Yang', 'Matthias Kayser', 'Ahmed Hendawy', 'George Eskandar', 'Mohamed Abdelsamad', 'Karim Guirguis']	2022-10-11	null	null	null	null	['few-shot-object-detection']	['computer-vision']	[ 3.56918216e-01 -6.71988353e-02 -1.68375388e-01 -3.67629945e-01 -6.33219540e-01 3.85129526e-02 7.34019220e-01 1.28531590e-01 -9.02297854e-01 4.89973128e-01 -3.35610151e-01 2.08283886e-01 2.28256971e-01 -6.56802356e-01 -7.50751495e-01 -6.25784755e-01 1.62192509e-01 1.63425580e-01 1.40136266e+00 -6.48561344e-02 1.57842964e-01 5.70821822e-01 -2.27527308e+00 5.17786503e-01 7.32664764e-01 1.19228029e+00 5.58044791e-01 7.74865925e-01 -2.97041327e-01 7.97494411e-01 -6.25668943e-01 -2.89523065e-01 3.71499002e-01 -4.05492246e-01 -4.61996913e-01 7.92907402e-02 8.56435716e-01 -4.72363710e-01 -1.95043936e-01 1.12857866e+00 6.91387057e-01 3.43798667e-01 6.93677783e-01 -1.20797062e+00 -4.14483219e-01 3.17171901e-01 -8.84436548e-01 7.65729427e-01 -7.76688755e-02 3.12837750e-01 7.69498348e-01 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0ad25f20-b742-4dc6-acf7-09efc78fe44a	hierarchical-clustering-using-auto-encoded	2101.03742	null	https://arxiv.org/abs/2101.03742v1	https://arxiv.org/pdf/2101.03742v1.pdf	Hierarchical Clustering using Auto-encoded Compact Representation for Time-series Analysis	Getting a robust time-series clustering with best choice of distance measure and appropriate representation is always a challenge. We propose a novel mechanism to identify the clusters combining learned compact representation of time-series, Auto Encoded Compact Sequence (AECS) and hierarchical clustering approach. Proposed algorithm aims to address the large computing time issue of hierarchical clustering as learned latent representation AECS has a length much less than the original length of time-series and at the same time want to enhance its performance.Our algorithm exploits Recurrent Neural Network (RNN) based under complete Sequence to Sequence(seq2seq) autoencoder and agglomerative hierarchical clustering with a choice of best distance measure to recommend the best clustering. Our scheme selects the best distance measure and corresponding clustering for both univariate and multivariate time-series. We have experimented with real-world time-series from UCR and UCI archive taken from diverse application domains like health, smart-city, manufacturing etc. Experimental results show that proposed method not only produce close to benchmark results but also in some cases outperform the benchmark.	['Arpan Pal', 'Anish Datta', 'Soma Bandyopadhyay']	2021-01-11	null	null	null	null	['time-series-clustering']	['time-series']	[ 1.38492942e-01 -4.34570283e-01 3.91170263e-01 -2.41690159e-01 -7.88796425e-01 -5.67082644e-01 3.61339539e-01 4.91447210e-01 -3.27554733e-01 5.46816468e-01 5.02887964e-01 2.90063303e-02 -6.63668215e-01 -5.70902407e-01 -3.09034258e-01 -8.64761353e-01 -6.95266128e-01 4.25779611e-01 -1.27032533e-01 9.84863117e-02 4.83041853e-01 3.64360064e-01 -1.93986380e+00 6.01563215e-01 6.57991052e-01 8.55881870e-01 4.57250446e-01 9.19333935e-01 1.92895651e-01 8.35805714e-01 -5.98466396e-01 5.05428493e-01 4.80570972e-01 -4.47833180e-01 -7.52969146e-01 1.61052093e-01 -3.99854988e-01 2.83445269e-01 -1.83735657e-02 7.35008478e-01 5.64236999e-01 6.68638825e-01 6.80951476e-01 -1.19978058e+00 -2.81357855e-01 7.62856007e-01 -5.60949445e-01 5.03041565e-01 -4.17921832e-03 -2.31665239e-01 6.90491140e-01 -4.35872436e-01 6.49404645e-01 1.01856065e+00 8.07294607e-01 -9.80281308e-02 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c5fcecfd-f582-465f-92d9-3358b73c55f0	autosplice-a-text-prompt-manipulated-image	2304.06870	null	https://arxiv.org/abs/2304.06870v1	https://arxiv.org/pdf/2304.06870v1.pdf	AutoSplice: A Text-prompt Manipulated Image Dataset for Media Forensics	Recent advancements in language-image models have led to the development of highly realistic images that can be generated from textual descriptions. However, the increased visual quality of these generated images poses a potential threat to the field of media forensics. This paper aims to investigate the level of challenge that language-image generation models pose to media forensics. To achieve this, we propose a new approach that leverages the DALL-E2 language-image model to automatically generate and splice masked regions guided by a text prompt. To ensure the creation of realistic manipulations, we have designed an annotation platform with human checking to verify reasonable text prompts. This approach has resulted in the creation of a new image dataset called AutoSplice, containing 5,894 manipulated and authentic images. Specifically, we have generated a total of 3,621 images by locally or globally manipulating real-world image-caption pairs, which we believe will provide a valuable resource for developing generalized detection methods in this area. The dataset is evaluated under two media forensic tasks: forgery detection and localization. Our extensive experiments show that most media forensic models struggle to detect the AutoSplice dataset as an unseen manipulation. However, when fine-tuned models are used, they exhibit improved performance in both tasks.	['Siwei Lyu', 'Jialing Cai', 'Yan Ju', 'Zhou Zhou', 'Mingzhen Huang', 'Shan Jia']	2023-04-14	null	null	null	null	['detect-forged-images-and-videos']	['computer-vision']	[ 4.54151332e-01 -6.94985762e-02 2.39485800e-01 -1.27232268e-01 -1.35524619e+00 -7.71197319e-01 7.29979515e-01 1.37082875e-01 -3.83591354e-01 4.14911330e-01 6.61604777e-02 -2.82974601e-01 3.86395037e-01 -4.66932148e-01 -8.59537899e-01 -2.50709444e-01 2.38585770e-01 1.73797503e-01 4.34234411e-01 7.70897511e-03 7.22226918e-01 5.88828564e-01 -1.34579468e+00 8.39416027e-01 2.67435104e-01 6.35619104e-01 2.48525918e-01 6.16533458e-01 -7.44848698e-02 9.67770696e-01 -1.01207113e+00 -1.08134198e+00 4.81467456e-01 -2.49334008e-01 -7.65121937e-01 3.69822890e-01 7.00497806e-01 -7.46219158e-01 -4.44691658e-01 1.05094624e+00 6.16074324e-01 -1.46405920e-01 2.88614869e-01 -1.31179452e+00 -7.95339584e-01 6.17596865e-01 -7.47073889e-01 5.89752853e-01 6.42446280e-01 6.34595394e-01 4.74511147e-01 -9.02831554e-01 1.09358859e+00 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feb96454-49bf-4ce3-aa95-835defe16552	annotation-imputation-to-individualize	2305.15070	null	https://arxiv.org/abs/2305.15070v1	https://arxiv.org/pdf/2305.15070v1.pdf	Annotation Imputation to Individualize Predictions: Initial Studies on Distribution Dynamics and Model Predictions	Annotating data via crowdsourcing is time-consuming and expensive. Owing to these costs, dataset creators often have each annotator label only a small subset of the data. This leads to sparse datasets with examples that are marked by few annotators; if an annotator is not selected to label an example, their opinion regarding it is lost. This is especially concerning for subjective NLP datasets where there is no correct label: people may have different valid opinions. Thus, we propose using imputation methods to restore the opinions of all annotators for all examples, creating a dataset that does not leave out any annotator's view. We then train and prompt models with data from the imputed dataset (rather than the original sparse dataset) to make predictions about majority and individual annotations. Unfortunately, the imputed data provided by our baseline methods does not improve predictions. However, through our analysis of it, we develop a strong understanding of how different imputation methods impact the original data in order to inform future imputation techniques. We make all of our code and data publicly available.	['Dongyeop Kang', 'Jaehyung Kim', 'Risako Owan', 'Ruyuan Wan', 'London Lowmanstone']	2023-05-24	null	null	null	null	['imputation', 'imputation', 'imputation']	['computer-vision', 'miscellaneous', 'time-series']	[ 1.93693057e-01 4.46842134e-01 -3.14421237e-01 -7.87772298e-01 -1.03523993e+00 -1.13688052e+00 7.21338838e-02 4.64468211e-01 -4.84729916e-01 1.31814241e+00 7.13963687e-01 1.66758880e-01 4.14934546e-01 -5.57057738e-01 -7.49596179e-01 -3.54499251e-01 8.25013757e-01 7.43276656e-01 -2.65530199e-01 1.89913288e-01 -3.18615362e-02 -9.70409513e-02 -1.25443745e+00 7.22519755e-01 1.06704366e+00 6.57757998e-01 -2.85506606e-01 1.91445068e-01 -1.27294451e-01 1.17269659e+00 -8.46159339e-01 -9.83696997e-01 5.03367841e-01 -3.67677182e-01 -8.45331967e-01 5.81083223e-02 5.23920834e-01 -2.75155574e-01 2.11578280e-01 9.12450314e-01 3.70618820e-01 -3.95302195e-03 6.01581693e-01 -1.47324705e+00 -9.68793631e-01 9.01923299e-01 -4.68086958e-01 -4.49767888e-01 6.54577196e-01 7.49080032e-02 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1e048b7c-4172-4cf6-8392-2f54c5ae1973	ltc-gif-attracting-more-clicks-on-feature	2201.09077	null	https://arxiv.org/abs/2201.09077v1	https://arxiv.org/pdf/2201.09077v1.pdf	LTC-GIF: Attracting More Clicks on Feature-length Sports Videos	This paper proposes a lightweight method to attract users and increase views of the video by presenting personalized artistic media -- i.e, static thumbnails and animated GIFs. This method analyzes lightweight thumbnail containers (LTC) using computational resources of the client device to recognize personalized events from full-length sports videos. In addition, instead of processing the entire video, small video segments are processed to generate artistic media. This makes the proposed approach more computationally efficient compared to the baseline approaches that create artistic media using the entire video. The proposed method retrieves and uses thumbnail containers and video segments, which reduces the required transmission bandwidth as well as the amount of locally stored data used during artistic media generation. When extensive experiments were conducted on the Nvidia Jetson TX2, the computational complexity of the proposed method was 3.57 times lower than that of the SoA method. In the qualitative assessment, GIFs generated using the proposed method received 1.02 higher overall ratings compared to the SoA method. To the best of our knowledge, this is the first technique that uses LTC to generate artistic media while providing lightweight and high-performance services even on resource-constrained devices.	['Eun-Seok Ryu', 'Jaehyuk Choi', 'Ghulam Mujtaba']	2022-01-22	null	null	null	null	['animated-gif-generation', 'sports-analytics', 'action-analysis', 'user-constrained-thumbnail-generation']	['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision']	[ 2.33693585e-01 -3.42394829e-01 7.56294280e-02 8.76606815e-03 -5.92733622e-01 -8.30753624e-01 2.22609892e-01 1.12487204e-01 -4.20081198e-01 3.85143995e-01 2.25770593e-01 -1.70099422e-01 1.50063947e-01 -8.85945439e-01 -6.17500842e-01 -3.92092705e-01 -8.48673955e-02 1.14013359e-01 7.45932639e-01 -1.22333393e-02 6.95020676e-01 4.10666704e-01 -2.42300224e+00 8.25668275e-01 7.13723063e-01 1.39079964e+00 4.25970286e-01 8.46513629e-01 -3.40560853e-01 1.08383310e+00 -1.01215518e+00 -4.11421090e-01 3.34402829e-01 -3.04082006e-01 -2.51186311e-01 1.66437447e-01 7.25812376e-01 -8.35927665e-01 -4.35258374e-02 5.43536127e-01 7.64727533e-01 3.04608732e-01 1.38833344e-01 -1.27721596e+00 -4.56848592e-01 5.50940335e-01 -5.80500484e-01 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1.48652434e-01 9.47089612e-01 2.39419296e-01 8.46479595e-01 5.29965818e-01 5.73044717e-01 -1.17431068e+00 1.17391936e-01 -5.06461680e-01 -3.88413101e-01 5.52799582e-01 -3.65189552e-01 -5.92322171e-01 -1.51864320e-01 4.45365220e-01]	[10.597084045410156, -0.9662665128707886]
3a0303d8-2aac-4fd8-93a1-8fcfa202efd0	new-results-and-open-questions-for-sir-ph	2205.03700	null	https://arxiv.org/abs/2205.03700v1	https://arxiv.org/pdf/2205.03700v1.pdf	New results and open questions for SIR-PH epidemic models with linear birth rate, loss of immunity, vaccination, and disease and vaccination fatalities	Our paper presents three new classes of models: SIR-PH, SIR-PH-FA, and SIR-PH-IA, and states two problems we would like to solve about them. Recall that deterministic mathematical epidemiology has one basic general law, the R0 alternative" of [52, 51], which states that the local stability condition of the disease free equilibrium may be expressed as R0 < 1, where R0 is the famous basic reproduction number, which plays also a major role in the theory of branching processes. The literature suggests that it is impossible to find general laws concerning the endemic points. However, it is quite common that 1. When R0 > 1, there exists a unique fixed endemic point, and 2. the endemic point is locally stable when R0 > 1. One would like to establish these properties for a large class of realistic epidemic models (and we do not include here epidemics without casualties). We have introduced in [7, 5] a "simple", but broad class of "SIR-PH models" with varying population, with the express purpose of establishing for these processes the two properties above. Since that seemed still hard, we have introduced a further class of "SIR-PH-FA" models, which may be interpreted as approximations for the SIR-PH models, and which includes simpler models typically studied in the literature (with constant population, without loss of immunity, etc). The goal of our paper is to draw attention to the two open problems above, for the SIR-PH, SIR-PH-FA, and also for a second, more refined "intermediate approximation" SIR-PH-IA. We illustrate the current status-quo by presenting new results on a generalization of the SAIRS epidemic model of [44, 40].	['Andrei Halanay', 'Rim Adenane', 'Florin Avram']	2022-05-07	null	null	null	null	['epidemiology']	['medical']	[-5.87349236e-02 2.39108413e-01 1.27271131e-01 5.34187257e-01 2.58807868e-01 -3.83462846e-01 6.33733869e-01 2.56069988e-01 -5.53918660e-01 1.03830171e+00 -1.98136136e-01 -6.15381598e-01 -8.78623307e-01 -9.87394750e-01 -2.99298078e-01 -1.17623532e+00 -6.07350647e-01 8.05908799e-01 4.51806456e-01 -9.22654688e-01 2.56664641e-02 7.46408343e-01 -1.31198692e+00 -6.64618790e-01 7.06641912e-01 5.62062919e-01 2.05371249e-02 7.70086944e-01 1.06007546e-01 5.06395519e-01 -6.23484254e-01 -6.70440555e-01 2.07169876e-01 -4.75419641e-01 -7.21688092e-01 -2.19978079e-01 -8.98477316e-01 -5.85844442e-02 -3.87305111e-01 1.02699435e+00 3.22924763e-01 -2.43228510e-01 8.92223656e-01 -1.50440598e+00 -6.86701536e-01 4.94962096e-01 -7.96787560e-01 5.09033740e-01 2.63372302e-01 3.00338082e-02 4.89300936e-01 9.77318734e-02 7.19565570e-01 1.24561858e+00 9.17660415e-01 7.81871259e-01 -1.27379632e+00 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a94e7a3f-00e4-4b12-b37f-36bcd6056ee9	pose-controllable-3d-facial-animation	2302.12532	null	https://arxiv.org/abs/2302.12532v1	https://arxiv.org/pdf/2302.12532v1.pdf	Pose-Controllable 3D Facial Animation Synthesis using Hierarchical Audio-Vertex Attention	Most of the existing audio-driven 3D facial animation methods suffered from the lack of detailed facial expression and head pose, resulting in unsatisfactory experience of human-robot interaction. In this paper, a novel pose-controllable 3D facial animation synthesis method is proposed by utilizing hierarchical audio-vertex attention. To synthesize real and detailed expression, a hierarchical decomposition strategy is proposed to encode the audio signal into both a global latent feature and a local vertex-wise control feature. Then the local and global audio features combined with vertex spatial features are used to predict the final consistent facial animation via a graph convolutional neural network by fusing the intrinsic spatial topology structure of the face model and the corresponding semantic feature of the audio. To accomplish pose-controllable animation, we introduce a novel pose attribute augmentation method by utilizing the 2D talking face technique. Experimental results indicate that the proposed method can produce more realistic facial expressions and head posture movements. Qualitative and quantitative experiments show that the proposed method achieves competitive performance against state-of-the-art methods.	['Yu-Kun Lai', 'Junjie Cao', 'Bo Li', 'Xiaolin Wei', 'Bin Liu']	2023-02-24	null	null	null	null	['face-model']	['computer-vision']	[-5.54513708e-02 3.15056682e-01 5.91843799e-02 -5.42144716e-01 -5.56437373e-01 9.32847615e-03 4.71471220e-01 -5.26891887e-01 2.89263010e-01 4.18147057e-01 5.35865426e-01 3.79074454e-01 1.23356678e-01 -4.13056552e-01 -5.69444478e-01 -8.27952504e-01 -1.27637297e-01 2.20069706e-01 -2.08697185e-01 -3.77011508e-01 -1.19890943e-01 8.35800767e-01 -1.82229483e+00 5.83493523e-02 4.21649814e-01 1.19044721e+00 -5.42825796e-02 2.83206850e-01 -9.70706940e-02 7.28574574e-01 -5.44149041e-01 -6.39609993e-02 7.43239745e-02 -7.06238985e-01 -4.04448241e-01 6.40861452e-01 1.61126181e-01 -3.30812454e-01 -9.74113718e-02 8.92911255e-01 7.93998241e-01 1.05658136e-01 6.25350118e-01 -1.67581964e+00 -4.45373476e-01 1.20069608e-01 -8.28110754e-01 -5.17028153e-01 7.72866726e-01 -6.18008971e-02 7.42900312e-01 -9.77079213e-01 7.72313833e-01 1.68801558e+00 6.29760861e-01 6.24861479e-01 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6d2cb042-c0f5-456b-9065-d92718036d9d	logai-a-library-for-log-analytics-and	2301.13415	null	https://arxiv.org/abs/2301.13415v1	https://arxiv.org/pdf/2301.13415v1.pdf	LogAI: A Library for Log Analytics and Intelligence	Software and System logs record runtime information about processes executing within a system. These logs have become the most critical and ubiquitous forms of observability data that help developers understand system behavior, monitor system health and resolve issues. However, the volume of logs generated can be humongous (of the order of petabytes per day) especially for complex distributed systems, such as cloud, search engine, social media, etc. This has propelled a lot of research on developing AI-based log based analytics and intelligence solutions that can process huge volume of raw logs and generate insights. In order to enable users to perform multiple types of AI-based log analysis tasks in a uniform manner, we introduce LogAI (https://github.com/salesforce/logai), a one-stop open source library for log analytics and intelligence. LogAI supports tasks such as log summarization, log clustering and log anomaly detection. It adopts the OpenTelemetry data model, to enable compatibility with different log management platforms. LogAI provides a unified model interface and provides popular time-series, statistical learning and deep learning models. Alongside this, LogAI also provides an out-of-the-box GUI for users to conduct interactive analysis. With LogAI, we can also easily benchmark popular deep learning algorithms for log anomaly detection without putting in redundant effort to process the logs. We have opensourced LogAI to cater to a wide range of applications benefiting both academic research and industrial prototyping.	['Steven Hoi', 'Doyen Sahoo', 'Chenghao Liu', 'Wenzhuo Yang', 'Amrita Saha', 'Qian Cheng']	2023-01-31	null	null	null	null	['log-parsing']	['computer-code']	[-4.84766781e-01 -3.63415629e-01 1.10335760e-01 -9.52460170e-02 -2.56562531e-01 -5.87820470e-01 4.15715814e-01 7.94220567e-01 9.56733525e-02 1.48215936e-03 -1.24771848e-01 -5.87260485e-01 -2.53022343e-01 -7.46596217e-01 -3.41691285e-01 -2.75745362e-01 -6.11213326e-01 5.81050277e-01 2.30948970e-01 -1.18023464e-02 3.34584385e-01 5.49882531e-01 -1.81371260e+00 3.17567527e-01 7.32137144e-01 1.12959421e+00 -1.38446406e-01 8.69345903e-01 -1.69917017e-01 1.19302905e+00 -8.18438828e-01 3.43158871e-01 2.00647891e-01 -9.11988989e-02 -5.23503959e-01 -2.53940046e-01 9.84911770e-02 -4.44039524e-01 -1.61558017e-01 7.53098428e-01 2.64839917e-01 -1.24154739e-01 1.46978498e-01 -1.68322790e+00 -4.76319164e-01 4.95362937e-01 -6.88323498e-01 6.69158578e-01 5.96915126e-01 4.44007486e-01 7.16231525e-01 -4.90708560e-01 1.92378014e-01 8.30972791e-01 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55e82a3a-3b3f-421e-86c3-2bf4a7da78ca	webly-supervised-learning-for-skin-lesion	1804.00177	null	https://arxiv.org/abs/1804.00177v2	https://arxiv.org/pdf/1804.00177v2.pdf	Webly Supervised Learning for Skin Lesion Classification	Within medical imaging, manual curation of sufficient well-labeled samples is cost, time and scale-prohibitive. To improve the representativeness of the training dataset, for the first time, we present an approach to utilize large amounts of freely available web data through web-crawling. To handle noise and weak nature of web annotations, we propose a two-step transfer learning based training process with a robust loss function, termed as Webly Supervised Learning (WSL) to train deep models for the task. We also leverage search by image to improve the search specificity of our web-crawling and reduce cross-domain noise. Within WSL, we explicitly model the noise structure between classes and incorporate it to selectively distill knowledge from the web data during model training. To demonstrate improved performance due to WSL, we benchmarked on a publicly available 10-class fine-grained skin lesion classification dataset and report a significant improvement of top-1 classification accuracy from 71.25 % to 80.53 % due to the incorporation of web-supervision.	['Sailesh Conjeti', 'Federico Tombari', 'Fernando Navarro', 'Nassir Navab']	2018-03-31	null	null	null	null	['skin-lesion-classification']	['medical']	[ 5.16299665e-01 5.29275881e-03 -3.85995209e-01 -6.14691198e-01 -1.77082789e+00 -6.78916812e-01 3.16204131e-01 2.64563501e-01 -6.64189756e-01 7.84920394e-01 1.16486885e-01 -2.61454344e-01 -2.62739271e-01 -5.69035769e-01 -9.19575393e-01 -6.40424192e-01 2.39886865e-01 3.77721936e-01 4.90785629e-01 2.69831896e-01 -1.65535696e-02 2.52192020e-01 -1.14913774e+00 8.65025222e-01 8.71460676e-01 1.16434014e+00 3.06709886e-01 6.41402364e-01 -4.37162146e-02 5.69553673e-01 -2.45882511e-01 -4.16559249e-01 2.48013347e-01 -1.83212385e-01 -9.35497463e-01 2.84843445e-01 5.51294088e-01 -2.21017584e-01 -1.13564953e-01 9.66136277e-01 8.33024442e-01 -2.68466830e-01 6.71391785e-01 -8.25491011e-01 -5.24647772e-01 3.01312953e-01 -5.98288715e-01 1.92170680e-01 7.04866797e-02 2.52788603e-01 9.37008440e-01 -7.88312674e-01 1.05119634e+00 5.38148999e-01 9.61754858e-01 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9b03854f-58a9-401c-9ac0-d8460d7e1757	copy-and-paste-networks-for-deep-video	1908.11587	null	https://arxiv.org/abs/1908.11587v1	https://arxiv.org/pdf/1908.11587v1.pdf	Copy-and-Paste Networks for Deep Video Inpainting	We present a novel deep learning based algorithm for video inpainting. Video inpainting is a process of completing corrupted or missing regions in videos. Video inpainting has additional challenges compared to image inpainting due to the extra temporal information as well as the need for maintaining the temporal coherency. We propose a novel DNN-based framework called the Copy-and-Paste Networks for video inpainting that takes advantage of additional information in other frames of the video. The network is trained to copy corresponding contents in reference frames and paste them to fill the holes in the target frame. Our network also includes an alignment network that computes affine matrices between frames for the alignment, enabling the network to take information from more distant frames for robustness. Our method produces visually pleasing and temporally coherent results while running faster than the state-of-the-art optimization-based method. In addition, we extend our framework for enhancing over/under exposed frames in videos. Using this enhancement technique, we were able to significantly improve the lane detection accuracy on road videos.	['Seoung Wug Oh', 'Seon Joo Kim', 'Sungho Lee', 'DaeYeun Won']	2019-08-30	copy-and-paste-networks-for-deep-video-1	http://openaccess.thecvf.com/content_ICCV_2019/html/Lee_Copy-and-Paste_Networks_for_Deep_Video_Inpainting_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Lee_Copy-and-Paste_Networks_for_Deep_Video_Inpainting_ICCV_2019_paper.pdf	iccv-2019-10	['video-inpainting']	['computer-vision']	[ 3.52354884e-01 -8.34682882e-02 1.24956155e-02 -1.89659759e-01 -6.00030065e-01 -1.67457044e-01 1.15955904e-01 -3.33453774e-01 -4.75182354e-01 7.73225427e-01 3.40809822e-01 -5.16210077e-03 2.07604736e-01 -6.25506222e-01 -1.23256671e+00 -5.31437457e-01 -5.07819839e-02 -2.11377457e-01 4.16858494e-01 -2.13536516e-01 1.70804501e-01 6.71680987e-01 -1.48920786e+00 4.04273629e-01 7.08437681e-01 8.44448030e-01 5.23303032e-01 8.03997159e-01 6.61399662e-02 1.30865991e+00 -4.63167280e-01 -1.98490888e-01 6.54592335e-01 -5.18493354e-01 -3.83136809e-01 3.72574061e-01 1.02367163e+00 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78f4a76d-1de3-4c76-a1f8-0c74621cf9b4	on-leveraging-the-visual-modality-for-neural	1910.02754	null	https://arxiv.org/abs/1910.02754v1	https://arxiv.org/pdf/1910.02754v1.pdf	On Leveraging the Visual Modality for Neural Machine Translation	Leveraging the visual modality effectively for Neural Machine Translation (NMT) remains an open problem in computational linguistics. Recently, Caglayan et al. posit that the observed gains are limited mainly due to the very simple, short, repetitive sentences of the Multi30k dataset (the only multimodal MT dataset available at the time), which renders the source text sufficient for context. In this work, we further investigate this hypothesis on a new large scale multimodal Machine Translation (MMT) dataset, How2, which has 1.57 times longer mean sentence length than Multi30k and no repetition. We propose and evaluate three novel fusion techniques, each of which is designed to ensure the utilization of visual context at different stages of the Sequence-to-Sequence transduction pipeline, even under full linguistic context. However, we still obtain only marginal gains under full linguistic context and posit that visual embeddings extracted from deep vision models (ResNet for Multi30k, ResNext for How2) do not lend themselves to increasing the discriminativeness between the vocabulary elements at token level prediction in NMT. We demonstrate this qualitatively by analyzing attention distribution and quantitatively through Principal Component Analysis, arriving at the conclusion that it is the quality of the visual embeddings rather than the length of sentences, which need to be improved in existing MMT datasets.	['Yi Xu', 'Quanyang Lu', 'Vikas Raunak', 'Sang Keun Choe', 'Florian Metze']	2019-10-07	on-leveraging-the-visual-modality-for-neural-1	https://aclanthology.org/W19-8620	https://aclanthology.org/W19-8620.pdf	ws-2019-10	['multimodal-machine-translation']	['natural-language-processing']	[ 2.46686161e-01 1.85431093e-01 -2.99061891e-02 -3.88635509e-02 -8.88235271e-01 -9.03313935e-01 9.65030849e-01 1.79997712e-01 -7.17892528e-01 4.72958773e-01 5.49953103e-01 -6.78119659e-01 2.41545677e-01 -1.95931599e-01 -7.99417436e-01 -5.37485301e-01 4.46690083e-01 3.56445819e-01 -2.23475769e-01 -3.37058812e-01 1.93863422e-01 1.91186637e-01 -1.20601976e+00 4.65712190e-01 8.05762947e-01 6.02331579e-01 3.41064394e-01 6.55883431e-01 -1.43750653e-01 3.40253949e-01 -3.56318623e-01 -6.72169268e-01 1.66880324e-01 -3.96154433e-01 -7.70919204e-01 -1.33395582e-01 7.48694897e-01 -1.34842128e-01 -4.00791228e-01 8.09519231e-01 5.99888682e-01 -1.66051481e-02 8.45325112e-01 -9.21711504e-01 -1.17930794e+00 7.78027296e-01 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8e7825cd-24ba-4221-b885-0acf7c988cfe	melon-playlist-dataset-a-public-dataset-for	2102.00201	null	https://arxiv.org/abs/2102.00201v1	https://arxiv.org/pdf/2102.00201v1.pdf	Melon Playlist Dataset: a public dataset for audio-based playlist generation and music tagging	One of the main limitations in the field of audio signal processing is the lack of large public datasets with audio representations and high-quality annotations due to restrictions of copyrighted commercial music. We present Melon Playlist Dataset, a public dataset of mel-spectrograms for 649,091tracks and 148,826 associated playlists annotated by 30,652 different tags. All the data is gathered from Melon, a popular Korean streaming service. The dataset is suitable for music information retrieval tasks, in particular, auto-tagging and automatic playlist continuation. Even though the latter can be addressed by collaborative filtering approaches, audio provides opportunities for research on track suggestions and building systems resistant to the cold-start problem, for which we provide a baseline. Moreover, the playlists and the annotations included in the Melon Playlist Dataset make it suitable for metric learning and representation learning.	['Dmitry Bogdanov', 'Xavier Serra', 'Sehwan Kim', 'Jungtaek Jang', 'Suyon Lim', 'Semi Lim', 'Namjun Jo', 'Biho Kim', 'Soohyeon Lee', 'Yuntae Kim', 'Andres Ferraro']	2021-01-30	null	null	null	null	['audio-signal-processing']	['audio']	[ 4.68330085e-03 -5.20204306e-01 -2.34596416e-01 -2.49265820e-01 -1.53683102e+00 -1.00218022e+00 -1.10314764e-01 4.06091720e-01 -3.90241563e-01 5.24319887e-01 7.62554705e-01 5.08328557e-01 -6.74181044e-01 -4.04550791e-01 -1.81512222e-01 -4.72857058e-01 -3.20185721e-01 1.21672839e-01 3.66885036e-01 -2.03486383e-01 2.57327318e-01 1.42756909e-01 -1.84824562e+00 8.23972404e-01 1.54476508e-01 1.41752946e+00 1.95664242e-01 8.35887849e-01 2.94318318e-01 9.55751300e-01 -8.11815023e-01 -3.36648256e-01 2.01051727e-01 -5.20486116e-01 -6.50001287e-01 -3.18292171e-01 5.69587231e-01 8.07889551e-02 -4.19283301e-01 8.05113137e-01 8.51456523e-01 6.06959820e-01 1.79278180e-01 -1.27205610e+00 1.05196983e-02 1.21863532e+00 -1.38484687e-01 4.19543922e-01 5.88984072e-01 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82137fc5-849d-448d-8e25-b85f4df26c6b	personalized-stress-monitoring-using-wearable	2108.00144	null	https://arxiv.org/abs/2108.00144v1	https://arxiv.org/pdf/2108.00144v1.pdf	Personalized Stress Monitoring using Wearable Sensors in Everyday Settings	Since stress contributes to a broad range of mental and physical health problems, the objective assessment of stress is essential for behavioral and physiological studies. Although several studies have evaluated stress levels in controlled settings, objective stress assessment in everyday settings is still largely under-explored due to challenges arising from confounding contextual factors and limited adherence for self-reports. In this paper, we explore the objective prediction of stress levels in everyday settings based on heart rate (HR) and heart rate variability (HRV) captured via low-cost and easy-to-wear photoplethysmography (PPG) sensors that are widely available on newer smart wearable devices. We present a layered system architecture for personalized stress monitoring that supports a tunable collection of data samples for labeling, and present a method for selecting informative samples from the stream of real-time data for labeling. We captured the stress levels of fourteen volunteers through self-reported questionnaires over periods of between 1-3 months, and explored binary stress detection based on HR and HRV using Machine Learning Methods. We observe promising preliminary results given that the dataset is collected in the challenging environments of everyday settings. The binary stress detector is fairly accurate and can detect stressful vs non-stressful samples with a macro-F1 score of up to \%76. Our study lays the groundwork for more sophisticated labeling strategies that generate context-aware, personalized models that will empower health professionals to provide personalized interventions.	['Marco Levorato', 'Amir M. Rahmani', 'Nikil Dutt', 'Stephanie M. Reich', 'Sina Labbaf', 'Ali Tazarv']	2021-07-31	null	null	null	null	['photoplethysmography-ppg', 'heart-rate-variability']	['medical', 'medical']	[ 4.64326292e-01 -2.78601825e-01 -5.40549099e-01 -7.43288398e-01 -2.37143472e-01 -2.56575465e-01 -3.48508656e-01 6.59780145e-01 -1.88684344e-01 7.16473639e-01 2.73223072e-01 4.03851876e-03 1.06847100e-01 -4.83723164e-01 2.53975838e-01 -2.98129678e-01 -3.71083170e-01 -1.44844264e-01 -3.94803315e-01 -4.55427840e-02 3.08658499e-02 3.96167040e-01 -1.64257109e+00 -7.33895274e-03 7.61326909e-01 1.17023134e+00 -2.63795704e-01 6.50014400e-01 3.59830409e-01 -1.87887046e-02 -7.37540483e-01 6.96521401e-02 -4.34360355e-02 -8.38646591e-01 -2.21164688e-01 5.98930158e-02 3.52882266e-01 -2.57653564e-01 2.09163800e-01 4.88598406e-01 1.03959394e+00 1.69072241e-01 -5.14402501e-02 -1.11544263e+00 -1.39626920e-01 1.82879224e-01 -4.53911200e-02 5.59638679e-01 6.19439602e-01 3.12164426e-01 2.83195347e-01 -5.01530945e-01 1.08666860e-01 7.55883157e-01 9.39123869e-01 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eff365fe-c908-4300-8dac-fd586742e872	implicit-differentiation-for-hyperparameter	2307.02130	null	https://arxiv.org/abs/2307.02130v1	https://arxiv.org/pdf/2307.02130v1.pdf	Implicit Differentiation for Hyperparameter Tuning the Weighted Graphical Lasso	We provide a framework and algorithm for tuning the hyperparameters of the Graphical Lasso via a bilevel optimization problem solved with a first-order method. In particular, we derive the Jacobian of the Graphical Lasso solution with respect to its regularization hyperparameters.	['Titouan Vayer', 'Mathurin Massias', 'Paulo Gonçalves', 'Can Pouliquen']	2023-07-05	null	null	null	null	['bilevel-optimization']	['methodology']	[-7.23505542e-02 1.87343568e-01 -4.06415910e-01 -5.27394354e-01 -1.05580676e+00 -4.92841899e-01 6.59203827e-02 -3.58106613e-01 -9.64087695e-02 8.75195384e-01 6.45779520e-02 -3.11986268e-01 -4.56516594e-01 -3.20777386e-01 -7.67899215e-01 -9.06621277e-01 -1.05913900e-01 6.64196789e-01 -7.00220704e-01 -1.22480929e-01 2.01752484e-01 4.58253980e-01 -3.59328210e-01 -4.90888953e-01 9.21091735e-01 8.75682354e-01 -6.09344482e-01 4.97758836e-01 2.99232513e-01 3.80177021e-01 -2.07669079e-01 -3.78156632e-01 4.89022315e-01 -4.04316753e-01 -5.33142507e-01 3.41748774e-01 7.25733459e-01 -2.20208257e-01 -5.45222282e-01 1.17856193e+00 3.76597881e-01 1.75261393e-01 8.73821497e-01 -1.32862413e+00 -2.46698543e-01 4.87851530e-01 -6.28101408e-01 -1.24869511e-01 1.36770576e-01 -7.64013529e-02 1.26235795e+00 -1.15020204e+00 7.83859313e-01 1.45334494e+00 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333754f9-b77f-4816-80d9-e14bfc17d07b	a-self-correcting-sequential-recommender	2303.02297	null	https://arxiv.org/abs/2303.02297v2	https://arxiv.org/pdf/2303.02297v2.pdf	A Self-Correcting Sequential Recommender	Sequential recommendations aim to capture users' preferences from their historical interactions so as to predict the next item that they will interact with. Sequential recommendation methods usually assume that all items in a user's historical interactions reflect her/his preferences and transition patterns between items. However, real-world interaction data is imperfect in that (i) users might erroneously click on items, i.e., so-called misclicks on irrelevant items, and (ii) users might miss items, i.e., unexposed relevant items due to inaccurate recommendations. To tackle the two issues listed above, we propose STEAM, a Self-correcTing sEquentiAl recoMmender. STEAM first corrects an input item sequence by adjusting the misclicked and/or missed items. It then uses the corrected item sequence to train a recommender and make the next item prediction.We design an item-wise corrector that can adaptively select one type of operation for each item in the sequence. The operation types are 'keep', 'delete' and 'insert.' In order to train the item-wise corrector without requiring additional labeling, we design two self-supervised learning mechanisms: (i) deletion correction (i.e., deleting randomly inserted items), and (ii) insertion correction (i.e., predicting randomly deleted items). We integrate the corrector with the recommender by sharing the encoder and by training them jointly. We conduct extensive experiments on three real-world datasets and the experimental results demonstrate that STEAM outperforms state-of-the-art sequential recommendation baselines. Our in-depth analyses confirm that STEAM benefits from learning to correct the raw item sequences.	['Pengjie Ren', 'Xiuzhen Cheng', 'Maarten de Rijke', 'Qiang Yan', 'Xin Xin', 'Zhaochun Ren', 'Zhumin Chen', 'Chenyang Wang', 'Yujie Lin']	2023-03-04	null	null	null	null	['sequential-recommendation']	['miscellaneous']	[ 4.61266726e-01 -3.73531878e-01 -6.63748682e-01 -5.91348529e-01 -2.38342851e-01 -6.94330752e-01 6.72206059e-02 2.14370161e-01 -5.33561587e-01 5.93702793e-01 4.66702372e-01 -5.12981236e-01 -1.06645850e-02 -6.31226540e-01 -1.01544607e+00 -2.91051388e-01 -7.05753490e-02 4.40722734e-01 1.99290618e-01 -1.89817920e-01 4.39956158e-01 -5.38673475e-02 -1.88761044e+00 6.59734309e-01 1.04225278e+00 8.66966426e-01 4.81341541e-01 5.87760568e-01 -2.05420598e-01 7.82832742e-01 -4.68191534e-01 -5.45699418e-01 2.10321084e-01 -5.09402037e-01 -5.16174197e-01 -1.97803825e-01 1.44915015e-01 -6.17260277e-01 -1.04397267e-01 8.68898034e-01 6.86818510e-02 4.60005015e-01 3.59334141e-01 -1.03930628e+00 -1.03087044e+00 1.05090511e+00 -3.38802725e-01 1.87767446e-01 6.52943552e-01 -7.32019776e-03 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b53a1f16-09f6-4739-a5fd-8e51bb1b1789	selective-token-generation-for-few-shot	null	null	https://openreview.net/forum?id=GthNKCqdDg	https://openreview.net/pdf?id=GthNKCqdDg	Selective Token Generation for Few-shot Language Modeling	Natural language modeling with limited training data is challenging problem, and many algorithms make use of large-scale pretrained language models (PLMs) for this due to its great generalization ability. Among these transfer learning algorithms from PLMs, additive learning that incorporates a task-specific adapter on top of the fixed PLM has been popularly used to alleviate the severe overfitting problem in the few-shot setting. However, this added task-specific adapter is generally trained by maximum likelihood estimation that can easily suffer from the so-called exposure bias problem, especially in sequential text generation. Therefore, in this work, we develop a novel additive learning algorithm based on reinforcement learning (RL) for few-shot natural language generation (NLG) tasks. In particular, we propose to use a selective token generation between the transformer-based PLM and the task-specific adapter during both training and inference. This output token selection between the two generators allows the adapter to take into account only on the task-relevant parts in sequence generation, and therefore makes it more robust to overfitting as well as more stable in RL training. In addition, in order to obtain the complementary adapter from the PLM for each few-shot task, we exploit a separate selecting module that is also simultaneously trained using RL. Experimental results on various few-shot NLG tasks including data-to-text generation and text summarization demonstrate that the proposed selective token generation significantly outperforms the previous additive learning algorithms based on the PLMs.	['Eun-Sol Kim', 'Sungwoong Kim', 'Taehwan Kwon', 'DaeJin Jo']	2021-09-29	null	null	null	null	['data-to-text-generation']	['natural-language-processing']	[ 5.12978196e-01 2.21932903e-01 -2.98143417e-01 -1.10267490e-01 -1.01059365e+00 -3.52435112e-02 7.97804534e-01 1.23120703e-01 -3.81625682e-01 9.07046735e-01 2.78266490e-01 -3.61946486e-02 1.34195864e-01 -1.08281720e+00 -6.61805153e-01 -8.98138762e-01 5.48752427e-01 4.87649590e-01 2.17664436e-01 -3.88120174e-01 1.84240401e-01 -1.64680406e-01 -1.40844250e+00 9.98493209e-02 1.70146108e+00 7.20418930e-01 9.10111010e-01 2.80248016e-01 -6.19131982e-01 9.13244605e-01 -6.28738582e-01 -2.41655931e-01 -1.31797761e-01 -8.43557060e-01 -2.67669231e-01 -3.27706225e-02 -5.51181659e-02 -3.81193042e-01 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e0fc3278-f9ef-4f82-8193-92dff268cab4	regeneration-learning-a-learning-paradigm-for	2301.08846	null	https://arxiv.org/abs/2301.08846v1	https://arxiv.org/pdf/2301.08846v1.pdf	Regeneration Learning: A Learning Paradigm for Data Generation	Machine learning methods for conditional data generation usually build a mapping from source conditional data X to target data Y. The target Y (e.g., text, speech, music, image, video) is usually high-dimensional and complex, and contains information that does not exist in source data, which hinders effective and efficient learning on the source-target mapping. In this paper, we present a learning paradigm called regeneration learning for data generation, which first generates Y' (an abstraction/representation of Y) from X and then generates Y from Y'. During training, Y' is obtained from Y through either handcrafted rules or self-supervised learning and is used to learn X-->Y' and Y'-->Y. Regeneration learning extends the concept of representation learning to data generation tasks, and can be regarded as a counterpart of traditional representation learning, since 1) regeneration learning handles the abstraction (Y') of the target data Y for data generation while traditional representation learning handles the abstraction (X') of source data X for data understanding; 2) both the processes of Y'-->Y in regeneration learning and X-->X' in representation learning can be learned in a self-supervised way (e.g., pre-training); 3) both the mappings from X to Y' in regeneration learning and from X' to Y in representation learning are simpler than the direct mapping from X to Y. We show that regeneration learning can be a widely-used paradigm for data generation (e.g., text generation, speech recognition, speech synthesis, music composition, image generation, and video generation) and can provide valuable insights into developing data generation methods.	['Yoshua Bengio', 'Tie-Yan Liu', 'Jiang Bian', 'Tao Qin', 'Xu Tan']	2023-01-21	null	null	null	null	['video-generation']	['computer-vision']	[ 7.20994115e-01 3.57343763e-01 -4.94437546e-01 -2.62980342e-01 -8.70779991e-01 -3.67899239e-01 9.64157104e-01 -2.58619599e-02 1.72950268e-01 8.82915258e-01 4.45113242e-01 -2.20153168e-01 -6.87709674e-02 -1.08107722e+00 -9.60378826e-01 -7.51098454e-01 1.24666005e-01 3.90129745e-01 -4.12143648e-01 -2.35877439e-01 2.06851318e-01 2.72780746e-01 -1.96647739e+00 5.20557046e-01 8.27819467e-01 1.00507545e+00 4.94554728e-01 7.43465066e-01 -6.05491877e-01 9.27615643e-01 -8.79737794e-01 1.56104654e-01 1.51197156e-02 -1.01701355e+00 -9.12307024e-01 2.95936495e-01 -2.77131493e-03 -5.09944111e-02 -1.66173458e-01 7.42905080e-01 1.50897533e-01 1.62365198e-01 1.01866555e+00 -1.55519819e+00 -9.64751601e-01 9.47695673e-01 -1.65470317e-01 -4.70567167e-01 6.47033274e-01 -1.35484889e-01 8.55496645e-01 -9.02036428e-01 8.74498725e-01 1.28292251e+00 2.90325701e-01 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-8.41732919e-01 -6.89378619e-01 7.24396110e-01 4.36749399e-01 -5.74386537e-01 -6.23616241e-02 7.09456444e-01]	[11.725205421447754, 0.1314777135848999]
82c2c7f4-134e-40fc-95f0-bfb834f27860	regulating-gatekeeper-ai-and-data	2212.04997	null	https://arxiv.org/abs/2212.04997v1	https://arxiv.org/pdf/2212.04997v1.pdf	Regulating Gatekeeper AI and Data: Transparency, Access, and Fairness under the DMA, the GDPR, and beyond	Artificial intelligence is not only increasingly used in business and administration contexts, but a race for its regulation is also underway, with the EU spearheading the efforts. Contrary to existing literature, this article suggests, however, that the most far-reaching and effective EU rules for AI applications in the digital economy will not be contained in the proposed AI Act - but have just been enacted in the Digital Markets Act. We analyze the impact of the DMA and related EU acts on AI models and their underlying data across four key areas: disclosure requirements; the regulation of AI training data; access rules; and the regime for fair rankings. The paper demonstrates that fairness, in the sense of the DMA, goes beyond traditionally protected categories of non-discrimination law on which scholarship at the intersection of AI and law has so far largely focused on. Rather, we draw on competition law and the FRAND criteria known from intellectual property law to interpret and refine the DMA provisions on fair rankings. Moreover, we show how, based on CJEU jurisprudence, a coherent interpretation of the concept of non-discrimination in both traditional non-discrimination and competition law may be found. The final part sketches specific proposals for a comprehensive framework of transparency, access, and fairness under the DMA and beyond.	['Janina Rochon', 'Johann Cordes', 'Philipp Hacker']	2022-12-09	null	null	null	null	['jurisprudence']	['miscellaneous']	[ 2.66854346e-01 5.78095615e-01 -5.85522175e-01 -2.89856941e-01 -3.34254444e-01 -7.49840915e-01 8.98986578e-01 1.32892147e-01 -7.29639649e-01 6.91710114e-01 6.02075517e-01 -7.78795958e-01 -7.63256848e-01 -5.54454505e-01 -2.93491423e-01 -2.79766649e-01 5.70048690e-01 3.86217266e-01 -3.65780979e-01 -1.49488300e-01 5.30741394e-01 3.89888167e-01 -1.43741119e+00 1.53837185e-02 1.05798304e+00 1.03298938e+00 -7.39124775e-01 1.18247367e-01 -9.90304127e-02 1.26954913e+00 -5.90296924e-01 -1.20359397e+00 9.77636993e-01 -4.49787676e-01 -1.00220704e+00 -4.79630828e-01 2.76315808e-01 -6.85581625e-01 -1.66198000e-01 1.26258886e+00 1.53971061e-01 -2.13211983e-01 7.64584899e-01 -1.19551528e+00 -1.14379025e+00 1.05725658e+00 -2.06031248e-01 -5.86815365e-02 -3.80497612e-02 3.48067462e-01 1.19671798e+00 -9.24446806e-02 6.82907164e-01 9.01729286e-01 2.60415941e-01 5.30085266e-01 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77673c2a-ac3c-4017-9f66-1cabc294bb00	a-question-focused-multi-factor-attention	1801.08290	null	http://arxiv.org/abs/1801.08290v1	http://arxiv.org/pdf/1801.08290v1.pdf	A Question-Focused Multi-Factor Attention Network for Question Answering	Neural network models recently proposed for question answering (QA) primarily focus on capturing the passage-question relation. However, they have minimal capability to link relevant facts distributed across multiple sentences which is crucial in achieving deeper understanding, such as performing multi-sentence reasoning, co-reference resolution, etc. They also do not explicitly focus on the question and answer type which often plays a critical role in QA. In this paper, we propose a novel end-to-end question-focused multi-factor attention network for answer extraction. Multi-factor attentive encoding using tensor-based transformation aggregates meaningful facts even when they are located in multiple sentences. To implicitly infer the answer type, we also propose a max-attentional question aggregation mechanism to encode a question vector based on the important words in a question. During prediction, we incorporate sequence-level encoding of the first wh-word and its immediately following word as an additional source of question type information. Our proposed model achieves significant improvements over the best prior state-of-the-art results on three large-scale challenging QA datasets, namely NewsQA, TriviaQA, and SearchQA.	['Souvik Kundu', 'Hwee Tou Ng']	2018-01-25	null	null	null	null	['triviaqa']	['miscellaneous']	[ 1.54706374e-01 5.19569889e-02 1.31253794e-01 -6.29914105e-01 -1.37320161e+00 -4.29943711e-01 2.83550739e-01 5.16251028e-01 -5.02184510e-01 6.74698472e-01 9.21899557e-01 -5.02079487e-01 -2.11037070e-01 -8.81189764e-01 -7.51781940e-01 -1.80523217e-01 4.62963432e-01 6.53338909e-01 3.29766184e-01 -6.87664747e-01 3.48359704e-01 -2.28999957e-01 -1.10617566e+00 9.10457969e-01 1.32016826e+00 1.11891818e+00 2.97078758e-01 6.88935339e-01 -7.75020242e-01 1.62975538e+00 -7.93922424e-01 -9.60629642e-01 -3.12378734e-01 -7.74848461e-01 -1.43456399e+00 -2.86727726e-01 6.69006586e-01 -4.09382999e-01 -2.36280382e-01 9.44920301e-01 3.01227450e-01 3.13866556e-01 5.36128223e-01 -5.41018128e-01 -1.00695336e+00 5.91638863e-01 -8.88585746e-02 8.32791507e-01 6.51636481e-01 4.62432243e-02 1.79340374e+00 -8.44928384e-01 3.26710284e-01 1.34931147e+00 3.48818541e-01 3.54189694e-01 -7.50540912e-01 -1.32106841e-01 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4.94064316e-02 3.96979421e-01 6.66157246e-01 -3.48070323e-01 -7.42828124e-04 -2.10780367e-01]	[11.177294731140137, 8.000874519348145]
77a16d03-4739-4612-ac58-5dcd66132b36	adaptersoup-weight-averaging-to-improve	2302.07027	null	https://arxiv.org/abs/2302.07027v3	https://arxiv.org/pdf/2302.07027v3.pdf	AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Models	Pretrained language models (PLMs) are trained on massive corpora, but often need to specialize to specific domains. A parameter-efficient adaptation method suggests training an adapter for each domain on the task of language modeling. This leads to good in-domain scores but can be impractical for domain- or resource-restricted settings. A solution is to use a related-domain adapter for the novel domain at test time. In this paper, we introduce AdapterSoup, an approach that performs weight-space averaging of adapters trained on different domains. Our approach is embarrassingly parallel: first, we train a set of domain-specific adapters; then, for each novel domain, we determine which adapters should be averaged at test time. We present extensive experiments showing that AdapterSoup consistently improves performance to new domains without extra training. We also explore weight averaging of adapters trained on the same domain with different hyper-parameters, and show that it preserves the performance of a PLM on new domains while obtaining strong in-domain results. We explore various approaches for choosing which adapters to combine, such as text clustering and semantic similarity. We find that using clustering leads to the most competitive results on novel domains.	['Jesse Dodge', 'Alexander Fraser', 'Matthew E. Peters', 'Alexandra Chronopoulou']	2023-02-14	null	null	null	null	['text-clustering', 'semantic-textual-similarity']	['natural-language-processing', 'natural-language-processing']	[ 6.62617907e-02 -2.74587929e-01 -2.02562481e-01 -5.50130904e-01 -1.12765944e+00 -7.77324319e-01 6.50100112e-01 8.48590881e-02 -6.68030441e-01 6.06414437e-01 2.79346079e-01 -2.96182781e-01 -4.41108830e-03 -5.64167261e-01 -5.48519254e-01 -5.46519756e-01 2.74954408e-01 1.20839036e+00 5.29538810e-01 -2.81682730e-01 4.61389944e-02 3.13051015e-01 -1.11620116e+00 4.69976723e-01 1.10072649e+00 6.06075644e-01 4.07069236e-01 5.00253260e-01 -6.19013727e-01 2.17497572e-01 -6.11341536e-01 -5.88719010e-01 2.49701321e-01 -5.25353014e-01 -1.02256393e+00 2.39605039e-01 3.67701858e-01 -8.75188969e-03 -8.91165342e-03 8.10465455e-01 4.84639168e-01 3.75156730e-01 9.05985951e-01 -9.78257716e-01 -5.14669716e-01 7.88361311e-01 -3.24112922e-01 2.53414303e-01 2.55827278e-01 -9.50691923e-02 9.59729373e-01 -1.09866309e+00 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3.93188357e-01 -5.21290064e-01 -6.84357762e-01 -1.04394245e+00 -5.39723158e-01 7.85602450e-01 3.48719098e-02 -7.09484816e-01 -3.76358539e-01 1.48174599e-01]	[10.788651466369629, 8.017595291137695]
3cdcb862-a2c6-4fa8-adbf-b8ccbc96b539	online-learning-with-regularized-kernel-for	1701.04508	null	http://arxiv.org/abs/1701.04508v2	http://arxiv.org/pdf/1701.04508v2.pdf	Online Learning with Regularized Kernel for One-class Classification	This paper presents an online learning with regularized kernel based one-class extreme learning machine (ELM) classifier and is referred as online RK-OC-ELM. The baseline kernel hyperplane model considers whole data in a single chunk with regularized ELM approach for offline learning in case of one-class classification (OCC). Further, the basic hyper plane model is adapted in an online fashion from stream of training samples in this paper. Two frameworks viz., boundary and reconstruction are presented to detect the target class in online RKOC-ELM. Boundary framework based one-class classifier consists of single node output architecture and classifier endeavors to approximate all data to any real number. However, one-class classifier based on reconstruction framework is an autoencoder architecture, where output nodes are identical to input nodes and classifier endeavor to reconstruct input layer at the output layer. Both these frameworks employ regularized kernel ELM based online learning and consistency based model selection has been employed to select learning algorithm parameters. The performance of online RK-OC-ELM has been evaluated on standard benchmark datasets as well as on artificial datasets and the results are compared with existing state-of-the art one-class classifiers. The results indicate that the online learning one-class classifier is slightly better or same as batch learning based approaches. As, base classifier used for the proposed classifiers are based on the ELM, hence, proposed classifiers would also inherit the benefit of the base classifier i.e. it will perform faster computation compared to traditional autoencoder based one-class classifier.	['Kapil Ahuja', 'Chandan Gautam', 'Aruna Tiwari', 'Sundaram Suresh']	2017-01-17	null	null	null	null	['one-class-classifier']	['methodology']	[-3.27394277e-01 1.29089832e-01 -3.29516828e-01 -3.78543198e-01 -8.97445232e-02 -2.62820154e-01 3.43980402e-01 3.99404556e-01 -6.02903605e-01 6.03031933e-01 -3.93806398e-01 -3.05732518e-01 -5.16587853e-01 -6.06624603e-01 -5.25292277e-01 -7.22667694e-01 -3.47580671e-01 5.57400763e-01 1.03530377e-01 -1.03865571e-01 2.26859331e-01 7.35656619e-01 -1.81348789e+00 6.07624948e-01 6.21133983e-01 1.28715599e+00 -8.29656720e-02 9.44616437e-01 -3.43845785e-01 8.45155239e-01 -2.50719666e-01 -1.80738539e-01 4.51321870e-01 -1.94033518e-01 -5.50229251e-01 -2.20487583e-02 1.73459932e-01 -7.75810704e-02 -1.84779912e-02 5.70804179e-01 2.59425700e-01 2.55945057e-01 1.32763815e+00 -1.55409563e+00 -4.44845229e-01 3.95000100e-01 -3.87000471e-01 -1.72044877e-02 5.56834601e-02 -1.22385398e-01 4.77022171e-01 -1.26323545e+00 5.20820856e-01 7.78523743e-01 9.19494331e-01 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