Split (2)

train · 44.6k rows

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4150482c-b6c2-4fa2-bd1c-be323a0fc176	roadtracer-automatic-extraction-of-road	1802.03680	null	http://arxiv.org/abs/1802.03680v2	http://arxiv.org/pdf/1802.03680v2.pdf	RoadTracer: Automatic Extraction of Road Networks from Aerial Images	Mapping road networks is currently both expensive and labor-intensive. High-resolution aerial imagery provides a promising avenue to automatically infer a road network. Prior work uses convolutional neural networks (CNNs) to detect which pixels belong to a road (segmentation), and then uses complex post-processing heuristics to infer graph connectivity. We show that these segmentation methods have high error rates because noisy CNN outputs are difficult to correct. We propose RoadTracer, a new method to automatically construct accurate road network maps from aerial images. RoadTracer uses an iterative search process guided by a CNN-based decision function to derive the road network graph directly from the output of the CNN. We compare our approach with a segmentation method on fifteen cities, and find that at a 5% error rate, RoadTracer correctly captures 45% more junctions across these cities.	['Hari Balakrishnan', 'Songtao He', 'Sofiane Abbar', 'Favyen Bastani', 'Sanjay Chawla', 'Mohammad Alizadeh', 'David DeWitt', 'Sam Madden']	2018-02-11	roadtracer-automatic-extraction-of-road-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Bastani_RoadTracer_Automatic_Extraction_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Bastani_RoadTracer_Automatic_Extraction_CVPR_2018_paper.pdf	cvpr-2018-6	['road-segementation']	['computer-vision']	[ 4.66723651e-01 4.02890682e-01 -1.45881996e-01 -3.50400269e-01 -5.58677197e-01 -8.36664796e-01 5.39545298e-01 -6.85001686e-02 -2.38343820e-01 6.09391868e-01 -1.23517752e-01 -7.82588959e-01 -1.40551358e-01 -1.69791496e+00 -8.41320217e-01 4.92995568e-02 -2.30285376e-01 6.34088814e-01 5.37126780e-01 -1.78984761e-01 2.08893135e-01 8.10596347e-01 -1.30883098e+00 2.40651481e-02 1.07512224e+00 7.98847258e-01 -4.69954871e-02 9.89936709e-01 -1.55170947e-01 3.33695620e-01 -2.30219871e-01 -3.18751037e-01 6.37623250e-01 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25bb1a2c-4406-4385-aa7a-cbe1f131d14d	spherical-kernel-for-efficient-graph	1909.09287	null	https://arxiv.org/abs/1909.09287v2	https://arxiv.org/pdf/1909.09287v2.pdf	Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds	We propose a spherical kernel for efficient graph convolution of 3D point clouds. Our metric-based kernels systematically quantize the local 3D space to identify distinctive geometric relationships in the data. Similar to the regular grid CNN kernels, the spherical kernel maintains translation-invariance and asymmetry properties, where the former guarantees weight sharing among similar local structures in the data and the latter facilitates fine geometric learning. The proposed kernel is applied to graph neural networks without edge-dependent filter generation, making it computationally attractive for large point clouds. In our graph networks, each vertex is associated with a single point location and edges connect the neighborhood points within a defined range. The graph gets coarsened in the network with farthest point sampling. Analogous to the standard CNNs, we define pooling and unpooling operations for our network. We demonstrate the effectiveness of the proposed spherical kernel with graph neural networks for point cloud classification and semantic segmentation using ModelNet, ShapeNet, RueMonge2014, ScanNet and S3DIS datasets. The source code and the trained models can be downloaded from https://github.com/hlei-ziyan/SPH3D-GCN.	['Huan Lei', 'Ajmal Mian', 'Naveed Akhtar']	2019-09-20	null	null	null	null	['3d-instance-segmentation-1', '3d-object-classification', '3d-part-segmentation']	['computer-vision', 'computer-vision', 'computer-vision']	[-4.81636047e-01 1.78000346e-01 3.50203440e-02 -3.43293071e-01 -5.45449778e-02 -5.83528817e-01 4.50071722e-01 3.87456357e-01 -1.92089632e-01 3.16196457e-02 -1.81180105e-01 -2.87435025e-01 -1.65336639e-01 -1.16726351e+00 -9.04700994e-01 -4.21661437e-01 -2.88076282e-01 4.67958540e-01 5.06291032e-01 6.90480173e-02 2.03939259e-01 1.25205016e+00 -1.22202492e+00 -2.59711817e-02 7.31119752e-01 1.28527093e+00 2.62571603e-01 4.51589644e-01 -4.64090198e-01 2.23677203e-01 -1.61538228e-01 -2.66495556e-01 4.74157512e-01 2.02738523e-01 -6.38154447e-01 -1.15539849e-01 6.96275830e-01 6.36271462e-02 -6.46198750e-01 1.23274922e+00 3.72661680e-01 2.50143439e-01 6.84647381e-01 -1.30352759e+00 -1.14284241e+00 2.63061911e-01 -4.75939542e-01 3.21616605e-02 -1.02638798e-02 -2.01706123e-03 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65ca5791-0a87-45ec-94be-287f8559b042	translating-a-math-word-problem-to-a	null	null	https://aclanthology.org/D18-1132	https://aclanthology.org/D18-1132.pdf	Translating a Math Word Problem to a Expression Tree	Sequence-to-sequence (SEQ2SEQ) models have been successfully applied to automatic math word problem solving. Despite its simplicity, a drawback still remains: a math word problem can be correctly solved by more than one equations. This non-deterministic transduction harms the performance of maximum likelihood estimation. In this paper, by considering the uniqueness of expression tree, we propose an equation normalization method to normalize the duplicated equations. Moreover, we analyze the performance of three popular SEQ2SEQ models on the math word problem solving. We find that each model has its own specialty in solving problems, consequently an ensemble model is then proposed to combine their advantages. Experiments on dataset Math23K show that the ensemble model with equation normalization significantly outperforms the previous state-of-the-art methods.	['Xiaojiang Liu', 'Deng Cai', 'Yan Wang', 'Lei Wang', 'Dongxiang Zhang']	2018-10-01	null	null	null	emnlp-2018-10	['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving']	['knowledge-base', 'reasoning', 'time-series']	[ 2.88303494e-01 -3.03975642e-01 1.16205327e-01 -4.14614379e-01 -5.48822880e-01 -7.34965920e-01 -7.77340680e-03 1.29110053e-01 -3.33427191e-01 8.69960546e-01 1.49781415e-02 -2.44935364e-01 -4.64736253e-01 -9.24393356e-01 -3.73732209e-01 -4.80943322e-01 5.83343983e-01 3.30007255e-01 -5.80136776e-02 -6.23628259e-01 5.72266877e-01 1.91168696e-01 -1.36231756e+00 3.41221958e-01 1.39555466e+00 6.31899416e-01 2.86998540e-01 6.78564131e-01 -9.38321829e-01 9.03882086e-01 -8.09788346e-01 -6.27873540e-01 7.83475861e-02 -7.32851028e-01 -9.19785142e-01 -5.25587916e-01 5.61088443e-01 9.99184400e-02 -2.99977828e-02 1.21838033e+00 6.38703823e-01 3.13577265e-01 7.15519726e-01 -1.04935956e+00 -7.73125887e-01 6.90903544e-01 -2.25963429e-01 6.67438209e-02 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5daf225b-cb5e-48a8-ba6d-12b851c1648d	yolo-v3-visual-and-real-time-object-detection	2209.12447	null	https://arxiv.org/abs/2209.12447v1	https://arxiv.org/pdf/2209.12447v1.pdf	YOLO v3: Visual and Real-Time Object Detection Model for Smart Surveillance Systems(3s)	Can we see it all? Do we know it All? These are questions thrown to human beings in our contemporary society to evaluate our tendency to solve problems. Recent studies have explored several models in object detection; however, most have failed to meet the demand for objectiveness and predictive accuracy, especially in developing and under-developed countries. Consequently, several global security threats have necessitated the development of efficient approaches to tackle these issues. This paper proposes an object detection model for cyber-physical systems known as Smart Surveillance Systems (3s). This research proposes a 2-phase approach, highlighting the advantages of YOLO v3 deep learning architecture in real-time and visual object detection. A transfer learning approach was implemented for this research to reduce training time and computing resources. The dataset utilized for training the model is the MS COCO dataset which contains 328,000 annotated image instances. Deep learning techniques such as Pre-processing, Data pipelining, and detection was implemented to improve efficiency. Compared to other novel research models, the proposed model's results performed exceedingly well in detecting WILD objects in surveillance footages. An accuracy of 99.71% was recorded, with an improved mAP of 61.5.	['Hashim Ibrahim Bisallah', 'Ozioma Collins Oguine', 'Kanyifeechukwu Jane Oguine']	2022-09-26	null	null	null	null	['real-time-object-detection']	['computer-vision']	[ 1.19717821e-01 -2.87786901e-01 1.11592799e-01 -8.93774629e-02 -1.63389787e-01 -3.08209389e-01 4.13191348e-01 7.38463327e-02 -4.15534288e-01 3.95769596e-01 -3.18799585e-01 -3.87123764e-01 7.94357713e-03 -8.15321505e-01 -4.48955089e-01 -6.58441126e-01 -1.70541808e-01 -5.53031825e-02 7.11089492e-01 -1.61338300e-01 4.85876262e-01 5.67956567e-01 -1.47446179e+00 4.38959837e-01 5.10311067e-01 1.15501761e+00 3.15917969e-01 1.03379774e+00 1.33527756e-01 1.02259743e+00 -1.02449107e+00 -1.92287385e-01 5.05816400e-01 -6.60873801e-02 -5.72425485e-01 1.63487568e-01 3.31267238e-01 -6.94226086e-01 -1.42946597e-02 1.02949691e+00 5.12770951e-01 -1.98143408e-01 5.04535794e-01 -1.31784403e+00 -6.07386231e-01 -8.32348317e-02 -7.60913730e-01 8.16643238e-01 2.29533777e-01 2.59751737e-01 2.53140509e-01 -5.67722082e-01 2.65274227e-01 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-6.02667630e-01 -4.92247522e-01 -1.08020806e+00 -1.92811295e-01 -1.57434435e-03 2.60541439e-01 -6.91717565e-01 9.14583206e-02 2.49671102e-01]	[8.6489839553833, -0.8654654622077942]
cf65e8f8-7bb2-4519-a649-5d0a79bbb839	learning-joint-semantic-parsers-from-disjoint	1804.05990	null	http://arxiv.org/abs/1804.05990v1	http://arxiv.org/pdf/1804.05990v1.pdf	Learning Joint Semantic Parsers from Disjoint Data	We present a new approach to learning semantic parsers from multiple datasets, even when the target semantic formalisms are drastically different, and the underlying corpora do not overlap. We handle such "disjoint" data by treating annotations for unobserved formalisms as latent structured variables. Building on state-of-the-art baselines, we show improvements both in frame-semantic parsing and semantic dependency parsing by modeling them jointly.	['Sam Thomson', 'Noah A. Smith', 'Swabha Swayamdipta', 'Hao Peng']	2018-04-17	learning-joint-semantic-parsers-from-disjoint-1	https://aclanthology.org/N18-1135	https://aclanthology.org/N18-1135.pdf	naacl-2018-6	['semantic-dependency-parsing']	['natural-language-processing']	[ 2.90342093e-01 8.66104305e-01 -4.44209278e-01 -7.67433524e-01 -1.36533964e+00 -1.02746034e+00 4.91741449e-01 1.56370640e-01 -2.05910280e-01 9.23732579e-01 5.13933480e-01 -2.58137107e-01 2.34317169e-01 -7.30518818e-01 -7.58706748e-01 -2.96573937e-01 3.00732851e-01 1.06105042e+00 4.42751467e-01 2.63361372e-02 -1.81694046e-01 5.26846293e-03 -1.12947917e+00 8.06111276e-01 6.04059517e-01 3.90185446e-01 3.80107850e-01 6.70622408e-01 -7.94477642e-01 9.68323410e-01 -6.52146995e-01 -8.09078038e-01 -1.22027695e-01 -1.90214798e-01 -1.48087108e+00 4.41173390e-02 4.91200626e-01 1.70166567e-01 -1.93669721e-01 1.19037318e+00 -1.19360648e-01 -4.30804826e-02 5.04383743e-01 -1.16461217e+00 -8.12941849e-01 1.06445193e+00 -1.10595204e-01 -2.95171235e-02 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b00cf451-28ea-42d7-a0ff-f6720e0f6f40	ciagan-conditional-identity-anonymization	2005.09544	null	https://arxiv.org/abs/2005.09544v2	https://arxiv.org/pdf/2005.09544v2.pdf	CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks	The unprecedented increase in the usage of computer vision technology in society goes hand in hand with an increased concern in data privacy. In many real-world scenarios like people tracking or action recognition, it is important to be able to process the data while taking careful consideration in protecting people's identity. We propose and develop CIAGAN, a model for image and video anonymization based on conditional generative adversarial networks. Our model is able to remove the identifying characteristics of faces and bodies while producing high-quality images and videos that can be used for any computer vision task, such as detection or tracking. Unlike previous methods, we have full control over the de-identification (anonymization) procedure, ensuring both anonymization as well as diversity. We compare our method to several baselines and achieve state-of-the-art results.	['Laura Leal-Taixé', 'Maxim Maximov', 'Ismail Elezi']	2020-05-19	ciagan-conditional-identity-anonymization-1	http://openaccess.thecvf.com/content_CVPR_2020/html/Maximov_CIAGAN_Conditional_Identity_Anonymization_Generative_Adversarial_Networks_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Maximov_CIAGAN_Conditional_Identity_Anonymization_Generative_Adversarial_Networks_CVPR_2020_paper.pdf	cvpr-2020-6	['face-anonymization']	['computer-vision']	[ 3.78231257e-01 1.26495391e-01 8.84969458e-02 -5.03105938e-01 -4.78409320e-01 -8.20131242e-01 6.30643010e-01 -1.69912666e-01 -6.73991680e-01 6.78589344e-01 2.30102852e-01 -1.17480800e-01 3.37365836e-01 -6.52103066e-01 -7.98569739e-01 -5.66628218e-01 1.15060084e-01 2.47334778e-01 -8.84926170e-02 1.45749539e-01 -1.71822399e-01 7.77854800e-01 -1.33596277e+00 8.58643949e-02 7.02906311e-01 9.14388895e-01 -6.85983121e-01 7.80521572e-01 3.97754788e-01 5.18500805e-01 -6.07453763e-01 -1.26688969e+00 8.45239878e-01 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40c38dc5-1f7a-4705-9e43-076cf80ae162	certifiable-robustness-for-naive-bayes	2303.04811	null	https://arxiv.org/abs/2303.04811v1	https://arxiv.org/pdf/2303.04811v1.pdf	Certifiable Robustness for Naive Bayes Classifiers	Data cleaning is crucial but often laborious in most machine learning (ML) applications. However, task-agnostic data cleaning is sometimes unnecessary if certain inconsistencies in the dirty data will not affect the prediction of ML models to the test points. A test point is certifiably robust for an ML classifier if the prediction remains the same regardless of which (among exponentially many) cleaned dataset it is trained on. In this paper, we study certifiable robustness for the Naive Bayes classifier (NBC) on dirty datasets with missing values. We present (i) a linear time algorithm in the number of entries in the dataset that decides whether a test point is certifiably robust for NBC, (ii) an algorithm that counts for each label, the number of cleaned datasets on which the NBC can be trained to predict that label, and (iii) an efficient optimal algorithm that poisons a clean dataset by inserting the minimum number of missing values such that a test point is not certifiably robust for NBC. We prove that (iv) poisoning a clean dataset such that multiple test points become certifiably non-robust is NP-hard for any dataset with at least three features. Our experiments demonstrate that our algorithms for the decision and data poisoning problems achieve up to $19.5\times$ and $3.06\times$ speed-up over the baseline algorithms across different real-world datasets.	['Paraschos Koutris', 'Zhiwei Fan', 'Xiating Ouyang', 'Song Bian']	2023-03-08	null	null	null	null	['data-poisoning']	['adversarial']	[ 2.20957428e-01 -5.92242964e-02 -1.00978516e-01 -4.23024267e-01 -1.29571640e+00 -1.01748705e+00 2.33057335e-01 7.45372474e-01 -3.08523923e-01 1.05293572e+00 -6.00244224e-01 -6.86494589e-01 -5.07904470e-01 -9.32784975e-01 -1.31720614e+00 -1.09579003e+00 -3.93352896e-01 9.33554351e-01 7.93115646e-02 3.95124406e-01 3.43863547e-01 5.17242610e-01 -1.69939518e+00 2.32636169e-01 1.01769984e+00 9.17998254e-01 -5.03854930e-01 8.23286891e-01 3.86895001e-01 5.99753439e-01 -8.24671745e-01 -4.69625056e-01 6.88614547e-01 -3.07456642e-01 -9.09330428e-01 -1.08709052e-01 6.39398873e-01 2.26070523e-01 2.97727406e-01 1.34832561e+00 8.91490728e-02 -2.32655808e-01 7.16194987e-01 -1.97994065e+00 -3.24421763e-01 6.89201057e-01 -5.81483006e-01 -1.07746229e-01 4.72043641e-02 2.53666759e-01 8.17231894e-01 -5.04671574e-01 4.32488620e-01 9.95233417e-01 5.64932406e-01 5.60753763e-01 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83d446f1-df83-43e5-8d89-84edc71be660	self-supervised-sentence-compression-for	2305.07988	null	https://arxiv.org/abs/2305.07988v1	https://arxiv.org/pdf/2305.07988v1.pdf	Self-Supervised Sentence Compression for Meeting Summarization	The conventional summarization model often fails to capture critical information in meeting transcripts, as meeting corpus usually involves multiple parties with lengthy conversations and is stuffed with redundant and trivial content. To tackle this problem, we present SVB, an effective and efficient framework for meeting summarization that `compress' the redundancy while preserving important content via three processes: sliding-window dialogue restoration and \textbf{S}coring, channel-wise importance score \textbf{V}oting, and relative positional \textbf{B}ucketing. Specifically, under the self-supervised paradigm, the sliding-window scoring aims to rate the importance of each token from multiple views. Then these ratings are aggregated by channel-wise voting. Tokens with high ratings will be regarded as salient information and labeled as \textit{anchors}. Finally, to tailor the lengthy input to an acceptable length for the language model, the relative positional bucketing algorithm is performed to retain the anchors while compressing other irrelevant contents in different granularities. Without large-scale pre-training or expert-grade annotating tools, our proposed method outperforms previous state-of-the-art approaches. A vast amount of evaluations and analyses are conducted to prove the effectiveness of our method.	['Linqi Song', 'Ding Liang', 'Zhaohui Hou', 'Mingjie Zhan', 'Xinyun Zhang', 'Wei Shao', 'Han Wu', 'Haochen Tan']	2023-05-13	null	null	null	null	['sentence-compression', 'meeting-summarization']	['natural-language-processing', 'natural-language-processing']	[ 4.91480172e-01 4.79102612e-01 -3.22590292e-01 -4.59769726e-01 -1.33797908e+00 -5.10420203e-01 3.93503010e-01 8.85519862e-01 -3.94867927e-01 9.97245669e-01 9.29789305e-01 1.40527159e-01 -1.39360219e-01 -2.83522457e-01 -2.20769241e-01 -5.94201148e-01 8.03510249e-02 6.92962170e-01 1.97258994e-01 -2.47161046e-01 6.71859980e-01 -1.62915036e-01 -1.51402843e+00 8.60608637e-01 1.14026988e+00 8.40289533e-01 1.88942283e-01 7.31578767e-01 -4.26436335e-01 6.48819685e-01 -1.13442385e+00 -4.76328462e-01 -9.07363147e-02 -5.75449824e-01 -9.39377487e-01 8.88081044e-02 1.44353583e-01 -2.48802885e-01 1.73521549e-01 9.27131772e-01 7.24145353e-01 3.41623008e-01 6.25511646e-01 -9.21636760e-01 -4.51979935e-02 1.12703335e+00 -6.03706598e-01 3.61498207e-01 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83292e12-1dc0-43b0-bfc7-6361db24ade3	semantic-structure-enhanced-event-causality	2305.12792	null	https://arxiv.org/abs/2305.12792v1	https://arxiv.org/pdf/2305.12792v1.pdf	Semantic Structure Enhanced Event Causality Identification	Event Causality Identification (ECI) aims to identify causal relations between events in unstructured texts. This is a very challenging task, because causal relations are usually expressed by implicit associations between events. Existing methods usually capture such associations by directly modeling the texts with pre-trained language models, which underestimate two kinds of semantic structures vital to the ECI task, namely, event-centric structure and event-associated structure. The former includes important semantic elements related to the events to describe them more precisely, while the latter contains semantic paths between two events to provide possible supports for ECI. In this paper, we study the implicit associations between events by modeling the above explicit semantic structures, and propose a Semantic Structure Integration model (SemSIn). It utilizes a GNN-based event aggregator to integrate the event-centric structure information, and employs an LSTM-based path aggregator to capture the event-associated structure information between two events. Experimental results on three widely used datasets show that SemSIn achieves significant improvements over baseline methods.	['Xueqi Cheng', 'Jiafeng Guo', 'Saiping Guan', 'Long Bai', 'Xiaolong Jin', 'Zixuan Li', 'Zhilei Hu']	2023-05-22	null	null	null	null	['event-causality-identification']	['natural-language-processing']	[ 1.72380105e-01 8.94718692e-02 -3.57061267e-01 -4.88212079e-01 -2.69361049e-01 -2.70258099e-01 1.05090332e+00 7.60964930e-01 -3.03607762e-01 8.17611098e-01 1.01371205e+00 -1.93243176e-01 -3.50184083e-01 -1.15858054e+00 -6.54635429e-01 -3.08879584e-01 -4.85275477e-01 5.20727277e-01 5.13295412e-01 -5.47416657e-02 8.17203447e-02 3.39463353e-02 -1.21698868e+00 7.11730540e-01 7.41622508e-01 8.06248665e-01 1.36630386e-01 1.86705083e-01 -7.41791666e-01 1.75169432e+00 -6.02940500e-01 -2.34080493e-01 -6.45805120e-01 -6.09090924e-01 -1.11641347e+00 -4.93016541e-01 -5.12071252e-01 -9.65469256e-02 -4.50910896e-01 9.23733056e-01 1.66107733e-02 3.53232503e-01 6.83890760e-01 -1.48727441e+00 -4.22874957e-01 1.40371525e+00 -2.09231377e-01 5.64779818e-01 5.37260413e-01 -5.05866885e-01 1.31957495e+00 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f2bfbac7-df73-4908-b022-d1cb52ebb14e	kpgt-knowledge-guided-pre-training-of-graph	2206.03364	null	https://arxiv.org/abs/2206.03364v1	https://arxiv.org/pdf/2206.03364v1.pdf	KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction	Designing accurate deep learning models for molecular property prediction plays an increasingly essential role in drug and material discovery. Recently, due to the scarcity of labeled molecules, self-supervised learning methods for learning generalizable and transferable representations of molecular graphs have attracted lots of attention. In this paper, we argue that there exist two major issues hindering current self-supervised learning methods from obtaining desired performance on molecular property prediction, that is, the ill-defined pre-training tasks and the limited model capacity. To this end, we introduce Knowledge-guided Pre-training of Graph Transformer (KPGT), a novel self-supervised learning framework for molecular graph representation learning, to alleviate the aforementioned issues and improve the performance on the downstream molecular property prediction tasks. More specifically, we first introduce a high-capacity model, named Line Graph Transformer (LiGhT), which emphasizes the importance of chemical bonds and is mainly designed to model the structural information of molecular graphs. Then, a knowledge-guided pre-training strategy is proposed to exploit the additional knowledge of molecules to guide the model to capture the abundant structural and semantic information from large-scale unlabeled molecular graphs. Extensive computational tests demonstrated that KPGT can offer superior performance over current state-of-the-art methods on several molecular property prediction tasks.	['Jianyang Zeng', 'Dan Zhao', 'Han Li']	2022-06-02	null	null	null	null	['molecular-property-prediction']	['miscellaneous']	[ 3.36306095e-01 -1.82294771e-02 -6.91123784e-01 -3.35521549e-01 -4.43464816e-01 -4.01549280e-01 1.42199710e-01 6.56493366e-01 5.30284569e-02 1.14996469e+00 -3.05278748e-02 -5.34710109e-01 -2.94499844e-01 -9.66564238e-01 -8.57309878e-01 -7.71550655e-01 -1.08934723e-01 2.32930824e-01 2.51841266e-02 -1.26673311e-01 -2.62516714e-03 5.77575684e-01 -8.85637820e-01 2.04169974e-01 1.35941529e+00 8.64052176e-01 2.32051149e-01 2.12028772e-02 -2.51190305e-01 1.13127804e+00 -6.20068386e-02 -4.26028371e-01 -3.02976966e-01 -5.76337516e-01 -7.89623916e-01 -9.15000737e-02 1.66464686e-01 -2.96737589e-02 -6.47513211e-01 9.59852695e-01 5.41769326e-01 2.08896801e-01 7.31453478e-01 -9.49802577e-01 -9.34509099e-01 6.22282028e-01 -2.47407481e-01 1.57859281e-01 2.26221934e-01 2.75456399e-01 1.12438774e+00 -6.78392529e-01 6.43817365e-01 9.11126971e-01 4.86890852e-01 6.21794999e-01 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a9de253b-2fd0-4f0e-99b0-19d818d5edaa	few-shot-learning-for-named-entity	1811.05468	null	http://arxiv.org/abs/1811.05468v1	http://arxiv.org/pdf/1811.05468v1.pdf	Few-shot Learning for Named Entity Recognition in Medical Text	Deep neural network models have recently achieved state-of-the-art performance gains in a variety of natural language processing (NLP) tasks (Young, Hazarika, Poria, & Cambria, 2017). However, these gains rely on the availability of large amounts of annotated examples, without which state-of-the-art performance is rarely achievable. This is especially inconvenient for the many NLP fields where annotated examples are scarce, such as medical text. To improve NLP models in this situation, we evaluate five improvements on named entity recognition (NER) tasks when only ten annotated examples are available: (1) layer-wise initialization with pre-trained weights, (2) hyperparameter tuning, (3) combining pre-training data, (4) custom word embeddings, and (5) optimizing out-of-vocabulary (OOV) words. Experimental results show that the F1 score of 69.3% achievable by state-of-the-art models can be improved to 78.87%.	['Alejo Nevado-Holgado', 'Maximilian Hofer', 'Paul Goldberg', 'Andrey Kormilitzin']	2018-11-13	null	null	null	null	['medical-named-entity-recognition']	['natural-language-processing']	[-3.77220730e-03 1.67540580e-01 -1.24227509e-01 -4.27595109e-01 -9.21485662e-01 -4.57613319e-01 5.04037797e-01 7.00209379e-01 -1.19042337e+00 7.49999285e-01 3.86838675e-01 -3.45229447e-01 1.07295133e-01 -6.94104791e-01 -3.62498283e-01 -3.98067623e-01 -1.10498168e-01 5.51320910e-01 -2.31198557e-02 -8.07898641e-02 8.03159364e-03 5.75886905e-01 -1.15947473e+00 1.05104282e-01 9.28453624e-01 8.27958167e-01 1.05379492e-01 6.03157282e-01 -6.47575438e-01 3.54158103e-01 -5.39222300e-01 -6.37646019e-01 8.64275545e-02 6.88470304e-02 -7.15175867e-01 -3.05693805e-01 4.74489257e-02 -2.16846570e-01 -2.36819386e-01 9.64821875e-01 7.24600255e-01 2.70332366e-01 4.30411011e-01 -7.92873621e-01 -9.34439003e-01 6.02238536e-01 -3.36436093e-01 1.43509120e-01 -7.49344081e-02 9.14917365e-02 1.03877425e+00 -9.92553949e-01 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3402cafd-83e5-406d-acb4-7b353b40d735	a-baseline-for-multi-label-image	1811.08412	null	https://arxiv.org/abs/1811.08412v3	https://arxiv.org/pdf/1811.08412v3.pdf	A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks	Recent studies on multi-label image classification have focused on designing more complex architectures of deep neural networks such as the use of attention mechanisms and region proposal networks. Although performance gains have been reported, the backbone deep models of the proposed approaches and the evaluation metrics employed in different works vary, making it difficult to compare each fairly. Moreover, due to the lack of properly investigated baselines, the advantage introduced by the proposed techniques are often ambiguous. To address these issues, we make a thorough investigation of the mainstream deep convolutional neural network architectures for multi-label image classification and present a strong baseline. With the use of proper data augmentation techniques and model ensembles, the basic deep architectures can achieve better performance than many existing more complex ones on three benchmark datasets, providing great insight for the future studies on multi-label image classification.	['Toby P. Breckon', 'Ning Jia', 'Qian Wang']	2018-11-20	null	null	null	null	['multi-label-image-classification']	['computer-vision']	[ 2.90140331e-01 -2.19722047e-01 -6.28039300e-01 -5.38170397e-01 -6.15243554e-01 -3.63731861e-01 7.16010690e-01 2.93591768e-01 -5.03145695e-01 6.61226571e-01 -3.03874128e-02 -3.13371718e-01 -4.05641310e-02 -4.52271014e-01 -4.38565403e-01 -9.94416237e-01 2.19874755e-01 2.76544720e-01 -1.28683653e-02 -4.00678366e-02 3.27061445e-01 4.23413336e-01 -1.68368506e+00 3.05837005e-01 5.68429232e-01 1.31246042e+00 -1.36963218e-01 4.70943302e-01 -2.23607406e-01 8.60606492e-01 -7.24894762e-01 -4.96328592e-01 1.05790757e-01 -2.97870696e-01 -9.13032711e-01 -1.74750760e-02 5.16943991e-01 -2.34807685e-01 -5.80336303e-02 1.19732046e+00 6.73646212e-01 -4.04511206e-02 9.37803864e-01 -1.23894298e+00 -1.02629471e+00 5.37125528e-01 -7.41402388e-01 1.41580507e-01 -1.38274103e-01 -2.13229343e-01 1.17249906e+00 -7.86474645e-01 2.88959324e-01 1.12640095e+00 8.64684701e-01 5.36820710e-01 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-5.96735060e-01 6.44704223e-01 1.81785882e-01 -8.92577112e-01 -1.39426783e-01 -3.18019420e-01]	[9.707778930664062, 4.163860321044922]
346ea3b4-6196-4391-836d-5e98b47178c1	codecmr-cross-modal-retrieval-for-function	null	null	http://proceedings.neurips.cc/paper/2020/hash/285f89b802bcb2651801455c86d78f2a-Abstract.html	http://proceedings.neurips.cc/paper/2020/file/285f89b802bcb2651801455c86d78f2a-Paper.pdf	CodeCMR: Cross-Modal Retrieval For Function-Level Binary Source Code Matching	Binary source code matching, especially on function-level, has a critical role in the field of computer security. Given binary code only, finding the corresponding source code improves the accuracy and efficiency in reverse engineering. Given source code only, related binary code retrieval contributes to known vulnerabilities confirmation. However, due to the vast difference between source and binary code, few studies have investigated binary source code matching. Previously published studies focus on code literals extraction such as strings and integers, then utilize traditional matching algorithms such as the Hungarian algorithm for code matching. Nevertheless, these methods have limitations on function-level, because they ignore the potential semantic features of code and a lot of code lacks sufficient code literals. Also, these methods indicate a need for expert experience for useful feature identification and feature engineering, which is timeconsuming. This paper proposes an end-to-end cross-modal retrieval network for binary source code matching, which achieves higher accuracy and requires less expert experience. We adopt Deep Pyramid Convolutional Neural Network (DPCNN) for source code feature extraction and Graph Neural Network (GNN) for binary code feature extraction. We also exploit neural network-based models to capture code literals, including strings and integers. Furthermore, we implement "norm weighted sampling" for negative sampling. We evaluate our model on two datasets, where it outperforms other methods significantly.	['Shi Wu', 'Sen Nie', 'Qiyi Tang', 'Jiaqi Wang', 'Wenxin Zheng', 'Zeping Yu']	2020-12-01	null	null	null	neurips-2020-12	['computer-security']	['miscellaneous']	[ 7.39391223e-02 -4.91399646e-01 -5.70728421e-01 -1.43244281e-01 -7.81312525e-01 -9.47843671e-01 2.35611550e-03 5.63397110e-01 1.72233824e-02 5.58678396e-02 -7.95193762e-03 -7.83494592e-01 -2.17847511e-01 -1.23876023e+00 -6.04523063e-01 -3.26178013e-03 -8.38686824e-02 -3.92045617e-01 2.97648937e-01 -4.26876783e-01 7.05361068e-01 3.40786666e-01 -1.40414679e+00 3.61652166e-01 8.71163487e-01 9.91542816e-01 -6.84439987e-02 5.00153601e-01 -4.92030591e-01 8.90572965e-01 -5.99026024e-01 -8.14763665e-01 3.57821524e-01 -1.06535017e-01 -5.64718902e-01 -8.12599123e-01 3.24549407e-01 -3.02222073e-01 -7.53188133e-01 1.75635600e+00 5.02542973e-01 -4.19388056e-01 3.89913827e-01 -1.37121260e+00 -1.20707655e+00 8.27277482e-01 -7.14244366e-01 5.92992157e-02 6.68403387e-01 -8.72361511e-02 1.16789532e+00 -6.64130390e-01 3.48554999e-01 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447dfb85-b0ed-4e8c-9722-21c684b0df07	a-data-efficient-deep-learning-framework-for	2207.06489	null	https://arxiv.org/abs/2207.06489v5	https://arxiv.org/pdf/2207.06489v5.pdf	A Data-Efficient Deep Learning Framework for Segmentation and Classification of Histopathology Images	The current study of cell architecture of inflammation in histopathology images commonly performed for diagnosis and research purposes excludes a lot of information available on the biopsy slide. In autoimmune diseases, major outstanding research questions remain regarding which cell types participate in inflammation at the tissue level, and how they interact with each other. While these questions can be partially answered using traditional methods, artificial intelligence approaches for segmentation and classification provide a much more efficient method to understand the architecture of inflammation in autoimmune disease, holding great promise for novel insights. In this paper, we empirically develop deep learning approaches that use dermatomyositis biopsies of human tissue to detect and identify inflammatory cells. Our approach improves classification performance by 26% and segmentation performance by 5%. We also propose a novel post-processing autoencoder architecture that improves segmentation performance by an additional 3%.	['Jacopo Cirrone', 'Pranav Singh']	2022-07-13	null	null	null	null	['classification']	['methodology']	[ 2.72338122e-01 -1.34276360e-01 -3.37454110e-01 -1.51728187e-02 -8.99649620e-01 -5.99840522e-01 1.16857670e-01 6.03389919e-01 -5.33192754e-01 6.00916445e-01 1.30045429e-01 -2.70439953e-01 1.20697670e-01 -7.61651099e-01 -1.67064160e-01 -1.39511132e+00 -1.05818957e-01 8.18980694e-01 -2.96010282e-02 1.79365024e-01 2.69235522e-02 8.29297960e-01 -9.70205069e-01 7.59101212e-01 4.97028947e-01 8.39518309e-01 -1.52609900e-01 1.09327292e+00 -2.41429880e-01 5.98606527e-01 -5.12804568e-01 9.18769315e-02 9.19067934e-02 -2.94525802e-01 -9.90149975e-01 2.21337363e-01 2.16368333e-01 -5.47115743e-01 -2.02845648e-01 7.65999198e-01 6.79781556e-01 -3.48688304e-01 9.24854934e-01 -6.14336967e-01 -1.47111878e-01 2.87644982e-01 -6.92717791e-01 4.99136388e-01 -2.03198552e-01 1.16231136e-01 7.76187062e-01 -3.28129411e-01 9.62635159e-01 8.74326587e-01 5.36038458e-01 6.44019485e-01 -1.52400208e+00 -2.03894779e-01 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2768b74d-c157-4e5f-a0d2-6154ade25f7e	gammae-gamma-embeddings-for-logical-queries	2210.15578	null	https://arxiv.org/abs/2210.15578v2	https://arxiv.org/pdf/2210.15578v2.pdf	GammaE: Gamma Embeddings for Logical Queries on Knowledge Graphs	Embedding knowledge graphs (KGs) for multi-hop logical reasoning is a challenging problem due to massive and complicated structures in many KGs. Recently, many promising works projected entities and queries into a geometric space to efficiently find answers. However, it remains challenging to model the negation and union operator. The negation operator has no strict boundaries, which generates overlapped embeddings and leads to obtaining ambiguous answers. An additional limitation is that the union operator is non-closure, which undermines the model to handle a series of union operators. To address these problems, we propose a novel probabilistic embedding model, namely Gamma Embeddings (GammaE), for encoding entities and queries to answer different types of FOL queries on KGs. We utilize the linear property and strong boundary support of the Gamma distribution to capture more features of entities and queries, which dramatically reduces model uncertainty. Furthermore, GammaE implements the Gamma mixture method to design the closed union operator. The performance of GammaE is validated on three large logical query datasets. Experimental results show that GammaE significantly outperforms state-of-the-art models on public benchmarks.	['Xiaodong Lin', 'Haonan Lu', 'Yang Li', 'Peijun Qing', 'Dong Yang']	2022-10-27	null	null	null	null	['logical-reasoning']	['reasoning']	[-5.07426500e-01 2.31977925e-01 -5.40380716e-01 -3.66946220e-01 -4.56811965e-01 -4.16067183e-01 1.96741924e-01 3.30700815e-01 -3.07704926e-01 5.77024579e-01 2.58263946e-01 -2.78530866e-01 -6.10707641e-01 -1.45088696e+00 -8.05407047e-01 -3.47316712e-01 -1.06502231e-02 6.20071590e-01 8.94712389e-01 -1.57578662e-01 5.67133836e-02 1.59445122e-01 -1.37137258e+00 1.46087915e-01 1.03177261e+00 1.07648766e+00 -1.18987285e-01 -1.61918011e-02 -4.93156672e-01 7.60479510e-01 -3.98961812e-01 -7.52076030e-01 -6.05624635e-03 1.32207856e-01 -9.45132852e-01 -6.64723873e-01 2.20270324e-02 -3.76984119e-01 -7.14903772e-01 1.21125233e+00 3.53050232e-01 8.16645771e-02 6.90664947e-01 -1.54667234e+00 -1.30674148e+00 7.97347307e-01 -3.38079363e-01 1.10955657e-02 5.62280655e-01 -3.74915332e-01 1.49113357e+00 -9.97454107e-01 4.86488312e-01 1.46350431e+00 6.63058698e-01 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e46eda07-8ebf-4f0b-9f13-00aa9f8474b7	speeding-up-the-hyperparameter-optimization	1807.07362	null	http://arxiv.org/abs/1807.07362v1	http://arxiv.org/pdf/1807.07362v1.pdf	Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks	Most learning algorithms require the practitioner to manually set the values of many hyperparameters before the learning process can begin. However, with modern algorithms, the evaluation of a given hyperparameter setting can take a considerable amount of time and the search space is often very high-dimensional. We suggest using a lower-dimensional representation of the original data to quickly identify promising areas in the hyperparameter space. This information can then be used to initialize the optimization algorithm for the original, higher-dimensional data. We compare this approach with the standard procedure of optimizing the hyperparameters only on the original input. We perform experiments with various state-of-the-art hyperparameter optimization algorithms such as random search, the tree of parzen estimators (TPEs), sequential model-based algorithm configuration (SMAC), and a genetic algorithm (GA). Our experiments indicate that it is possible to speed up the optimization process by using lower-dimensional data representations at the beginning, while increasing the dimensionality of the input later in the optimization process. This is independent of the underlying optimization procedure, making the approach promising for many existing hyperparameter optimization algorithms.	['Nicolás Navarro-Guerrero', 'Tobias Hinz', 'Sven Magg', 'Stefan Wermter']	2018-07-19	null	null	null	null	['smac-1', 'smac']	['playing-games', 'playing-games']	[ 1.12804413e-01 -2.23632812e-01 -1.19132578e-01 -2.64412344e-01 -8.24114501e-01 -7.24310517e-01 4.88405973e-01 1.91951662e-01 -7.19101667e-01 7.93556094e-01 -2.14330573e-02 -3.85317087e-01 -4.96658623e-01 -7.62280166e-01 -2.86609411e-01 -9.87276971e-01 1.81703940e-01 1.18462288e+00 2.23022595e-01 -3.84674817e-02 7.91693985e-01 6.98999465e-01 -1.67399263e+00 -1.34431913e-01 6.99500024e-01 6.28890276e-01 -5.45814149e-02 9.09348011e-01 -2.07696319e-01 -3.09234202e-01 -6.93970919e-01 -1.53593257e-01 3.97705942e-01 -5.00532925e-01 -7.65004575e-01 1.98817149e-01 2.18804926e-02 2.04259560e-01 3.07176620e-01 8.42192233e-01 5.78779697e-01 5.21538436e-01 7.34377146e-01 -1.04792237e+00 2.12560579e-01 4.73028719e-01 -3.22729290e-01 -2.88874544e-02 2.30887666e-01 3.06327522e-01 7.86235392e-01 -6.59640670e-01 5.14496565e-01 1.31971550e+00 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e4ee16f6-2447-4f86-bc46-a07c9f95851a	convolutional-neural-networks-applied-to-sky	2005.11246	null	https://arxiv.org/abs/2005.11246v1	https://arxiv.org/pdf/2005.11246v1.pdf	Convolutional Neural Networks applied to sky images for short-term solar irradiance forecasting	Despite the advances in the field of solar energy, improvements of solar forecasting techniques, addressing the intermittent electricity production, remain essential for securing its future integration into a wider energy supply. A promising approach to anticipate irradiance changes consists of modeling the cloud cover dynamics from ground taken or satellite images. This work presents preliminary results on the application of deep Convolutional Neural Networks for 2 to 20 min irradiance forecasting using hemispherical sky images and exogenous variables. We evaluate the models on a set of irradiance measurements and corresponding sky images collected in Palaiseau (France) over 8 months with a temporal resolution of 2 min. To outline the learning of neural networks in the context of short-term irradiance forecasting, we implemented visualisation techniques revealing the types of patterns recognised by trained algorithms in sky images. In addition, we show that training models with past samples of the same day improves their forecast skill, relative to the smart persistence model based on the Mean Square Error, by around 10% on a 10 min ahead prediction. These results emphasise the benefit of integrating previous same-day data in short-term forecasting. This, in turn, can be achieved through model fine tuning or using recurrent units to facilitate the extraction of relevant temporal features from past data.	['Quentin Paletta', 'Joan Lasenby']	2020-05-22	null	null	null	null	['solar-irradiance-forecasting']	['time-series']	[ 2.01005444e-01 -3.00641328e-01 2.59900481e-01 -4.98383075e-01 -4.27632853e-02 -8.41335237e-01 1.06982315e+00 -4.65745628e-02 -3.11252102e-02 9.73815739e-01 1.81556925e-01 -6.06052101e-01 -2.36188486e-01 -1.05554247e+00 -5.70266247e-01 -1.02713919e+00 -3.76024365e-01 -1.93630800e-01 -2.79145956e-01 -4.66921717e-01 1.35251790e-01 8.73566031e-01 -2.01227069e+00 1.59319103e-01 8.15678418e-01 9.72711742e-01 4.06099439e-01 9.99122798e-01 -7.49362409e-02 4.05137092e-01 -8.07589233e-01 3.42947215e-01 3.28126609e-01 -4.08408672e-01 -2.33653888e-01 -1.71154961e-01 6.25627875e-01 -1.36923447e-01 4.35884558e-02 5.85134268e-01 5.06326973e-01 1.49636671e-01 5.02323389e-01 -7.05738842e-01 -1.70889303e-01 1.01670004e-01 9.37574953e-02 6.99219644e-01 1.55919626e-01 2.85636753e-01 5.54872930e-01 -5.47346473e-01 3.99859995e-01 5.40300071e-01 7.53371358e-01 -1.36479408e-01 -1.15383315e+00 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-1.12250827e-01 8.11179638e-01 -1.33767381e-01 -6.46486402e-01 4.66665551e-02]	[6.330924987792969, 2.766744375228882]
d9c335ff-4bb8-438b-afcd-53d7932dc498	dynamic-term-structure-models-with	2305.11001	null	https://arxiv.org/abs/2305.11001v1	https://arxiv.org/pdf/2305.11001v1.pdf	Dynamic Term Structure Models with Nonlinearities using Gaussian Processes	The importance of unspanned macroeconomic variables for Dynamic Term Structure Models has been intensively discussed in the literature. To our best knowledge the earlier studies considered only linear interactions between the economy and the real-world dynamics of interest rates in DTSMs. We propose a generalized modelling setup for Gaussian DTSMs which allows for unspanned nonlinear associations between the two and we exploit it in forecasting. Specifically, we construct a custom sequential Monte Carlo estimation and forecasting scheme where we introduce Gaussian Process priors to model nonlinearities. Sequential scheme we propose can also be used with dynamic portfolio optimization to assess the potential of generated economic value to investors. The methodology is presented using US Treasury data and selected macroeconomic indices. Namely, we look at core inflation and real economic activity. We contrast the results obtained from the nonlinear model with those stemming from an application of a linear model. Unlike for real economic activity, in case of core inflation we find that, compared to linear models, application of nonlinear models leads to statistically significant gains in economic value across considered maturities.	['Nikolaos Karouzakis', 'Konstantinos Kalogeropoulos', 'Tomasz Dubiel-Teleszynski']	2023-05-18	null	null	null	null	['portfolio-optimization']	['time-series']	[-2.54532516e-01 1.40359119e-01 1.49794951e-01 -1.37660384e-01 -5.62123597e-01 -8.21791351e-01 1.29439783e+00 -1.58590171e-02 -4.91596669e-01 8.62941206e-01 2.90782064e-01 -8.25688243e-01 -4.04889226e-01 -9.41389263e-01 -4.13240224e-01 -9.25523281e-01 -2.86585577e-02 7.21482277e-01 -1.58701062e-01 -1.02536313e-01 3.26640636e-01 3.44524741e-01 -1.06482363e+00 -2.61128962e-01 6.23068154e-01 8.99965525e-01 1.61615998e-01 4.43847001e-01 5.99063991e-04 3.31154346e-01 -3.85752529e-01 -6.84036136e-01 4.65402871e-01 1.68810368e-01 -3.36519212e-01 -8.89509246e-02 -5.25434792e-01 7.61978552e-02 1.76093683e-01 9.91903305e-01 4.83759850e-01 3.17853153e-01 9.88680422e-01 -5.38581908e-01 -4.63756830e-01 9.58942890e-01 -4.40777063e-01 5.38558662e-01 4.49351184e-02 1.88968167e-01 8.64131749e-01 -6.62145793e-01 1.30625844e-01 1.49497402e+00 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6667b46e-28bc-4596-83c8-614459595b3a	autotoon-automatic-geometric-warping-for-face	2004.02377	null	https://arxiv.org/abs/2004.02377v1	https://arxiv.org/pdf/2004.02377v1.pdf	AutoToon: Automatic Geometric Warping for Face Cartoon Generation	Caricature, a type of exaggerated artistic portrait, amplifies the distinctive, yet nuanced traits of human faces. This task is typically left to artists, as it has proven difficult to capture subjects' unique characteristics well using automated methods. Recent development of deep end-to-end methods has achieved promising results in capturing style and higher-level exaggerations. However, a key part of caricatures, face warping, has remained challenging for these systems. In this work, we propose AutoToon, the first supervised deep learning method that yields high-quality warps for the warping component of caricatures. Completely disentangled from style, it can be paired with any stylization method to create diverse caricatures. In contrast to prior art, we leverage an SENet and spatial transformer module and train directly on artist warping fields, applying losses both prior to and after warping. As shown by our user studies, we achieve appealing exaggerations that amplify distinguishing features of the face while preserving facial detail.	['Yannick Hold-Geoffroy', 'Jingwan Lu', 'Julia Gong']	2020-04-06	null	null	null	null	['caricature']	['computer-vision']	[ 3.36956561e-01 2.85900235e-01 2.08045572e-01 -6.90687120e-01 -5.26976645e-01 -7.48773754e-01 7.44594157e-01 -6.37205541e-01 5.08557335e-02 6.30637944e-01 5.65098405e-01 2.76842654e-01 1.58499762e-01 -4.49534297e-01 -5.95284164e-01 -3.25170010e-01 2.78138995e-01 3.21665138e-01 -4.76850599e-01 -5.66384852e-01 -4.92345951e-02 8.47655535e-01 -1.38658595e+00 4.19958919e-01 6.06834650e-01 8.47521126e-01 -4.48423505e-01 5.08944750e-01 7.88096488e-02 4.45320666e-01 -6.00378335e-01 -1.19662094e+00 5.02647936e-01 -4.94746655e-01 -2.59741396e-01 3.92881259e-02 1.29717672e+00 -6.44447803e-01 -4.32955176e-01 9.14534748e-01 6.21985972e-01 -2.86446214e-01 5.33643305e-01 -1.25927663e+00 -1.08182311e+00 6.51267886e-01 -9.08376276e-01 -3.53068620e-01 2.19966441e-01 4.31454837e-01 1.11544120e+00 -9.96020913e-01 8.63796294e-01 1.62684381e+00 1.04117143e+00 8.48157585e-01 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-9.66846347e-01 -6.15667522e-01 9.79152501e-01 6.17386639e-01 -6.15307271e-01 -3.02824676e-01 1.44207850e-01]	[12.230623245239258, -0.33452433347702026]
bd5fc5e2-2fb3-4ba5-bafe-17ea9d42ad31	comparative-study-of-machine-learning-models-1	2202.03156	null	https://arxiv.org/abs/2202.03156v1	https://arxiv.org/pdf/2202.03156v1.pdf	Comparative Study of Machine Learning Models for Stock Price Prediction	In this work, we apply machine learning techniques to historical stock prices to forecast future prices. To achieve this, we use recursive approaches that are appropriate for handling time series data. In particular, we apply a linear Kalman filter and different varieties of long short-term memory (LSTM) architectures to historical stock prices over a 10-year range (1/1/2011 - 1/1/2021). We quantify the results of these models by computing the error of the predicted values versus the historical values of each stock. We find that of the algorithms we investigated, a simple linear Kalman filter can predict the next-day value of stocks with low-volatility (e.g., Microsoft) surprisingly well. However, in the case of high-volatility stocks (e.g., Tesla) the more complex LSTM algorithms significantly outperform the Kalman filter. Our results show that we can classify different types of stocks and then train an LSTM for each stock type. This method could be used to automate portfolio generation for a target return rate.	['Sasha S. Yamada', 'Ogulcan E. Orsel']	2022-01-31	null	null	null	null	['stock-price-prediction']	['time-series']	[-6.23525202e-01 -2.97209799e-01 1.21455304e-02 -1.83675930e-01 -3.98456722e-01 -9.10167098e-01 8.96282971e-01 -1.23445727e-01 -5.74092090e-01 9.40409660e-01 1.92585289e-01 -8.92770648e-01 -1.53272182e-01 -1.21357882e+00 -7.43317246e-01 -4.76723015e-01 -2.24729806e-01 2.49036446e-01 1.14676870e-01 -3.56426507e-01 5.78187943e-01 6.09640598e-01 -1.35630560e+00 2.17003927e-01 2.79630840e-01 1.51380301e+00 7.25897178e-02 6.08817637e-01 -6.09490037e-01 7.49623775e-01 -7.02182651e-01 -4.50112849e-01 6.76978827e-01 -2.26367973e-02 -2.27855325e-01 -5.07079244e-01 6.22651465e-02 -5.82644880e-01 -2.62757152e-01 1.05645621e+00 1.00380838e-01 2.01284178e-02 3.60944480e-01 -7.20270336e-01 -4.16864574e-01 1.02075768e+00 -5.17299026e-02 5.23510098e-01 4.10107113e-02 1.03694580e-01 8.87922466e-01 -6.67072952e-01 3.28126073e-01 9.34916914e-01 8.79134655e-01 4.58050407e-02 -9.93477881e-01 -7.29417980e-01 3.49852473e-01 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d375592b-7ac1-4954-8106-e0fadf6fa5b3	on-vision-features-in-multimodal-machine-1	2203.09173	null	https://arxiv.org/abs/2203.09173v1	https://arxiv.org/pdf/2203.09173v1.pdf	On Vision Features in Multimodal Machine Translation	Previous work on multimodal machine translation (MMT) has focused on the way of incorporating vision features into translation but little attention is on the quality of vision models. In this work, we investigate the impact of vision models on MMT. Given the fact that Transformer is becoming popular in computer vision, we experiment with various strong models (such as Vision Transformer) and enhanced features (such as object-detection and image captioning). We develop a selective attention model to study the patch-level contribution of an image in MMT. On detailed probing tasks, we find that stronger vision models are helpful for learning translation from the visual modality. Our results also suggest the need of carefully examining MMT models, especially when current benchmarks are small-scale and biased. Our code could be found at \url{https://github.com/libeineu/fairseq_mmt}.	['Jingbo Zhu', 'Anxiang Ma', 'Tong Xiao', 'Tao Zhou', 'Zefan Zhou', 'Chuanhao Lv', 'Bei Li']	2022-03-17	null	https://aclanthology.org/2022.acl-long.438	https://aclanthology.org/2022.acl-long.438.pdf	acl-2022-5	['multimodal-machine-translation']	['natural-language-processing']	[ 1.69398144e-01 3.00340215e-03 -1.94158882e-01 -1.63658723e-01 -8.70120108e-01 -6.10157490e-01 9.23500240e-01 -3.85115296e-01 -3.37490052e-01 4.09663230e-01 5.22712529e-01 -6.04642749e-01 5.01914203e-01 -3.77407312e-01 -9.83414114e-01 -3.72742444e-01 5.47766864e-01 3.13252926e-01 -4.69428627e-03 -2.83004075e-01 1.99387133e-01 1.33295700e-01 -1.10422933e+00 5.23051262e-01 7.56308675e-01 6.56019151e-01 2.79302031e-01 7.49604702e-01 3.95856798e-02 6.65982604e-01 -5.00384271e-01 -9.34237123e-01 3.94722283e-01 -6.97593689e-01 -7.69761622e-01 1.76138416e-01 8.86889398e-01 -4.22105968e-01 -5.32412112e-01 1.04957950e+00 7.10444272e-01 -3.18378210e-02 6.70268118e-01 -1.40053558e+00 -1.12804925e+00 6.06870711e-01 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-1.82745785e-01 -2.47825503e-01 5.46787620e-01 7.25896895e-01 4.94259536e-01 3.33290815e-01 2.96206892e-01 -8.58683646e-01 -3.15195858e-01 -1.04752815e+00 9.12640318e-02 3.88662010e-01 3.87404889e-01 -4.79667217e-01 -1.70355946e-01 1.67251706e-01]	[11.343172073364258, 1.4568474292755127]
6f6723b0-9736-4d4a-9e65-7f686a7257d6	quadratic-decomposable-submodular-function-1	1902.10132	null	https://arxiv.org/abs/1902.10132v4	https://arxiv.org/pdf/1902.10132v4.pdf	Quadratic Decomposable Submodular Function Minimization: Theory and Practice (Computation and Analysis of PageRank over Hypergraphs)	We introduce a new convex optimization problem, termed quadratic decomposable submodular function minimization (QDSFM), which allows to model a number of learning tasks on graphs and hypergraphs. The problem exhibits close ties to decomposable submodular function minimization (DSFM), yet is much more challenging to solve. We approach the problem via a new dual strategy and formulate an objective that can be optimized through a number of double-loop algorithms. The outer-loop uses either random coordinate descent (RCD) or alternative projection (AP) methods, for both of which we prove linear convergence rates. The inner-loop computes projections onto cones generated by base polytopes of the submodular functions, via the modified min-norm-point or Frank-Wolfe algorithm. We also describe two new applications of QDSFM: hypergraph-adapted PageRank and semi-supervised learning. The proposed hypergraph-based PageRank algorithm can be used for local hypergraph partitioning, and comes with provable performance guarantees. For hypergraph-adapted semi-supervised learning, we provide numerical experiments demonstrating the efficiency of our QDSFM solvers and their significant improvements on prediction accuracy when compared to state-of-the-art methods.	['Olgica Milenkovic', 'Niao He', 'Pan Li']	2019-02-26	null	null	null	null	['hypergraph-partitioning']	['graphs']	[ 2.42834046e-01 5.34243345e-01 -6.44117296e-01 -1.03504121e-01 -1.08947241e+00 -7.63682306e-01 1.85893372e-01 1.31210297e-01 1.24317877e-01 8.87975097e-01 8.47789943e-02 -2.85234630e-01 -7.24509537e-01 -7.42959738e-01 -9.59627330e-01 -8.14414442e-01 -1.63577363e-01 1.16289675e+00 5.43664284e-02 -4.03856486e-02 4.94651236e-02 3.67242724e-01 -1.06836975e+00 1.64059341e-01 8.95361245e-01 8.49220395e-01 7.70690909e-04 6.46633565e-01 -2.69838963e-02 6.22232437e-01 7.66252428e-02 -4.98420298e-01 7.63682425e-01 -1.96482405e-01 -1.05734956e+00 5.43172836e-01 7.99996197e-01 6.72717616e-02 -5.16006649e-01 1.14556098e+00 2.06131577e-01 -4.59776074e-02 5.21057189e-01 -1.68286300e+00 -5.29306173e-01 5.48265934e-01 -9.41622436e-01 -2.13782370e-01 3.92536819e-01 -3.79442871e-01 1.54033077e+00 -9.56924856e-01 8.16800475e-01 1.30655575e+00 4.57691967e-01 1.41545907e-01 -1.61639893e+00 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1.77929729e-01 6.04891106e-02 -4.82162774e-01 -5.73399365e-01 -3.19733948e-01]	[7.000067234039307, 5.019751071929932]
d83d3425-9c69-43f1-861b-b31fbb4758cd	convergence-to-the-fixed-node-limit-in-deep	2010.05316	null	https://arxiv.org/abs/2010.05316v2	https://arxiv.org/pdf/2010.05316v2.pdf	Convergence to the fixed-node limit in deep variational Monte Carlo	Variational quantum Monte Carlo (QMC) is an ab-initio method for solving the electronic Schr\"odinger equation that is exact in principle, but limited by the flexibility of the available ansatzes in practice. The recently introduced deep QMC approach, specifically two deep-neural-network ansatzes PauliNet and FermiNet, allows variational QMC to reach the accuracy of diffusion QMC, but little is understood about the convergence behavior of such ansatzes. Here, we analyze how deep variational QMC approaches the fixed-node limit with increasing network size. First, we demonstrate that a deep neural network can overcome the limitations of a small basis set and reach the mean-field complete-basis-set limit. Moving to electron correlation, we then perform an extensive hyperparameter scan of a deep Jastrow factor for LiH and H$_4$ and find that variational energies at the fixed-node limit can be obtained with a sufficiently large network. Finally, we benchmark mean-field and many-body ansatzes on H$_2$O, increasing the fraction of recovered fixed-node correlation energy of single-determinant Slater--Jastrow-type ansatzes by half an order of magnitude compared to previous variational QMC results and demonstrate that a single-determinant Slater--Jastrow--backflow version of the ansatz overcomes the fixed-node limitations. This analysis helps understanding the superb accuracy of deep variational ansatzes in comparison to the traditional trial wavefunctions at the respective level of theory, and will guide future improvements of the neural network architectures in deep QMC.	['Frank Noé', 'Jan Hermann', 'Zeno Schätzle']	2020-10-11	null	null	null	null	['variational-monte-carlo']	['miscellaneous']	[-1.28597915e-01 -1.97666049e-01 1.58690706e-01 -1.04380026e-01 -1.04035842e+00 -2.02133045e-01 3.95625442e-01 -2.90446639e-01 -6.52553797e-01 1.24568117e+00 -2.72417217e-02 -4.57717061e-01 -4.65166062e-01 -8.29547524e-01 -5.43552637e-01 -1.32215333e+00 -1.53083563e-01 5.82674205e-01 -1.63223028e-01 -6.60346568e-01 2.55806893e-01 5.70187807e-01 -1.07013917e+00 2.44813979e-01 7.32491493e-01 7.45760679e-01 -2.02468425e-01 3.90938729e-01 3.74435097e-01 4.11499947e-01 -1.86360762e-01 -1.24432795e-01 2.72575498e-01 -7.05025673e-01 -1.03071916e+00 -8.90874505e-01 3.52262676e-01 -9.08686891e-02 -8.42662811e-01 1.33581281e+00 7.96648741e-01 6.47677302e-01 7.55488276e-01 -5.44952095e-01 -7.19087780e-01 7.23311841e-01 -1.19613014e-01 1.72660694e-01 -2.41524637e-01 8.40789318e-01 1.26913476e+00 -8.08576226e-01 7.69765377e-01 8.32586169e-01 1.10510373e+00 9.62927222e-01 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1624e5f0-b6d5-4494-beb9-1b0203b62775	video-action-recognition-with-attentive	2303.09756	null	https://arxiv.org/abs/2303.09756v1	https://arxiv.org/pdf/2303.09756v1.pdf	Video Action Recognition with Attentive Semantic Units	Visual-Language Models (VLMs) have significantly advanced action video recognition. Supervised by the semantics of action labels, recent works adapt the visual branch of VLMs to learn video representations. Despite the effectiveness proved by these works, we believe that the potential of VLMs has yet to be fully harnessed. In light of this, we exploit the semantic units (SU) hiding behind the action labels and leverage their correlations with fine-grained items in frames for more accurate action recognition. SUs are entities extracted from the language descriptions of the entire action set, including body parts, objects, scenes, and motions. To further enhance the alignments between visual contents and the SUs, we introduce a multi-region module (MRA) to the visual branch of the VLM. The MRA allows the perception of region-aware visual features beyond the original global feature. Our method adaptively attends to and selects relevant SUs with visual features of frames. With a cross-modal decoder, the selected SUs serve to decode spatiotemporal video representations. In summary, the SUs as the medium can boost discriminative ability and transferability. Specifically, in fully-supervised learning, our method achieved 87.8\% top-1 accuracy on Kinetics-400. In K=2 few-shot experiments, our method surpassed the previous state-of-the-art by +7.1% and +15.0% on HMDB-51 and UCF-101, respectively.	['Wei Peng', 'Hao Li', 'Ruijin Liu', 'Dapeng Chen', 'Yifei Chen']	2023-03-17	null	null	null	null	['video-recognition']	['computer-vision']	[ 3.41102958e-01 -1.60450473e-01 -8.24722409e-01 -2.42747590e-01 -7.22286105e-01 -3.68857980e-01 7.27122247e-01 -2.66172796e-01 -4.59580868e-01 4.59107578e-01 7.59048760e-01 3.29169571e-01 2.94248641e-01 -3.35063666e-01 -7.87438571e-01 -6.34647369e-01 -1.68584988e-01 -1.11037023e-01 5.39812624e-01 -6.18308559e-02 1.36670783e-01 2.52896756e-01 -1.71832252e+00 1.06544375e+00 2.27860451e-01 1.29675460e+00 3.63634765e-01 4.96128738e-01 -6.82835206e-02 1.51879370e+00 -3.14137161e-01 2.56126113e-02 8.51099566e-02 -6.81205332e-01 -6.67270660e-01 3.95410240e-01 5.58946848e-01 -7.24973202e-01 -1.00471592e+00 7.07353234e-01 3.13631326e-01 3.48619878e-01 7.28638709e-01 -1.33553374e+00 -7.26223707e-01 4.44934845e-01 -5.91950774e-01 5.86615682e-01 6.56673551e-01 4.27648187e-01 1.16437864e+00 -1.21067154e+00 9.54688847e-01 1.36526012e+00 1.11129530e-01 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420d32e8-f66f-4e71-9b7e-393198608224	prediction-of-kidney-function-from-biopsy	1702.01816	null	http://arxiv.org/abs/1702.01816v1	http://arxiv.org/pdf/1702.01816v1.pdf	Prediction of Kidney Function from Biopsy Images Using Convolutional Neural Networks	A Convolutional Neural Network was used to predict kidney function in patients with chronic kidney disease from high-resolution digital pathology scans of their kidney biopsies. Kidney biopsies were taken from participants of the NEPTUNE study, a longitudinal cohort study whose goal is to set up infrastructure for observing the evolution of 3 forms of idiopathic nephrotic syndrome, including developing predictors for progression of kidney disease. The knowledge of future kidney function is desirable as it can identify high-risk patients and influence treatment decisions, reducing the likelihood of irreversible kidney decline.	['David Ledbetter', 'Long Ho', 'Kevin V Lemley']	2017-02-06	null	null	null	null	['kidney-function']	['medical']	[ 1.78751007e-01 2.46338099e-02 -3.44865531e-01 -8.78742039e-01 -2.34318078e-01 -3.48380595e-01 1.09714396e-01 4.83011127e-01 -4.60856050e-01 4.92808491e-01 6.08621478e-01 -4.72110450e-01 -5.51859200e-01 -1.30764627e+00 -2.57742137e-01 -1.80588543e-01 -7.14372158e-01 1.21552122e+00 -2.71864176e-01 4.05622721e-01 -8.73648077e-02 1.12348890e+00 -5.54527700e-01 2.39590883e-01 7.27962255e-01 4.87546474e-01 1.40653804e-01 1.01200199e+00 -2.41430834e-01 9.24115002e-01 1.95928380e-01 -8.14231113e-02 5.64238250e-01 -6.15422487e-01 -6.18807256e-01 -1.66917130e-01 8.91621590e-01 -7.28037894e-01 -5.70522308e-01 6.89709008e-01 6.23906553e-01 -4.58136290e-01 4.71408248e-01 -2.00049043e-01 -7.93273032e-01 9.36248064e-01 -5.25346547e-02 5.72435558e-01 -5.70743263e-01 5.48651636e-01 4.86952454e-01 -4.70459700e-01 7.81277180e-01 9.99832451e-01 7.87729025e-01 5.03572762e-01 -1.65106773e+00 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-9.23260152e-01 2.88447201e-01 4.77056950e-01 -1.36352167e-01 1.09554976e-02 -2.08739623e-01]	[14.144893646240234, -2.455165386199951]
26b82f51-af6c-4393-8aaa-e4cee2fc6c6a	3d-shape-reconstruction-of-semi-transparent	2304.14841	null	https://arxiv.org/abs/2304.14841v1	https://arxiv.org/pdf/2304.14841v1.pdf	3D shape reconstruction of semi-transparent worms	3D shape reconstruction typically requires identifying object features or textures in multiple images of a subject. This approach is not viable when the subject is semi-transparent and moving in and out of focus. Here we overcome these challenges by rendering a candidate shape with adaptive blurring and transparency for comparison with the images. We use the microscopic nematode Caenorhabditis elegans as a case study as it freely explores a 3D complex fluid with constantly changing optical properties. We model the slender worm as a 3D curve using an intrinsic parametrisation that naturally admits biologically-informed constraints and regularisation. To account for the changing optics we develop a novel differentiable renderer to construct images from 2D projections and compare against raw images to generate a pixel-wise error to jointly update the curve, camera and renderer parameters using gradient descent. The method is robust to interference such as bubbles and dirt trapped in the fluid, stays consistent through complex sequences of postures, recovers reliable estimates from blurry images and provides a significant improvement on previous attempts to track C. elegans in 3D. Our results demonstrate the potential of direct approaches to shape estimation in complex physical environments in the absence of ground-truth data.	['David C. Hogg', 'Netta Cohen', 'Thomas Ranner', 'Omer Yuval', 'Thomas P. Ilett']	2023-04-28	null	http://openaccess.thecvf.com//content/CVPR2023/html/Ilett_3D_Shape_Reconstruction_of_Semi-Transparent_Worms_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Ilett_3D_Shape_Reconstruction_of_Semi-Transparent_Worms_CVPR_2023_paper.pdf	cvpr-2023-1	['3d-shape-reconstruction']	['computer-vision']	[ 5.99598527e-01 -2.49832287e-01 8.25934470e-01 4.28495258e-02 -2.10584193e-01 -8.82695794e-01 6.60580218e-01 4.72058691e-02 -8.35956633e-01 6.87834680e-01 -2.83032894e-01 -2.62173153e-02 1.34575590e-01 -1.79496735e-01 -9.05182540e-01 -7.87012935e-01 -3.94243568e-01 6.34039879e-01 5.51937521e-01 1.71738952e-01 5.46489000e-01 7.92997181e-01 -1.46261466e+00 -2.11335167e-01 5.14011562e-01 3.84353995e-01 5.85153520e-01 1.26932704e+00 8.08949694e-02 2.86696315e-01 -5.65298319e-01 -2.32772738e-01 4.59240377e-01 -3.24014753e-01 -5.75668752e-01 4.38970506e-01 7.04768479e-01 -6.35219693e-01 2.95882940e-01 1.03022444e+00 4.91986185e-01 -9.63977724e-03 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4b6a2a6c-ad17-4bf8-8057-47cf56f8d78e	comparative-analysis-of-melodia-and-time	null	null	https://aclanthology.org/2021.smp-1.4	https://aclanthology.org/2021.smp-1.4.pdf	Comparative Analysis of Melodia and Time-Domain Adaptive Filtering based Model for Melody Extraction from Polyphonic Music	Among the many applications of Music Information Retrieval (MIR), melody extraction is one of the most essential. It has risen to the top of the list of current research challenges in the field of MIR applications. We now need new means of defining, indexing, finding, and interacting with musical information, given the tremendous amount of music available at our fingertips. This article looked at some of the approaches that open the door to a broad variety of applications, such as automatically predicting the pitch sequence of a melody straight from the audio signal of a polyphonic music recording, commonly known as melody extraction. It is pretty easy for humans to identify the pitch of a melody, but doing so on an automated basis is very difficult and time-consuming. In this article, a comparison is made between the performance of the currently available melody extraction approach that is state-of-the-art Melodia and the technique based on time-domain adaptive filtering for melody extraction in terms of evaluation metrics introduced in MIREX 2005. Motivating by the same, this paper focuses on the discussion of datasets and state-of-the-art approaches for the extraction of the main melody from music signals. Additionally, a summary of the evaluation matrices based on which methodologies have been examined on various datasets is also present in this paper.	['Yeshwant Singh', 'Pinki Roy', 'Anupam Biswas', 'Ranjeet Kumar']	null	null	null	null	smp-icon-2021-12	['melody-extraction', 'music-information-retrieval']	['music', 'music']	[ 3.96698177e-01 -5.31891286e-01 1.59458920e-01 2.37661943e-01 -9.53124881e-01 -9.09482360e-01 3.47839296e-01 2.76680738e-01 -4.58779752e-01 3.77877355e-01 2.89086908e-01 1.02633432e-01 -8.27082813e-01 -4.62731630e-01 9.59794596e-02 -6.39279842e-01 -2.04612195e-01 2.47629762e-01 2.51427352e-01 -5.26204348e-01 4.94810432e-01 4.75977153e-01 -2.10628009e+00 3.59497964e-01 6.38249815e-02 1.09125388e+00 1.70896307e-01 9.61685121e-01 -1.09066099e-01 5.02456605e-01 -8.20740223e-01 -2.31607556e-01 2.62058020e-01 -8.40316653e-01 -7.16811299e-01 -1.77779764e-01 9.61542353e-02 2.97035426e-01 4.42364030e-02 9.09279644e-01 7.45520711e-01 3.21884304e-01 4.48501170e-01 -6.90511227e-01 3.29133213e-01 9.58368599e-01 -1.60718828e-01 2.70900160e-01 6.52773023e-01 -4.17822182e-01 1.11264586e+00 -8.73915076e-01 5.69908738e-01 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08b2488d-f5ce-4c7c-9639-d309a82e24e8	learning-to-rank-query-graphs-for-complex	1811.01118	null	http://arxiv.org/abs/1811.01118v1	http://arxiv.org/pdf/1811.01118v1.pdf	Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs	In this paper, we conduct an empirical investigation of neural query graph ranking approaches for the task of complex question answering over knowledge graphs. We experiment with six different ranking models and propose a novel self-attention based slot matching model which exploits the inherent structure of query graphs, our logical form of choice. Our proposed model generally outperforms the other models on two QA datasets over the DBpedia knowledge graph, evaluated in different settings. In addition, we show that transfer learning from the larger of those QA datasets to the smaller dataset yields substantial improvements, effectively offsetting the general lack of training data.	['Denis Lukovnikov', 'Gaurav Maheshwari', 'Priyansh Trivedi', 'Asja Fischer', 'Nilesh Chakraborty', 'Jens Lehmann']	2018-11-02	null	null	null	null	['graph-ranking']	['graphs']	[ 1.30785838e-01 7.62711942e-01 -4.41871226e-01 -3.96919787e-01 -9.26275611e-01 -4.73065555e-01 5.27617753e-01 4.66632873e-01 -4.61994022e-01 8.32820833e-01 4.19990152e-01 -4.46212888e-01 -7.27264524e-01 -1.13651061e+00 -8.63870382e-01 5.38082719e-02 -5.67712402e-03 1.01655209e+00 6.36571288e-01 -8.48955214e-01 1.59771115e-01 -1.02861656e-03 -1.28361022e+00 3.80965412e-01 9.52953637e-01 1.15534222e+00 -2.00869605e-01 3.86611432e-01 -3.42686027e-01 1.55067587e+00 -6.77500725e-01 -8.47488940e-01 -1.52338743e-01 -1.58894390e-01 -1.56199229e+00 -6.91506982e-01 1.03108537e+00 -1.62947118e-01 -7.89782763e-01 9.05763805e-01 5.57578802e-01 4.26885456e-01 4.46855843e-01 -9.93338227e-01 -1.03257716e+00 8.23735058e-01 1.67436719e-01 4.96905118e-01 5.97630143e-01 -4.38760340e-01 1.76792395e+00 -4.73979563e-01 8.91209126e-01 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3.89939278e-01 2.35424209e-02 2.28424206e-01 -6.74906135e-01 -1.07778382e+00 -4.04975444e-01 5.62549412e-01 2.61656582e-01 -8.50854516e-01 -4.80547585e-02 -5.21910965e-01]	[10.509058952331543, 7.980247974395752]
6b9229c2-54e1-417d-b248-baa7fde010f4	lsvc-a-learning-based-stereo-video	null	null	http://openaccess.thecvf.com//content/CVPR2022/html/Chen_LSVC_A_Learning-Based_Stereo_Video_Compression_Framework_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_LSVC_A_Learning-Based_Stereo_Video_Compression_Framework_CVPR_2022_paper.pdf	LSVC: A Learning-Based Stereo Video Compression Framework	In this work, we propose the first end-to-end optimized framework for compressing automotive stereo videos (i.e., stereo videos from autonomous driving applications) from both left and right views. Specifically, when compressing the current frame from each view, our framework reduces temporal redundancy by performing motion compensation using the reconstructed intra-view adjacent frame and at the same time exploits binocular redundancy by conducting disparity compensation using the latest reconstructed cross-view frame. Moreover, to effectively compress the introduced motion and disparity offsets for better compensation, we further propose two novel schemes called motion residual compression and disparity residual compression to respectively generate the predicted motion offset and disparity offset from the previously compressed motion offset and disparity offset, such that we can more effectively compress residual offset information for better bit-rate saving. Overall, the entire framework is implemented by the fully-differentiable modules and can be optimized in an end-to-end manner. Our comprehensive experiments on three automotive stereo video benchmarks Cityscapes, KITTI 2012 and KITTI 2015 demonstrate that our proposed framework outperforms the learning-based single-view video codec and the traditional hand-crafted multi-view video codec.	['Dong Xu', 'Wei Jiang', 'Shan Liu', 'Zhihao Hu', 'Guo Lu', 'Zhenghao Chen']	2022-01-01	null	null	null	cvpr-2022-1	['motion-compensation']	['computer-vision']	[ 3.46119195e-01 -1.34215578e-01 -2.45837927e-01 -4.11829025e-01 -7.16568112e-01 -2.62290597e-01 4.61878568e-01 -5.64693868e-01 -2.63345480e-01 4.37698811e-01 3.44191223e-01 -2.97012776e-01 2.11901888e-01 -6.28874719e-01 -1.05456758e+00 -6.54010475e-01 3.11233044e-01 -1.18629821e-01 3.73150647e-01 -1.61031350e-01 6.00556433e-01 3.00162639e-02 -1.93511939e+00 5.09326220e-01 8.39418471e-01 1.12430072e+00 7.26887465e-01 6.95530236e-01 3.82180303e-01 1.10845840e+00 -2.28526462e-02 -5.12681663e-01 6.51998341e-01 -1.55910507e-01 -3.64031702e-01 4.02783960e-01 9.13519442e-01 -9.95050907e-01 -9.37013268e-01 1.11153924e+00 3.32335591e-01 -3.62392291e-02 9.64309052e-02 -9.23424423e-01 -2.70278901e-01 6.93001002e-02 -8.11338842e-01 1.25992045e-01 2.70944715e-01 4.26011354e-01 7.33805120e-01 -1.02166975e+00 8.14183712e-01 1.39216053e+00 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af750588-6747-49c6-9fcb-85d8783fcdc9	video-coding-for-machines-a-paradigm-of	2001.03569	null	https://arxiv.org/abs/2001.03569v2	https://arxiv.org/pdf/2001.03569v2.pdf	Video Coding for Machines: A Paradigm of Collaborative Compression and Intelligent Analytics	Video coding, which targets to compress and reconstruct the whole frame, and feature compression, which only preserves and transmits the most critical information, stand at two ends of the scale. That is, one is with compactness and efficiency to serve for machine vision, and the other is with full fidelity, bowing to human perception. The recent endeavors in imminent trends of video compression, e.g. deep learning based coding tools and end-to-end image/video coding, and MPEG-7 compact feature descriptor standards, i.e. Compact Descriptors for Visual Search and Compact Descriptors for Video Analysis, promote the sustainable and fast development in their own directions, respectively. In this paper, thanks to booming AI technology, e.g. prediction and generation models, we carry out exploration in the new area, Video Coding for Machines (VCM), arising from the emerging MPEG standardization efforts1. Towards collaborative compression and intelligent analytics, VCM attempts to bridge the gap between feature coding for machine vision and video coding for human vision. Aligning with the rising Analyze then Compress instance Digital Retina, the definition, formulation, and paradigm of VCM are given first. Meanwhile, we systematically review state-of-the-art techniques in video compression and feature compression from the unique perspective of MPEG standardization, which provides the academic and industrial evidence to realize the collaborative compression of video and feature streams in a broad range of AI applications. Finally, we come up with potential VCM solutions, and the preliminary results have demonstrated the performance and efficiency gains. Further direction is discussed as well.	['Ling-Yu Duan', 'Jiaying Liu', 'Wen Gao', 'Wenhan Yang', 'Tiejun Huang']	2020-01-10	null	null	null	null	['feature-compression']	['computer-vision']	[ 5.06933093e-01 -8.18875507e-02 -2.93110579e-01 -1.16054334e-01 -3.39277864e-01 1.31346345e-01 6.22571647e-01 -1.39884979e-01 -2.86824971e-01 2.34130383e-01 3.00435215e-01 2.99152672e-01 -3.65031362e-01 -6.54211283e-01 -4.87170815e-01 -6.63038015e-01 -2.63498485e-01 -1.70693561e-01 3.39704081e-02 -2.10140646e-02 5.34081221e-01 3.61673713e-01 -2.25901961e+00 4.43691432e-01 5.78314066e-01 1.73376393e+00 5.75839996e-01 6.42276466e-01 -2.13868856e-01 1.03103173e+00 -1.96339458e-01 -7.69199550e-01 2.21247613e-01 -4.18110043e-01 -5.80028117e-01 2.70062596e-01 2.78153151e-01 -4.63040501e-01 -9.69919264e-01 1.08869886e+00 1.17213279e-01 -3.34267691e-02 6.05337858e-01 -1.36443365e+00 -8.23813677e-01 2.10786089e-01 -4.17024344e-01 9.19499174e-02 3.21471721e-01 1.23170018e-01 1.00102341e+00 -7.78549433e-01 7.12627769e-01 1.07528400e+00 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c8b98265-f8c9-4c14-bfa4-aee5a171fd5c	efficient-deep-learning-models-for-land-cover	2111.09451	null	https://arxiv.org/abs/2111.09451v3	https://arxiv.org/pdf/2111.09451v3.pdf	Benchmarking and scaling of deep learning models for land cover image classification	The availability of the sheer volume of Copernicus Sentinel-2 imagery has created new opportunities for exploiting deep learning (DL) methods for land use land cover (LULC) image classification. However, an extensive set of benchmark experiments is currently lacking, i.e. DL models tested on the same dataset, with a common and consistent set of metrics, and in the same hardware. In this work, we use the BigEarthNet Sentinel-2 dataset to benchmark for the first time different state-of-the-art DL models for the multi-label, multi-class LULC image classification problem, contributing with an exhaustive zoo of 60 trained models. Our benchmark includes standard CNNs, as well as non-convolutional methods. We put to the test EfficientNets and Wide Residual Networks (WRN) architectures, and leverage classification accuracy, training time and inference rate. Furthermore, we propose to use the EfficientNet framework for the compound scaling of a lightweight WRN. Enhanced with an Efficient Channel Attention mechanism, our scaled lightweight model emerged as the new state-of-the-art. It achieves 4.5% higher averaged F-Score classification accuracy for all 19 LULC classes compared to a standard ResNet50 baseline model, with an order of magnitude less trainable parameters. We provide access to all trained models, along with our code for distributed training on multiple GPU nodes. This model zoo of pre-trained encoders can be used for transfer learning and rapid prototyping in different remote sensing tasks that use Sentinel-2 data, instead of exploiting backbone models trained with data from a different domain, e.g., from ImageNet. We validate their suitability for transfer learning in different datasets of diverse volumes. Our top-performing WRN achieves state-of-the-art performance (71.1% F-Score) on the SEN12MS dataset while being exposed to only a small fraction of the training dataset.	['Christos Tryfonopoulos', 'Dimitrios Michail', 'Angelos Zavras', 'Nikolaos-Ioannis Bountos', 'Ioannis Papoutsis']	2021-11-18	null	null	null	null	['multi-label-image-classification']	['computer-vision']	[ 1.94065139e-01 -7.38205835e-02 -4.26726311e-01 -3.59038502e-01 -9.46533680e-01 -5.40552258e-01 4.51526612e-01 -1.22096390e-01 -7.59364426e-01 8.02708745e-01 -9.73384529e-02 -6.63671494e-01 8.94502029e-02 -9.97417808e-01 -9.66742456e-01 -7.85853386e-01 -5.06201804e-01 5.10933638e-01 6.22013807e-02 -3.73532861e-01 -4.53404486e-01 7.49446988e-01 -1.79236448e+00 2.62983471e-01 5.93334019e-01 1.20165265e+00 2.41597399e-01 7.22261548e-01 1.72088668e-01 8.62318575e-01 -1.80516019e-01 8.56054761e-03 5.50563514e-01 1.55473575e-01 -6.89491212e-01 -5.40965736e-01 1.07731915e+00 -6.19534671e-01 -2.43643016e-01 7.04760611e-01 8.44163299e-01 -1.75458536e-01 2.82272607e-01 -1.00403881e+00 -1.22093067e-01 4.79239613e-01 -3.31730962e-01 2.35903963e-01 -4.62235868e-01 2.61643887e-01 9.63600159e-01 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1afb79b6-1770-4289-987e-35f01c424377	u-pass-an-uncertainty-guided-deep-learning	2306.04663	null	https://arxiv.org/abs/2306.04663v1	https://arxiv.org/pdf/2306.04663v1.pdf	U-PASS: an Uncertainty-guided deep learning Pipeline for Automated Sleep Staging	As machine learning becomes increasingly prevalent in critical fields such as healthcare, ensuring the safety and reliability of machine learning systems becomes paramount. A key component of reliability is the ability to estimate uncertainty, which enables the identification of areas of high and low confidence and helps to minimize the risk of error. In this study, we propose a machine learning pipeline called U-PASS tailored for clinical applications that incorporates uncertainty estimation at every stage of the process, including data acquisition, training, and model deployment. The training process is divided into a supervised pre-training step and a semi-supervised finetuning step. We apply our uncertainty-guided deep learning pipeline to the challenging problem of sleep staging and demonstrate that it systematically improves performance at every stage. By optimizing the training dataset, actively seeking informative samples, and deferring the most uncertain samples to an expert, we achieve an expert-level accuracy of 85% on a challenging clinical dataset of elderly sleep apnea patients, representing a significant improvement over the baseline accuracy of 75%. U-PASS represents a promising approach to incorporating uncertainty estimation into machine learning pipelines, thereby improving their reliability and unlocking their potential in clinical settings.	['Maarten De Vos', 'Mihaela van der Schaar', 'Dries Testelmans', 'Bertien Buyse', 'Nabeel Seedat', 'Elisabeth R. M. Heremans']	2023-06-07	null	null	null	null	['sleep-staging']	['medical']	[ 1.51320040e-01 4.99019563e-01 -2.93279290e-01 -6.77120030e-01 -1.27217579e+00 -3.43450904e-01 1.41755641e-01 7.08268285e-01 -6.34731412e-01 8.43104303e-01 1.85537681e-01 -5.99271953e-01 -8.99063498e-02 -3.94802988e-01 -6.19410157e-01 -3.84902567e-01 -9.00992826e-02 8.47618699e-01 -7.44503457e-04 5.04888773e-01 -6.44196570e-02 1.65242180e-01 -9.92913067e-01 3.59136701e-01 1.04698479e+00 1.38805914e+00 -3.20787936e-01 6.50360525e-01 3.01636934e-01 8.32367778e-01 -4.63099003e-01 -2.02740118e-01 3.88504006e-03 -2.09687382e-01 -7.99967945e-01 -2.66114742e-01 4.56624553e-02 -4.64216381e-01 1.72865033e-01 7.05477238e-01 6.79850042e-01 -4.56405655e-02 5.51696777e-01 -8.26181412e-01 9.15255118e-03 6.53328240e-01 -1.32428885e-01 3.62182617e-01 -1.94973368e-02 3.93361747e-01 8.99598360e-01 -5.39347172e-01 -7.87986070e-02 6.09163284e-01 9.58479285e-01 6.23133242e-01 -1.25808096e+00 -5.20904720e-01 -1.20927654e-01 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ca3f67e3-3752-4509-b07c-9d0cbc6f4653	joint-covariate-alignment-and-concept	2208.00898	null	https://arxiv.org/abs/2208.00898v1	https://arxiv.org/pdf/2208.00898v1.pdf	Joint covariate-alignment and concept-alignment: a framework for domain generalization	In this paper, we propose a novel domain generalization (DG) framework based on a new upper bound to the risk on the unseen domain. Particularly, our framework proposes to jointly minimize both the covariate-shift as well as the concept-shift between the seen domains for a better performance on the unseen domain. While the proposed approach can be implemented via an arbitrary combination of covariate-alignment and concept-alignment modules, in this work we use well-established approaches for distributional alignment namely, Maximum Mean Discrepancy (MMD) and covariance Alignment (CORAL), and use an Invariant Risk Minimization (IRM)-based approach for concept alignment. Our numerical results show that the proposed methods perform as well as or better than the state-of-the-art for domain generalization on several data sets.	['Shuchin Aeron', 'Matthias Scheutz', 'Prakash Ishwar', 'Boyang Lyu', 'Thuan Nguyen']	2022-08-01	null	null	null	null	['concept-alignment']	['computer-vision']	[ 3.10608298e-01 4.11062315e-02 1.13352433e-01 -6.60543442e-01 -9.62784350e-01 -6.60915732e-01 6.06635332e-01 4.37223136e-01 -5.45284748e-01 6.51452661e-01 -5.21275550e-02 -7.47861713e-02 -5.30872941e-01 -6.40567541e-01 -4.96630907e-01 -8.30730319e-01 2.87362278e-01 6.65180743e-01 2.36005470e-01 -2.39669323e-01 2.43748307e-01 4.53145146e-01 -1.57510841e+00 -1.92540318e-01 1.35819185e+00 9.56422210e-01 -4.65047210e-02 6.38515279e-02 -3.49312164e-02 -1.83227256e-01 -5.30529141e-01 -5.67927957e-01 5.26176572e-01 -3.13066840e-01 -6.90621197e-01 1.94261357e-01 5.86061358e-01 -3.89905386e-02 1.42399892e-01 1.18674123e+00 6.02425337e-01 5.04706144e-01 1.03669691e+00 -1.18140757e+00 -4.24785763e-01 2.10942492e-01 -5.74863672e-01 1.42946556e-01 3.11235547e-01 -4.93820101e-01 7.65394866e-01 -7.90836275e-01 4.32225257e-01 1.11465347e+00 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a0244fa5-5873-4924-8bbb-e0037c5943bc	multi-feature-distance-metric-learning-for	1901.03031	null	http://arxiv.org/abs/1901.03031v1	http://arxiv.org/pdf/1901.03031v1.pdf	Multi-feature Distance Metric Learning for Non-rigid 3D Shape Retrieval	In the past decades, feature-learning-based 3D shape retrieval approaches have been received widespread attention in the computer graphic community. These approaches usually explored the hand-crafted distance metric or conventional distance metric learning methods to compute the similarity of the single feature. The single feature always contains onefold geometric information, which cannot characterize the 3D shapes well. Therefore, the multiple features should be used for the retrieval task to overcome the limitation of single feature and further improve the performance. However, most conventional distance metric learning methods fail to integrate the complementary information from multiple features to construct the distance metric. To address these issue, a novel multi-feature distance metric learning method for non-rigid 3D shape retrieval is presented in this study, which can make full use of the complimentary geometric information from multiple shape features by utilizing the KL-divergences. Minimizing KL-divergence between different metric of features and a common metric is a consistency constraints, which can lead the consistency shared latent feature space of the multiple features. We apply the proposed method to 3D model retrieval, and test our method on well known benchmark database. The results show that our method substantially outperforms the state-of-the-art non-rigid 3D shape retrieval methods.	['Huibing Wang', 'Haohao Li', 'Xianping Fu']	2019-01-10	null	null	null	null	['3d-shape-retrieval']	['computer-vision']	[-3.39351088e-01 -7.72959173e-01 -1.13121018e-01 -3.55533540e-01 -9.49542642e-01 -5.98123014e-01 6.65686488e-01 4.20950651e-02 -1.73968360e-01 1.28845900e-01 1.30225092e-01 6.58265948e-02 -7.70081639e-01 -7.58834541e-01 -8.53344705e-03 -9.20894802e-01 2.58014381e-01 5.16107559e-01 3.31500471e-01 -7.06946701e-02 6.27196550e-01 7.91114450e-01 -1.43592191e+00 -9.57970545e-02 6.69405043e-01 1.10191607e+00 2.26561561e-01 -5.88197373e-02 -3.87632400e-01 -2.02233031e-01 -2.01829702e-01 4.21801805e-02 4.41522717e-01 -1.56091422e-01 -3.83963436e-01 -1.09421171e-01 3.16833317e-01 -3.48833501e-01 -4.90489334e-01 9.90233958e-01 8.21640193e-01 1.95270643e-01 1.13230813e+00 -1.13719296e+00 -8.98474038e-01 -2.02981234e-01 -7.13246822e-01 4.00541872e-02 5.95621407e-01 -3.32768053e-01 1.04836905e+00 -1.32279348e+00 5.70089996e-01 1.55070639e+00 5.66584587e-01 4.31986377e-02 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e1d07d69-ca85-4bb6-9527-8cf328d6cd82	the-chai-platform-s-ai-safety-framework	2306.02979	null	https://arxiv.org/abs/2306.02979v1	https://arxiv.org/pdf/2306.02979v1.pdf	The Chai Platform's AI Safety Framework	Chai empowers users to create and interact with customized chatbots, offering unique and engaging experiences. Despite the exciting prospects, the work recognizes the inherent challenges of a commitment to modern safety standards. Therefore, this paper presents the integrated AI safety principles into Chai to prioritize user safety, data protection, and ethical technology use. The paper specifically explores the multidimensional domain of AI safety research, demonstrating its application in Chai's conversational chatbot platform. It presents Chai's AI safety principles, informed by well-established AI research centres and adapted for chat AI. This work proposes the following safety framework: Content Safeguarding; Stability and Robustness; and Operational Transparency and Traceability. The subsequent implementation of these principles is outlined, followed by an experimental analysis of Chai's AI safety framework's real-world impact. We emphasise the significance of conscientious application of AI safety principles and robust safety measures. The successful implementation of the safe AI framework in Chai indicates the practicality of mitigating potential risks for responsible and ethical use of AI technologies. The ultimate vision is a transformative AI tool fostering progress and innovation while prioritizing user safety and ethical standards.	['William Beauchamp', 'Zongyi Liu', 'Aleksey Korshuk', 'Xiaoding Lu']	2023-06-05	null	null	null	null	['chatbot', 'chatbot']	['methodology', 'natural-language-processing']	[ 1.47770792e-01 6.07447684e-01 9.70254373e-03 1.63968503e-02 -3.11951876e-01 -7.67951667e-01 8.69447410e-01 4.80368882e-02 -3.25222880e-01 6.34801090e-01 5.33185720e-01 -2.80906230e-01 -3.87430966e-01 -3.34143698e-01 -1.17581010e-01 -2.65473813e-01 1.32826433e-01 1.25100851e-01 -1.08924456e-01 -5.60675502e-01 5.11783242e-01 5.05355418e-01 -1.45592082e+00 8.98374841e-02 7.43855596e-01 7.79379547e-01 -6.23472095e-01 7.22773612e-01 3.82888496e-01 1.68821216e+00 -8.68068218e-01 -8.18149805e-01 2.49787480e-01 -4.70194459e-01 -1.42638636e+00 -6.46434844e-01 1.61778525e-01 -6.28210485e-01 5.47712818e-02 7.39651084e-01 3.13349426e-01 1.41974345e-01 4.25085515e-01 -2.24949765e+00 -7.63357997e-01 5.45747161e-01 4.25614208e-01 -1.94814071e-01 6.34559751e-01 9.75362897e-01 8.30654144e-01 6.41400516e-02 6.58324897e-01 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acd6e0e7-c053-4648-8849-a813506a5a6a	adversarial-robustness-of-representation	2210.00122	null	https://arxiv.org/abs/2210.00122v1	https://arxiv.org/pdf/2210.00122v1.pdf	Adversarial Robustness of Representation Learning for Knowledge Graphs	Knowledge graphs represent factual knowledge about the world as relationships between concepts and are critical for intelligent decision making in enterprise applications. New knowledge is inferred from the existing facts in the knowledge graphs by encoding the concepts and relations into low-dimensional feature vector representations. The most effective representations for this task, called Knowledge Graph Embeddings (KGE), are learned through neural network architectures. Due to their impressive predictive performance, they are increasingly used in high-impact domains like healthcare, finance and education. However, are the black-box KGE models adversarially robust for use in domains with high stakes? This thesis argues that state-of-the-art KGE models are vulnerable to data poisoning attacks, that is, their predictive performance can be degraded by systematically crafted perturbations to the training knowledge graph. To support this argument, two novel data poisoning attacks are proposed that craft input deletions or additions at training time to subvert the learned model's performance at inference time. These adversarial attacks target the task of predicting the missing facts in knowledge graphs using KGE models, and the evaluation shows that the simpler attacks are competitive with or outperform the computationally expensive ones. The thesis contributions not only highlight and provide an opportunity to fix the security vulnerabilities of KGE models, but also help to understand the black-box predictive behaviour of KGE models.	['Peru Bhardwaj']	2022-09-30	null	null	null	null	['data-poisoning', 'knowledge-graph-embeddings', 'knowledge-graph-embeddings']	['adversarial', 'graphs', 'methodology']	[ 1.21673737e-02 8.04695010e-01 -4.10994917e-01 -4.87628346e-03 1.11668192e-01 -7.80456424e-01 5.84335089e-01 5.22498786e-01 -1.88452393e-01 8.37755561e-01 5.44410013e-02 -7.43345022e-01 -4.89632398e-01 -1.43582940e+00 -9.75377142e-01 -3.74650717e-01 -2.91855186e-01 3.97403717e-01 2.81753778e-01 -5.50150216e-01 1.72061458e-01 7.01317251e-01 -1.42864180e+00 2.78146833e-01 6.84922159e-01 6.48105860e-01 -6.72653317e-01 6.57494068e-01 -7.47633353e-02 1.33370221e+00 -9.23725843e-01 -1.35178554e+00 2.51068115e-01 1.34740785e-01 -1.03775084e+00 -6.21859133e-01 3.58369142e-01 -2.47302666e-01 -8.13058674e-01 1.12534976e+00 3.43407571e-01 -8.25826749e-02 5.81933916e-01 -1.69413185e+00 -1.23773146e+00 7.27941453e-01 -4.09485586e-02 4.45062280e-01 4.64697987e-01 7.07181692e-02 9.57529843e-01 -4.03857738e-01 6.77187264e-01 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e9af178b-db3b-419a-849b-fe9c04672a2f	contaminated-speech-training-methods-for	1710.03538	null	http://arxiv.org/abs/1710.03538v1	http://arxiv.org/pdf/1710.03538v1.pdf	Contaminated speech training methods for robust DNN-HMM distant speech recognition	Despite the significant progress made in the last years, state-of-the-art speech recognition technologies provide a satisfactory performance only in the close-talking condition. Robustness of distant speech recognition in adverse acoustic conditions, on the other hand, remains a crucial open issue for future applications of human-machine interaction. To this end, several advances in speech enhancement, acoustic scene analysis as well as acoustic modeling, have recently contributed to improve the state-of-the-art in the field. One of the most effective approaches to derive a robust acoustic modeling is based on using contaminated speech, which proved helpful in reducing the acoustic mismatch between training and testing conditions. In this paper, we revise this classical approach in the context of modern DNN-HMM systems, and propose the adoption of three methods, namely, asymmetric context windowing, close-talk based supervision, and close-talk based pre-training. The experimental results, obtained using both real and simulated data, show a significant advantage in using these three methods, overall providing a 15% error rate reduction compared to the baseline systems. The same trend in performance is confirmed either using a high-quality training set of small size, and a large one.	['Mirco Ravanelli', 'Maurizio Omologo']	2017-10-10	null	null	null	null	['distant-speech-recognition']	['speech']	[ 3.89768124e-01 -8.07135329e-02 4.69830185e-01 -4.84245777e-01 -1.07561684e+00 -1.35848716e-01 6.43291593e-01 7.03459233e-02 -5.99297822e-01 6.16981447e-01 1.20942175e-01 -3.31086010e-01 -5.42565137e-02 -2.55942762e-01 -2.99744457e-01 -1.01742649e+00 4.64913324e-02 1.45279109e-01 5.81655383e-01 -3.74163926e-01 1.41633034e-01 5.27513027e-01 -2.00338340e+00 2.23307088e-02 7.40110576e-01 8.37500930e-01 6.05289280e-01 7.89432347e-01 -8.36378634e-02 1.34386778e-01 -8.90044808e-01 -3.20489138e-01 -1.44779786e-01 -3.41563851e-01 -3.95438224e-01 -2.28190180e-02 3.33283283e-02 -6.52825385e-02 -1.32076591e-01 1.00010443e+00 9.78018939e-01 3.91753048e-01 3.20452988e-01 -6.00781620e-01 3.18308175e-02 4.35503811e-01 -8.12418759e-03 2.98892111e-01 3.91143113e-01 6.54782727e-02 5.58962166e-01 -9.92063582e-01 1.81230590e-01 1.07686567e+00 4.01717514e-01 6.18234515e-01 -1.06055129e+00 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-5.79797447e-01 3.18936378e-01 3.80659997e-01 -6.78147793e-01 -6.37821794e-01 -1.83144696e-02]	[14.845169067382812, 5.848887920379639]
ea102079-876e-4425-a404-c139dc17c1c6	video-driven-neural-physically-based-facial	2202.05592	null	https://arxiv.org/abs/2202.05592v4	https://arxiv.org/pdf/2202.05592v4.pdf	Video-driven Neural Physically-based Facial Asset for Production	Production-level workflows for producing convincing 3D dynamic human faces have long relied on an assortment of labor-intensive tools for geometry and texture generation, motion capture and rigging, and expression synthesis. Recent neural approaches automate individual components but the corresponding latent representations cannot provide artists with explicit controls as in conventional tools. In this paper, we present a new learning-based, video-driven approach for generating dynamic facial geometries with high-quality physically-based assets. For data collection, we construct a hybrid multiview-photometric capture stage, coupling with ultra-fast video cameras to obtain raw 3D facial assets. We then set out to model the facial expression, geometry and physically-based textures using separate VAEs where we impose a global MLP based expression mapping across the latent spaces of respective networks, to preserve characteristics across respective attributes. We also model the delta information as wrinkle maps for the physically-based textures, achieving high-quality 4K dynamic textures. We demonstrate our approach in high-fidelity performer-specific facial capture and cross-identity facial motion retargeting. In addition, our multi-VAE-based neural asset, along with the fast adaptation schemes, can also be deployed to handle in-the-wild videos. Besides, we motivate the utility of our explicit facial disentangling strategy by providing various promising physically-based editing results with high realism. Comprehensive experiments show that our technique provides higher accuracy and visual fidelity than previous video-driven facial reconstruction and animation methods.	['Jingyi Yu', 'Lan Xu', 'Wei Yang', 'Ruixiang Cao', 'Hongyang Lin', 'Qixuan Zhang', 'Chuxiao Zeng', 'Longwen Zhang']	2022-02-11	null	null	null	null	['texture-synthesis', 'motion-retargeting']	['computer-vision', 'computer-vision']	[ 1.95899442e-01 -3.40705663e-02 3.23473334e-01 -3.12171042e-01 -7.11134911e-01 -6.26567483e-01 6.97294414e-01 -7.96974540e-01 2.65523851e-01 4.63582993e-01 -3.50286551e-02 2.50539511e-01 -5.48268184e-02 -8.10063064e-01 -9.06202853e-01 -7.11008787e-01 1.90717325e-01 3.44631821e-01 -3.68132770e-01 -4.11040664e-01 -2.66159594e-01 1.14716697e+00 -1.98039782e+00 3.37039143e-01 4.31397170e-01 1.04769516e+00 -2.57579952e-01 9.36599553e-01 -3.74931209e-02 6.05430484e-01 -3.45642388e-01 -6.35004520e-01 4.90735590e-01 -4.46411580e-01 -3.80939305e-01 3.26672792e-01 1.01471817e+00 -6.15240157e-01 -1.26578346e-01 6.67468250e-01 5.54523587e-01 6.45273328e-02 5.88648379e-01 -1.32081091e+00 -7.37633884e-01 -1.12373292e-01 -6.50433362e-01 -6.86121404e-01 5.87386847e-01 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98e30395-7d13-4e42-ae6d-05fa87068c6f	tased-net-temporally-aggregating-spatial	1908.05786	null	https://arxiv.org/abs/1908.05786v1	https://arxiv.org/pdf/1908.05786v1.pdf	TASED-Net: Temporally-Aggregating Spatial Encoder-Decoder Network for Video Saliency Detection	TASED-Net is a 3D fully-convolutional network architecture for video saliency detection. It consists of two building blocks: first, the encoder network extracts low-resolution spatiotemporal features from an input clip of several consecutive frames, and then the following prediction network decodes the encoded features spatially while aggregating all the temporal information. As a result, a single prediction map is produced from an input clip of multiple frames. Frame-wise saliency maps can be predicted by applying TASED-Net in a sliding-window fashion to a video. The proposed approach assumes that the saliency map of any frame can be predicted by considering a limited number of past frames. The results of our extensive experiments on video saliency detection validate this assumption and demonstrate that our fully-convolutional model with temporal aggregation method is effective. TASED-Net significantly outperforms previous state-of-the-art approaches on all three major large-scale datasets of video saliency detection: DHF1K, Hollywood2, and UCFSports. After analyzing the results qualitatively, we observe that our model is especially better at attending to salient moving objects.	['Kyle Min', 'Jason J. Corso']	2019-08-15	tased-net-temporally-aggregating-spatial-1	http://openaccess.thecvf.com/content_ICCV_2019/html/Min_TASED-Net_Temporally-Aggregating_Spatial_Encoder-Decoder_Network_for_Video_Saliency_Detection_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Min_TASED-Net_Temporally-Aggregating_Spatial_Encoder-Decoder_Network_for_Video_Saliency_Detection_ICCV_2019_paper.pdf	iccv-2019-10	['video-saliency-detection']	['computer-vision']	[ 3.92494410e-01 -8.53642151e-02 -5.25300801e-01 -2.54646480e-01 -5.03026664e-01 -3.75219658e-02 3.64663929e-01 3.77605818e-02 -2.82321751e-01 5.65225184e-01 4.97065872e-01 8.13519023e-03 2.52160847e-01 -5.28141499e-01 -1.01474822e+00 -3.03578436e-01 -4.66497689e-01 -2.74294734e-01 1.17752433e+00 -2.52066791e-01 3.14796746e-01 1.51492760e-01 -1.85404849e+00 5.83680928e-01 6.69412732e-01 1.34261191e+00 7.51243711e-01 6.86418235e-01 2.25103542e-01 1.39923012e+00 -1.92806244e-01 1.54724479e-01 1.07621588e-01 -4.29132253e-01 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75f3ebf5-9899-4322-9442-d2cdbe2de513	road-damage-detection-and-classification-in	1811.04535	null	http://arxiv.org/abs/1811.04535v1	http://arxiv.org/pdf/1811.04535v1.pdf	Road Damage Detection And Classification In Smartphone Captured Images Using Mask R-CNN	This paper summarizes the design, experiments and results of our solution to the Road Damage Detection and Classification Challenge held as part of the 2018 IEEE International Conference On Big Data Cup. Automatic detection and classification of damage in roads is an essential problem for multiple applications like maintenance and autonomous driving. We demonstrate that convolutional neural net based instance detection and classfication approaches can be used to solve this problem. In particular we show that Mask-RCNN, one of the state-of-the-art algorithms for object detection, localization and instance segmentation of natural images, can be used to perform this task in a fast manner with effective results. We achieve a mean F1 score of 0.528 at an IoU of 50% on the task of detection and classification of different types of damages in real-world road images acquired using a smartphone camera and our average inference time for each image is 0.105 seconds on an NVIDIA GeForce 1080Ti graphic card. The code and saved models for our approach can be found here : https://github.com/sshkhr/BigDataCup18 Submission	['Shashank Shekhar', 'Janpreet Singh']	2018-11-12	null	null	null	null	['road-damage-detection']	['computer-vision']	[-4.66344990e-02 -1.44039914e-01 -7.06505543e-03 -3.59188259e-01 -7.83525050e-01 -2.46249527e-01 3.12594801e-01 -1.85009819e-02 -5.36588788e-01 6.42960608e-01 -3.15513581e-01 -4.32100326e-01 1.28262788e-01 -1.19482636e+00 -1.06162369e+00 -5.49640179e-01 1.63853109e-01 3.45417231e-01 6.16357923e-01 -1.48223992e-02 2.31570199e-01 8.00539255e-01 -2.34599090e+00 2.09345207e-01 1.03335643e+00 1.12869501e+00 2.40000278e-01 1.08272290e+00 2.94530988e-01 7.85406828e-01 -6.37686908e-01 -1.69385299e-01 8.97156894e-02 5.44830441e-01 -8.79078388e-01 -2.56523281e-01 7.84507871e-01 -5.49932718e-01 -5.84822893e-01 1.01057947e+00 7.69925892e-01 1.11905672e-01 4.51986998e-01 -1.26688230e+00 -1.92333728e-01 2.24798143e-01 -6.29192889e-01 7.62973726e-01 -2.12539002e-01 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614cbf95-0335-402e-b839-531ccbf6128c	cmu-arc-factored-discriminative-semantic	null	null	https://aclanthology.org/S14-2027	https://aclanthology.org/S14-2027.pdf	CMU: Arc-Factored, Discriminative Semantic Dependency Parsing	null	["Brendan O{'}Connor", 'Jesse Dodge', 'Jeffrey Flanigan', 'Sam Thomson', 'Noah A. Smith', 'David Bamman', 'Chris Dyer', 'Swabha Swayamdipta', 'Nathan Schneider']	2014-08-01	null	null	null	semeval-2014-8	['semantic-dependency-parsing']	['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 -8.59008506e-02 -2.09758401e-01 9.22211111e-01 5.37373662e-01 4.49454933e-02 1.08379531e+00 -1.42548037e+00 -5.91619551e-01 6.10313773e-01 -4.01565880e-02 1.21627934e-01 1.03678203e+00 -5.39447069e-01 1.19919395e+00 -1.13026452e+00 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7c35be4b-bdc5-4717-ac64-f3ee290e415c	joint-device-edge-digital-semantic	2305.13553	null	https://arxiv.org/abs/2305.13553v1	https://arxiv.org/pdf/2305.13553v1.pdf	Joint Device-Edge Digital Semantic Communication with Adaptive Network Split and Learned Non-Linear Quantization	Semantic communication, an intelligent communication paradigm that aims to transmit useful information in the semantic domain, is facilitated by deep learning techniques. Although robust semantic features can be learned and transmitted in an analog fashion, it poses new challenges to hardware, protocol, and encryption. In this paper, we propose a digital semantic communication system, which consists of an encoding network deployed on a resource-limited device and a decoding network deployed at the edge. To acquire better semantic representation for digital transmission, a novel non-linear quantization module is proposed with the trainable quantization levels that efficiently quantifies semantic features. Additionally, structured pruning by a sparse scaling vector is incorporated to reduce the dimension of the transmitted features. We also introduce a semantic learning loss (SLL) function to reduce semantic error. To adapt to various channel conditions and inputs under constraints of communication and computing resources, a policy network is designed to adaptively choose the split point and the dimension of the transmitted semantic features. Experiments using the CIFAR-10 dataset for image classification are employed to evaluate the proposed digital semantic communication network, and ablation studies are conducted to assess the proposed modules including the quantization module, structured pruning and SLL.	['Bo Ai', 'Yuxuan Sun', 'Wei Chen', 'Lei Guo']	2023-05-22	null	null	null	null	['intelligent-communication']	['time-series']	[ 7.43823886e-01 1.63087711e-01 -3.33339542e-01 -6.47401392e-01 -5.49034417e-01 -1.50871128e-01 4.33449149e-01 9.94557589e-02 -5.35601079e-01 6.41302466e-01 1.67917103e-01 -2.12386549e-01 -3.97068292e-01 -1.01830781e+00 -5.59787333e-01 -7.57847190e-01 -9.18860957e-02 -8.38689208e-02 1.50686383e-01 -8.80179107e-02 4.62684363e-01 2.26837650e-01 -1.28562129e+00 3.70538503e-01 7.77721941e-01 1.94381547e+00 5.72426140e-01 3.25745910e-01 -3.72311711e-01 4.91850972e-01 -7.80054867e-01 -3.03464442e-01 3.64676118e-01 -4.76468593e-01 -6.56355500e-01 -1.63067400e-01 -3.17496806e-02 -3.88012379e-01 -5.77384889e-01 1.29775751e+00 5.75274229e-01 -2.18241334e-01 4.58211273e-01 -1.17426872e+00 -4.62917179e-01 8.68258953e-01 -7.57091045e-02 1.56523943e-01 2.84240723e-01 -2.65827626e-01 7.50667214e-01 -3.10319185e-01 2.77485341e-01 1.25206530e+00 4.73643929e-01 5.34748673e-01 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7e42bf46-fc6e-4a2a-93ed-fae403ba7412	investigating-explainability-of-generative-ai	2202.04903	null	https://arxiv.org/abs/2202.04903v1	https://arxiv.org/pdf/2202.04903v1.pdf	Investigating Explainability of Generative AI for Code through Scenario-based Design	What does it mean for a generative AI model to be explainable? The emergent discipline of explainable AI (XAI) has made great strides in helping people understand discriminative models. Less attention has been paid to generative models that produce artifacts, rather than decisions, as output. Meanwhile, generative AI (GenAI) technologies are maturing and being applied to application domains such as software engineering. Using scenario-based design and question-driven XAI design approaches, we explore users' explainability needs for GenAI in three software engineering use cases: natural language to code, code translation, and code auto-completion. We conducted 9 workshops with 43 software engineers in which real examples from state-of-the-art generative AI models were used to elicit users' explainability needs. Drawing from prior work, we also propose 4 types of XAI features for GenAI for code and gathered additional design ideas from participants. Our work explores explainability needs for GenAI for code and demonstrates how human-centered approaches can drive the technical development of XAI in novel domains.	['Justin D. Weisz', 'Kartik Talamadupula', 'Stephanie Houde', 'Mayank Agarwal', 'Michael Muller', 'Q. Vera Liao', 'Jiao Sun']	2022-02-10	null	null	null	null	['code-translation']	['computer-code']	[ 3.42655033e-01 9.39214230e-01 2.71984339e-01 -6.35819495e-01 -3.63310307e-01 -4.31957960e-01 5.64369977e-01 -2.52926201e-01 1.00590670e+00 2.18867227e-01 6.55692697e-01 -5.76858819e-01 -4.05721158e-01 -6.20409250e-01 -5.75387836e-01 1.60058618e-01 2.02455491e-01 6.22138560e-01 -6.69449389e-01 -4.47615564e-01 2.75854111e-01 -2.99801920e-02 -1.67674994e+00 7.78545082e-01 1.28117430e+00 1.06067590e-01 6.52777478e-02 6.55430138e-01 -4.17459637e-01 1.26214528e+00 -8.56281996e-01 -1.72967806e-01 -1.58049956e-01 -1.24095917e+00 -1.19185662e+00 1.19522206e-01 1.99642733e-01 8.85351002e-02 9.65880081e-02 5.78360796e-01 5.54691032e-02 -3.30281705e-01 4.95313019e-01 -1.81595695e+00 -1.84860718e+00 1.30195820e+00 -6.05585836e-02 -3.46685767e-01 7.91352570e-01 4.12038177e-01 9.01974499e-01 -8.05606127e-01 8.37818980e-01 1.24237978e+00 6.50407553e-01 1.02047956e+00 -1.59569752e+00 -7.20972419e-01 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6.34309500e-02 2.88048595e-01 -1.58243150e-01 -1.05997644e-01 -1.03367828e-02 6.26030982e-01]	[8.094544410705566, 7.495131492614746]
92696544-771f-40f9-82ab-f8073c11b1ad	learning-from-multi-view-representation-for	2306.02558	null	https://arxiv.org/abs/2306.02558v1	https://arxiv.org/pdf/2306.02558v1.pdf	Learning from Multi-View Representation for Point-Cloud Pre-Training	A critical problem in the pre-training of 3D point clouds is leveraging massive 2D data. A fundamental challenge is to address the 2D-3D domain gap. This paper proposes a novel approach to point-cloud pre-training that enables learning 3D representations by leveraging pre-trained 2D-based networks. In particular, it avoids overfitting to 2D representations and potentially discarding critical 3D features for 3D recognition tasks. The key to our approach is a novel multi-view representation, which learns a shared 3D feature volume consistent with deep features extracted from multiple 2D camera views. The 2D deep features are regularized using pre-trained 2D networks through the 2D knowledge transfer loss. To prevent the resulting 3D feature representations from discarding 3D signals, we introduce the multi-view consistency loss that forces the projected 2D feature representations to capture pixel-wise correspondences across different views. Such correspondences induce 3D geometry and effectively retain 3D features in the projected 2D features. Experimental results demonstrate that our pre-trained model can be successfully transferred to various downstream tasks, including 3D detection and semantic segmentation, and achieve state-of-the-art performance.	['QiXing Huang', 'Youkang Kong', 'Chen Song', 'Siming Yan']	2023-06-05	null	null	null	null	['point-cloud-pre-training']	['computer-vision']	[ 7.00074658e-02 2.03905553e-01 -1.42743558e-01 -5.10508001e-01 -9.12484944e-01 -6.67200267e-01 5.30029595e-01 -1.51762128e-01 -4.38834056e-02 -7.99641162e-02 -2.32055232e-01 -2.61561453e-01 -5.52433506e-02 -8.26835096e-01 -1.21195531e+00 -4.23461199e-01 4.47905511e-02 5.92025101e-01 2.13384554e-01 -5.42114340e-02 3.31502080e-01 1.20209694e+00 -1.66404152e+00 1.71461254e-01 6.22003496e-01 1.26686132e+00 2.76142180e-01 4.02073920e-01 -3.95894349e-01 -3.68148019e-03 -7.91431218e-02 3.07666138e-04 8.84379387e-01 1.34120032e-01 -5.12314320e-01 4.48093891e-01 9.76558328e-01 -5.62059402e-01 -2.77913690e-01 8.52861166e-01 2.93818295e-01 4.81531583e-03 6.96451664e-01 -1.07084417e+00 -7.30576217e-01 -2.75085121e-01 -9.14430439e-01 -8.83617997e-02 1.62782416e-01 7.02912360e-02 9.38390911e-01 -1.33430195e+00 5.81191719e-01 1.41463077e+00 8.60137999e-01 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-4.45874661e-01 4.76023257e-01 5.93069494e-01 -1.04071391e+00 -4.32851940e-01 -2.21784189e-01]	[8.158373832702637, -3.38466477394104]
bcd46a45-d35e-4095-bd36-e76daa57df8f	meta-learning-for-multi-objective	1811.03376	null	https://arxiv.org/abs/1811.03376v2	https://arxiv.org/pdf/1811.03376v2.pdf	Meta-Learning for Multi-objective Reinforcement Learning	Multi-objective reinforcement learning (MORL) is the generalization of standard reinforcement learning (RL) approaches to solve sequential decision making problems that consist of several, possibly conflicting, objectives. Generally, in such formulations, there is no single optimal policy which optimizes all the objectives simultaneously, and instead, a number of policies has to be found each optimizing a preference of the objectives. In other words, the MORL is framed as a meta-learning problem, with the task distribution given by a distribution over the preferences. We demonstrate that such a formulation results in a better approximation of the Pareto optimal solutions in terms of both the optimality and the computational efficiency. We evaluated our method on obtaining Pareto optimal policies using a number of continuous control problems with high degrees of freedom.	['Patric Jensfelt', 'Mårten Björkman', 'Ali Ghadirzadeh', 'Xi Chen']	2018-11-08	null	null	null	null	['multi-objective-reinforcement-learning']	['methodology']	[ 2.43034139e-02 -8.29197187e-03 -6.47975028e-01 -6.38224557e-02 -9.21117842e-01 -4.98559386e-01 2.85137385e-01 2.91215658e-01 -6.69265866e-01 1.42111146e+00 3.76267321e-02 -1.97545305e-01 -8.00288737e-01 -5.51136613e-01 -4.80970800e-01 -9.09629941e-01 -1.87667876e-01 8.85414004e-01 -2.31922537e-01 -1.27817884e-01 3.99339378e-01 1.73992753e-01 -1.54157710e+00 -2.79431567e-02 1.06078649e+00 1.05179620e+00 5.12120008e-01 6.23617828e-01 7.74363205e-02 6.48725986e-01 -7.95640886e-01 -9.85354632e-02 1.56911477e-01 -2.33569145e-01 -9.29764211e-01 2.14789167e-01 -9.14230198e-02 -1.70778707e-01 3.07736129e-01 1.08879769e+00 4.14357543e-01 4.90110546e-01 7.90112793e-01 -1.51315045e+00 -2.72970915e-01 4.37978029e-01 -6.47296250e-01 -1.51737197e-03 2.48099491e-01 7.70391151e-02 1.40243196e+00 -1.62750155e-01 2.14241475e-01 1.50524032e+00 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050930d7-4390-4793-9556-2495691176a5	topic-aware-response-generation-in-task-1	2212.05373	null	https://arxiv.org/abs/2212.05373v1	https://arxiv.org/pdf/2212.05373v1.pdf	Topic-Aware Response Generation in Task-Oriented Dialogue with Unstructured Knowledge Access	To alleviate the problem of structured databases' limited coverage, recent task-oriented dialogue systems incorporate external unstructured knowledge to guide the generation of system responses. However, these usually use word or sentence level similarities to detect the relevant knowledge context, which only partially capture the topical level relevance. In this paper, we examine how to better integrate topical information in knowledge grounded task-oriented dialogue and propose ``Topic-Aware Response Generation'' (TARG), an end-to-end response generation model. TARG incorporates multiple topic-aware attention mechanisms to derive the importance weighting scheme over dialogue utterances and external knowledge sources towards a better understanding of the dialogue history. Experimental results indicate that TARG achieves state-of-the-art performance in knowledge selection and response generation, outperforming previous state-of-the-art by 3.2, 3.6, and 4.2 points in EM, F1 and BLEU-4 respectively on Doc2Dial, and performing comparably with previous work on DSTC9; both being knowledge-grounded task-oriented dialogue datasets.	['Ignacio Iacobacci', 'Gerasimos Lampouras', 'Yue Feng']	2022-12-10	null	null	null	null	['response-generation', 'task-oriented-dialogue-systems']	['natural-language-processing', 'natural-language-processing']	[ 1.82687029e-01 7.39066899e-01 -1.48940176e-01 -4.31114614e-01 -1.36185825e+00 -5.78073025e-01 1.11705863e+00 2.20488757e-01 -5.52778184e-01 1.35611999e+00 8.73654962e-01 4.82569933e-02 -6.25401549e-03 -5.81234992e-01 -1.17405273e-01 -2.60204792e-01 4.25508678e-01 1.08127427e+00 5.49525797e-01 -9.67106938e-01 6.18321121e-01 -4.13281888e-01 -1.02551281e+00 8.60815704e-01 1.22891843e+00 6.29288614e-01 2.73425668e-01 8.96719575e-01 -4.97092485e-01 8.92733991e-01 -1.00521302e+00 -5.86802900e-01 -4.20023620e-01 -7.68679619e-01 -1.70566463e+00 3.51747945e-02 2.30560899e-02 -1.79641575e-01 -2.40909040e-01 5.91865003e-01 8.80701125e-01 3.96912932e-01 7.36306548e-01 -6.49302483e-01 -7.73994505e-01 1.06230474e+00 -3.29498239e-02 1.82863504e-01 8.31565022e-01 -2.53441129e-02 8.86959314e-01 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57bb4ba8-36f2-4160-bf35-089143bad4f4	spatiotemporally-discriminative-video	2303.16341	null	https://arxiv.org/abs/2303.16341v1	https://arxiv.org/pdf/2303.16341v1.pdf	Spatiotemporally Discriminative Video-Language Pre-Training with Text Grounding	Most of existing video-language pre-training methods focus on instance-level alignment between video clips and captions via global contrastive learning but neglect rich fine-grained local information, which is of importance to downstream tasks requiring temporal localization and semantic reasoning. In this work, we propose a simple yet effective video-language pre-training framework, namely G-ViLM, to learn discriminative spatiotemporal features. Two novel designs involving spatiotemporal grounding and temporal grouping promote learning local region-noun alignment and temporal-aware features simultaneously. Specifically, spatiotemporal grounding aggregates semantically similar video tokens and aligns them with noun phrases extracted from the caption to promote local region-noun correspondences. Moreover, temporal grouping leverages cut-and-paste to manually create temporal scene changes and then learns distinguishable features from different scenes. Comprehensive evaluations demonstrate that G-ViLM performs favorably against existing approaches on four representative downstream tasks, covering text-video retrieval, video question answering, video action recognition and temporal action localization. G-ViLM performs competitively on all evaluated tasks and in particular achieves R@10 of 65.1 on zero-shot MSR-VTT retrieval, over 9% higher than the state-of-the-art method.	['Liangzhe Yuan', 'Cho-Jui Hsieh', 'Ting Liu', 'Florian Schroff', 'Ming-Hsuan Yang', 'Boqing Gong', 'Long Zhao', 'Yuanhao Xiong']	2023-03-28	null	null	null	null	['video-question-answering', 'video-retrieval', 'action-recognition-in-videos', 'action-localization']	['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision']	[ 2.74967015e-01 -5.81268609e-01 -8.15640986e-01 -4.18379754e-01 -1.33499789e+00 -6.56336904e-01 7.57751226e-01 2.37983987e-01 -6.17183566e-01 1.82310537e-01 6.69622183e-01 1.81327939e-01 2.97806598e-02 -7.88241476e-02 -9.09072459e-01 -4.83772516e-01 -3.48959565e-01 3.90890129e-02 5.03015339e-01 -1.01177692e-01 2.32109249e-01 1.00157999e-01 -1.46041858e+00 1.03829110e+00 4.50389266e-01 1.15839624e+00 1.82056040e-01 6.29245043e-01 -9.85403545e-03 1.23256505e+00 -2.75929302e-01 -2.34986633e-01 2.47648805e-01 -3.89203668e-01 -8.91741753e-01 7.66034573e-02 9.76479828e-01 -5.76389909e-01 -8.36944580e-01 6.72553897e-01 2.09911644e-01 5.26034892e-01 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c09b811f-6e7e-4db3-949d-7ef9b022f7a0	student-collaboration-improves-self	2205.05194	null	https://arxiv.org/abs/2205.05194v3	https://arxiv.org/pdf/2205.05194v3.pdf	Multiplexed Immunofluorescence Brain Image Analysis Using Self-Supervised Dual-Loss Adaptive Masked Autoencoder	Reliable large-scale cell detection and segmentation is the fundamental first step to understanding biological processes in the brain. The ability to phenotype cells at scale can accelerate preclinical drug evaluation and system-level brain histology studies. The impressive advances in deep learning offer a practical solution to cell image detection and segmentation. Unfortunately, categorizing cells and delineating their boundaries for training deep networks is an expensive process that requires skilled biologists. This paper presents a novel self-supervised Dual-Loss Adaptive Masked Autoencoder (DAMA) for learning rich features from multiplexed immunofluorescence brain images. DAMA's objective function minimizes the conditional entropy in pixel-level reconstruction and feature-level regression. Unlike existing self-supervised learning methods based on a random image masking strategy, DAMA employs a novel adaptive mask sampling strategy to maximize mutual information and effectively learn brain cell data. To the best of our knowledge, this is the first effort to develop a self-supervised learning method for multiplexed immunofluorescence brain images. Our extensive experiments demonstrate that DAMA features enable superior cell detection, segmentation, and classification performance without requiring many annotations.	['Hien V. Nguyen', 'Badri Roysam', 'Dragan Maric', 'Hung Q. Vo', 'Bai Lin', 'Son T. Ly']	2022-05-10	null	null	null	null	['self-supervised-image-classification', 'cell-detection']	['computer-vision', 'computer-vision']	[ 3.48963678e-01 -3.89229238e-01 9.01923031e-02 -4.79606211e-01 -7.26244926e-01 -2.25858435e-01 2.66106069e-01 4.12789553e-01 -9.52950537e-01 1.15882885e+00 -5.30504405e-01 -4.32904996e-02 3.22359622e-01 -6.41119838e-01 -6.74340665e-01 -1.22968161e+00 -1.49140075e-01 6.00908518e-01 8.82228389e-02 1.05030484e-01 2.07723022e-01 9.87915337e-01 -1.26661539e+00 2.67206132e-01 7.15394437e-01 1.00445068e+00 3.10460985e-01 7.18366861e-01 -2.13684887e-01 7.96693087e-01 -6.12423062e-01 8.62889886e-02 9.19090137e-02 -4.15886194e-01 -7.68743992e-01 3.71830948e-02 3.03823948e-01 -3.89033824e-01 1.24727543e-02 1.11917555e+00 7.78939366e-01 -1.03429742e-01 9.95973527e-01 -1.05938792e+00 -3.55624110e-01 2.05733746e-01 -5.03699958e-01 6.19907260e-01 -1.94180474e-01 8.52412432e-02 5.17519176e-01 -6.60080612e-01 6.10915303e-01 7.45616198e-01 5.17168343e-01 7.71138966e-01 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b1cbf940-8f70-458b-9eb0-8bbb274a3f82	relationrs-relationship-representation	2110.06730	null	https://arxiv.org/abs/2110.06730v1	https://arxiv.org/pdf/2110.06730v1.pdf	RelationRS: Relationship Representation Network for Object Detection in Aerial Images	Object detection is a basic and important task in the field of aerial image processing and has gained much attention in computer vision. However, previous aerial image object detection approaches have insufficient use of scene semantic information between different regions of large-scale aerial images. In addition, complex background and scale changes make it difficult to improve detection accuracy. To address these issues, we propose a relationship representation network for object detection in aerial images (RelationRS): 1) Firstly, multi-scale features are fused and enhanced by a dual relationship module (DRM) with conditional convolution. The dual relationship module learns the potential relationship between features of different scales and learns the relationship between different scenes from different patches in a same iteration. In addition, the dual relationship module dynamically generates parameters to guide the fusion of multi-scale features. 2) Secondly, The bridging visual representations module (BVR) is introduced into the field of aerial images to improve the object detection effect in images with complex backgrounds. Experiments with a publicly available object detection dataset for aerial images demonstrate that the proposed RelationRS achieves a state-of-the-art detection performance.	['Jihong Xiu', 'Haipeng Kuang', 'Yu Liu', 'Qingjun Li', 'Pu Huang', 'Weifeng Sun', 'Bin Li', 'Chao Sun', 'Hao Wang', 'Chongyang Liu', 'Xuefei Zhang', 'Zhiming Liu']	2021-10-13	null	null	null	null	['object-detection-in-aerial-images']	['computer-vision']	[ 4.47557777e-01 -5.67089975e-01 2.64328778e-01 -1.65726185e-01 7.38298055e-03 -5.17721593e-01 2.93563277e-01 1.50151804e-01 -4.43973482e-01 1.19482554e-01 -4.34627056e-01 -9.49892327e-02 -1.51686400e-01 -1.18494058e+00 -4.62928593e-01 -6.75890386e-01 -9.90196243e-02 -3.97435427e-01 9.81217802e-01 -4.54206735e-01 1.17392940e-02 6.13677323e-01 -1.54524827e+00 4.50144768e-01 8.44209135e-01 1.07581246e+00 7.53198802e-01 5.50190151e-01 7.60162622e-02 6.17770135e-01 -5.75389862e-01 1.45529611e-02 7.48026550e-01 -3.64706784e-01 -5.42097867e-01 4.34641272e-01 4.61204439e-01 -6.41871989e-01 -5.07782876e-01 1.31740272e+00 4.46567982e-01 1.21144190e-01 4.18284923e-01 -1.18750393e+00 -6.30741894e-01 3.80570948e-01 -1.12856889e+00 7.73790777e-01 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6eaf1e6c-1a99-40a8-b187-791fe9eac565	learning-the-human-judgment-for-the-automatic	null	null	https://aclanthology.org/2020.lrec-1.198	https://aclanthology.org/2020.lrec-1.198.pdf	Learning the Human Judgment for the Automatic Evaluation of Chatbot	It is hard to evaluate the quality of the generated text by a generative dialogue system. Currently, dialogue evaluation relies on human judges to label the quality of the generated text. It is not a reusable mechanism that can give consistent evaluation for system developers. We believe that it is easier to get consistent results on comparing two generated dialogue by two systems and it is hard to give a consistent quality score on only one system at a time. In this paper, we propose a machine learning approach to reduce the effort of human evaluation by learning the human judgment on comparing two dialogue systems. Training from the human labeling result, the evaluation model learns which generative models is better in each dialog context. Thus, it can be used for system developers to compare the fine-tuned models over and over again without the human labor. In our experiment we find the agreement between the learned model and human judge is 70{\%}. The experiment is conducted on comparing two attention based GRU-RNN generative models.	['Sheng-Lun Chien', 'Shih-Hung Wu']	2020-05-01	null	null	null	lrec-2020-5	['dialogue-evaluation']	['natural-language-processing']	[-1.03883468e-01 6.70600772e-01 4.49369341e-01 -6.83278620e-01 -8.90581548e-01 -7.25849926e-01 5.32011986e-01 -5.32834008e-02 -3.31151068e-01 8.61811519e-01 1.62125081e-01 -2.92032301e-01 2.87662029e-01 -6.26984537e-01 -1.57603726e-01 -2.81919777e-01 8.71125162e-01 9.40577090e-01 1.59185648e-01 -5.57786942e-01 5.07167935e-01 -2.78393835e-01 -1.27040279e+00 5.86252868e-01 1.06785619e+00 5.68349659e-01 4.91327554e-01 1.06501317e+00 -3.52328032e-01 1.19948041e+00 -1.15174401e+00 -8.19005787e-01 5.75162023e-02 -1.07449853e+00 -1.34304976e+00 4.89984155e-02 2.15881303e-01 -6.48553908e-01 3.46216977e-01 1.19793952e+00 6.90436900e-01 -1.64313868e-01 7.76015460e-01 -1.21279168e+00 -9.26805973e-01 1.14833033e+00 -3.49879861e-02 -1.33751109e-01 8.30897629e-01 1.26149848e-01 1.02113497e+00 -6.60852790e-01 5.66777229e-01 1.40540552e+00 3.23671401e-01 9.18762505e-01 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bc28ee20-8770-4fc2-8bde-20346ee8f9a2	deep-gaussian-processes-for-air-quality	2211.10174	null	https://arxiv.org/abs/2211.10174v1	https://arxiv.org/pdf/2211.10174v1.pdf	Deep Gaussian Processes for Air Quality Inference	Air pollution kills around 7 million people annually, and approximately 2.4 billion people are exposed to hazardous air pollution. Accurate, fine-grained air quality (AQ) monitoring is essential to control and reduce pollution. However, AQ station deployment is sparse, and thus air quality inference for unmonitored locations is crucial. Conventional interpolation methods fail to learn the complex AQ phenomena. This work demonstrates that Deep Gaussian Process models (DGPs) are a promising model for the task of AQ inference. We implement Doubly Stochastic Variational Inference, a DGP algorithm, and show that it performs comparably to the state-of-the-art models.	['Nipun Batra', 'Zeel Patel', 'Sachin Yadav', 'Saagar Parikh', 'Eshan Gujarathi', 'Aadesh Desai']	2022-11-18	null	null	null	null	['air-quality-inference']	['miscellaneous']	[-1.22049868e-01 -4.84607637e-01 7.40372157e-03 1.29172742e-01 -1.19100809e+00 -4.43681151e-01 5.56333601e-01 1.82306901e-01 -1.80504218e-01 1.19182050e+00 1.31552741e-01 -5.43694317e-01 -1.71895176e-01 -1.19670141e+00 -6.52546525e-01 -1.00677240e+00 2.68062919e-01 6.81265593e-01 2.08011474e-02 3.85829002e-01 -2.07296222e-01 4.14576501e-01 -9.75256264e-01 -2.74993449e-01 1.25973260e+00 7.70110488e-01 -2.56186366e-01 8.21922421e-01 -2.51051635e-01 2.90594846e-01 -5.01957893e-01 -2.48219326e-01 -6.40580729e-02 -2.23110169e-01 -2.56997645e-01 -5.02696812e-01 6.00424945e-01 -2.07504332e-01 -3.06215137e-01 9.79887724e-01 5.10461807e-01 5.44312477e-01 9.37564790e-01 -1.08229721e+00 -5.81752181e-01 -2.67451167e-01 -3.97219092e-01 4.15356249e-01 -1.97185017e-02 5.95416367e-01 8.06910634e-01 -8.01416337e-01 -4.15577650e-01 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75973619-0c30-475b-9bf8-dc9a626ae77a	analyzing-the-impact-of-foursquare-and	2006.07516	null	https://arxiv.org/abs/2006.07516v1	https://arxiv.org/pdf/2006.07516v1.pdf	Analyzing the Impact of Foursquare and Streetlight Data with Human Demographics on Future Crime Prediction	Finding the factors contributing to criminal activities and their consequences is essential to improve quantitative crime research. To respond to this concern, we examine an extensive set of features from different perspectives and explanations. Our study aims to build data-driven models for predicting future crime occurrences. In this paper, we propose the use of streetlight infrastructure and Foursquare data along with demographic characteristics for improving future crime incident prediction. We evaluate the classification performance based on various feature combinations as well as with the baseline model. Our proposed model was tested on each smallest geographic region in Halifax, Canada. Our findings demonstrate the effectiveness of integrating diverse sources of data to gain satisfactory classification performance.	['Lucas May Petry', 'Stan Matwin', 'Fateha Khanam Bappee', 'Amilcar Soares']	2020-06-13	null	null	null	null	['crime-prediction']	['miscellaneous']	[-1.04698218e-01 -4.42995757e-01 -2.93797165e-01 -5.04432499e-01 -5.67666411e-01 -9.25086737e-02 6.06513679e-01 5.56850553e-01 -6.47786558e-01 8.22058737e-01 8.43422294e-01 -6.25650048e-01 -3.81386369e-01 -9.47123289e-01 -1.01688527e-01 -1.47342607e-01 1.58626422e-01 -1.62504986e-01 -1.53541937e-01 -2.88370758e-01 9.36564505e-01 7.09969878e-01 -1.17405212e+00 3.61048818e-01 1.22944605e+00 2.00937465e-01 -3.47120315e-02 3.24356258e-01 1.05287880e-01 7.77956724e-01 -5.32587230e-01 -6.78129613e-01 1.78160205e-01 -1.39803728e-02 -5.23503721e-01 -1.54686838e-01 2.73538858e-01 -4.73768294e-01 -3.87362301e-01 4.78325009e-01 3.78819287e-01 2.02721447e-01 8.59374046e-01 -1.17449331e+00 -9.42704260e-01 2.83216089e-01 -6.12916887e-01 8.56190562e-01 6.51416302e-01 1.37487799e-01 8.17047298e-01 -9.88128066e-01 3.66247177e-01 8.33280325e-01 8.49398434e-01 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6ece59b7-c1cc-4b02-91dd-ea6e61b95230	autoregressive-structured-prediction-with	2210.14698	null	https://arxiv.org/abs/2210.14698v2	https://arxiv.org/pdf/2210.14698v2.pdf	Autoregressive Structured Prediction with Language Models	Recent years have seen a paradigm shift in NLP towards using pretrained language models ({PLM}) for a wide range of tasks. However, there are many difficult design decisions to represent structures (e.g. tagged text, coreference chains) in a way such that they can be captured by PLMs. Prior work on structured prediction with PLMs typically flattens the structured output into a sequence, which limits the quality of structural information being learned and leads to inferior performance compared to classic discriminative models. In this work, we describe an approach to model structures as sequences of actions in an autoregressive manner with PLMs, allowing in-structure dependencies to be learned without any loss. Our approach achieves the new state-of-the-art on all the structured prediction tasks we looked at, namely, named entity recognition, end-to-end relation extraction, and coreference resolution.	['Mrinmaya Sachan', 'Ryan Cotterell', 'Nicholas Monath', 'Yuchen Jiang', 'Tianyu Liu']	2022-10-26	null	null	null	null	['coreference-resolution']	['natural-language-processing']	[ 5.36108434e-01 8.03129673e-01 -6.06252670e-01 -7.11835027e-01 -1.11316490e+00 -7.29986250e-01 7.16403067e-01 1.61548287e-01 -1.18151754e-01 8.15874755e-01 8.51255000e-01 -4.68281716e-01 -6.93370551e-02 -3.87364626e-01 -7.58995533e-01 -3.77151668e-01 -4.02063839e-02 1.18451858e+00 2.30595946e-01 -1.27858752e-02 -4.41202894e-02 5.54692745e-01 -1.03594315e+00 8.07548404e-01 6.72558248e-01 6.69056058e-01 1.66666374e-01 4.45529968e-01 -4.89048094e-01 1.32690358e+00 -3.62648040e-01 -7.09577441e-01 -1.64850786e-01 -3.05308580e-01 -1.47093487e+00 -2.67919570e-01 3.94694924e-01 1.99369416e-01 -3.45669776e-01 6.84469879e-01 1.53184354e-01 1.54655546e-01 7.26449907e-01 -9.24675763e-01 -2.63925374e-01 1.16069674e+00 -3.99974555e-01 -6.57495717e-03 6.76675856e-01 -4.17799234e-01 1.53507757e+00 -6.66361928e-01 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5188ba54-463a-406f-be41-4476d2719c6f	pointgrow-autoregressively-learned-point	1810.05591	null	https://arxiv.org/abs/1810.05591v3	https://arxiv.org/pdf/1810.05591v3.pdf	PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention	Generating 3D point clouds is challenging yet highly desired. This work presents a novel autoregressive model, PointGrow, which can generate diverse and realistic point cloud samples from scratch or conditioned on semantic contexts. This model operates recurrently, with each point sampled according to a conditional distribution given its previously-generated points, allowing inter-point correlations to be well-exploited and 3D shape generative processes to be better interpreted. Since point cloud object shapes are typically encoded by long-range dependencies, we augment our model with dedicated self-attention modules to capture such relations. Extensive evaluations show that PointGrow achieves satisfying performance on both unconditional and conditional point cloud generation tasks, with respect to realism and diversity. Several important applications, such as unsupervised feature learning and shape arithmetic operations, are also demonstrated.	['Yongbin Sun', 'Joshua E. Siegel', 'Yue Wang', 'Ziwei Liu', 'Sanjay E. Sarma']	2018-10-12	null	null	null	null	['point-cloud-generation', 'generating-3d-point-clouds']	['computer-vision', 'computer-vision']	[ 1.08618978e-02 5.98230958e-02 7.23122135e-02 -5.27541041e-01 -8.96418214e-01 -3.60375792e-01 9.61306393e-01 -7.09980801e-02 3.01819265e-01 5.24559379e-01 1.00542726e-02 3.93396989e-02 1.43918827e-01 -1.19375432e+00 -1.24467599e+00 -7.11671293e-01 -3.79849859e-02 1.11120737e+00 -2.37689272e-01 -1.44542038e-01 3.13997239e-01 1.14460886e+00 -1.87870812e+00 1.18552677e-01 9.11012113e-01 7.27381051e-01 3.81163865e-01 8.58757138e-01 -4.89912480e-01 5.04451275e-01 -6.64239526e-01 -2.97190905e-01 1.29260510e-01 1.45731166e-01 -3.67596745e-01 3.47924471e-01 3.93664867e-01 -7.45063499e-02 -9.60200205e-02 7.39446044e-01 3.16005021e-01 1.25707984e-01 1.13937283e+00 -1.18835247e+00 -1.13474953e+00 4.30251062e-01 -3.66233677e-01 -4.79544818e-01 2.08539486e-01 5.08816361e-01 9.30671394e-01 -1.26577449e+00 4.65167165e-01 1.47490156e+00 5.53322136e-01 4.48723912e-01 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cf925dd4-c88f-460b-a24b-e746b5b87cf3	learning-event-guided-high-dynamic-range	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Yang_Learning_Event_Guided_High_Dynamic_Range_Video_Reconstruction_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Learning_Event_Guided_High_Dynamic_Range_Video_Reconstruction_CVPR_2023_paper.pdf	Learning Event Guided High Dynamic Range Video Reconstruction	Limited by the trade-off between frame rate and exposure time when capturing moving scenes with conventional cameras, frame based HDR video reconstruction suffers from scene-dependent exposure ratio balancing and ghosting artifacts. Event cameras provide an alternative visual representation with a much higher dynamic range and temporal resolution free from the above issues, which could be an effective guidance for HDR imaging from LDR videos. In this paper, we propose a multimodal learning framework for event guided HDR video reconstruction. In order to better leverage the knowledge of the same scene from the two modalities of visual signals, a multimodal representation alignment strategy to learn a shared latent space and a fusion module tailored to complementing two types of signals for different dynamic ranges in different regions are proposed. Temporal correlations are utilized recurrently to suppress the flickering effects in the reconstructed HDR video. The proposed HDRev-Net demonstrates state-of-the-art performance quantitatively and qualitatively for both synthetic and real-world data.	['Boxin Shi', 'Imari Sato', 'Jinxiu Liang', 'Jin Han', 'Yixin Yang']	2023-01-01	null	null	null	cvpr-2023-1	['video-reconstruction']	['computer-vision']	[ 3.39775503e-01 -6.27705336e-01 -1.88611627e-01 -1.06568381e-01 -8.07231486e-01 -2.50689864e-01 5.24828672e-01 -4.21886802e-01 -8.81514251e-02 6.72694385e-01 6.15896225e-01 2.36941800e-01 -7.99324885e-02 -4.50100482e-01 -4.40702260e-01 -1.13555789e+00 8.35614949e-02 -4.44352984e-01 1.27067819e-01 -1.00333676e-01 8.06655884e-02 4.29344833e-01 -1.70306194e+00 5.36958456e-01 6.44707680e-01 8.94396186e-01 5.97031295e-01 6.92181945e-01 2.77593195e-01 1.49302387e+00 -2.38227278e-01 1.21901937e-01 3.01374108e-01 -4.89485353e-01 -1.18431300e-01 4.67190832e-01 3.86102498e-01 -7.00697124e-01 -1.29571378e+00 8.58313441e-01 6.07644498e-01 3.68254870e-01 3.46877187e-01 -1.06861889e+00 -3.90289396e-01 3.14341664e-01 -9.56005931e-01 2.51750171e-01 6.62945569e-01 4.35395867e-01 5.76751471e-01 -8.06487381e-01 6.86078668e-01 1.11629570e+00 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d28f58d2-6add-49c0-93ad-d7f12ec5b831	envisioning-a-next-generation-extended	2306.16541	null	https://arxiv.org/abs/2306.16541v1	https://arxiv.org/pdf/2306.16541v1.pdf	Envisioning a Next Generation Extended Reality Conferencing System with Efficient Photorealistic Human Rendering	Meeting online is becoming the new normal. Creating an immersive experience for online meetings is a necessity towards more diverse and seamless environments. Efficient photorealistic rendering of human 3D dynamics is the core of immersive meetings. Current popular applications achieve real-time conferencing but fall short in delivering photorealistic human dynamics, either due to limited 2D space or the use of avatars that lack realistic interactions between participants. Recent advances in neural rendering, such as the Neural Radiance Field (NeRF), offer the potential for greater realism in metaverse meetings. However, the slow rendering speed of NeRF poses challenges for real-time conferencing. We envision a pipeline for a future extended reality metaverse conferencing system that leverages monocular video acquisition and free-viewpoint synthesis to enhance data and hardware efficiency. Towards an immersive conferencing experience, we explore an accelerated NeRF-based free-viewpoint synthesis algorithm for rendering photorealistic human dynamics more efficiently. We show that our algorithm achieves comparable rendering quality while performing training and inference 44.5% and 213% faster than state-of-the-art methods, respectively. Our exploration provides a design basis for constructing metaverse conferencing systems that can handle complex application scenarios, including dynamic scene relighting with customized themes and multi-user conferencing that harmonizes real-world people into an extended world.	['Heather Yu', 'Liang Peng', 'Xiyun Song', 'Masood Mortazavi', 'Zhangsihao Yang', 'Letian Zhang', 'Chuanyue Shen']	2023-06-28	null	null	null	null	['neural-rendering', 'human-dynamics']	['computer-vision', 'computer-vision']	[ 4.31180224e-02 7.71050155e-02 8.20736468e-01 -2.46995434e-01 -7.76272774e-01 -4.46467310e-01 7.20355511e-01 -5.34249485e-01 2.12895244e-01 4.34249610e-01 4.91324037e-01 -2.84373939e-01 1.83135822e-01 -8.78951490e-01 -5.12242496e-01 -4.01259989e-01 -3.69277835e-01 2.80637681e-01 -1.21929958e-01 -8.70540738e-01 -2.16959957e-02 3.52317244e-01 -2.06203842e+00 5.17363250e-01 7.47015238e-01 3.94358814e-01 1.83561027e-01 1.57278800e+00 2.43209779e-01 7.06777513e-01 -8.55134070e-01 -9.52410102e-02 2.30958372e-01 -3.94697040e-01 -3.43050450e-01 -2.85003364e-01 8.49016607e-01 -7.19296575e-01 -7.31286049e-01 1.39321700e-01 1.00372016e+00 4.01235163e-01 5.49492016e-02 -1.32410347e+00 -2.90848255e-01 1.64879244e-02 -4.48835760e-01 1.78669523e-02 1.14737737e+00 2.15289354e-01 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61bde8bd-bfc1-4bf7-8852-f18062e500d0	self-replicating-machines-in-continuous-space	cs/0304022	null	https://arxiv.org/abs/cs/0304022v1	https://arxiv.org/pdf/cs/0304022v1.pdf	Self-Replicating Machines in Continuous Space with Virtual Physics	JohnnyVon is an implementation of self-replicating machines in continuous two-dimensional space. Two types of particles drift about in a virtual liquid. The particles are automata with discrete internal states but continuous external relationships. Their internal states are governed by finite state machines but their external relationships are governed by a simulated physics that includes Brownian motion, viscosity, and spring-like attractive and repulsive forces. The particles can be assembled into patterns that can encode arbitrary strings of bits. We demonstrate that, if an arbitrary "seed" pattern is put in a "soup" of separate individual particles, the pattern will replicate by assembling the individual particles into copies of itself. We also show that, given sufficient time, a soup of separate individual particles will eventually spontaneously form self-replicating patterns. We discuss the implications of JohnnyVon for research in nanotechnology, theoretical biology, and artificial life.	['Robert Ewaschuk', 'Peter Turney', 'Arnold Smith']	2003-04-15	null	null	null	null	['artificial-life']	['miscellaneous']	[ 2.90042460e-01 2.06316233e-01 8.30619857e-02 3.63550872e-01 5.16592860e-01 -1.02193999e+00 9.66760099e-01 -1.49460807e-01 -3.07964414e-01 8.41080785e-01 -8.12221784e-03 -3.12444180e-01 3.06623757e-01 -1.38545990e+00 -7.74556458e-01 -1.13702083e+00 -2.02593610e-01 6.05314910e-01 6.09879792e-01 -4.55386102e-01 2.61690915e-01 5.68399370e-01 -1.63786006e+00 3.94014502e-03 4.68694210e-01 3.45664054e-01 2.78245658e-01 1.14051723e+00 -2.10624009e-01 5.73736608e-01 -4.20286864e-01 5.71487918e-02 2.68988132e-01 -7.82868624e-01 -3.38592052e-01 -1.81369439e-01 -3.52963716e-01 2.78358728e-01 -3.50421995e-01 1.03932428e+00 -3.54434103e-02 -6.42011240e-02 8.14260542e-01 -7.95651138e-01 -1.07870805e+00 5.03209174e-01 7.18041137e-02 -3.42313379e-01 3.68264526e-01 5.63576758e-01 3.53703946e-01 -4.05909061e-01 9.52979028e-01 1.10059047e+00 5.98622143e-01 1.24638498e+00 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-7.79820561e-01 1.65464967e-01 3.51218969e-01 -3.89999866e-01 -5.49125016e-01 -2.14137603e-02]	[5.624760150909424, 4.181692600250244]
544a982a-d56e-4ed7-a12c-cee0806f8c66	l1-gp-l1-adaptive-control-with-bayesian	null	null	https://openreview.net/forum?id=TZhplO4Q3YM	https://openreview.net/pdf?id=TZhplO4Q3YM	L1-GP: L1 Adaptive Control with Bayesian Learning	We present L1-GP, an architecture based on L1 adaptive control and Gaussian Process Regression (GPR) for safe simultaneous control and learning. On one hand, the L1 adaptive control provides stability and transient performance guarantees, which allows for GPR to efficiently and safely learn the uncertain dynamics. On the other hand, the learned dynamics can be conveniently incorporated into the L1 control architecture without sacrificing robustness and tracking performance. Subsequently, the learned dynamics can lead to less conservative designs for performance/robustness tradeoff. We illustrate the efficacy of the proposed architecture via numerical simulations.	['Evangelos Theodorou', 'Naira Hovakimyan', 'Andrew Patterson', 'Pan Zhao', 'Aditya Gahlawat']	2020-06-08	null	null	null	l4dc-2020-6	['gpr', 'gpr']	['computer-vision', 'miscellaneous']	[-3.88220400e-01 3.59261751e-01 -4.35609341e-01 2.50412107e-01 -7.48707294e-01 -3.57103676e-01 2.88981259e-01 1.19437709e-01 2.35859267e-02 1.02746522e+00 -2.04680189e-01 -2.70288199e-01 -6.33633018e-01 -4.92991120e-01 -6.31270409e-01 -1.21426201e+00 -9.76194143e-02 -1.49126992e-01 -8.23893026e-02 1.83529437e-01 -8.64039361e-02 2.09965512e-01 -8.00532877e-01 -8.74708533e-01 1.17854381e+00 1.28290892e+00 1.01100758e-01 3.85106474e-01 4.92566437e-01 7.70268202e-01 4.07590605e-02 2.55028397e-01 2.40733072e-01 1.86799675e-01 1.15117729e-01 4.85454835e-02 -2.28780538e-01 -3.21700484e-01 -3.97637129e-01 1.18933535e+00 6.37637317e-01 5.50352395e-01 5.52930176e-01 -1.13633835e+00 -4.87138838e-01 3.26444536e-01 -7.06836343e-01 -2.36124352e-01 -6.54516146e-02 4.47040647e-01 6.58436537e-01 -8.88052285e-01 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e46ec5a0-6dcd-4eda-b254-de07ea1971e4	an-ensemble-deep-learning-based-cyber-attack	2005.00936	null	https://arxiv.org/abs/2005.00936v1	https://arxiv.org/pdf/2005.00936v1.pdf	An Ensemble Deep Learning-based Cyber-Attack Detection in Industrial Control System	The integration of communication networks and the Internet of Things (IoT) in Industrial Control Systems (ICSs) increases their vulnerability towards cyber-attacks, causing devastating outcomes. Traditional Intrusion Detection Systems (IDSs), which are mainly developed to support Information Technology (IT) systems, count vastly on predefined models and are trained mostly on specific cyber-attacks. Besides, most IDSs do not consider the imbalanced nature of ICS datasets, thereby suffering from low accuracy and high false positive on real datasets. In this paper, we propose a deep representation learning model to construct new balanced representations of the imbalanced dataset. The new representations are fed into an ensemble deep learning attack detection model specifically designed for an ICS environment. The proposed attack detection model leverages Deep Neural Network (DNN) and Decision Tree (DT) classifiers to detect cyber-attacks from the new representations. The performance of the proposed model is evaluated based on 10-fold cross-validation on two real ICS datasets. The results show that the proposed method outperforms conventional classifiers, including Random Forest (RF), DNN, and AdaBoost, as well as recent existing models in the literature. The proposed approach is a generalized technique, which can be implemented in existing ICS infrastructures with minimum changes.	['Reza M. Parizi', 'Ali Dehghantanha', 'Hadis Karimipour', 'Abdulrahman Al-Abassi']	2020-05-02	null	null	null	null	['cyber-attack-detection']	['miscellaneous']	[ 1.83010042e-01 -2.06454545e-01 -2.63572097e-01 -3.61657739e-01 -2.55820919e-02 -3.91543746e-01 5.09819567e-01 1.82943761e-01 1.83000609e-01 6.85186923e-01 -3.53826374e-01 -7.11083829e-01 -4.35062081e-01 -1.23269892e+00 -1.98318258e-01 -6.35297656e-01 2.45870531e-01 6.37279928e-01 3.47560830e-02 1.04348687e-02 4.30672884e-01 9.38423812e-01 -1.28518176e+00 4.12703395e-01 6.11687720e-01 1.36944604e+00 -6.05292141e-01 3.93342644e-01 -1.79110467e-01 8.08238328e-01 -1.20978904e+00 -2.60391146e-01 3.40116292e-01 3.14421169e-02 -5.63208878e-01 -4.90362704e-01 -1.10979997e-01 -3.13875675e-01 -3.01073521e-01 8.99005771e-01 4.40673947e-01 -3.20624381e-01 6.37625754e-01 -1.74664044e+00 -3.20063680e-01 4.71068114e-01 -5.14685273e-01 3.47591639e-01 7.80584291e-02 1.57537341e-01 4.75209087e-01 -3.51502031e-01 2.85201311e-01 1.35741055e+00 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e941372b-6bd6-405d-bff0-395dca6ecd60	frequency-selective-mesh-to-mesh-resampling	2203.09224	null	https://arxiv.org/abs/2203.09224v2	https://arxiv.org/pdf/2203.09224v2.pdf	Frequency-Selective Mesh-to-Mesh Resampling for Color Upsampling of Point Clouds	With the increased use of virtual and augmented reality applications, the importance of point cloud data rises. High-quality capturing of point clouds is still expensive and thus, the need for point cloud super-resolution or point cloud upsampling techniques emerges. In this paper, we propose an interpolation scheme for color upsampling of three-dimensional color point clouds. As a point cloud represents an object's surface in three-dimensional space, we first conduct a local transform of the surface into a two-dimensional plane. Secondly, we propose to apply a novel Frequency-Selective Mesh-to-Mesh Resampling (FSMMR) technique for the interpolation of the points in 2D. FSMMR generates a model of weighted superpositions of basis functions on scattered points. This model is then evaluated for the final points in order to increase the resolution of the original point cloud. Evaluation shows that our approach outperforms common interpolation schemes. Visual comparisons of the jaguar point cloud underlines the quality of our upsampling results. The high performance of FSMMR holds for various sampling densities of the input point cloud.	['André Kaup', 'Andreas Spruck', 'Viktoria Heimann']	2022-03-17	null	null	null	null	['point-cloud-super-resolution']	['computer-vision']	[ 2.33160108e-01 -4.40705746e-01 4.70197648e-01 5.24952449e-02 -7.59225667e-01 -8.99479389e-02 5.73740602e-01 3.63359004e-02 -9.81322080e-02 7.79794216e-01 -3.50479722e-01 -9.20010656e-02 1.76768228e-01 -1.19438803e+00 -7.07602561e-01 -5.40054440e-01 -9.97446626e-02 6.85944557e-01 4.93268132e-01 -3.00428092e-01 3.70137960e-01 1.28033626e+00 -2.23196197e+00 2.75610447e-01 1.12348163e+00 8.57611716e-01 1.79300874e-01 8.23323965e-01 -5.27539611e-01 -1.76423699e-01 -4.47569281e-01 -9.27607641e-02 4.21091288e-01 -3.56689207e-02 -4.83236730e-01 3.75308953e-02 6.82087898e-01 -3.01065028e-01 3.60403329e-01 1.02338290e+00 1.54750794e-01 1.44429043e-01 6.10241890e-01 -1.06155193e+00 -5.40834665e-01 -4.29563493e-01 -9.05059874e-01 -4.08658206e-01 5.70903599e-01 -2.20581695e-01 3.38490814e-01 -1.52151012e+00 6.76095128e-01 1.28631735e+00 9.64083850e-01 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ca9b5e87-8895-4701-bf00-012c63f88f25	time-domain-audio-source-separation-based-on	2001.10190	null	https://arxiv.org/abs/2001.10190v1	https://arxiv.org/pdf/2001.10190v1.pdf	Time-Domain Audio Source Separation Based on Wave-U-Net Combined with Discrete Wavelet Transform	We propose a time-domain audio source separation method using down-sampling (DS) and up-sampling (US) layers based on a discrete wavelet transform (DWT). The proposed method is based on one of the state-of-the-art deep neural networks, Wave-U-Net, which successively down-samples and up-samples feature maps. We find that this architecture resembles that of multiresolution analysis, and reveal that the DS layers of Wave-U-Net cause aliasing and may discard information useful for the separation. Although the effects of these problems may be reduced by training, to achieve a more reliable source separation method, we should design DS layers capable of overcoming the problems. With this belief, focusing on the fact that the DWT has an anti-aliasing filter and the perfect reconstruction property, we design the proposed layers. Experiments on music source separation show the efficacy of the proposed method and the importance of simultaneously considering the anti-aliasing filters and the perfect reconstruction property.	['Tomohiko Nakamura', 'Hiroshi Saruwatari']	2020-01-28	null	null	null	null	['audio-source-separation', 'music-source-separation']	['audio', 'music']	[ 1.75095752e-01 -3.68175298e-01 1.14557311e-01 7.53905326e-02 -6.47509098e-01 -2.77138263e-01 2.35907927e-01 -1.06989995e-01 -1.02697343e-01 4.94473189e-01 4.39325392e-01 1.00076333e-01 -5.11274278e-01 -7.41784751e-01 -4.36070442e-01 -8.78004789e-01 -2.05454811e-01 -4.07510519e-01 3.56405199e-01 -2.41986662e-01 4.45842594e-02 3.75569463e-01 -1.84810674e+00 5.59898734e-01 9.15366292e-01 1.19103026e+00 1.50234699e-01 5.88903546e-01 4.68931906e-03 8.06427956e-01 -7.09611535e-01 1.16342837e-02 2.20243469e-01 -7.30614722e-01 -2.90652096e-01 -3.46586823e-01 2.13937744e-01 -5.22172391e-01 -2.68958986e-01 1.20774198e+00 8.46443594e-01 1.93072617e-01 5.72779596e-01 -1.00143993e+00 -5.79928458e-01 6.87178135e-01 -6.53778374e-01 5.01063645e-01 2.77371764e-01 -4.78091776e-01 9.46600616e-01 -8.48343134e-01 1.87507227e-01 9.96696353e-01 1.20788693e+00 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ee6e409f-60cc-4fb7-909f-4d6dd146f691	efficient-twitter-sentiment-classification	1701.03051	null	http://arxiv.org/abs/1701.03051v1	http://arxiv.org/pdf/1701.03051v1.pdf	Efficient Twitter Sentiment Classification using Subjective Distant Supervision	As microblogging services like Twitter are becoming more and more influential in today's globalised world, its facets like sentiment analysis are being extensively studied. We are no longer constrained by our own opinion. Others opinions and sentiments play a huge role in shaping our perspective. In this paper, we build on previous works on Twitter sentiment analysis using Distant Supervision. The existing approach requires huge computation resource for analysing large number of tweets. In this paper, we propose techniques to speed up the computation process for sentiment analysis. We use tweet subjectivity to select the right training samples. We also introduce the concept of EFWS (Effective Word Score) of a tweet that is derived from polarity scores of frequently used words, which is an additional heuristic that can be used to speed up the sentiment classification with standard machine learning algorithms. We performed our experiments using 1.6 million tweets. Experimental evaluations show that our proposed technique is more efficient and has higher accuracy compared to previously proposed methods. We achieve overall accuracies of around 80% (EFWS heuristic gives an accuracy around 85%) on a training dataset of 100K tweets, which is half the size of the dataset used for the baseline model. The accuracy of our proposed model is 2-3% higher than the baseline model, and the model effectively trains at twice the speed of the baseline model.	['Naveen Reddy Chedeti', 'Chinmay Chandak', 'Manish Singh', 'Tapan Sahni']	2017-01-11	null	null	null	null	['twitter-sentiment-analysis']	['natural-language-processing']	[-7.01153800e-02 -1.25021681e-01 -4.44021165e-01 -7.05252588e-01 -5.45911372e-01 -4.99564826e-01 6.82884276e-01 5.47408700e-01 -9.07891631e-01 7.95693219e-01 3.32148641e-01 -3.34645599e-01 3.30528319e-01 -9.54725325e-01 -3.22892398e-01 -6.22656107e-01 1.43092439e-01 3.47008914e-01 4.88521665e-01 -7.49995172e-01 6.51958525e-01 -4.52161804e-02 -1.74074602e+00 5.15018225e-01 6.98441446e-01 1.22246194e+00 -1.15892719e-02 6.89990163e-01 -3.87770414e-01 1.01128137e+00 -7.64449477e-01 -6.45323694e-01 4.32686359e-02 -1.06277071e-01 -1.00530207e+00 -1.06047221e-01 1.83969438e-02 1.83018640e-01 5.88076293e-01 9.71193790e-01 3.98895830e-01 1.29994616e-01 5.51798701e-01 -1.11078691e+00 -2.75634259e-01 7.76833236e-01 -7.25285709e-01 5.06280303e-01 3.34023237e-01 -6.10075653e-01 1.10544813e+00 -9.43753839e-01 3.95677954e-01 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a935b0b7-ca90-40a8-b46d-0470bd9303d6	revise-self-supervised-speech-resynthesis-1	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Hsu_ReVISE_Self-Supervised_Speech_Resynthesis_With_Visual_Input_for_Universal_and_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Hsu_ReVISE_Self-Supervised_Speech_Resynthesis_With_Visual_Input_for_Universal_and_CVPR_2023_paper.pdf	ReVISE: Self-Supervised Speech Resynthesis With Visual Input for Universal and Generalized Speech Regeneration	Prior works on improving speech quality with visual input typically study each type of auditory distortion separately (e.g., separation, inpainting, video-to-speech) and present tailored algorithms. This paper proposes to unify these subjects and study Generalized Speech Regeneration, where the goal is not to reconstruct the exact reference clean signal, but to focus on improving certain aspects of speech while not necessarily preserving the rest such as voice. In particular, this paper concerns intelligibility, quality, and video synchronization. We cast the problem as audio-visual speech resynthesis, which is composed of two steps: pseudo audio-visual speech recognition (P-AVSR) and pseudo text-to-speech synthesis (P-TTS). P-AVSR and P-TTS are connected by discrete units derived from a self-supervised speech model. Moreover, we utilize self-supervised audio-visual speech model to initialize P-AVSR. The proposed model is coined ReVISE. ReVISE is the first high-quality model for in-the-wild video-to-speech synthesis and achieves superior performance on all LRS3 audio-visual regeneration tasks with a single model. To demonstrates its applicability in the real world, ReVISE is also evaluated on EasyCom, an audio-visual benchmark collected under challenging acoustic conditions with only 1.6 hours of training data. Similarly, ReVISE greatly suppresses noise and improves quality. Project page: https://wnhsu.github.io/ReVISE.	['Yossi Adi', 'Jacob Donley', 'Bowen Shi', 'Tal Remez', 'Wei-Ning Hsu']	2023-01-01	null	null	null	cvpr-2023-1	['video-synchronization', 'text-to-speech-synthesis', 'speech-synthesis', 'visual-speech-recognition', 'audio-visual-speech-recognition']	['computer-vision', 'speech', 'speech', 'speech', 'speech']	[ 2.29637817e-01 -1.73360661e-01 2.08575614e-02 9.76500139e-02 -1.23399770e+00 -4.41310406e-01 3.76892000e-01 -4.80547160e-01 2.45767310e-02 5.59034526e-01 6.81546807e-01 -4.24320161e-01 3.21546972e-01 -1.33681640e-01 -8.98543775e-01 -7.72614062e-01 3.21467668e-01 -1.52638718e-01 3.08383517e-02 -1.68070942e-01 -9.34864059e-02 3.36831212e-01 -1.74260366e+00 4.91601825e-01 6.37331843e-01 8.44149411e-01 6.53766692e-01 1.18223345e+00 9.68670323e-02 7.68142879e-01 -8.68357420e-01 -5.18970676e-02 2.35908180e-01 -6.27907872e-01 -5.15223384e-01 2.72962958e-01 3.34107339e-01 -4.79101002e-01 -7.67726719e-01 1.02165699e+00 9.53288376e-01 1.84565082e-01 5.07457256e-01 -1.38665056e+00 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e06414ca-e640-4afb-ba17-40ba71afc1f6	cross-align-modeling-deep-cross-lingual	2210.04141	null	https://arxiv.org/abs/2210.04141v1	https://arxiv.org/pdf/2210.04141v1.pdf	Cross-Align: Modeling Deep Cross-lingual Interactions for Word Alignment	Word alignment which aims to extract lexicon translation equivalents between source and target sentences, serves as a fundamental tool for natural language processing. Recent studies in this area have yielded substantial improvements by generating alignments from contextualized embeddings of the pre-trained multilingual language models. However, we find that the existing approaches capture few interactions between the input sentence pairs, which degrades the word alignment quality severely, especially for the ambiguous words in the monolingual context. To remedy this problem, we propose Cross-Align to model deep interactions between the input sentence pairs, in which the source and target sentences are encoded separately with the shared self-attention modules in the shallow layers, while cross-lingual interactions are explicitly constructed by the cross-attention modules in the upper layers. Besides, to train our model effectively, we propose a two-stage training framework, where the model is trained with a simple Translation Language Modeling (TLM) objective in the first stage and then finetuned with a self-supervised alignment objective in the second stage. Experiments show that the proposed Cross-Align achieves the state-of-the-art (SOTA) performance on four out of five language pairs.	['Jie zhou', 'Jinan Xu', 'Yufeng Chen', 'Fandong Meng', 'Zhen Yang', 'Siyu Lai']	2022-10-09	null	null	null	null	['word-alignment']	['natural-language-processing']	[ 1.95611343e-01 -1.60938390e-02 -2.46954218e-01 -5.04291236e-01 -9.74308729e-01 -3.61867398e-01 6.56294286e-01 -2.14186776e-02 -5.96864223e-01 6.24534190e-01 4.74821389e-01 -6.37762666e-01 6.10077620e-01 -6.38416111e-01 -9.23296332e-01 -4.14425671e-01 5.58991313e-01 5.65692544e-01 -3.13632399e-01 -4.91275281e-01 -8.47802982e-02 -1.73520833e-01 -7.01721430e-01 4.45740759e-01 1.30074167e+00 4.64304239e-01 4.21382308e-01 2.61379838e-01 -5.16430497e-01 4.17399168e-01 -2.39846408e-01 -8.60975444e-01 2.90522277e-01 -8.43449712e-01 -7.75693834e-01 -2.15777829e-01 4.24242854e-01 -1.51056334e-01 -1.92517549e-01 1.29453671e+00 6.45459592e-01 -2.57522196e-01 3.82273465e-01 -7.97081590e-01 -1.17107666e+00 9.81528819e-01 -5.77163517e-01 2.32574761e-01 2.67801255e-01 3.05101424e-01 1.28423095e+00 -1.26343548e+00 3.22067857e-01 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5.82134128e-01 7.38753796e-01 -7.26801693e-01 -6.84263229e-01 -1.36594629e+00 2.00426936e-01 4.19688314e-01 2.28859469e-01 -2.14644641e-01 4.79348339e-02 -4.67698686e-02]	[11.59030532836914, 10.210981369018555]
5d40dd76-5b3b-48b1-833f-f82a03b675a4	deeppruner-learning-efficient-stereo-matching	1909.05845	null	https://arxiv.org/abs/1909.05845v1	https://arxiv.org/pdf/1909.05845v1.pdf	DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch	Our goal is to significantly speed up the runtime of current state-of-the-art stereo algorithms to enable real-time inference. Towards this goal, we developed a differentiable PatchMatch module that allows us to discard most disparities without requiring full cost volume evaluation. We then exploit this representation to learn which range to prune for each pixel. By progressively reducing the search space and effectively propagating such information, we are able to efficiently compute the cost volume for high likelihood hypotheses and achieve savings in both memory and computation. Finally, an image guided refinement module is exploited to further improve the performance. Since all our components are differentiable, the full network can be trained end-to-end. Our experiments show that our method achieves competitive results on KITTI and SceneFlow datasets while running in real-time at 62ms.	['Wei-Chiu Ma', 'Raquel Urtasun', 'Shivam Duggal', 'Shenlong Wang', 'Rui Hu']	2019-09-12	deeppruner-learning-efficient-stereo-matching-1	http://openaccess.thecvf.com/content_ICCV_2019/html/Duggal_DeepPruner_Learning_Efficient_Stereo_Matching_via_Differentiable_PatchMatch_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Duggal_DeepPruner_Learning_Efficient_Stereo_Matching_via_Differentiable_PatchMatch_ICCV_2019_paper.pdf	iccv-2019-10	['stereo-matching']	['computer-vision']	[ 3.09040397e-01 -3.78800593e-02 -4.35758289e-03 -5.52912176e-01 -1.01189554e+00 -4.56147552e-01 5.63110411e-01 4.00563441e-02 -7.45014250e-01 5.68818152e-01 1.13949832e-03 -4.03191924e-01 2.95270979e-01 -9.04181600e-01 -9.36824679e-01 -3.38533908e-01 -4.00949754e-02 5.74791968e-01 6.94701612e-01 2.08026052e-01 5.22868395e-01 5.35781741e-01 -1.87354553e+00 1.70302138e-01 9.69979644e-01 1.08355534e+00 4.30153072e-01 7.74796247e-01 1.05580516e-01 8.49058092e-01 -1.37011200e-01 -1.84253588e-01 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70d465bc-f50a-44a0-bdab-a4d194315d1c	leveraging-summary-guidance-on-medical-report	2302.04001	null	https://arxiv.org/abs/2302.04001v1	https://arxiv.org/pdf/2302.04001v1.pdf	Leveraging Summary Guidance on Medical Report Summarization	This study presents three deidentified large medical text datasets, named DISCHARGE, ECHO and RADIOLOGY, which contain 50K, 16K and 378K pairs of report and summary that are derived from MIMIC-III, respectively. We implement convincing baselines of automated abstractive summarization on the proposed datasets with pre-trained encoder-decoder language models, including BERT2BERT, T5-large and BART. Further, based on the BART model, we leverage the sampled summaries from the train set as prior knowledge guidance, for encoding additional contextual representations of the guidance with the encoder and enhancing the decoding representations in the decoder. The experimental results confirm the improvement of ROUGE scores and BERTScore made by the proposed method, outperforming the larger model T5-large.	['Wensheng Zhang', 'Yuanyuan Wu', 'Xuebing Yang', 'Yunqi Zhu']	2023-02-08	null	null	null	null	['abstractive-text-summarization']	['natural-language-processing']	[ 1.82948351e-01 8.41538191e-01 -1.18797667e-01 -3.84734035e-01 -1.59037054e+00 -2.67692059e-01 5.50108075e-01 7.23548412e-01 -3.89963508e-01 1.13032019e+00 1.60320818e+00 -1.69305816e-01 -2.82624543e-01 -2.53269881e-01 -6.79812193e-01 -3.90054405e-01 -3.28193426e-01 5.87004185e-01 -2.57331222e-01 2.14729607e-01 3.19363475e-01 -1.74281180e-01 -7.27775216e-01 9.48248148e-01 1.08163536e+00 8.39439869e-01 -1.77885187e-04 1.26071453e+00 1.37086332e-01 1.17129195e+00 -7.32668698e-01 -5.98466933e-01 -2.61405051e-01 -5.37915468e-01 -8.27646673e-01 -1.55164480e-01 -1.97000522e-02 -8.29508722e-01 -9.01667595e-01 4.82098550e-01 1.12386692e+00 -1.56103015e-01 1.10569620e+00 -3.26266468e-01 -7.00604022e-01 1.12181425e+00 -4.50961232e-01 3.68558884e-01 6.37853205e-01 -8.97779688e-03 6.71310306e-01 -7.64137328e-01 6.56121254e-01 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4b488f79-b632-473f-a2de-27448059d762	dctd-deep-conditional-target-densities-for	1909.12297	null	https://arxiv.org/abs/1909.12297v4	https://arxiv.org/pdf/1909.12297v4.pdf	Energy-Based Models for Deep Probabilistic Regression	While deep learning-based classification is generally tackled using standardized approaches, a wide variety of techniques are employed for regression. In computer vision, one particularly popular such technique is that of confidence-based regression, which entails predicting a confidence value for each input-target pair (x,y). While this approach has demonstrated impressive results, it requires important task-dependent design choices, and the predicted confidences lack a natural probabilistic meaning. We address these issues by proposing a general and conceptually simple regression method with a clear probabilistic interpretation. In our proposed approach, we create an energy-based model of the conditional target density p(y\|x), using a deep neural network to predict the un-normalized density from (x,y). This model of p(y\|x) is trained by directly minimizing the associated negative log-likelihood, approximated using Monte Carlo sampling. We perform comprehensive experiments on four computer vision regression tasks. Our approach outperforms direct regression, as well as other probabilistic and confidence-based methods. Notably, our model achieves a 2.2% AP improvement over Faster-RCNN for object detection on the COCO dataset, and sets a new state-of-the-art on visual tracking when applied for bounding box estimation. In contrast to confidence-based methods, our approach is also shown to be directly applicable to more general tasks such as age and head-pose estimation. Code is available at https://github.com/fregu856/ebms_regression.	['Thomas B. Schön', 'Goutam Bhat', 'Fredrik K. Gustafsson', 'Martin Danelljan']	2019-09-26	null	https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3472_ECCV_2020_paper.php	https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123650324.pdf	eccv-2020-8	['head-pose-estimation']	['computer-vision']	[-9.68602598e-02 -4.13381495e-02 -3.66794527e-01 -5.27396262e-01 -1.26403284e+00 -2.01181784e-01 6.59379423e-01 1.69456065e-01 -6.98270738e-01 8.41590226e-01 -7.43129775e-02 -1.07868701e-01 1.16446979e-01 -4.71471518e-01 -9.37354863e-01 -8.86624694e-01 -2.51811184e-02 4.92420554e-01 2.38809690e-01 2.85185784e-01 3.66940051e-02 5.26820898e-01 -1.54074299e+00 -3.25968117e-01 6.44488871e-01 1.28996813e+00 -7.03345537e-02 6.53730214e-01 3.07778984e-01 3.80257636e-01 -6.13937318e-01 -5.73580742e-01 -1.56754285e-01 -7.29542077e-02 -3.51769924e-01 -4.99563098e-01 7.20469117e-01 -2.74182498e-01 -2.84445375e-01 1.07289922e+00 5.63259959e-01 1.63170978e-01 1.25067198e+00 -1.39953828e+00 -5.01122713e-01 2.21176937e-01 -1.00547326e+00 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07b0dc7e-ee43-417d-b222-823cd29fa7cb	table-to-text-generation-with-effective	1909.02304	null	https://arxiv.org/abs/1909.02304v1	https://arxiv.org/pdf/1909.02304v1.pdf	Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)	Although Seq2Seq models for table-to-text generation have achieved remarkable progress, modeling table representation in one dimension is inadequate. This is because (1) the table consists of multiple rows and columns, which means that encoding a table should not depend only on one dimensional sequence or set of records and (2) most of the tables are time series data (e.g. NBA game data, stock market data), which means that the description of the current table may be affected by its historical data. To address aforementioned problems, not only do we model each table cell considering other records in the same row, we also enrich table's representation by modeling each table cell in context of other cells in the same column or with historical (time dimension) data respectively. In addition, we develop a table cell fusion gate to combine representations from row, column and time dimension into one dense vector according to the saliency of each dimension's representation. We evaluated our methods on ROTOWIRE, a benchmark dataset of NBA basketball games. Both automatic and human evaluation results demonstrate the effectiveness of our model with improvement of 2.66 in BLEU over the strong baseline and outperformance of state-of-the-art model.	['Xiaocheng Feng', 'Ting Liu', 'Heng Gong', 'Bing Qin']	2019-09-05	table-to-text-generation-with-effective-1	https://aclanthology.org/D19-1310	https://aclanthology.org/D19-1310.pdf	ijcnlp-2019-11	['table-to-text-generation']	['natural-language-processing']	[ 2.70165019e-02 -2.21459344e-01 -3.44126135e-01 -5.43177351e-02 -7.48928845e-01 -9.91724491e-01 6.13468170e-01 5.01878619e-01 -3.11891109e-01 1.12341261e+00 9.34627891e-01 -2.63926178e-01 1.14968434e-01 -1.08751011e+00 -7.39874959e-01 -4.41617250e-01 3.16801593e-02 5.46480417e-01 1.82612821e-01 -7.52714515e-01 1.93456531e-01 1.12072080e-01 -1.39283550e+00 6.60389721e-01 3.84996921e-01 1.23408926e+00 1.63133275e-02 4.81799453e-01 -4.95103776e-01 1.09030795e+00 -9.21731174e-01 -6.36906564e-01 3.52106452e-01 -5.86814761e-01 -6.02639437e-01 -2.27168232e-01 -1.85054049e-01 -1.65174976e-01 -6.87703371e-01 9.41868544e-01 4.09760326e-01 1.00962259e-01 5.64274788e-01 -1.09510434e+00 -5.41400194e-01 1.21915185e+00 -7.48548627e-01 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4a1d88fb-f5fc-4cb2-ac2e-78e12f2b0807	alf-a-fitness-based-artificial-life-form-for	2104.08252	null	https://arxiv.org/abs/2104.08252v1	https://arxiv.org/pdf/2104.08252v1.pdf	ALF -- A Fitness-Based Artificial Life Form for Evolving Large-Scale Neural Networks	Machine Learning (ML) is becoming increasingly important in daily life. In this context, Artificial Neural Networks (ANNs) are a popular approach within ML methods to realize an artificial intelligence. Usually, the topology of ANNs is predetermined. However, there are problems where it is difficult to find a suitable topology. Therefore, Topology and Weight Evolving Artificial Neural Network (TWEANN) algorithms have been developed that can find ANN topologies and weights using genetic algorithms. A well-known downside for large-scale problems is that TWEANN algorithms often evolve inefficient ANNs and require long runtimes. To address this issue, we propose a new TWEANN algorithm called Artificial Life Form (ALF) with the following technical advancements: (1) speciation via structural and semantic similarity to form better candidate solutions, (2) dynamic adaptation of the observed candidate solutions for better convergence properties, and (3) integration of solution quality into genetic reproduction to increase the probability of optimization success. Experiments on large-scale ML problems confirm that these approaches allow the fast solving of these problems and lead to efficient evolved ANNs.	['Rolf Drechsler', 'Mirco Bockholt', 'Marcel Merten', 'Rune Krauss']	2021-04-16	null	null	null	null	['artificial-life']	['miscellaneous']	[ 1.93936765e-01 -2.40238085e-01 1.31242186e-01 -1.79343373e-01 1.77428439e-01 -2.21243545e-01 9.97167230e-02 1.76092461e-01 -4.19872493e-01 1.05552471e+00 -5.13959587e-01 1.02377869e-01 -4.04221833e-01 -1.08218670e+00 -6.60623848e-01 -9.71046984e-01 1.56310707e-01 5.75434744e-01 1.94033951e-01 -3.31830174e-01 5.18779695e-01 5.08436799e-01 -1.99518502e+00 -3.72394621e-01 1.77408719e+00 9.06287432e-01 2.61957884e-01 2.76087970e-01 -7.24050879e-01 5.93124926e-02 -8.53997529e-01 -2.94523060e-01 3.44027400e-01 -9.45362568e-01 -3.55565429e-01 -5.64184785e-02 -4.52550977e-01 5.84686279e-01 3.02951217e-01 1.11872900e+00 6.26400709e-01 1.63708434e-01 5.46906888e-01 -1.19292772e+00 -7.13828087e-01 9.16384339e-01 -6.25159800e-01 -2.98443586e-02 -1.23374812e-01 1.05682760e-01 5.20650148e-01 -5.42539239e-01 2.61348426e-01 1.16009319e+00 7.71375060e-01 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63a96e30-0a7f-41e7-a28d-03451a8bca9e	unsupervised-domain-expansion-for-visual	2104.00233	null	https://arxiv.org/abs/2104.00233v1	https://arxiv.org/pdf/2104.00233v1.pdf	Unsupervised Domain Expansion for Visual Categorization	Expanding visual categorization into a novel domain without the need of extra annotation has been a long-term interest for multimedia intelligence. Previously, this challenge has been approached by unsupervised domain adaptation (UDA). Given labeled data from a source domain and unlabeled data from a target domain, UDA seeks for a deep representation that is both discriminative and domain-invariant. While UDA focuses on the target domain, we argue that the performance on both source and target domains matters, as in practice which domain a test example comes from is unknown. In this paper we extend UDA by proposing a new task called unsupervised domain expansion (UDE), which aims to adapt a deep model for the target domain with its unlabeled data, meanwhile maintaining the model's performance on the source domain. We propose Knowledge Distillation Domain Expansion (KDDE) as a general method for the UDE task. Its domain-adaptation module can be instantiated with any existing model. We develop a knowledge distillation based learning mechanism, enabling KDDE to optimize a single objective wherein the source and target domains are equally treated. Extensive experiments on two major benchmarks, i.e., Office-Home and DomainNet, show that KDDE compares favorably against four competitive baselines, i.e., DDC, DANN, DAAN, and CDAN, for both UDA and UDE tasks. Our study also reveals that the current UDA models improve their performance on the target domain at the cost of noticeable performance loss on the source domain.	['Xirong Li', 'Gang Yang', 'Dayong Ding', 'Kaibin Tian', 'Jie Wang']	2021-04-01	null	null	null	null	['unsupervised-domain-expansion']	['methodology']	[ 1.31528601e-01 -6.98474795e-02 -4.57211047e-01 -3.76795948e-01 -6.60768986e-01 -7.95097828e-01 6.65717959e-01 -1.81560174e-01 -4.35076475e-01 6.82881296e-01 1.95184037e-01 -2.09090993e-01 1.94380224e-01 -7.50055611e-01 -5.57495952e-01 -5.13901412e-01 3.59295398e-01 6.35710835e-01 4.46010649e-01 -1.64691001e-01 -2.50554562e-01 3.51508737e-01 -1.27290630e+00 2.95011342e-01 9.18169916e-01 1.08878696e+00 3.26528490e-01 2.27353841e-01 -2.21321225e-01 6.98807657e-01 -5.18740475e-01 -3.67586613e-01 3.73709381e-01 -3.99312496e-01 -1.12023842e+00 3.80064279e-01 3.29434037e-01 -3.67027253e-01 -5.94855130e-01 8.83321404e-01 5.90456367e-01 3.64315897e-01 1.01623487e+00 -1.40675545e+00 -1.12582386e+00 1.51701853e-01 -7.01110005e-01 1.83275416e-01 1.15763824e-02 7.63613284e-02 8.24876308e-01 -1.09815609e+00 9.83257890e-01 1.15521026e+00 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f60c61a2-5d79-40c0-ba86-e4e445d5aa1a	pose-guided-feature-alignment-for-occluded	null	null	http://openaccess.thecvf.com/content_ICCV_2019/html/Miao_Pose-Guided_Feature_Alignment_for_Occluded_Person_Re-Identification_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Miao_Pose-Guided_Feature_Alignment_for_Occluded_Person_Re-Identification_ICCV_2019_paper.pdf	Pose-Guided Feature Alignment for Occluded Person Re-Identification	Persons are often occluded by various obstacles in person retrieval scenarios. Previous person re-identification (re-id) methods, either overlook this issue or resolve it based on an extreme assumption. To alleviate the occlusion problem, we propose to detect the occluded regions, and explicitly exclude those regions during feature generation and matching. In this paper, we introduce a novel method named Pose-Guided Feature Alignment (PGFA), exploiting pose landmarks to disentangle the useful information from the occlusion noise. During the feature constructing stage, our method utilizes human landmarks to generate attention maps. The generated attention maps indicate if a specific body part is occluded and guide our model to attend to the non-occluded regions. During matching, we explicitly partition the global feature into parts and use the pose landmarks to indicate which partial features belonging to the target person. Only the visible regions are utilized for the retrieval. Besides, we construct a large-scale dataset for the Occluded Person Re-ID problem, namely Occluded-DukeMTMC, which is by far the largest dataset for the Occlusion Person Re-ID. Extensive experiments are conducted on our constructed occluded re-id dataset, two partial re-id datasets, and two commonly used holistic re-id datasets. Our method largely outperforms existing person re-id methods on three occlusion datasets, while remains top performance on two holistic datasets.	[' Yi Yang', ' Yuhang Ding', ' Ping Liu', ' Yu Wu', 'Jiaxu Miao']	2019-10-01	null	null	null	iccv-2019-10	['person-retrieval']	['computer-vision']	[-1.10052414e-01 -2.26460278e-01 -1.58455953e-01 -2.99344957e-01 -7.64472723e-01 -2.78957814e-01 6.35850728e-01 -1.02815196e-01 -3.48821372e-01 5.89822114e-01 7.43989289e-01 4.12058264e-01 -2.60469764e-01 -5.86535156e-01 -2.47468442e-01 -4.93308127e-01 3.24441969e-01 8.84772778e-01 -2.22324178e-01 -1.07878624e-02 9.36060548e-02 4.89988506e-01 -1.82915974e+00 -3.96250151e-02 8.63571703e-01 5.83487391e-01 -1.74371704e-01 2.14419737e-01 1.28706813e-01 -4.19272743e-02 -7.28513181e-01 -4.75516945e-01 4.65418458e-01 -1.65434614e-01 -6.70864582e-01 2.73168623e-01 1.03487217e+00 -4.83491123e-01 -5.04755020e-01 9.08745050e-01 1.01634276e+00 3.94003421e-01 5.40922880e-01 -1.32350743e+00 -6.94540381e-01 1.14650778e-01 -9.35160041e-01 2.31931552e-01 1.03922546e+00 -6.79376572e-02 7.98342407e-01 -1.24414670e+00 7.09421933e-01 1.67787457e+00 7.26391017e-01 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db27b1fa-7782-406a-af88-c5b8fcb3192b	stereobj-1m-large-scale-stereo-image-dataset	2109.10115	null	https://arxiv.org/abs/2109.10115v3	https://arxiv.org/pdf/2109.10115v3.pdf	StereOBJ-1M: Large-scale Stereo Image Dataset for 6D Object Pose Estimation	We present a large-scale stereo RGB image object pose estimation dataset named the $\textbf{StereOBJ-1M}$ dataset. The dataset is designed to address challenging cases such as object transparency, translucency, and specular reflection, in addition to the common challenges of occlusion, symmetry, and variations in illumination and environments. In order to collect data of sufficient scale for modern deep learning models, we propose a novel method for efficiently annotating pose data in a multi-view fashion that allows data capturing in complex and flexible environments. Fully annotated with 6D object poses, our dataset contains over 393K frames and over 1.5M annotations of 18 objects recorded in 182 scenes constructed in 11 different environments. The 18 objects include 8 symmetric objects, 7 transparent objects, and 8 reflective objects. We benchmark two state-of-the-art pose estimation frameworks on StereOBJ-1M as baselines for future work. We also propose a novel object-level pose optimization method for computing 6D pose from keypoint predictions in multiple images. Project website: https://sites.google.com/view/stereobj-1m.	['Kris M. Kitani', 'Shun Iwase', 'Xingyu Liu']	2021-09-21	null	http://openaccess.thecvf.com//content/ICCV2021/html/Liu_StereOBJ-1M_Large-Scale_Stereo_Image_Dataset_for_6D_Object_Pose_Estimation_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Liu_StereOBJ-1M_Large-Scale_Stereo_Image_Dataset_for_6D_Object_Pose_Estimation_ICCV_2021_paper.pdf	iccv-2021-1	['transparent-objects']	['computer-vision']	[-3.72544639e-02 -1.85485497e-01 2.15772599e-01 -5.63010991e-01 -9.35040653e-01 -7.17899799e-01 2.59422839e-01 -5.16568661e-01 -2.72461712e-01 2.31668785e-01 2.62538850e-01 2.62838304e-01 1.11652754e-01 -2.29741096e-01 -1.24767220e+00 -4.63216394e-01 1.60482347e-01 7.77102053e-01 3.64870101e-01 4.34657857e-02 2.78198183e-01 7.29657888e-01 -1.66286886e+00 3.49463463e-01 3.16737831e-01 1.38933218e+00 4.70225960e-01 6.18017256e-01 2.41116941e-01 5.21190882e-01 -4.42098677e-01 -5.66930056e-01 7.00757682e-01 4.04539466e-01 -7.65743315e-01 3.95385802e-01 1.33391309e+00 -8.67179751e-01 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51379499-547a-4146-b81e-ed03199be4b3	geomae-masked-geometric-target-prediction-for	2305.08808	null	https://arxiv.org/abs/2305.08808v1	https://arxiv.org/pdf/2305.08808v1.pdf	GeoMAE: Masked Geometric Target Prediction for Self-supervised Point Cloud Pre-Training	This paper tries to address a fundamental question in point cloud self-supervised learning: what is a good signal we should leverage to learn features from point clouds without annotations? To answer that, we introduce a point cloud representation learning framework, based on geometric feature reconstruction. In contrast to recent papers that directly adopt masked autoencoder (MAE) and only predict original coordinates or occupancy from masked point clouds, our method revisits differences between images and point clouds and identifies three self-supervised learning objectives peculiar to point clouds, namely centroid prediction, normal estimation, and curvature prediction. Combined with occupancy prediction, these four objectives yield an nontrivial self-supervised learning task and mutually facilitate models to better reason fine-grained geometry of point clouds. Our pipeline is conceptually simple and it consists of two major steps: first, it randomly masks out groups of points, followed by a Transformer-based point cloud encoder; second, a lightweight Transformer decoder predicts centroid, normal, and curvature for points in each voxel. We transfer the pre-trained Transformer encoder to a downstream peception model. On the nuScene Datset, our model achieves 3.38 mAP improvment for object detection, 2.1 mIoU gain for segmentation, and 1.7 AMOTA gain for multi-object tracking. We also conduct experiments on the Waymo Open Dataset and achieve significant performance improvements over baselines as well.	['Hang Zhao', 'Yue Wang', 'Haoxi Ran', 'Xiaoyu Tian']	2023-05-15	null	http://openaccess.thecvf.com//content/CVPR2023/html/Tian_GeoMAE_Masked_Geometric_Target_Prediction_for_Self-Supervised_Point_Cloud_Pre-Training_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Tian_GeoMAE_Masked_Geometric_Target_Prediction_for_Self-Supervised_Point_Cloud_Pre-Training_CVPR_2023_paper.pdf	cvpr-2023-1	['point-cloud-pre-training']	['computer-vision']	[ 7.04953894e-02 3.19241017e-01 -9.03735682e-02 -3.09281379e-01 -1.13600385e+00 -5.94672263e-01 6.89909518e-01 1.64530694e-01 -2.56351948e-01 2.14829043e-01 -1.44166410e-01 -2.46537670e-01 1.67102918e-01 -8.71294975e-01 -1.55804181e+00 -5.98581553e-01 -9.75528732e-02 8.78114581e-01 4.41035002e-01 1.47975996e-01 2.70780325e-01 7.61365473e-01 -1.76278496e+00 2.93289900e-01 5.07218122e-01 1.24733090e+00 2.13719457e-01 6.63908124e-01 -2.27800503e-01 5.30633807e-01 -1.89991847e-01 -1.72790393e-01 4.21559125e-01 3.12411547e-01 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4740ce11-b2fa-4c5a-b379-bd8641cc2d12	traffic-accident-benchmark-for-causality	null	null	https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/312_ECCV_2020_paper.php	https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520528.pdf	Traffic Accident Benchmark for Causality Recognition	We propose a brand new benchmark for analyzing causality in traffic accident videos by decomposing an accident into a pair of events, cause and effect. We collect videos containing traffic accident scenes and annotate cause and effect events for each accident with their temporal intervals and semantic labels; such annotations are not available in existing datasets for accident anticipation task. Our dataset has the following two advantages over the existing ones, which would facilitate practical research for causality analysis. First, the decomposition of an accident into cause and effect events provides atomic cues for reasoning on a complex environment and planning future actions. Second, the prediction of cause and effect in an accident makes a system more interpretable to humans, which mitigates the ambiguity of legal liabilities among agents engaged in the accident. Using the proposed dataset, we analyze accidents by localizing the temporal intervals of their causes and effects and classifying the semantic labels of the accidents. The dataset as well as the implementations of baseline models are available in the code repository.	['Bohyung Han', 'Tackgeun You']	null	null	null	null	eccv-2020-8	['accident-anticipation']	['computer-vision']	[ 2.66809106e-01 1.42792761e-01 -4.16248709e-01 -5.42199016e-01 -3.96728754e-01 -7.03179181e-01 5.77087343e-01 4.89004016e-01 -2.72592515e-01 7.70469666e-01 9.89558101e-01 -3.64162952e-01 -3.18016142e-01 -6.47791564e-01 -7.64020026e-01 -5.85604668e-01 -2.60558337e-01 1.19919084e-01 3.99110764e-01 6.06137291e-02 -8.70805308e-02 3.91481608e-01 -1.68284512e+00 7.28701055e-01 3.52419168e-01 9.92902279e-01 2.63723463e-01 4.63328689e-01 3.54412556e-01 1.59348214e+00 -5.25936544e-01 -5.08022845e-01 9.04276744e-02 -3.94832253e-01 -9.96263325e-01 3.19422372e-02 2.13524297e-01 -5.04453897e-01 -7.64792800e-01 7.63744771e-01 2.38304529e-02 2.44790927e-01 6.59368634e-01 -1.81190896e+00 -2.90827096e-01 6.58130109e-01 -6.41966164e-02 5.10070026e-01 8.48291039e-01 3.47869873e-01 1.02492070e+00 -2.69838452e-01 6.93530262e-01 1.49137461e+00 3.03448439e-01 4.07373220e-01 -5.77100933e-01 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075cba77-a19b-431c-88b0-9e3c2dd9c7ae	gaitmixer-skeleton-based-gait-representation	2210.15491	null	https://arxiv.org/abs/2210.15491v2	https://arxiv.org/pdf/2210.15491v2.pdf	GaitMixer: Skeleton-based Gait Representation Learning via Wide-spectrum Multi-axial Mixer	Most existing gait recognition methods are appearance-based, which rely on the silhouettes extracted from the video data of human walking activities. The less-investigated skeleton-based gait recognition methods directly learn the gait dynamics from 2D/3D human skeleton sequences, which are theoretically more robust solutions in the presence of appearance changes caused by clothes, hairstyles, and carrying objects. However, the performance of skeleton-based solutions is still largely behind the appearance-based ones. This paper aims to close such performance gap by proposing a novel network model, GaitMixer, to learn more discriminative gait representation from skeleton sequence data. In particular, GaitMixer follows a heterogeneous multi-axial mixer architecture, which exploits the spatial self-attention mixer followed by the temporal large-kernel convolution mixer to learn rich multi-frequency signals in the gait feature maps. Experiments on the widely used gait database, CASIA-B, demonstrate that GaitMixer outperforms the previous SOTA skeleton-based methods by a large margin while achieving a competitive performance compared with the representative appearance-based solutions. Code will be available at https://github.com/exitudio/gaitmixer	['Chen Chen', 'Minwoo Lee', 'Pu Wang', 'Ayman Ali', 'Ekkasit Pinyoanuntapong']	2022-10-27	null	null	null	null	['gait-recognition', 'multiview-gait-recognition']	['computer-vision', 'computer-vision']	[-1.06658630e-01 -6.31879508e-01 -1.55999094e-01 -2.18019988e-02 -4.19119388e-01 -2.23246329e-02 3.07072461e-01 -4.24028277e-01 -3.10695201e-01 4.33829784e-01 2.62273282e-01 5.29084086e-01 1.65812433e-01 -5.09739101e-01 -4.14957225e-01 -9.44911957e-01 -3.37807685e-01 2.38191262e-01 3.40382755e-01 -1.86888158e-01 -5.51887751e-02 1.50426134e-01 -1.62133658e+00 -7.29979053e-02 6.26982689e-01 8.08945954e-01 -4.62155527e-04 6.10111237e-01 2.39144564e-01 6.23135746e-01 -3.82567704e-01 -2.00621381e-01 1.82854265e-01 -5.86335599e-01 -2.77311176e-01 2.22732350e-01 5.13771653e-01 -2.35139236e-01 -8.39922726e-01 9.49106276e-01 6.38367653e-01 1.54971436e-01 7.91741073e-01 -1.13078749e+00 -5.48030853e-01 5.08065112e-02 -9.83011127e-01 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c1be1f96-d265-4795-b1c3-ee7b07fe972b	graphical-contrastive-losses-for-scene-graph	1903.02728	null	https://arxiv.org/abs/1903.02728v5	https://arxiv.org/pdf/1903.02728v5.pdf	Graphical Contrastive Losses for Scene Graph Parsing	Most scene graph parsers use a two-stage pipeline to detect visual relationships: the first stage detects entities, and the second predicts the predicate for each entity pair using a softmax distribution. We find that such pipelines, trained with only a cross entropy loss over predicate classes, suffer from two common errors. The first, Entity Instance Confusion, occurs when the model confuses multiple instances of the same type of entity (e.g. multiple cups). The second, Proximal Relationship Ambiguity, arises when multiple subject-predicate-object triplets appear in close proximity with the same predicate, and the model struggles to infer the correct subject-object pairings (e.g. mis-pairing musicians and their instruments). We propose a set of contrastive loss formulations that specifically target these types of errors within the scene graph parsing problem, collectively termed the Graphical Contrastive Losses. These losses explicitly force the model to disambiguate related and unrelated instances through margin constraints specific to each type of confusion. We further construct a relationship detector, called RelDN, using the aforementioned pipeline to demonstrate the efficacy of our proposed losses. Our model outperforms the winning method of the OpenImages Relationship Detection Challenge by 4.7\% (16.5\% relative) on the test set. We also show improved results over the best previous methods on the Visual Genome and Visual Relationship Detection datasets.	['Andrew Tao', 'Kevin J. Shih', 'Ahmed Elgammal', 'Ji Zhang', 'Bryan Catanzaro']	2019-03-07	graphical-contrastive-losses-for-scene-graph-1	http://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_Graphical_Contrastive_Losses_for_Scene_Graph_Parsing_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Graphical_Contrastive_Losses_for_Scene_Graph_Parsing_CVPR_2019_paper.pdf	cvpr-2019-6	['visual-relationship-detection']	['computer-vision']	[ 6.03501141e-01 4.58476901e-01 2.89980266e-02 -5.46776056e-01 -7.85369873e-01 -8.78969789e-01 5.09735405e-01 6.41246438e-01 -2.26432174e-01 3.99588108e-01 -1.89431071e-01 -2.76051223e-01 9.82650649e-03 -6.84832454e-01 -1.14633036e+00 -3.11996460e-01 -1.89753637e-01 5.56217432e-01 2.21615463e-01 2.99597889e-01 6.76288232e-02 1.30877733e-01 -1.43017972e+00 4.72050697e-01 7.04045892e-01 1.10857761e+00 3.20164979e-01 5.17316580e-01 -1.22745775e-01 7.78986394e-01 -8.24454963e-01 -9.97403085e-01 2.99005359e-01 -2.07639858e-01 -8.84505868e-01 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d8e62d2f-f4b5-48ba-a2f9-734f16de1393	investigating-the-effect-of-auxiliary	null	null	https://aclanthology.org/2020.acl-main.206	https://aclanthology.org/2020.acl-main.206.pdf	Investigating the effect of auxiliary objectives for the automated grading of learner English speech transcriptions	We address the task of automatically grading the language proficiency of spontaneous speech based on textual features from automatic speech recognition transcripts. Motivated by recent advances in multi-task learning, we develop neural networks trained in a multi-task fashion that learn to predict the proficiency level of non-native English speakers by taking advantage of inductive transfer between the main task (grading) and auxiliary prediction tasks: morpho-syntactic labeling, language modeling, and native language identification (L1). We encode the transcriptions with both bi-directional recurrent neural networks and with bi-directional representations from transformers, compare against a feature-rich baseline, and analyse performance at different proficiency levels and with transcriptions of varying error rates. Our best performance comes from a transformer encoder with L1 prediction as an auxiliary task. We discuss areas for improvement and potential applications for text-only speech scoring.	['Paula Buttery', 'Helen Yannakoudakis', 'Hannah Craighead', 'Andrew Caines']	2020-07-01	null	null	null	acl-2020-6	['native-language-identification']	['natural-language-processing']	[ 5.20316124e-01 2.75605619e-01 -2.35275179e-01 -7.39888191e-01 -1.77928734e+00 -7.59040594e-01 4.43171948e-01 1.26286194e-01 -6.51514947e-01 5.53437173e-01 9.87034798e-01 -8.02286863e-01 5.26273698e-02 -4.59596664e-01 -5.53638637e-01 -1.16185084e-01 3.76008809e-01 5.73476434e-01 8.04689303e-02 -2.41246000e-01 -1.46881461e-01 -3.82907242e-02 -1.34866524e+00 9.72485304e-01 9.45953310e-01 8.81239057e-01 1.81887418e-01 9.80085790e-01 -1.94976613e-01 1.33859468e+00 -7.14006364e-01 -7.00436890e-01 -2.59048194e-01 -3.90230477e-01 -1.26822901e+00 -8.91120657e-02 7.54608512e-01 -9.59406421e-02 -1.67970672e-01 6.46734297e-01 4.86619055e-01 1.10985085e-01 6.75308645e-01 -6.90630376e-01 -8.06954801e-01 9.32132542e-01 4.13246080e-02 3.09861779e-01 6.37863934e-01 -1.07277334e-01 1.29069328e+00 -7.92334259e-01 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6.27758980e-01 -5.76996744e-01 -8.60615790e-01 -1.41601372e+00 -3.00599128e-01 2.28940889e-01 7.68663466e-01 -2.20044523e-01 -5.49854159e-01 -5.62381148e-02]	[14.142291069030762, 6.9161696434021]
8bace296-5ae6-4adb-be1c-ca639e930708	invariant-representation-learning-for	2011.12379	null	https://arxiv.org/abs/2011.12379v2	https://arxiv.org/pdf/2011.12379v2.pdf	Invariant Representation Learning for Treatment Effect Estimation	The defining challenge for causal inference from observational data is the presence of `confounders', covariates that affect both treatment assignment and the outcome. To address this challenge, practitioners collect and adjust for the covariates, hoping that they adequately correct for confounding. However, including every observed covariate in the adjustment runs the risk of including `bad controls', variables that induce bias when they are conditioned on. The problem is that we do not always know which variables in the covariate set are safe to adjust for and which are not. To address this problem, we develop Nearly Invariant Causal Estimation (NICE). NICE uses invariant risk minimization (IRM) [Arj19] to learn a representation of the covariates that, under some assumptions, strips out bad controls but preserves sufficient information to adjust for confounding. Adjusting for the learned representation, rather than the covariates themselves, avoids the induced bias and provides valid causal inferences. We evaluate NICE on both synthetic and semi-synthetic data. When the covariates contain unknown collider variables and other bad controls, NICE performs better than adjusting for all the covariates.	['David Blei', 'Victor Veitch', 'Claudia Shi']	2020-11-24	null	null	null	null	['causal-identification']	['reasoning']	[ 3.84932458e-01 3.03740203e-01 -9.25495386e-01 -5.55305779e-01 -9.67902958e-01 -5.84402382e-01 3.68094057e-01 4.26573187e-01 -3.34685415e-01 1.19060004e+00 1.04257166e+00 -4.05411184e-01 -3.19689840e-01 -8.12849045e-01 -1.01078415e+00 -7.58721352e-01 -4.44180399e-01 4.89409059e-01 -3.46960098e-01 1.81799993e-01 -6.85959030e-03 2.15725318e-01 -1.00882006e+00 4.18814085e-02 8.58234167e-01 3.98510508e-03 -4.46893781e-01 3.50970000e-01 4.58471566e-01 9.55904067e-01 -2.82042801e-01 -3.48312378e-01 1.61148727e-01 -4.56966281e-01 -4.80499059e-01 -4.56111789e-01 5.74724793e-01 -3.74716282e-01 -2.42081210e-01 8.51830542e-01 3.62535775e-01 -1.00744009e-01 1.31535435e+00 -1.36023653e+00 -6.12968326e-01 1.06369734e+00 -9.61439133e-01 1.93841428e-01 -5.44751296e-03 1.08251208e-02 1.10101056e+00 -4.51607168e-01 5.18554330e-01 1.49744737e+00 7.51749754e-01 4.82880503e-01 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fe49f64d-85f0-4e64-8d28-c53cad841525	efficient-uncertainty-estimation-with	2303.08599	null	https://arxiv.org/abs/2303.08599v1	https://arxiv.org/pdf/2303.08599v1.pdf	Efficient Uncertainty Estimation with Gaussian Process for Reliable Dialog Response Retrieval	Deep neural networks have achieved remarkable performance in retrieval-based dialogue systems, but they are shown to be ill calibrated. Though basic calibration methods like Monte Carlo Dropout and Ensemble can calibrate well, these methods are time-consuming in the training or inference stages. To tackle these challenges, we propose an efficient uncertainty calibration framework GPF-BERT for BERT-based conversational search, which employs a Gaussian Process layer and the focal loss on top of the BERT architecture to achieve a high-quality neural ranker. Extensive experiments are conducted to verify the effectiveness of our method. In comparison with basic calibration methods, GPF-BERT achieves the lowest empirical calibration error (ECE) in three in-domain datasets and the distributional shift tasks, while yielding the highest $R_{10}@1$ and MAP performance on most cases. In terms of time consumption, our GPF-BERT has an 8$\times$ speedup.	['Jing Xiao', 'Ning Cheng', 'Jianzong Wang', 'Zhitao Li', 'Tong Ye']	2023-03-15	null	null	null	null	['conversational-search']	['natural-language-processing']	[-2.83127934e-01 1.03452116e-01 2.80060899e-02 -8.18741798e-01 -1.50378323e+00 -4.11042571e-01 6.39110267e-01 -2.05105096e-01 -7.32111931e-01 1.07684541e+00 1.48033664e-01 -3.83509159e-01 -2.05145329e-01 -4.72874254e-01 -6.06959820e-01 -4.98160154e-01 2.19413295e-01 1.36696637e+00 6.08741157e-02 -2.56883532e-01 1.48023620e-01 5.84128313e-03 -8.39099586e-01 7.84325674e-02 1.25790215e+00 1.07820141e+00 -1.20683558e-01 7.53364861e-01 -2.52022773e-01 5.51866293e-01 -8.90425682e-01 -1.00762284e+00 -9.23418906e-03 -1.52115896e-01 -9.89484251e-01 -5.56078732e-01 1.14496030e-01 -6.98765695e-01 -6.50411546e-01 1.06212878e+00 1.00303376e+00 4.93932843e-01 8.88749778e-01 -9.95969653e-01 -3.96092355e-01 1.19733238e+00 -4.74408656e-01 -5.26280701e-02 1.04520380e-01 1.68843850e-01 1.21843994e+00 -8.72707784e-01 1.45029873e-01 1.68391812e+00 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d05fe0e7-b71e-41ef-add4-74f971d57c33	generated-graph-detection	2306.07758	null	https://arxiv.org/abs/2306.07758v1	https://arxiv.org/pdf/2306.07758v1.pdf	Generated Graph Detection	Graph generative models become increasingly effective for data distribution approximation and data augmentation. While they have aroused public concerns about their malicious misuses or misinformation broadcasts, just as what Deepfake visual and auditory media has been delivering to society. Hence it is essential to regulate the prevalence of generated graphs. To tackle this problem, we pioneer the formulation of the generated graph detection problem to distinguish generated graphs from real ones. We propose the first framework to systematically investigate a set of sophisticated models and their performance in four classification scenarios. Each scenario switches between seen and unseen datasets/generators during testing to get closer to real-world settings and progressively challenge the classifiers. Extensive experiments evidence that all the models are qualified for generated graph detection, with specific models having advantages in specific scenarios. Resulting from the validated generality and oblivion of the classifiers to unseen datasets/generators, we draw a safe conclusion that our solution can sustain for a decent while to curb generated graph misuses.	['Yang Zhang', 'Yun Shen', 'Michael Backes', 'Xinlei He', 'Ning Yu', 'Zhikun Zhang', 'Yihan Ma']	2023-06-13	null	null	null	null	['face-swapping', 'misinformation']	['computer-vision', 'miscellaneous']	[ 2.44388029e-01 6.18627489e-01 2.65981387e-02 -2.08075251e-03 -3.41112226e-01 -6.82671487e-01 1.03039002e+00 3.95127505e-01 -8.94313902e-02 6.09666467e-01 -5.24686165e-02 -4.74160671e-01 -1.47866264e-01 -9.32413459e-01 -4.83948827e-01 -6.41808689e-01 -4.67463434e-01 5.61818719e-01 3.13690156e-01 -4.08846438e-01 1.62473962e-01 6.26500010e-01 -1.75384462e+00 2.31326029e-01 6.46301329e-01 7.22632349e-01 -2.24682108e-01 9.23757732e-01 -2.71442160e-02 9.51585770e-01 -1.08773088e+00 -7.24550724e-01 1.65398061e-01 -2.81920731e-01 -6.97640955e-01 2.26878643e-01 2.26925373e-01 -1.67769730e-01 -3.78926098e-01 1.18477046e+00 5.79955161e-01 -3.20005566e-01 5.21260858e-01 -1.80139589e+00 -6.71510935e-01 8.15529883e-01 -4.37053502e-01 3.56997073e-01 6.83665454e-01 1.21359646e-01 6.39973402e-01 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84758e87-c86a-46af-8099-c31b98d6bd50	biomechanics-guided-facial-action-unit	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Cui_Biomechanics-Guided_Facial_Action_Unit_Detection_Through_Force_Modeling_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Cui_Biomechanics-Guided_Facial_Action_Unit_Detection_Through_Force_Modeling_CVPR_2023_paper.pdf	Biomechanics-Guided Facial Action Unit Detection Through Force Modeling	Existing AU detection algorithms are mainly based on appearance information extracted from 2D images, and well-established facial biomechanics that governs 3D facial skin deformation is rarely considered. In this paper, we propose a biomechanics-guided AU detection approach, where facial muscle activation forces are modelled, and are employed to predict AU activation. Specifically, our model consists of two branches: 3D physics branch and 2D image branch. In 3D physics branch, we first derive the Euler-Lagrange equation governing facial deformation. The Euler-Lagrange equation represented as an ordinary differential equation (ODE) is embedded into a differentiable ODE solver. Muscle activation forces together with other physics parameters are firstly regressed, and then are utilized to simulate 3D deformation by solving the ODE. By leveraging facial biomechanics, we obtain physically plausible facial muscle activation forces. 2D image branch compensates 3D physics branch by employing additional appearance information from 2D images. Both estimated forces and appearance features are employed for AU detection. The proposed approach achieves competitive AU detection performance on two benchmark datasets. Furthermore, by leveraging biomechanics, our approach achieves outstanding performance with reduced training data.	['Qiang Ji', 'Kartik Talamadupula', 'Tian Gao', 'Chenyi Kuang', 'Zijun Cui']	2023-01-01	null	null	null	cvpr-2023-1	['action-unit-detection', 'facial-action-unit-detection']	['computer-vision', 'computer-vision']	[ 1.54606938e-01 2.85862803e-01 -3.15821081e-01 1.26163736e-02 -2.30465814e-01 -1.89165100e-01 2.08825156e-01 -3.07924300e-01 -2.18356460e-01 2.69818306e-01 -1.45718381e-01 1.50293022e-01 3.65395129e-01 -6.62642956e-01 -6.92107260e-01 -8.09382677e-01 6.66215867e-02 -1.08111851e-01 8.25470686e-02 -2.45581344e-01 1.42939314e-01 8.27191710e-01 -1.40335834e+00 -2.62589782e-01 8.93752098e-01 1.44470394e+00 -2.83231914e-01 6.33101046e-01 -4.29558605e-02 1.47893041e-01 -4.96706739e-02 -2.34504551e-01 4.73815858e-01 -6.09326839e-01 -5.13106823e-01 5.04891813e-01 4.15765822e-01 -6.40687346e-01 -3.38665336e-01 8.04666638e-01 3.50356013e-01 4.45264056e-02 8.97622645e-01 -1.04912865e+00 -5.18925786e-01 -4.26360667e-01 -1.05956006e+00 -3.37787747e-01 4.83613163e-01 4.10514295e-01 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d2e7a1eb-679b-4b58-890d-2c2c5b0fac22	deep-video-inpainting	1905.01639	null	https://arxiv.org/abs/1905.01639v1	https://arxiv.org/pdf/1905.01639v1.pdf	Deep Video Inpainting	Video inpainting aims to fill spatio-temporal holes with plausible content in a video. Despite tremendous progress of deep neural networks for image inpainting, it is challenging to extend these methods to the video domain due to the additional time dimension. In this work, we propose a novel deep network architecture for fast video inpainting. Built upon an image-based encoder-decoder model, our framework is designed to collect and refine information from neighbor frames and synthesize still-unknown regions. At the same time, the output is enforced to be temporally consistent by a recurrent feedback and a temporal memory module. Compared with the state-of-the-art image inpainting algorithm, our method produces videos that are much more semantically correct and temporally smooth. In contrast to the prior video completion method which relies on time-consuming optimization, our method runs in near real-time while generating competitive video results. Finally, we applied our framework to video retargeting task, and obtain visually pleasing results.	['Joon-Young Lee', 'Dahun Kim', 'Sanghyun Woo', 'In So Kweon']	2019-05-05	deep-video-inpainting-1	http://openaccess.thecvf.com/content_CVPR_2019/html/Kim_Deep_Video_Inpainting_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Kim_Deep_Video_Inpainting_CVPR_2019_paper.pdf	cvpr-2019-6	['video-denoising', 'video-to-video-synthesis', 'video-inpainting']	['computer-vision', 'computer-vision', 'computer-vision']	[ 3.48767906e-01 5.46744503e-02 -1.11909159e-01 -1.73186809e-01 -7.31044352e-01 -2.88721085e-01 3.46813232e-01 -3.19319457e-01 -2.01046064e-01 8.08773518e-01 3.99447292e-01 -6.37242869e-02 3.53732198e-01 -5.69742203e-01 -1.23061919e+00 -2.39075392e-01 2.54386008e-01 2.42396723e-02 1.68931440e-01 -4.12729234e-02 8.93074051e-02 3.62351745e-01 -1.42579067e+00 5.69437206e-01 6.91065550e-01 9.57058430e-01 4.35047865e-01 6.88136458e-01 1.64247185e-01 1.34557652e+00 -2.51809388e-01 -2.45708197e-01 3.62577826e-01 -8.10786784e-01 -6.81582153e-01 5.92187107e-01 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ad11365c-5d91-4a4f-bdf9-c23d1d62d0a2	derivation-of-document-vectors-from	null	null	https://aclanthology.org/E17-2073	https://aclanthology.org/E17-2073.pdf	Derivation of Document Vectors from Adaptation of LSTM Language Model	In many natural language processing (NLP) tasks, a document is commonly modeled as a bag of words using the term frequency-inverse document frequency (TF-IDF) vector. One major shortcoming of the frequency-based TF-IDF feature vector is that it ignores word orders that carry syntactic and semantic relationships among the words in a document. This paper proposes a novel distributed vector representation of a document, which will be labeled as DV-LSTM, and is derived from the result of adapting a long short-term memory recurrent neural network language model by the document. DV-LSTM is expected to capture some high-level sequential information in the document, which other current document representations fail to do. It was evaluated in document genre classification in the Brown Corpus and the BNC Baby Corpus. The results show that DV-LSTM significantly outperforms TF-IDF vector and paragraph vector (PV-DM) in most cases, and their combinations may further improve the classification performance.	['Wei Li', 'Brian Mak']	2017-04-01	null	null	null	eacl-2017-4	['genre-classification']	['computer-vision']	[ 1.98195428e-01 -3.83580208e-01 -4.08999681e-01 -6.32813156e-01 -2.65621901e-01 -4.91979390e-01 9.16272223e-01 4.10365164e-01 -5.83593011e-01 6.19678497e-01 7.77054369e-01 -4.91843432e-01 -1.88894019e-01 -6.63799524e-01 -4.06196445e-01 -6.09188318e-01 -1.64931342e-01 2.51174837e-01 -1.86041877e-01 -4.17078994e-02 5.87365568e-01 3.38639796e-01 -1.51524305e+00 7.50208139e-01 3.00906271e-01 9.87223327e-01 6.89121962e-01 5.85395753e-01 -1.03014815e+00 8.54717016e-01 -9.94667649e-01 1.77039951e-02 -4.32660908e-01 -4.06447709e-01 -1.08886218e+00 -2.83685833e-01 1.87024593e-01 -1.22311741e-01 -3.18544686e-01 7.29641616e-01 2.17945740e-01 6.57596588e-01 1.04659212e+00 -6.41224623e-01 -8.63265932e-01 9.92028296e-01 -3.80861044e-01 4.63508844e-01 2.65563875e-01 -7.60121644e-01 1.04890382e+00 -1.08725441e+00 5.95448375e-01 1.40782928e+00 5.04676938e-01 4.12541509e-01 -8.24200451e-01 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5.09200811e-01 -5.25358260e-01 -4.40274894e-01 3.22048739e-02]	[11.047313690185547, 7.696857452392578]
2ef752ed-1c6f-4bd4-a6d4-e6dff9a51cd7	filtered-cophy-unsupervised-learning-of-1	2202.00368	null	https://arxiv.org/abs/2202.00368v1	https://arxiv.org/pdf/2202.00368v1.pdf	Filtered-CoPhy: Unsupervised Learning of Counterfactual Physics in Pixel Space	Learning causal relationships in high-dimensional data (images, videos) is a hard task, as they are often defined on low dimensional manifolds and must be extracted from complex signals dominated by appearance, lighting, textures and also spurious correlations in the data. We present a method for learning counterfactual reasoning of physical processes in pixel space, which requires the prediction of the impact of interventions on initial conditions. Going beyond the identification of structural relationships, we deal with the challenging problem of forecasting raw video over long horizons. Our method does not require the knowledge or supervision of any ground truth positions or other object or scene properties. Our model learns and acts on a suitable hybrid latent representation based on a combination of dense features, sets of 2D keypoints and an additional latent vector per keypoint. We show that this better captures the dynamics of physical processes than purely dense or sparse representations. We introduce a new challenging and carefully designed counterfactual benchmark for predictions in pixel space and outperform strong baselines in physics-inspired ML and video prediction.	['Christian Wolf', 'Greg Mori', 'Madiha Nadri', 'Natalia Neverova', 'Fabien Baradel', 'Steeven Janny']	2022-02-01	filtered-cophy-unsupervised-learning-of	https://openreview.net/forum?id=1L0C5ROtFp	https://openreview.net/pdf?id=1L0C5ROtFp	iclr-2022-4	['video-prediction']	['computer-vision']	[ 4.72063214e-01 4.87483032e-02 -1.70448437e-01 -5.26651815e-02 -3.15349519e-01 -3.97368014e-01 1.42881238e+00 1.12856500e-01 6.41872734e-02 8.90071034e-01 8.97759259e-01 -7.48881027e-02 -4.51022029e-01 -5.98712504e-01 -1.25322282e+00 -9.86308575e-01 -5.15463948e-01 2.60099798e-01 -1.31528527e-01 1.43198907e-01 2.75944084e-01 5.93349814e-01 -1.45843565e+00 3.21224719e-01 1.48157492e-01 8.44590068e-01 4.04562503e-02 9.86768484e-01 2.67544270e-01 1.28867185e+00 -2.78648715e-02 -2.90439695e-01 3.42868239e-01 -3.27546418e-01 -4.99257177e-01 -4.55471268e-03 9.27843392e-01 -2.67829448e-01 -1.12035966e+00 6.81974530e-01 1.79549262e-01 1.65786117e-01 1.08734810e+00 -1.21403635e+00 -6.21512353e-01 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da4361cf-71ce-4563-9a1e-46284f347e2e	the-methodius-corpus-of-rhetorical-discourse	null	null	https://aclanthology.org/L16-1273	https://aclanthology.org/L16-1273.pdf	The Methodius Corpus of Rhetorical Discourse Structures and Generated Texts	Using the Methodius Natural Language Generation (NLG) System, we have created a corpus which consists of a collection of generated texts which describe ancient Greek artefacts. Each text is linked to two representations created as part of the NLG process. The first is a content plan, which uses rhetorical relations to describe the high-level discourse structure of the text, and the second is a logical form describing the syntactic structure, which is sent to the OpenCCG surface realization module to produce the final text output. In recent work, White and Howcroft (2015) have used the SPaRKy restaurant corpus, which contains similar combination of texts and representations, for their research on the induction of rules for the combination of clauses. In the first instance this corpus will be used to test their algorithms on an additional domain, and extend their work to include the learning of referring expression generation rules. As far as we know, the SPaRKy restaurant corpus is the only existing corpus of this type, and we hope that the creation of this new corpus in a different domain will provide a useful resource to the Natural Language Generation community.	['Amy Isard']	2016-05-01	the-methodius-corpus-of-rhetorical-discourse-1	https://aclanthology.org/L16-1273	https://aclanthology.org/L16-1273.pdf	lrec-2016-5	['referring-expression-generation']	['computer-vision']	[ 3.09856087e-01 1.00547934e+00 1.19815528e-01 -2.70550609e-01 -7.08409548e-01 -6.05174363e-01 1.13828635e+00 4.18142974e-01 5.96185699e-02 1.06040204e+00 9.89556551e-01 -5.04105449e-01 7.74357542e-02 -1.14591038e+00 -4.90129322e-01 -2.76743144e-01 1.44537121e-01 7.95324802e-01 3.51100713e-01 -6.76017940e-01 5.27349770e-01 -1.42784238e-01 -1.67691982e+00 8.14163148e-01 7.62961030e-01 5.31504810e-01 4.83346909e-01 3.28744680e-01 -4.46526438e-01 1.22821057e+00 -7.46183813e-01 -4.27673876e-01 -7.20466524e-02 -9.25995171e-01 -1.30594671e+00 -5.04531860e-02 -2.17399240e-01 2.51696259e-01 2.65205830e-01 6.43522799e-01 3.14151734e-01 1.83386385e-01 6.82835519e-01 -6.68713510e-01 -4.49504584e-01 1.43133938e+00 1.61940083e-01 -2.51896113e-01 9.02012646e-01 -8.33064243e-02 1.30513799e+00 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a100ab68-2173-4998-bc00-1c90b6ce12d1	projection-based-point-convolution-for	2202.01991	null	https://arxiv.org/abs/2202.01991v1	https://arxiv.org/pdf/2202.01991v1.pdf	Projection-based Point Convolution for Efficient Point Cloud Segmentation	Understanding point cloud has recently gained huge interests following the development of 3D scanning devices and the accumulation of large-scale 3D data. Most point cloud processing algorithms can be classified as either point-based or voxel-based methods, both of which have severe limitations in processing time or memory, or both. To overcome these limitations, we propose Projection-based Point Convolution (PPConv), a point convolutional module that uses 2D convolutions and multi-layer perceptrons (MLPs) as its components. In PPConv, point features are processed through two branches: point branch and projection branch. Point branch consists of MLPs, while projection branch transforms point features into a 2D feature map and then apply 2D convolutions. As PPConv does not use point-based or voxel-based convolutions, it has advantages in fast point cloud processing. When combined with a learnable projection and effective feature fusion strategy, PPConv achieves superior efficiency compared to state-of-the-art methods, even with a simple architecture based on PointNet++. We demonstrate the efficiency of PPConv in terms of the trade-off between inference time and segmentation performance. The experimental results on S3DIS and ShapeNetPart show that PPConv is the most efficient method among the compared ones. The code is available at github.com/pahn04/PPConv.	['Junmo Kim', 'Chanho Lee', 'Eojindl Yi', 'JuYoung Yang', 'Pyunghwan Ahn']	2022-02-04	null	null	null	null	['point-cloud-segmentation']	['computer-vision']	[-2.93747067e-01 -2.23898396e-01 1.15607202e-01 -3.41862857e-01 -3.17321658e-01 -2.34111473e-01 5.74125946e-01 2.88365185e-01 -5.15325844e-01 1.45623773e-01 -5.43538690e-01 -5.22273660e-01 -1.05840296e-01 -1.33981884e+00 -9.77408111e-01 -3.71304512e-01 -8.56039375e-02 6.04189694e-01 7.05338895e-01 6.01098593e-03 3.39385033e-01 1.28481078e+00 -1.62693548e+00 1.57051146e-01 7.52351642e-01 1.48594248e+00 3.13684404e-01 3.33331048e-01 -8.52641284e-01 -5.72463088e-02 -2.25327253e-01 -1.97411850e-01 5.40489435e-01 5.29278517e-01 -5.65303981e-01 -3.07439744e-01 1.69792876e-01 -2.95184672e-01 -1.79291144e-01 8.44510615e-01 4.80167419e-01 2.10967995e-02 3.79617363e-01 -1.35415375e+00 -8.04307684e-02 3.05131018e-01 -6.44204855e-01 -9.21260715e-02 -1.26706446e-02 1.81814656e-01 5.42149782e-01 -1.23797548e+00 1.77984059e-01 1.35181820e+00 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15bace52-8c42-42c9-b312-dc1128ea9806	placing-historical-facts-on-a-timeline-a	2206.14089	null	https://arxiv.org/abs/2206.14089v1	https://arxiv.org/pdf/2206.14089v1.pdf	Placing (Historical) Facts on a Timeline: A Classification cum Coref Resolution Approach	A timeline provides one of the most effective ways to visualize the important historical facts that occurred over a period of time, presenting the insights that may not be so apparent from reading the equivalent information in textual form. By leveraging generative adversarial learning for important sentence classification and by assimilating knowledge based tags for improving the performance of event coreference resolution we introduce a two staged system for event timeline generation from multiple (historical) text documents. We demonstrate our results on two manually annotated historical text documents. Our results can be extremely helpful for historians, in advancing research in history and in understanding the socio-political landscape of a country as reflected in the writings of famous personas.	['Animesh Mukherjee', 'Aditya Basu', 'Altaf Ahmad', 'Sayantan Adak']	2022-06-28	null	null	null	null	['sentence-classification']	['natural-language-processing']	[ 4.79470402e-01 4.31028873e-01 -2.94146668e-02 -3.29162568e-01 -1.18234992e+00 -9.23708558e-01 1.25477469e+00 7.96216249e-01 -3.82344872e-01 1.04327476e+00 1.23201692e+00 -5.07466257e-01 -1.12300083e-01 -8.78710270e-01 -5.25139987e-01 -3.50980997e-01 -2.82455772e-01 5.01697004e-01 -3.82582843e-02 -5.95633924e-01 3.78446192e-01 5.21059692e-01 -9.62104142e-01 4.30730820e-01 5.03940165e-01 2.68204570e-01 -7.35800192e-02 5.15366018e-01 -2.85997331e-01 1.13193464e+00 -1.37308216e+00 -8.99581611e-01 -1.84978649e-01 -7.14856267e-01 -8.47407639e-01 -4.57932711e-01 4.55511957e-01 2.09776044e-01 -4.66036171e-01 8.03867936e-01 4.13459986e-01 2.25758299e-01 6.84627891e-01 -7.47128367e-01 -3.87848020e-01 1.40369987e+00 -2.93332785e-01 8.33715379e-01 6.50134087e-01 -1.54964626e-01 9.41097140e-01 -3.38848501e-01 1.09936106e+00 1.40596616e+00 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a07e3ecc-f1e9-42f5-90ca-32b6d1848856	crosswoz-a-large-scale-chinese-cross-domain	2002.11893	null	https://arxiv.org/abs/2002.11893v2	https://arxiv.org/pdf/2002.11893v2.pdf	CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset	To advance multi-domain (cross-domain) dialogue modeling as well as alleviate the shortage of Chinese task-oriented datasets, we propose CrossWOZ, the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. Moreover, the corpus contains rich annotation of dialogue states and dialogue acts at both user and system sides. About 60% of the dialogues have cross-domain user goals that favor inter-domain dependency and encourage natural transition across domains in conversation. We also provide a user simulator and several benchmark models for pipelined task-oriented dialogue systems, which will facilitate researchers to compare and evaluate their models on this corpus. The large size and rich annotation of CrossWOZ make it suitable to investigate a variety of tasks in cross-domain dialogue modeling, such as dialogue state tracking, policy learning, user simulation, etc.	['Qi Zhu', 'Minlie Huang', 'Xiaoyan Zhu', 'Zheng Zhang', 'Kaili Huang']	2020-02-27	crosswoz-a-large-scale-chinese-cross-domain-1	https://aclanthology.org/2020.tacl-1.19	https://aclanthology.org/2020.tacl-1.19.pdf	tacl-2020-1	['user-simulation']	['natural-language-processing']	[-4.71519589e-01 4.26871777e-01 -1.32653549e-01 -5.91402769e-01 -6.09035075e-01 -8.72996151e-01 8.65224063e-01 8.79043117e-02 -3.40832144e-01 1.06312287e+00 7.01496363e-01 -5.59422493e-01 2.55888760e-01 -5.10422289e-01 1.43205076e-01 -1.66471928e-01 -8.02715048e-02 1.26567256e+00 4.81994539e-01 -1.14096582e+00 8.46611857e-02 -2.00575590e-01 -7.54705548e-01 5.83949685e-01 9.31687236e-01 4.26975250e-01 3.00107479e-01 9.92489398e-01 -2.41293028e-01 9.68557775e-01 -9.84389722e-01 -4.65153933e-01 -1.89935014e-01 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d19477cd-2c5c-4809-88d8-4350dc7e73e3	visual-context-driven-audio-feature	2207.06020	null	https://arxiv.org/abs/2207.06020v1	https://arxiv.org/pdf/2207.06020v1.pdf	Visual Context-driven Audio Feature Enhancement for Robust End-to-End Audio-Visual Speech Recognition	This paper focuses on designing a noise-robust end-to-end Audio-Visual Speech Recognition (AVSR) system. To this end, we propose Visual Context-driven Audio Feature Enhancement module (V-CAFE) to enhance the input noisy audio speech with a help of audio-visual correspondence. The proposed V-CAFE is designed to capture the transition of lip movements, namely visual context and to generate a noise reduction mask by considering the obtained visual context. Through context-dependent modeling, the ambiguity in viseme-to-phoneme mapping can be refined for mask generation. The noisy representations are masked out with the noise reduction mask resulting in enhanced audio features. The enhanced audio features are fused with the visual features and taken to an encoder-decoder model composed of Conformer and Transformer for speech recognition. We show the proposed end-to-end AVSR with the V-CAFE can further improve the noise-robustness of AVSR. The effectiveness of the proposed method is evaluated in noisy speech recognition and overlapped speech recognition experiments using the two largest audio-visual datasets, LRS2 and LRS3.	['Yong Man Ro', 'Daehun Yoo', 'Minsu Kim', 'Joanna Hong']	2022-07-13	null	null	null	null	['noisy-speech-recognition', 'audio-visual-speech-recognition']	['speech', 'speech']	[ 3.80151153e-01 -4.06340003e-01 4.29614902e-01 -3.42405409e-01 -1.16832113e+00 -3.04693073e-01 5.21693289e-01 -1.60070017e-01 -3.14873189e-01 2.53447890e-01 6.57868385e-01 -1.21836536e-01 8.31275284e-02 -2.24292904e-01 -4.48139548e-01 -7.89702356e-01 3.95048231e-01 -4.34181780e-01 7.49735087e-02 -1.59267813e-01 4.10724789e-01 4.35485989e-01 -2.19150376e+00 7.96959162e-01 6.41807914e-01 1.22389376e+00 7.46945977e-01 1.13751519e+00 -2.11572185e-01 3.88407707e-01 -6.48000598e-01 2.47078687e-01 7.04388022e-02 -3.57437104e-01 -2.26999983e-01 4.32404697e-01 2.28943542e-01 -1.68667063e-01 -4.16589588e-01 1.04902470e+00 1.00597191e+00 3.59056473e-01 6.10221207e-01 -1.27745855e+00 -4.34131563e-01 3.41282785e-01 -3.68786842e-01 2.08970681e-01 5.54532886e-01 3.44711900e-01 5.93373179e-01 -1.56099224e+00 4.74988222e-01 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bd0d8d71-305d-43c0-b6b5-a6d74ec9102c	real-time-neural-radiance-caching-for-path	2106.12372	null	https://arxiv.org/abs/2106.12372v2	https://arxiv.org/pdf/2106.12372v2.pdf	Real-time Neural Radiance Caching for Path Tracing	We present a real-time neural radiance caching method for path-traced global illumination. Our system is designed to handle fully dynamic scenes, and makes no assumptions about the lighting, geometry, and materials. The data-driven nature of our approach sidesteps many difficulties of caching algorithms, such as locating, interpolating, and updating cache points. Since pretraining neural networks to handle novel, dynamic scenes is a formidable generalization challenge, we do away with pretraining and instead achieve generalization via adaptation, i.e. we opt for training the radiance cache while rendering. We employ self-training to provide low-noise training targets and simulate infinite-bounce transport by merely iterating few-bounce training updates. The updates and cache queries incur a mild overhead -- about 2.6ms on full HD resolution -- thanks to a streaming implementation of the neural network that fully exploits modern hardware. We demonstrate significant noise reduction at the cost of little induced bias, and report state-of-the-art, real-time performance on a number of challenging scenarios.	['Alexander Keller', 'Jan Novák', 'Fabrice Rousselle', 'Thomas Müller']	2021-06-23	null	null	null	null	['neural-radiance-caching']	['computer-vision']	[ 5.74045070e-02 -5.93663275e-01 3.33038419e-01 -5.39371789e-01 -8.55857909e-01 -3.00242096e-01 2.89688736e-01 -1.45770788e-01 -7.22164094e-01 4.67746049e-01 -1.99635644e-02 -5.33064425e-01 1.84053779e-01 -1.03220522e+00 -1.02585948e+00 -7.21037447e-01 -2.39503875e-01 1.51176199e-01 4.41456020e-01 -2.07507744e-01 -1.28350511e-01 8.66467297e-01 -2.05460262e+00 3.25573653e-01 4.75600749e-01 1.18240952e+00 1.72250032e-01 1.15392518e+00 -5.38942479e-02 8.54674578e-01 -6.06065631e-01 -4.12512273e-02 4.68832195e-01 -1.94938242e-01 -4.24614787e-01 -2.15881139e-01 9.79563594e-01 -9.12563026e-01 -4.92673546e-01 7.44805992e-01 7.12401271e-01 4.79053110e-01 2.27009077e-02 -6.78403497e-01 -4.46916640e-01 -1.30763754e-01 -5.31381071e-01 3.48032832e-01 -9.29685682e-02 4.05019611e-01 5.24523258e-01 -8.85861039e-01 4.55854237e-01 9.55112100e-01 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9cc27d57-e6a8-4076-b23f-21dd7334489b	upb-at-semeval-2022-task-5-enhancing-uniter	2205.14769	null	https://arxiv.org/abs/2205.14769v1	https://arxiv.org/pdf/2205.14769v1.pdf	UPB at SemEval-2022 Task 5: Enhancing UNITER with Image Sentiment and Graph Convolutional Networks for Multimedia Automatic Misogyny Identification	In recent times, the detection of hate-speech, offensive, or abusive language in online media has become an important topic in NLP research due to the exponential growth of social media and the propagation of such messages, as well as their impact. Misogyny detection, even though it plays an important part in hate-speech detection, has not received the same attention. In this paper, we describe our classification systems submitted to the SemEval-2022 Task 5: MAMI - Multimedia Automatic Misogyny Identification. The shared task aimed to identify misogynous content in a multi-modal setting by analysing meme images together with their textual captions. To this end, we propose two models based on the pre-trained UNITER model, one enhanced with an image sentiment classifier, whereas the second leverages a Vocabulary Graph Convolutional Network (VGCN). Additionally, we explore an ensemble using the aforementioned models. Our best model reaches an F1-score of 71.4% in Sub-task A and 67.3% for Sub-task B positioning our team in the upper third of the leaderboard. We release the code and experiments for our models on GitHub	['Dumitru-Clementin Cercel', 'Mihai Dascalu', 'Andrei Paraschiv']	2022-05-29	null	https://aclanthology.org/2022.semeval-1.85	https://aclanthology.org/2022.semeval-1.85.pdf	semeval-naacl-2022-7	['abusive-language']	['natural-language-processing']	[ 7.88261443e-02 2.86085531e-02 4.68308702e-02 1.91821918e-01 -7.07280338e-01 -8.02796245e-01 1.01948750e+00 5.23249447e-01 -4.43204045e-01 3.66498739e-01 3.08164239e-01 -1.36007100e-01 4.41042721e-01 -2.61813164e-01 -5.21161020e-01 -4.71274793e-01 1.40569717e-01 1.69083849e-01 4.54403497e-02 -3.25826466e-01 3.89760822e-01 1.64091289e-01 -1.15237451e+00 4.57459748e-01 7.67769456e-01 1.00781083e+00 -2.67214626e-01 7.85699904e-01 4.87188436e-02 1.38588417e+00 -8.32501471e-01 -9.10707593e-01 -1.76958755e-01 -3.75656694e-01 -7.83816576e-01 -5.50836772e-02 8.02055120e-01 -2.78247088e-01 -4.87380803e-01 1.22975278e+00 4.72441524e-01 -1.51853636e-01 7.82711267e-01 -1.27630520e+00 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592d2894-69ca-4835-a577-481ee6bac0b7	security-and-privacy-problems-in-voice	2304.09486	null	https://arxiv.org/abs/2304.09486v1	https://arxiv.org/pdf/2304.09486v1.pdf	Security and Privacy Problems in Voice Assistant Applications: A Survey	Voice assistant applications have become omniscient nowadays. Two models that provide the two most important functions for real-life applications (i.e., Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR) models and Speaker Identification (SI) models. According to recent studies, security and privacy threats have also emerged with the rapid development of the Internet of Things (IoT). The security issues researched include attack techniques toward machine learning models and other hardware components widely used in voice assistant applications. The privacy issues include technical-wise information stealing and policy-wise privacy breaches. The voice assistant application takes a steadily growing market share every year, but their privacy and security issues never stopped causing huge economic losses and endangering users' personal sensitive information. Thus, it is important to have a comprehensive survey to outline the categorization of the current research regarding the security and privacy problems of voice assistant applications. This paper concludes and assesses five kinds of security attacks and three types of privacy threats in the papers published in the top-tier conferences of cyber security and voice domain.	['Jun Zhang', 'Hossein Ghodosi', 'Mostafa Rahimi Azghadi', 'Lei Pan', 'Chao Chen', 'Jingjin Li']	2023-04-19	null	null	null	null	['speaker-identification']	['speech']	[-1.15477182e-01 -5.25510646e-02 -2.15801582e-01 -4.66700315e-01 -2.08202273e-01 -7.04457939e-01 5.50118268e-01 -3.18730623e-01 -4.84781027e-01 8.12157452e-01 8.11029822e-02 -5.60989976e-01 -1.68607801e-01 -5.39636314e-01 2.20910043e-01 -6.90721035e-01 3.91137302e-01 1.36991248e-01 1.56260207e-01 -1.55888721e-01 3.12137961e-01 8.21637571e-01 -1.68735874e+00 -5.72304614e-02 6.51019633e-01 1.19747841e+00 -2.83867180e-01 4.72152710e-01 -4.49741483e-01 2.05516428e-01 -9.04160559e-01 -8.58413696e-01 3.66594076e-01 1.79172173e-01 -7.23442256e-01 -2.41696790e-01 -3.72643381e-01 -2.72898287e-01 -4.68364686e-01 1.53999412e+00 5.23280442e-01 3.98915149e-02 3.07975337e-03 -1.90548813e+00 -4.78969544e-01 5.40700316e-01 -5.12943447e-01 4.23229858e-02 3.01410705e-01 -1.63459361e-01 5.84408283e-01 -2.86567032e-01 1.20315123e-02 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42a58852-45de-464c-a843-2f80ed266f03	minimal-time-deadbeat-consensus-and	2304.06224	null	https://arxiv.org/abs/2304.06224v1	https://arxiv.org/pdf/2304.06224v1.pdf	Minimal-time Deadbeat Consensus and Individual Disagreement Degree Prediction for High-order Linear Multi-agent Systems	In this paper, a Hankel matrix-based fully distributed algorithm is proposed to address a minimal-time deadbeat consensus prediction problem for discrete-time high-order multi-agent systems (MASs). Therein, each agent can predict the consensus value with the minimum number of observable historical outputs of its own. Accordingly, compared to most existing algorithms only yielding asymptotic convergence, the present method can attain deadbeat consensus instead. Moreover, based on the consensus value prediction, instant individual disagreement degree value of MASs can be calculated in advance as well. Sufficient conditions are derived to guarantee both the minimal-time deadbeat consensus and the instant individual disagreement degree prediction. Finally, both the effectiveness and superiority of the proposed deadbeat consensus algorithm are substantiated by numerical simulations.	['Wei Ren', 'Zhe Hu', 'Bowen Xu', 'Hai-Tao Zhang', 'Fu-Long Hu']	2023-04-13	null	null	null	null	['value-prediction']	['computer-code']	[-0.28346643 0.19660294 0.09108175 0.12764938 -0.6314055 -0.6618075 0.4836923 0.22105238 -0.04145888 1.1182323 -0.514189 -0.09952964 -0.53072894 -0.31305656 -0.06455656 -1.1977823 -0.196339 0.59214365 -0.11559355 -0.13954239 0.02307089 0.12401674 -0.7906868 -0.53324854 1.1140726 1.1573117 0.04390902 0.6899475 1.0718143 0.73343694 -0.5020735 0.3683022 0.53065264 -0.47103038 -0.20593439 0.30516374 -0.2859012 -0.47881073 -0.34845415 1.3190517 0.51561546 0.23421827 0.46082312 -1.6569659 -0.17496964 0.66029847 -0.57290083 -0.02234216 -0.1176234 0.19079433 0.95172876 -0.8517058 0.49259 0.7617676 0.43217748 0.3365277 -1.1514674 -0.6281946 0.2654559 0.31833646 -1.5339328 -0.22093385 0.74057615 -0.32971126 0.33011168 0.473115 0.8730205 0.21653214 0.644144 0.54681414 1.1918377 0.06205035 0.5682892 -0.48414332 -0.12480967 0.39782143 0.7135386 0.08937202 -0.1916701 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50c58701-1608-4442-bc81-c8ffd6faea70	spontaneous-emotion-recognition-from-facial	2012.06973	null	https://arxiv.org/abs/2012.06973v1	https://arxiv.org/pdf/2012.06973v1.pdf	Spontaneous Emotion Recognition from Facial Thermal Images	One of the key research areas in computer vision addressed by a vast number of publications is the processing and understanding of images containing human faces. The most often addressed tasks include face detection, facial landmark localization, face recognition and facial expression analysis. Other, more specialized tasks such as affective computing, the extraction of vital signs from videos or analysis of social interaction usually require one or several of the aforementioned tasks that have to be performed. In our work, we analyze that a large number of tasks for facial image processing in thermal infrared images that are currently solved using specialized rule-based methods or not solved at all can be addressed with modern learning-based approaches. We have used USTC-NVIE database for training of a number of machine learning algorithms for facial landmark localization.	['Chirag Kyal']	2020-12-13	null	null	null	null	['face-alignment']	['computer-vision']	[ 3.72366846e-01 -2.02883810e-01 1.34632021e-01 -7.65059114e-01 -2.77327359e-01 -4.82800335e-01 3.56370836e-01 -1.04295544e-01 -4.86505687e-01 4.70876038e-01 -5.06533384e-01 7.45285451e-02 -7.81826023e-03 -1.58587471e-01 -2.16413230e-01 -7.34737217e-01 -1.84419319e-01 2.47905463e-01 -2.50839442e-01 -1.63955048e-01 2.90769368e-01 9.51349854e-01 -2.07350850e+00 1.29706770e-01 1.54772058e-01 1.45934010e+00 -2.88566172e-01 6.83029652e-01 -1.03820018e-01 5.03435493e-01 -9.93268117e-02 -2.17308044e-01 1.95691794e-01 -8.45757797e-02 -6.43376112e-01 1.49046317e-01 5.43225884e-01 -1.86616182e-01 5.28019965e-01 1.04502487e+00 5.63937426e-01 2.60524511e-01 7.62478054e-01 -1.41861749e+00 -8.23786259e-02 -3.44477385e-01 -9.21979606e-01 6.11966103e-02 5.06203592e-01 -4.49663818e-01 2.47813195e-01 -1.12668407e+00 3.92743260e-01 1.35241222e+00 5.23673713e-01 7.05913186e-01 -9.14672673e-01 -9.08500791e-01 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3.22607487e-01 7.52708986e-02 -7.73569167e-01 -2.56660253e-01 3.00276875e-01]	[13.284937858581543, 0.9757174253463745]
5626d6dd-0a83-4b3a-9b46-87d99a862a3a	self-sustaining-ultra-wideband-positioning	2212.04896	null	https://arxiv.org/abs/2212.04896v2	https://arxiv.org/pdf/2212.04896v2.pdf	Self-sustaining Ultra-wideband Positioning System for Event-driven Indoor Localization	Smart and unobtrusive mobile sensor nodes that accurately track their own position have the potential to augment data collection with location-based functions. To attain this vision of unobtrusiveness, the sensor nodes must have a compact form factor and operate over long periods without battery recharging or replacement. This paper presents a self-sustaining and accurate ultra-wideband-based indoor location system with conservative infrastructure overhead. An event-driven sensing approach allows for balancing the limited energy harvested in indoor conditions with the power consumption of ultra-wideband transceivers. The presented tag-centralized concept, which combines heterogeneous system design with embedded processing, minimizes idle consumption without sacrificing functionality. Despite modest infrastructure requirements, high localization accuracy is achieved with error-correcting double-sided two-way ranging and embedded optimal multilateration. Experimental results demonstrate the benefits of the proposed system: the node achieves a quiescent current of $47~nA$ and operates at $1.2~\mu A$ while performing energy harvesting and motion detection. The energy consumption for position updates, with an accuracy of $40~cm$ (2D) in realistic non-line-of-sight conditions, is $10.84~mJ$. In an asset tracking case study within a $200~m^2$ multi-room office space, the achieved accuracy level allows for identifying 36 different desk and storage locations with an accuracy of over $95~{\%}$. The system`s long-time self-sustainability has been analyzed over $700~days$ in multiple indoor lighting situations.	['Luca Benini', 'Michele Magno', 'Philipp Mayer']	2022-12-09	null	null	null	null	['motion-detection', 'indoor-localization']	['computer-vision', 'computer-vision']	[ 0.12350588 0.22407174 0.08826767 -0.2392431 -1.0324303 -0.63650477 -0.13581045 0.38517007 -0.6494533 1.0734566 -0.52385014 -0.4711321 -0.4290177 -0.8512843 -0.4846285 -0.9699974 -0.42159528 -0.11763798 -0.18712902 0.17587526 -0.15399969 0.28897923 -1.4125953 -0.98158634 0.79390234 1.6989744 0.3548724 0.7776722 0.6087793 0.14256361 -0.91230834 0.05916441 0.10753296 0.25449577 0.1454037 -0.7125076 0.22101946 -0.67260575 -0.20924008 0.7310245 1.1176288 -0.12661293 0.2899045 -1.0686469 0.12337152 0.3484704 -0.43816757 0.13577688 0.5517416 -0.29192787 0.49958086 -0.14217497 0.1381106 0.25702795 1.0456703 0.33727014 -0.93961465 -0.9969822 -0.64217204 -0.28845102 -1.8672978 -0.7876461 0.82483387 0.25410208 1.1916753 0.80363905 0.69174355 0.57969856 0.8130015 -0.01916822 1.0266644 -0.5042943 0.5599061 0.3075934 -0.06358039 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9439d745-f282-443d-81c9-e87468a27eba	vcsum-a-versatile-chinese-meeting	2305.05280	null	https://arxiv.org/abs/2305.05280v2	https://arxiv.org/pdf/2305.05280v2.pdf	VCSUM: A Versatile Chinese Meeting Summarization Dataset	Compared to news and chat summarization, the development of meeting summarization is hugely decelerated by the limited data. To this end, we introduce a versatile Chinese meeting summarization dataset, dubbed VCSum, consisting of 239 real-life meetings, with a total duration of over 230 hours. We claim our dataset is versatile because we provide the annotations of topic segmentation, headlines, segmentation summaries, overall meeting summaries, and salient sentences for each meeting transcript. As such, the dataset can adapt to various summarization tasks or methods, including segmentation-based summarization, multi-granularity summarization and retrieval-then-generate summarization. Our analysis confirms the effectiveness and robustness of VCSum. We also provide a set of benchmark models regarding different downstream summarization tasks on VCSum to facilitate further research. The dataset and code will be released at https://github.com/hahahawu/VCSum.	['Linqi Song', 'Ding Liang', 'Zhaohui Hou', 'Haochen Tan', 'Mingjie Zhan', 'Han Wu']	2023-05-09	null	null	null	null	['meeting-summarization']	['natural-language-processing']	[ 1.85249344e-01 4.94322896e-01 -1.26923233e-01 -2.14126766e-01 -1.48440778e+00 -7.45993137e-01 7.67137229e-01 5.56757867e-01 1.26978848e-02 1.13649631e+00 1.18306422e+00 3.75797376e-02 2.00438708e-01 -3.59463990e-01 -3.52456838e-01 -3.00789595e-01 8.76509845e-02 3.53975475e-01 1.45214900e-01 -1.74039319e-01 6.50495768e-01 -2.88936764e-01 -1.16355205e+00 5.51781178e-01 1.29197383e+00 3.87395084e-01 3.17354172e-01 1.03658211e+00 -1.62751526e-01 4.67996150e-01 -1.39781654e+00 -3.27157050e-01 -3.15929651e-01 -8.00507486e-01 -9.76334393e-01 1.92084581e-01 2.57968783e-01 -3.16578656e-01 -2.79187024e-01 6.63301826e-01 8.30164850e-01 2.48686135e-01 7.27697194e-01 -1.02418947e+00 -5.38474262e-01 1.38095975e+00 -6.21318877e-01 2.87495315e-01 9.24320877e-01 -1.75196290e-01 1.12916231e+00 -6.14549279e-01 8.35042596e-01 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ee506a66-c2f7-4e2d-a36e-fa201a732581	time-aware-dynamic-graph-embedding-for	2207.00594	null	https://arxiv.org/abs/2207.00594v2	https://arxiv.org/pdf/2207.00594v2.pdf	Time-aware Dynamic Graph Embedding for Asynchronous Structural Evolution	Dynamic graphs refer to graphs whose structure dynamically changes over time. Despite the benefits of learning vertex representations (i.e., embeddings) for dynamic graphs, existing works merely view a dynamic graph as a sequence of changes within the vertex connections, neglecting the crucial asynchronous nature of such dynamics where the evolution of each local structure starts at different times and lasts for various durations. To maintain asynchronous structural evolutions within the graph, we innovatively formulate dynamic graphs as temporal edge sequences associated with joining time of vertices (ToV) and timespan of edges (ToE). Then, a time-aware Transformer is proposed to embed vertices' dynamic connections and ToEs into the learned vertex representations. Meanwhile, we treat each edge sequence as a whole and embed its ToV of the first vertex to further encode the time-sensitive information. Extensive evaluations on several datasets show that our approach outperforms the state-of-the-art in a wide range of graph mining tasks. At the same time, it is very efficient and scalable for embedding large-scale dynamic graphs.	['Lei Chen', 'Xiaofang Zhou', 'Quoc Viet Hung Nguyen', 'Tong Chen', 'Jiannong Cao', 'Hongzhi Yin', 'Yu Yang']	2022-07-01	null	null	null	null	['graph-mining', 'dynamic-graph-embedding']	['graphs', 'graphs']	[-1.45904496e-01 1.88425452e-01 -4.37411457e-01 -1.52947262e-01 3.38575035e-01 -9.28130388e-01 7.62593567e-01 6.84677780e-01 -3.13788168e-02 3.60040039e-01 1.84327513e-01 -3.54014874e-01 -2.56533414e-01 -1.29396236e+00 -6.42399967e-01 -7.53244042e-01 -6.53121114e-01 5.71388721e-01 4.23883080e-01 -1.57992661e-01 -4.25009131e-02 5.14031947e-01 -1.17359984e+00 -1.14404112e-01 1.88450724e-01 6.10373318e-01 -2.78321896e-02 4.89738077e-01 -4.74188477e-01 4.79370415e-01 -3.94485116e-01 -6.38963699e-01 4.75488119e-02 -8.49289522e-02 -5.12484431e-01 2.17034250e-01 -1.20524734e-01 2.18431670e-02 -1.26715636e+00 9.85151470e-01 9.82736573e-02 -1.17939254e-02 4.13062930e-01 -1.73284614e+00 -1.14375710e+00 1.00337112e+00 -8.58024895e-01 5.68519413e-01 2.50086039e-01 -1.02153882e-01 1.40232468e+00 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d2f4461b-b375-4837-928b-289df69b0306	c-4-net-contextual-compression-and	2110.11887	null	https://arxiv.org/abs/2110.11887v1	https://arxiv.org/pdf/2110.11887v1.pdf	C$^{4}$Net: Contextual Compression and Complementary Combination Network for Salient Object Detection	Deep learning solutions of the salient object detection problem have achieved great results in recent years. The majority of these models are based on encoders and decoders, with a different multi-feature combination. In this paper, we show that feature concatenation works better than other combination methods like multiplication or addition. Also, joint feature learning gives better results, because of the information sharing during their processing. We designed a Complementary Extraction Module (CEM) to extract necessary features with edge preservation. Our proposed Excessiveness Loss (EL) function helps to reduce false-positive predictions and purifies the edges with other weighted loss functions. Our designed Pyramid-Semantic Module (PSM) with Global guiding flow (G) makes the prediction more accurate by providing high-level complementary information to shallower layers. Experimental results show that the proposed model outperforms the state-of-the-art methods on all benchmark datasets under three evaluation metrics.	['Hazarapet Tunanyan']	2021-10-22	null	null	null	null	['salient-object-detection']	['computer-vision']	[ 2.06600130e-02 8.61225277e-03 -2.61488110e-01 -4.11390364e-01 -5.01857877e-01 2.82847703e-01 4.93142456e-01 1.44968256e-01 -4.10657108e-01 5.65096796e-01 3.97753179e-01 2.95026153e-01 4.43073809e-02 -9.43449438e-01 -7.56520331e-01 -5.46112418e-01 -3.78203273e-01 -3.58895093e-01 8.98561895e-01 -2.23541662e-01 5.63784003e-01 2.62195975e-01 -1.69198573e+00 6.44983232e-01 7.55661666e-01 1.39102042e+00 6.20814860e-01 1.72091663e-01 -2.43450314e-01 9.79856789e-01 -1.88107148e-01 -5.67194045e-01 5.08051038e-01 -8.06243345e-02 -5.02917945e-01 -1.24800324e-01 2.02256799e-01 -5.07543862e-01 -3.91826689e-01 1.06341982e+00 5.99931479e-01 -1.51950225e-01 4.80934024e-01 -1.32901382e+00 -8.34941983e-01 6.70084476e-01 -1.08280623e+00 2.60969222e-01 -1.84548795e-02 9.04529244e-02 1.30373144e+00 -1.30375934e+00 4.00065243e-01 1.35296333e+00 8.24927807e-01 1.36495516e-01 -6.82999372e-01 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587bb871-8b19-401e-8603-a3c207f582f7	pipeline-for-3d-reconstruction-of-the-human	2111.05409	null	https://arxiv.org/abs/2111.05409v1	https://arxiv.org/pdf/2111.05409v1.pdf	Pipeline for 3D reconstruction of the human body from AR/VR headset mounted egocentric cameras	In this paper, we propose a novel pipeline for the 3D reconstruction of the full body from egocentric viewpoints. 3-D reconstruction of the human body from egocentric viewpoints is a challenging task as the view is skewed and the body parts farther from the cameras are occluded. One such example is the view from cameras installed below VR headsets. To achieve this task, we first make use of conditional GANs to translate the egocentric views to full body third-person views. This increases the comprehensibility of the image and caters to occlusions. The generated third-person view is further sent through the 3D reconstruction module that generates a 3D mesh of the body. We also train a network that can take the third person full-body view of the subject and generate the texture maps for applying on the mesh. The generated mesh has fairly realistic body proportions and is fully rigged allowing for further applications such as real-time animation and pose transfer in games. This approach can be key to a new domain of mobile human telepresence.	['Vanita Jain', 'Kshitij Sidana', 'Shivam Grover']	2021-11-09	null	null	null	null	['pose-transfer']	['computer-vision']	[ 6.12809062e-02 6.94028795e-01 2.82026172e-01 -3.70918989e-01 -1.84774280e-01 -3.40741932e-01 3.98228705e-01 -7.40120411e-01 -4.52401163e-03 4.40636605e-01 4.06564504e-01 2.55777121e-01 5.33831239e-01 -8.18473160e-01 -8.04189026e-01 -2.37376988e-01 1.68386564e-01 9.88155842e-01 3.92472237e-01 -4.12269592e-01 -3.06811482e-01 4.57775116e-01 -1.73533833e+00 1.53523535e-01 4.11871254e-01 3.92264396e-01 1.43472090e-01 9.82589066e-01 5.23449004e-01 2.19523519e-01 -3.59053463e-01 -5.33353865e-01 5.28166354e-01 -5.35139680e-01 -4.56505686e-01 5.90971708e-01 6.31405115e-01 -8.65178823e-01 -1.64114758e-01 6.81977332e-01 6.43314838e-01 1.04439750e-01 2.71473557e-01 -1.07744694e+00 4.43874866e-01 1.27533540e-01 -8.59857738e-01 -4.53671873e-01 1.13948882e+00 -4.05857973e-02 3.52882236e-01 -6.41834021e-01 1.20808709e+00 1.60713184e+00 5.32680392e-01 1.02287757e+00 -9.53864098e-01 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f15563d9-f2b3-40a7-9e1d-64a78a6fe4f6	crowdsourcing-lung-nodules-detection-and	1809.06402	null	http://arxiv.org/abs/1809.06402v1	http://arxiv.org/pdf/1809.06402v1.pdf	Crowdsourcing Lung Nodules Detection and Annotation	We present crowdsourcing as an additional modality to aid radiologists in the diagnosis of lung cancer from clinical chest computed tomography (CT) scans. More specifically, a complete workflow is introduced which can help maximize the sensitivity of lung nodule detection by utilizing the collective intelligence of the crowd. We combine the concept of overlapping thin-slab maximum intensity projections (TS-MIPs) and cine viewing to render short videos that can be outsourced as an annotation task to the crowd. These videos are generated by linearly interpolating overlapping TS-MIPs of CT slices through the depth of each quadrant of a patient's lung. The resultant videos are outsourced to an online community of non-expert users who, after a brief tutorial, annotate suspected nodules in these video segments. Using our crowdsourcing workflow, we achieved a lung nodule detection sensitivity of over 90% for 20 patient CT datasets (containing 178 lung nodules with sizes between 1-30mm), and only 47 false positives from a total of 1021 annotations on nodules of all sizes (96% sensitivity for nodules$>$4mm). These results show that crowdsourcing can be a robust and scalable modality to aid radiologists in screening for lung cancer, directly or in combination with computer-aided detection (CAD) algorithms. For CAD algorithms, the presented workflow can provide highly accurate training data to overcome the high false-positive rate (per scan) problem. We also provide, for the first time, analysis on nodule size and position which can help improve CAD algorithms.	['Saeed Boorboor', 'Ji Hwan Park', 'Arie Kaufman', 'Saad Nadeem', 'Kevin Baker']	2018-09-17	null	null	null	null	['lung-nodule-detection']	['medical']	[ 1.72448054e-01 5.30368447e-01 -1.38705596e-01 -3.95254344e-02 -1.23685539e+00 -8.25032592e-01 -1.15051856e-02 2.27192968e-01 -5.48600554e-01 4.13451910e-01 -2.02098954e-02 -6.31372631e-01 2.19882667e-01 -6.43612981e-01 -5.69814026e-01 -7.34001935e-01 -5.90212569e-02 9.97728229e-01 9.23397720e-01 1.56163782e-01 -4.55619425e-01 6.84889734e-01 -1.10221422e+00 6.04714274e-01 7.28072405e-01 6.46069229e-01 4.71867502e-01 1.17341995e+00 9.68754292e-02 1.18317068e+00 -4.29298490e-01 -4.08692867e-01 4.21447009e-01 -3.95654738e-01 -7.53811538e-01 6.08996749e-01 3.18685709e-03 -7.18846917e-01 -1.37570933e-01 6.86142385e-01 6.15339816e-01 -2.95473576e-01 3.79679531e-01 -8.84186387e-01 1.63858794e-02 3.64458710e-01 -3.97041112e-01 2.60691226e-01 6.06529653e-01 5.11478364e-01 5.81199944e-01 -9.92003024e-01 8.16990077e-01 5.24863780e-01 6.82766378e-01 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