paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5ea65d5f-c8c0-41d1-9aa6-bb603abcd638 | proportional-response-contextual-bandits-for | 2307.02108 | null | https://arxiv.org/abs/2307.02108v1 | https://arxiv.org/pdf/2307.02108v1.pdf | Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization | Simple regret minimization is a critical problem in learning optimal treatment assignment policies across various domains, including healthcare and e-commerce. However, it remains understudied in the contextual bandit setting. We propose a new family of computationally efficient bandit algorithms for the stochastic con... | ['Emma Brunskill', 'Susan Athey', 'Ruohan Zhan', 'Sanath Kumar Krishnamurthy'] | 2023-07-05 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 2.96000361e-01 2.46013641e-01 -1.16730297e+00 -5.76271772e-01
-1.48709190e+00 -8.34359884e-01 7.26474375e-02 -4.34629880e-02
-2.12050870e-01 1.22009313e+00 5.18455684e-01 -8.34761262e-01
-1.19385135e+00 -4.96440440e-01 -1.00141037e+00 -8.05298924e-01
7.02552646e-02 6.69638932e-01 -7.03758895e-01 1.96094111... | [4.535053253173828, 3.295076370239258] |
49514c1f-6332-442d-bdb3-57531b0e74c3 | llmva-gebc-large-language-model-with-video | 2306.10354 | null | https://arxiv.org/abs/2306.10354v1 | https://arxiv.org/pdf/2306.10354v1.pdf | LLMVA-GEBC: Large Language Model with Video Adapter for Generic Event Boundary Captioning | Our winning entry for the CVPR 2023 Generic Event Boundary Captioning (GEBC) competition is detailed in this paper. Unlike conventional video captioning tasks, GEBC demands that the captioning model possess an understanding of immediate changes in status around the designated video boundary, making it a difficult task.... | ['Feng Zheng', 'Teng Wang', 'Xiangchen Wang', 'Jinrui Zhang', 'Yunlong Tang'] | 2023-06-17 | null | null | null | null | ['video-captioning', 'boundary-captioning'] | ['computer-vision', 'computer-vision'] | [ 2.19052881e-01 2.50710130e-01 -3.10730964e-01 -2.27860257e-01
-1.33778441e+00 -5.97848892e-01 5.44819891e-01 -3.66373181e-01
-3.09843481e-01 7.81512678e-01 5.50284088e-01 -3.11774731e-01
5.90130627e-01 -1.26810521e-01 -1.29412150e+00 -2.03621790e-01
-3.66203859e-02 5.20761967e-01 1.50855571e-01 -9.08003747... | [10.525189399719238, 0.6823262572288513] |
0e9d5949-54cc-4915-b2d3-7d70b12f45a5 | occluded-video-instance-segmentation | 2102.01558 | null | https://arxiv.org/abs/2102.01558v6 | https://arxiv.org/pdf/2102.01558v6.pdf | Occluded Video Instance Segmentation: A Benchmark | Can our video understanding systems perceive objects when a heavy occlusion exists in a scene? To answer this question, we collect a large-scale dataset called OVIS for occluded video instance segmentation, that is, to simultaneously detect, segment, and track instances in occluded scenes. OVIS consists of 296k high-qu... | ['Serge Belongie', 'Song Bai', 'Alan Yuille', 'Philip H. S. Torr', 'Xiang Bai', 'Xinggang Wang', 'Yao Hu', 'Xiaoyu Liu', 'Yan Gao', 'Jiyang Qi'] | 2021-02-02 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 2.82019824e-01 5.82904443e-02 -5.54059744e-01 -4.26783592e-01
-6.76802754e-01 -6.79420829e-01 2.60969043e-01 -3.07513237e-01
-5.18811829e-02 3.42976928e-01 4.34861472e-03 5.50157353e-02
1.41334608e-01 -4.37488556e-01 -1.14152014e+00 -3.42537671e-01
1.04699887e-01 4.75986511e-01 6.62865341e-01 1.78031579... | [9.206570625305176, 0.08136147260665894] |
64d61393-401a-42d4-813a-7ea8f1832da3 | ginex-ssd-enabled-billion-scale-graph-neural | 2208.09151 | null | https://arxiv.org/abs/2208.09151v1 | https://arxiv.org/pdf/2208.09151v1.pdf | Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching | Recently, Graph Neural Networks (GNNs) have been receiving a spotlight as a powerful tool that can effectively serve various inference tasks on graph structured data. As the size of real-world graphs continues to scale, the GNN training system faces a scalability challenge. Distributed training is a popular approach to... | ['Jae W. Lee', 'Sunhong Min', 'Yeonhong Park'] | 2022-08-19 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [-2.83779263e-01 -4.52836156e-02 -5.98976314e-01 -1.61641002e-01
-2.24296615e-01 -3.13469678e-01 4.01493013e-01 3.65871191e-01
-4.96875733e-01 2.38934413e-01 -8.97596627e-02 -1.13977921e+00
-5.68371499e-03 -1.38285398e+00 -9.14893031e-01 -3.92890692e-01
-9.46415514e-02 6.53896570e-01 5.02334416e-01 -1.21354222... | [7.018374919891357, 5.725513458251953] |
18c0224d-3fc7-4d7d-9a58-33a00c2aa51a | the-effect-of-heterogeneous-data-for | 1811.12254 | null | http://arxiv.org/abs/1811.12254v1 | http://arxiv.org/pdf/1811.12254v1.pdf | The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech | Speech datasets for identifying Alzheimer's disease (AD) are generally
restricted to participants performing a single task, e.g. describing an image
shown to them. As a result, models trained on linguistic features derived from
such datasets may not be generalizable across tasks. Building on prior work
demonstrating th... | ['Jekaterina Novikova', 'Frank Rudzicz', 'Aparna Balagopalan', 'Marzyeh Ghassemi'] | 2018-11-29 | null | null | null | null | ['alzheimer-s-disease-detection'] | ['medical'] | [ 3.93328071e-01 4.04781163e-01 1.16084710e-01 -9.81632531e-01
-1.18919134e+00 -4.13805753e-01 9.02170897e-01 2.06120044e-01
-7.05600381e-01 3.55333030e-01 8.32569182e-01 -1.44199416e-01
-4.99168076e-02 -1.99506834e-01 -2.93435931e-01 -1.26623064e-01
1.15263369e-02 5.60594261e-01 -1.59979914e-04 1.20325848... | [11.088162422180176, 1.4616367816925049] |
7308487c-a128-4e84-8719-41f3d7625e2c | video-salient-object-detection-using | 1708.01447 | null | http://arxiv.org/abs/1708.01447v3 | http://arxiv.org/pdf/1708.01447v3.pdf | Video Salient Object Detection Using Spatiotemporal Deep Features | This paper presents a method for detecting salient objects in videos where
temporal information in addition to spatial information is fully taken into
account. Following recent reports on the advantage of deep features over
conventional hand-crafted features, we propose a new set of SpatioTemporal Deep
(STD) features t... | ['Trung-Nghia Le', 'Akihiro Sugimoto'] | 2017-08-04 | null | null | null | null | ['video-salient-object-detection'] | ['computer-vision'] | [ 3.92622948e-01 -4.96940285e-01 -3.62449557e-01 -4.64864075e-01
-6.87117040e-01 -3.69982392e-01 6.05414450e-01 3.28096092e-01
-5.20970941e-01 6.35679305e-01 2.65444756e-01 3.47330153e-01
-1.85531564e-02 -6.17553055e-01 -8.68860483e-01 -4.40898120e-01
-2.71258146e-01 -3.14263701e-01 1.29953754e+00 -5.75664602... | [9.662165641784668, -0.2765635848045349] |
2f2d7a27-196d-44e6-b26e-bc11e358317b | rethinking-document-level-relation-extraction | 2306.08953 | null | https://arxiv.org/abs/2306.08953v1 | https://arxiv.org/pdf/2306.08953v1.pdf | Rethinking Document-Level Relation Extraction: A Reality Check | Recently, numerous efforts have continued to push up performance boundaries of document-level relation extraction (DocRE) and have claimed significant progress in DocRE. In this paper, we do not aim at proposing a novel model for DocRE. Instead, we take a closer look at the field to see if these performance gains are a... | ['Min Zhang', 'Shuai Zhang', 'Yequan Wang', 'Jing Li'] | 2023-06-15 | null | null | null | null | ['document-level-relation-extraction', 'relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-1.00903884e-02 3.21529776e-01 -3.51593107e-01 -2.35491827e-01
-7.80867934e-01 -9.64541614e-01 6.17611051e-01 3.59385431e-01
-4.71608698e-01 7.01022685e-01 2.35802144e-01 -8.24505985e-01
-1.70636281e-01 -5.88991106e-01 -4.53475088e-01 1.07529595e-01
-7.72621110e-02 4.41065878e-01 4.05210823e-01 -1.36273980... | [9.376128196716309, 8.71340274810791] |
530e3b4b-04e4-4b9b-80d9-8a55f7c286f5 | spatio-temporal-deepkriging-for-interpolation | 2306.11472 | null | https://arxiv.org/abs/2306.11472v1 | https://arxiv.org/pdf/2306.11472v1.pdf | Spatio-temporal DeepKriging for Interpolation and Probabilistic Forecasting | Gaussian processes (GP) and Kriging are widely used in traditional spatio-temporal mod-elling and prediction. These techniques typically presuppose that the data are observed from a stationary GP with parametric covariance structure. However, processes in real-world applications often exhibit non-Gaussianity and nonsta... | ['Brian J Reich', 'Ying Sun', 'Pratik Nag'] | 2023-06-20 | null | null | null | null | ['imputation', 'gaussian-processes', 'imputation', 'imputation'] | ['computer-vision', 'methodology', 'miscellaneous', 'time-series'] | [-3.02772492e-01 -3.86522025e-01 9.39327255e-02 -4.62750465e-01
-8.34542453e-01 -1.07546061e-01 6.35797918e-01 2.18954295e-01
-2.58262187e-01 1.11841309e+00 6.98595271e-02 -7.17831969e-01
-3.00826281e-01 -1.34256160e+00 -1.03319323e+00 -9.88124132e-01
-3.66765052e-01 5.09170830e-01 -1.37545660e-01 2.04051390... | [6.8524556159973145, 3.1675448417663574] |
ee505e68-3de2-45bf-92bf-7c02767fc75a | plume-efficient-3d-object-detection-from | 2101.06594 | null | https://arxiv.org/abs/2101.06594v3 | https://arxiv.org/pdf/2101.06594v3.pdf | PLUMENet: Efficient 3D Object Detection from Stereo Images | 3D object detection is a key component of many robotic applications such as self-driving vehicles. While many approaches rely on expensive 3D sensors such as LiDAR to produce accurate 3D estimates, methods that exploit stereo cameras have recently shown promising results at a lower cost. Existing approaches tackle this... | ['Raquel Urtasun', 'Ming Liang', 'Rui Hu', 'Bin Yang', 'Yan Wang'] | 2021-01-17 | null | null | null | null | ['3d-object-detection-from-stereo-images'] | ['computer-vision'] | [ 8.48908648e-02 -3.62951577e-01 -1.25061512e-01 -4.26586419e-01
-7.69095182e-01 -5.95698953e-01 6.56184673e-01 8.42856467e-02
-7.24331379e-01 3.74497771e-01 -3.48164439e-01 -3.09571624e-01
1.97660491e-01 -7.86806583e-01 -7.45937347e-01 -5.06920934e-01
3.88365448e-01 9.20758069e-01 7.39188969e-01 1.30310863... | [7.839908599853516, -2.651840925216675] |
4f318ed9-9b35-457f-ab75-eb86030016e0 | s-3-track-self-supervised-tracking-with-soft | 2305.09981 | null | https://arxiv.org/abs/2305.09981v1 | https://arxiv.org/pdf/2305.09981v1.pdf | S$^3$Track: Self-supervised Tracking with Soft Assignment Flow | In this work, we study self-supervised multiple object tracking without using any video-level association labels. We propose to cast the problem of multiple object tracking as learning the frame-wise associations between detections in consecutive frames. To this end, we propose differentiable soft object assignment for... | ['Felix Heide', 'Fahim Mannan', 'Fatemeh Azimi'] | 2023-05-17 | null | null | null | null | ['multiple-object-tracking'] | ['computer-vision'] | [-6.28533363e-02 -1.99283913e-01 -3.87805223e-01 -4.27578300e-01
-7.63048112e-01 -6.18474364e-01 6.09275579e-01 2.40254119e-01
-7.41236091e-01 6.99609697e-01 -3.09814394e-01 3.73245299e-01
-3.16165179e-01 -3.41655165e-01 -9.89954829e-01 -7.49868095e-01
-2.70878345e-01 6.47288799e-01 4.46569204e-01 1.77130595... | [6.417240142822266, -2.1082136631011963] |
560761cc-12ba-4484-97eb-4ce177e14f22 | making-language-models-better-tool-learners | 2305.13068 | null | https://arxiv.org/abs/2305.13068v1 | https://arxiv.org/pdf/2305.13068v1.pdf | Making Language Models Better Tool Learners with Execution Feedback | Tools serve as pivotal interfaces that enable humans to understand and reshape the world. With the advent of foundational models, AI systems can utilize tools to expand their capabilities and interact with the world. Existing tool learning methodologies, encompassing supervised fine-tuning and prompt engineering approa... | ['Ningyu Zhang', 'Huajun Chen', 'Honghao Gui', 'Shuofei Qiao'] | 2023-05-22 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [ 2.72909440e-02 4.62741554e-02 -2.64831632e-01 -2.28711843e-01
-2.10762754e-01 -8.83821249e-01 3.93999904e-01 -1.08794212e-01
-2.01595917e-01 2.42400363e-01 -2.16307789e-01 -6.41869307e-01
-2.88234279e-02 -8.18046451e-01 -5.98784626e-01 1.84484899e-01
2.14953184e-01 2.58034289e-01 2.50982314e-01 -2.77309269... | [8.573668479919434, 7.445566654205322] |
89a32201-5f8a-4190-8a1a-5a231af87d4c | introducing-semantics-into-speech-encoders | 2211.08402 | null | https://arxiv.org/abs/2211.08402v1 | https://arxiv.org/pdf/2211.08402v1.pdf | Introducing Semantics into Speech Encoders | Recent studies find existing self-supervised speech encoders contain primarily acoustic rather than semantic information. As a result, pipelined supervised automatic speech recognition (ASR) to large language model (LLM) systems achieve state-of-the-art results on semantic spoken language tasks by utilizing rich semant... | ['Wei Wang', 'Yizhou Sun', 'Hung-Yi Lee', 'Guan-Ting Lin', 'Alexei Baevski', 'Liang-Hsuan Tseng', 'Shang-Wen Li', 'Akshat Shrivastava', 'Zhaojiang Lin', 'Suyoun Kim', 'Changhan Wang', 'Shuyan Dong', 'Derek Xu'] | 2022-11-15 | null | null | null | null | ['spoken-language-understanding', 'entity-resolution', 'intent-classification', 'slot-filling', 'spoken-language-understanding'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 3.81212920e-01 7.08423853e-01 -2.74356872e-01 -9.25288022e-01
-1.49969733e+00 -3.06069553e-01 4.54028130e-01 1.22250564e-01
-5.68642557e-01 5.32922983e-01 8.46194208e-01 -2.79461384e-01
5.55396855e-01 -3.92859608e-01 -7.87218988e-01 4.61088158e-02
1.34830400e-01 6.73375010e-01 2.69534364e-02 -1.84038073... | [14.016847610473633, 6.992152690887451] |
0977af1a-a4b2-43e8-9d1a-a8d3c91a1b11 | a-point-cloud-generative-model-via-tree | 2107.09923 | null | https://arxiv.org/abs/2107.09923v1 | https://arxiv.org/pdf/2107.09923v1.pdf | A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction | Fusing medical images and the corresponding 3D shape representation can provide complementary information and microstructure details to improve the operational performance and accuracy in brain surgery. However, compared to the substantial image data, it is almost impossible to obtain the intraoperative 3D shape inform... | ['Shuqiang Wang', 'Yong liu', 'Yanyan Shen', 'Baiying Lei', 'Bowen Hu'] | 2021-07-21 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [ 1.32199913e-01 5.75836360e-01 3.38009745e-01 -1.47187695e-01
-4.49587435e-01 -2.03477949e-01 4.58471715e-01 -2.80727953e-01
-1.10093750e-01 5.58602154e-01 -3.23614217e-02 -2.53113717e-01
-3.59785333e-02 -8.90004694e-01 -7.68245459e-01 -9.39214885e-01
2.30254129e-01 6.31271064e-01 -1.06572524e-01 -2.01959729... | [8.983933448791504, -3.587024688720703] |
b892112d-72ee-4c0c-8f7b-3c1305555cc3 | improving-multilingual-named-entity | 1707.02459 | null | http://arxiv.org/abs/1707.02459v1 | http://arxiv.org/pdf/1707.02459v1.pdf | Improving Multilingual Named Entity Recognition with Wikipedia Entity Type Mapping | The state-of-the-art named entity recognition (NER) systems are statistical
machine learning models that have strong generalization capability (i.e., can
recognize unseen entities that do not appear in training data) based on lexical
and contextual information. However, such a model could still make mistakes if
its fea... | ['Jian Ni', 'Radu Florian'] | 2017-07-08 | improving-multilingual-named-entity-1 | https://aclanthology.org/D16-1135 | https://aclanthology.org/D16-1135.pdf | emnlp-2016-11 | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-4.85182405e-01 1.22866638e-01 -1.01886854e-01 -4.58693355e-01
-7.48217523e-01 -8.56419623e-01 4.77357626e-01 4.29941028e-01
-9.40181553e-01 1.24666345e+00 -6.11261539e-02 -2.54567742e-01
3.03583443e-01 -1.00634205e+00 -7.69240975e-01 -1.51975170e-01
1.43408328e-01 5.21978915e-01 4.36515391e-01 -5.08687854... | [9.788394927978516, 9.638354301452637] |
01128f28-e2fc-43ba-940b-3f9ee851e025 | corrpus-detecting-story-inconsistencies-via | 2212.10754 | null | https://arxiv.org/abs/2212.10754v3 | https://arxiv.org/pdf/2212.10754v3.pdf | CoRRPUS: Code-based Structured Prompting for Neurosymbolic Story Understanding | Story generation and understanding -- as with all NLG/NLU tasks -- has seen a surge in neurosymbolic work. Researchers have recognized that, while large language models (LLMs) have tremendous utility, they can be augmented with symbolic means to be even better and to make up for any flaws that the neural networks might... | ['Chris Callison-Burch', 'Lara J. Martin', 'Yijiang River Dong'] | 2022-12-21 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 3.48536104e-01 7.30650783e-01 -1.18267305e-01 -4.22940999e-01
-8.27438593e-01 -6.51885092e-01 7.37978280e-01 2.42570713e-01
2.87121505e-01 6.34109497e-01 5.74816406e-01 -7.65683115e-01
-9.59893689e-02 -7.46165395e-01 -7.82647252e-01 1.56299815e-01
-2.42580716e-02 6.71460092e-01 2.76509553e-01 -6.59238994... | [11.162739753723145, 8.75307846069336] |
6639b24f-f20e-4389-96b5-fcf3fd1e386f | systematic-evaluation-of-cnn-advances-on-the | 1606.02228 | null | http://arxiv.org/abs/1606.02228v2 | http://arxiv.org/pdf/1606.02228v2.pdf | Systematic evaluation of CNN advances on the ImageNet | The paper systematically studies the impact of a range of recent advances in
CNN architectures and learning methods on the object categorization (ILSVRC)
problem. The evalution tests the influence of the following choices of the
architecture: non-linearity (ReLU, ELU, maxout, compatibility with batch
normalization), po... | ['Nikolay Sergievskiy', 'Jiri Matas', 'Dmytro Mishkin'] | 2016-06-07 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 3.48127820e-02 -2.19424181e-02 1.19487062e-01 -4.13277894e-01
1.82945311e-01 -5.34011483e-01 8.43122065e-01 3.75882566e-01
-1.07484281e+00 6.29418910e-01 1.72680438e-01 -4.78450477e-01
-5.35003960e-01 -5.42927742e-01 -5.12119114e-01 -7.01832891e-01
-2.99340874e-01 -2.05059811e-01 5.66058517e-01 -1.98974848... | [8.639728546142578, 2.907447338104248] |
d2209a33-f2b9-4df0-9438-5cfbfb1a046b | variational-hierarchical-dialog-autoencoder | 2001.08604 | null | https://arxiv.org/abs/2001.08604v3 | https://arxiv.org/pdf/2001.08604v3.pdf | Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation | Recent works have shown that generative data augmentation, where synthetic samples generated from deep generative models complement the training dataset, benefit NLP tasks. In this work, we extend this approach to the task of dialog state tracking for goal-oriented dialogs. Due to the inherent hierarchical structure of... | ['Sang-goo Lee', 'Walter Chang', 'Kang Min Yoo', 'Franck Dernoncourt', 'Trung Bui', 'Hanbit Lee'] | 2020-01-23 | null | https://aclanthology.org/2020.emnlp-main.274 | https://aclanthology.org/2020.emnlp-main.274.pdf | emnlp-2020-11 | ['user-simulation'] | ['natural-language-processing'] | [-2.55422413e-01 9.00710046e-01 -5.69834560e-03 -7.56243110e-01
-8.33367825e-01 -7.57837713e-01 9.37064886e-01 -3.56447190e-01
1.74632743e-01 7.55182683e-01 8.84105861e-01 -1.53985083e-01
1.35617897e-01 -5.99489033e-01 -3.63019317e-01 -5.74673414e-01
1.58568487e-01 1.10943699e+00 -6.09599389e-02 -7.45187759... | [12.810866355895996, 8.126664161682129] |
d7e8b046-cf75-4599-ad5a-4088ba33e865 | human-activity-recognition-using-self | 2304.14912 | null | https://arxiv.org/abs/2304.14912v1 | https://arxiv.org/pdf/2304.14912v1.pdf | Human Activity Recognition Using Self-Supervised Representations of Wearable Data | Automated and accurate human activity recognition (HAR) using body-worn sensors enables practical and cost efficient remote monitoring of Activity of DailyLiving (ADL), which are shown to provide clinical insights across multiple therapeutic areas. Development of accurate algorithms for human activity recognition(HAR) ... | ['Niranjan Sridhar', 'Maximilien Burq'] | 2023-04-26 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 5.90000093e-01 2.24194191e-02 -7.48160601e-01 -4.23358262e-01
-1.04907906e+00 -1.63189188e-01 4.00586426e-01 4.46338564e-01
-6.52201056e-01 8.99844587e-01 5.75191081e-01 -2.35197261e-01
-1.93554893e-01 -4.43494380e-01 -4.23601180e-01 -5.71152151e-01
-4.05539840e-01 3.25561702e-01 1.92678301e-03 2.04789653... | [7.428934574127197, 0.7530152797698975] |
330b9415-6968-4b30-b4c6-414d11baa42f | mimo-is-all-you-need-a-strong-multi-in-multi | 2212.04655 | null | https://arxiv.org/abs/2212.04655v3 | https://arxiv.org/pdf/2212.04655v3.pdf | MIMO Is All You Need : A Strong Multi-In-Multi-Out Baseline for Video Prediction | The mainstream of the existing approaches for video prediction builds up their models based on a Single-In-Single-Out (SISO) architecture, which takes the current frame as input to predict the next frame in a recursive manner. This way often leads to severe performance degradation when they try to extrapolate a longer ... | ['Shuguang Cui', 'Xiaoguang Han', 'Xunlai Chen', 'Qian Chen', 'Chaofeng Chen', 'Yanran Li', 'Mengcheng Lan', 'Shuliang Ning'] | 2022-12-09 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 2.62039602e-01 -1.63857505e-01 -4.72862273e-01 -2.95835853e-01
-3.77656907e-01 -3.04946955e-02 4.95395690e-01 -4.35035408e-01
-9.88843888e-02 6.17042601e-01 2.59794861e-01 -5.09368122e-01
1.50706217e-01 -5.41983724e-01 -1.00116432e+00 -6.36057138e-01
-2.16471627e-01 -8.73730034e-02 8.35825324e-01 -4.55683291... | [9.02558708190918, 0.23071494698524475] |
c59884a8-567b-463d-a2f3-2f57b4d693b8 | adaptive-wind-driven-optimization-trained | 1911.08942 | null | https://arxiv.org/abs/1911.08942v1 | https://arxiv.org/pdf/1911.08942v1.pdf | Adaptive Wind Driven Optimization Trained Artificial Neural Networks | This paper presents the application of a newly developed nature-inspired metaheuristic optimization method, namely the Adaptive Wind Driven Optimization (AWDO), to the training of feedforward artificial neural networks (NN) and presents a discussion into the future research of AWDO implementation in Deep Learning (DL).... | ['Zikri Bayraktar'] | 2019-11-20 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [ 4.32572290e-02 -1.84595987e-01 2.37505585e-01 -4.63698417e-01
7.93445885e-01 -2.26444483e-01 3.35060060e-01 -1.77821234e-01
-7.76962876e-01 1.34653592e+00 -1.57398880e-01 -6.18946970e-01
-7.87052751e-01 -9.59877729e-01 -2.51114190e-01 -1.11791778e+00
-4.99416366e-02 5.57749212e-01 -2.89433300e-01 -7.78442502... | [8.180985450744629, 3.279120683670044] |
349d1d26-ad02-4927-9c1f-a25e90c57cbb | deformable-medical-image-registration-setting | null | null | https://www.ncbi.nlm.nih.gov/pubmed/21568711 | https://pdfs.semanticscholar.org/0c5f/277d357e3667b18b5420fd660f221c935fcc.pdf | Deformable medical image registration: setting the state of the art with discrete methods | This review introduces a novel deformable image registration paradigm that exploits Markov random field formulation and powerful discrete optimization algorithms. We express deformable registration as a minimal cost graph problem, where nodes correspond to the deformation grid, a node's connectivity corresponds to regu... | ['Paragios N.', 'Komodakis N.', 'Sotiras A.', 'Glocker B.'] | 2011-08-15 | null | null | null | annual-review-of-biomedical-engineering-2011 | ['deformable-medical-image-registration', 'birl-cima'] | ['medical', 'medical'] | [ 4.24686730e-01 1.04842521e-01 -2.85927087e-01 -2.39134967e-01
-8.10838342e-01 -5.26105464e-01 3.53578240e-01 4.43305999e-01
-5.96306562e-01 7.08483279e-01 -8.10146332e-02 -9.04968604e-02
-5.35194993e-01 -6.01653039e-01 -3.30886632e-01 -1.00145769e+00
-2.18369558e-01 6.75865591e-01 1.67931691e-01 -3.88779104... | [13.993165016174316, -2.671675443649292] |
891021b4-71de-48e9-8fb3-e78bad3a75ec | idn-sum-a-new-dataset-for-interactive-digital | null | null | https://aclanthology.org/2022.creativesumm-29.1 | https://aclanthology.org/2022.creativesumm-29.1.pdf | IDN-Sum: A New Dataset for Interactive Digital Narrative Extractive Text Summarisation | Summarizing Interactive Digital Narratives (IDN) presents some unique challenges to existing text summarization models especially around capturing interactive elements in addition to important plot points. In this paper, we describe the first IDN dataset (IDN-Sum) designed specifically for training and testing IDN text... | ['David E. Millard', 'Stuart E. Middleton', 'Ashwathy T. Revi'] | null | null | https://aclanthology.org/2022.creativesumm-1.1 | https://aclanthology.org/2022.creativesumm-1.1.pdf | coling-creativesumm-2022-10 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 6.63374901e-01 4.90452409e-01 -4.33068961e-01 -1.67192239e-02
-1.30875969e+00 -1.02354360e+00 1.09181786e+00 6.40103281e-01
-6.63309395e-02 7.82153010e-01 1.62544560e+00 3.17839120e-04
-3.17777812e-01 -3.44226539e-01 -2.60619938e-01 2.55136967e-01
-1.61968283e-02 6.99424088e-01 2.07821608e-01 -4.65762675... | [12.274041175842285, 9.494693756103516] |
e06022eb-d9ef-417a-96e7-d71ce94c0919 | deep-hurdle-networks-for-zero-inflated-multi | 2010.16040 | null | https://arxiv.org/abs/2010.16040v1 | https://arxiv.org/pdf/2010.16040v1.pdf | Deep Hurdle Networks for Zero-Inflated Multi-Target Regression: Application to Multiple Species Abundance Estimation | A key problem in computational sustainability is to understand the distribution of species across landscapes over time. This question gives rise to challenging large-scale prediction problems since (i) hundreds of species have to be simultaneously modeled and (ii) the survey data are usually inflated with zeros due to ... | ['Carla P. Gomes', 'Katherine Mills', 'Malin Pinsky', 'Michelle Stuart', 'Andrew Allyn', 'Di Chen', 'Jae Hee Lee', 'Junwen Bai', 'Shufeng Kong'] | 2020-10-30 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 1.81253940e-01 -4.16060388e-01 -2.94071794e-01 -1.67019367e-01
-6.54852033e-01 -5.26646435e-01 5.99763274e-01 3.00441295e-01
-6.36007071e-01 8.42240036e-01 1.60003260e-01 -4.52355683e-01
-2.96486467e-01 -1.04333830e+00 -9.75556254e-01 -7.80561090e-01
-2.27107376e-01 4.96334165e-01 1.41613200e-01 -1.76162347... | [8.013596534729004, 4.043440341949463] |
52e58230-2b24-4eac-a476-aaedf669e503 | unsupervised-superpixel-generation-using-edge | 2211.15474 | null | https://arxiv.org/abs/2211.15474v2 | https://arxiv.org/pdf/2211.15474v2.pdf | Unsupervised Superpixel Generation using Edge-Sparse Embedding | Partitioning an image into superpixels based on the similarity of pixels with respect to features such as colour or spatial location can significantly reduce data complexity and improve subsequent image processing tasks. Initial algorithms for unsupervised superpixel generation solely relied on local cues without prior... | ['Ender Konukoglu', 'Tianfei Zhou', 'Gustav Bredell', 'Jakob Geusen'] | 2022-11-28 | null | null | null | null | ['superpixels'] | ['computer-vision'] | [ 7.39631772e-01 2.46093497e-01 1.79922834e-01 -4.96374220e-01
-5.75250626e-01 -3.97988915e-01 3.95702392e-01 2.26096317e-01
-7.56432176e-01 6.39959693e-01 -4.04544584e-02 4.11445089e-02
-1.50606595e-02 -7.72162437e-01 -8.84045124e-01 -1.10304594e+00
4.58462872e-02 3.99782002e-01 4.97239530e-01 3.29334676... | [9.601276397705078, 0.4427917003631592] |
de4c9394-2119-46e0-9b89-d88c67fc5ea7 | avatargen-a-3d-generative-model-for | 2208.00561 | null | https://arxiv.org/abs/2208.00561v1 | https://arxiv.org/pdf/2208.00561v1.pdf | AvatarGen: a 3D Generative Model for Animatable Human Avatars | Unsupervised generation of clothed virtual humans with various appearance and animatable poses is important for creating 3D human avatars and other AR/VR applications. Existing methods are either limited to rigid object modeling, or not generative and thus unable to synthesize high-quality virtual humans and animate th... | ['Jiashi Feng', 'Xinchao Wang', 'Zhongcong Xu', 'Guoxian Song', 'Yichun Shi', 'Hongyi Xu', 'Dingdong Yang', 'Zihang Jiang', 'Jianfeng Zhang'] | 2022-08-01 | null | null | null | null | ['3d-human-reconstruction'] | ['computer-vision'] | [ 1.84109032e-01 4.39708501e-01 2.11750448e-01 -7.63136074e-02
-4.27492380e-01 -7.19931960e-01 5.61193764e-01 -7.44186878e-01
3.83096278e-01 5.70504487e-01 2.02912807e-01 4.63546030e-02
5.54792345e-01 -9.42668438e-01 -8.30532789e-01 -6.58454657e-01
4.36213344e-01 7.03144014e-01 -1.25790656e-01 -5.16874313... | [12.049690246582031, -0.6578266620635986] |
df109661-f845-426d-a8e3-59a497f3c1a0 | edge-directionality-improves-learning-on | 2305.10498 | null | https://arxiv.org/abs/2305.10498v1 | https://arxiv.org/pdf/2305.10498v1.pdf | Edge Directionality Improves Learning on Heterophilic Graphs | Graph Neural Networks (GNNs) have become the de-facto standard tool for modeling relational data. However, while many real-world graphs are directed, the majority of today's GNN models discard this information altogether by simply making the graph undirected. The reasons for this are historical: 1) many early variants ... | ['Michael Bronstein', 'Stephan Günnemann', 'Fabrizio Frasca', 'Francesco Di Giovanni', 'Bertrand Charpentier', 'Emanuele Rossi'] | 2023-05-17 | null | null | null | null | ['node-classification-on-non-homophilic'] | ['graphs'] | [-9.81522426e-02 4.99082178e-01 -4.24759537e-01 -2.82258689e-01
3.74678791e-01 -5.85903943e-01 1.01032770e+00 2.52946466e-01
2.86649968e-02 6.69812977e-01 3.63774300e-01 -8.03584397e-01
-4.38339114e-01 -1.57365155e+00 -9.10199106e-01 -5.54354191e-01
-6.44039154e-01 9.88768160e-01 3.76198590e-01 -5.12444198... | [6.9644012451171875, 6.222329616546631] |
36f96dd1-2ee1-4ca9-94db-bc7c29c38b6c | domain-adaptation-using-silver-standard | 2307.03872 | null | https://arxiv.org/abs/2307.03872v1 | https://arxiv.org/pdf/2307.03872v1.pdf | Domain Adaptation using Silver Standard Labels for Ki-67 Scoring in Digital Pathology: A Step Closer to Widescale Deployment | Deep learning systems have been proposed to improve the objectivity and efficiency of Ki- 67 PI scoring. The challenge is that while very accurate, deep learning techniques suffer from reduced performance when applied to out-of-domain data. This is a critical challenge for clinical translation, as models are typically ... | ['April Khademi', 'Susan Done', 'Dimitrios Androutsos', 'Fei-Fei Liu', 'Wei Shi', 'Anthony Fyles', 'Melanie Dawe', 'Seyed Hossein Mirjahanmardi', 'Ngoc-Nhu Jennifer Nguyen', 'Amanda Dy'] | 2023-07-08 | null | null | null | null | ['domain-adaptation'] | ['methodology'] | [ 6.09951913e-01 -6.44891113e-02 -5.13018906e-01 -3.50287437e-01
-1.44253182e+00 -6.67581022e-01 2.75149584e-01 5.40578485e-01
-3.85265857e-01 1.00188482e+00 -1.52362175e-02 -4.11037236e-01
-2.09363267e-01 -6.90661669e-01 -6.56475663e-01 -9.44490016e-01
2.30057985e-01 9.90685940e-01 2.98812747e-01 3.75037305... | [15.176328659057617, -2.8842287063598633] |
0c897ba9-264d-4f27-baa2-7e4ff8b6c2bf | multi-person-3d-pose-estimation-from | 2212.08731 | null | https://arxiv.org/abs/2212.08731v1 | https://arxiv.org/pdf/2212.08731v1.pdf | Multi-person 3D pose estimation from unlabelled data | Its numerous applications make multi-human 3D pose estimation a remarkably impactful area of research. Nevertheless, assuming a multiple-view system composed of several regular RGB cameras, 3D multi-pose estimation presents several challenges. First of all, each person must be uniquely identified in the different views... | ['Luis J. Manso', 'George Vogiatzis', 'Pilar Bachiller', 'Daniel Rodriguez-Criado'] | 2022-12-16 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [-1.12748832e-01 1.50926067e-02 1.86090797e-01 -3.41591895e-01
-3.94321322e-01 -4.49326396e-01 3.90732139e-01 -3.16641256e-02
-4.42428380e-01 2.89001107e-01 1.02659263e-01 2.78048664e-01
1.36500314e-01 -6.01464748e-01 -7.10945666e-01 -3.90585005e-01
2.54533112e-01 8.91522527e-01 5.23045696e-02 -3.43441963... | [7.056605815887451, -1.0324230194091797] |
c311ea2b-380b-4df1-8520-f213e39907b6 | discovering-new-intents-with-deep-aligned | 2012.08987 | null | https://arxiv.org/abs/2012.08987v7 | https://arxiv.org/pdf/2012.08987v7.pdf | Discovering New Intents with Deep Aligned Clustering | Discovering new intents is a crucial task in dialogue systems. Most existing methods are limited in transferring the prior knowledge from known intents to new intents. They also have difficulties in providing high-quality supervised signals to learn clustering-friendly features for grouping unlabeled intents. In this w... | ['Rui Lyu', 'Ting-En Lin', 'Hua Xu', 'Hanlei Zhang'] | 2020-12-16 | null | null | null | null | ['text-clustering', 'open-intent-discovery', 'short-text-clustering'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 5.87759428e-02 3.39108258e-02 -3.70474577e-01 -1.02817762e+00
-8.60147834e-01 -6.25805140e-01 4.80519354e-01 -7.69619718e-02
-3.73233885e-01 5.87192237e-01 4.50347632e-01 9.59840119e-02
2.10982963e-01 -2.44345874e-01 -2.47271046e-01 -6.23995781e-01
1.17519379e-01 7.44668663e-01 -2.10202694e-01 -1.16871156... | [12.412796020507812, 7.468223571777344] |
bc6c523a-4879-41f8-be9e-67483ec92fe2 | dynamic-clustering-transformer-network-for | 2306.08073 | null | https://arxiv.org/abs/2306.08073v1 | https://arxiv.org/pdf/2306.08073v1.pdf | Dynamic Clustering Transformer Network for Point Cloud Segmentation | Point cloud segmentation is one of the most important tasks in computer vision with widespread scientific, industrial, and commercial applications. The research thereof has resulted in many breakthroughs in 3D object and scene understanding. Previous methods typically utilized hierarchical architectures for feature rep... | ['Jonathan Li', 'Linlin Xu', 'Jing Du', 'Dilong Li', 'Kyle Yilin Gao', 'Jun Zhou', 'Dening Lu'] | 2023-05-30 | null | null | null | null | ['point-cloud-segmentation', 'scene-understanding', 'clustering'] | ['computer-vision', 'computer-vision', 'methodology'] | [-6.76259547e-02 -3.28817099e-01 -2.84459114e-01 -4.32908058e-01
-6.25392973e-01 -2.45011151e-01 3.96738350e-01 6.98867664e-02
-2.58489579e-01 2.35948026e-01 -4.48382109e-01 -2.31588274e-01
-2.44010270e-01 -1.03954208e+00 -6.90050066e-01 -5.60149848e-01
-1.48430854e-01 7.60762691e-01 5.98642945e-01 -2.25876253... | [7.90795373916626, -3.1311264038085938] |
0004a75e-29f9-4f2c-b050-aa6351312e62 | a-simple-framework-for-open-vocabulary | 2303.08131 | null | https://arxiv.org/abs/2303.08131v3 | https://arxiv.org/pdf/2303.08131v3.pdf | A Simple Framework for Open-Vocabulary Segmentation and Detection | We present OpenSeeD, a simple Open-vocabulary Segmentation and Detection framework that jointly learns from different segmentation and detection datasets. To bridge the gap of vocabulary and annotation granularity, we first introduce a pre-trained text encoder to encode all the visual concepts in two tasks and learn a ... | ['Lei Zhang', 'Jianwei Yang', 'Jianfeng Gao', 'Chunyuan Li', 'Shilong Liu', 'Xueyan Zou', 'Feng Li', 'Hao Zhang'] | 2023-03-14 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 2.56299853e-01 1.85912266e-01 -1.57390833e-01 -2.18929201e-01
-1.10172343e+00 -9.18998957e-01 6.84852064e-01 -1.41185150e-01
-5.30222774e-01 3.67186457e-01 -1.41744483e-02 -4.19179797e-01
3.43205780e-01 -7.17908561e-01 -7.53515840e-01 -4.97613162e-01
9.50274169e-02 7.34531045e-01 6.13114238e-01 -9.43149105... | [9.658027648925781, 0.6628656983375549] |
38bb8e5d-d61b-4731-8dd5-84900a147cb1 | feature-relevance-analysis-to-explain-concept | 2301.08453 | null | https://arxiv.org/abs/2301.08453v1 | https://arxiv.org/pdf/2301.08453v1.pdf | Feature Relevance Analysis to Explain Concept Drift -- A Case Study in Human Activity Recognition | This article studies how to detect and explain concept drift. Human activity recognition is used as a case study together with a online batch learning situation where the quality of the labels used in the model updating process starts to decrease. Drift detection is based on identifying a set of features having the lar... | ['Juha Röning', 'Pekka Siirtola'] | 2023-01-20 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 2.76923716e-01 6.91574300e-03 -4.39581990e-01 -3.16576511e-01
1.42669395e-01 -3.07886630e-01 6.07964396e-01 7.45848298e-01
-3.35344136e-01 8.76593053e-01 -2.18358323e-01 -6.44877693e-03
-5.09090185e-01 -3.86554837e-01 -5.98763525e-01 -8.22414756e-01
-1.50711596e-01 5.42805493e-01 3.55866462e-01 -3.94479111... | [7.449616432189941, 3.2769885063171387] |
ab9cf34a-4ae1-4638-be13-81ce687584ac | teach-me-how-to-learn-a-perspective-review | 2307.03853 | null | https://arxiv.org/abs/2307.03853v1 | https://arxiv.org/pdf/2307.03853v1.pdf | Teach Me How to Learn: A Perspective Review towards User-centered Neuro-symbolic Learning for Robotic Surgical Systems | Recent advances in machine learning models allowed robots to identify objects on a perceptual nonsymbolic level (e.g., through sensor fusion and natural language understanding). However, these primarily black-box learning models still lack interpretation and transferability and require high data and computational deman... | ['Antonio Krüger', 'Frank Kirchner', 'Michael Feld', 'Niko Kleer', 'Bilal Mahdy', 'Amr Gomaa'] | 2023-07-07 | null | null | null | null | ['sensor-fusion', 'natural-language-understanding'] | ['computer-vision', 'natural-language-processing'] | [ 3.28122601e-02 9.93074477e-01 -6.12672687e-01 -1.68896824e-01
-5.00697017e-01 -5.96269667e-01 1.72935545e-01 4.36906815e-01
-4.24525201e-01 5.20838797e-01 -2.18164504e-01 -4.41028178e-01
-6.24819040e-01 -4.62302566e-01 -8.02655160e-01 -5.44562399e-01
-2.76458323e-01 5.79690158e-01 -5.75011596e-02 -4.53825206... | [14.040669441223145, -3.403916597366333] |
8ed86ca6-429a-49f1-a5e0-875ecb2ae98a | uncertainty-dtw-for-time-series-and-sequences | 2211.00005 | null | https://arxiv.org/abs/2211.00005v1 | https://arxiv.org/pdf/2211.00005v1.pdf | Uncertainty-DTW for Time Series and Sequences | Dynamic Time Warping (DTW) is used for matching pairs of sequences and celebrated in applications such as forecasting the evolution of time series, clustering time series or even matching sequence pairs in few-shot action recognition. The transportation plan of DTW contains a set of paths; each path matches frames betw... | ['Piotr Koniusz', 'Lei Wang'] | 2022-10-30 | null | null | null | null | ['few-shot-action-recognition', 'time-series-clustering'] | ['computer-vision', 'time-series'] | [ 1.85876355e-01 -9.47528258e-02 -3.95290926e-02 -3.09052050e-01
-8.56838644e-01 -3.45823377e-01 7.06380248e-01 -1.59795478e-01
-3.16348791e-01 5.00516772e-01 3.96509796e-01 2.80771911e-01
-6.85741305e-01 -5.89133263e-01 -6.77781582e-01 -8.62126529e-01
-6.24168396e-01 5.35490870e-01 4.08141255e-01 -1.46128759... | [7.419969081878662, 3.4414634704589844] |
3d33eb28-4097-4e99-bf63-98e3636f6e1b | learning-graph-enhanced-commander-executor | 2302.04094 | null | https://arxiv.org/abs/2302.04094v1 | https://arxiv.org/pdf/2302.04094v1.pdf | Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation | This paper investigates the multi-agent navigation problem, which requires multiple agents to reach the target goals in a limited time. Multi-agent reinforcement learning (MARL) has shown promising results for solving this issue. However, it is inefficient for MARL to directly explore the (nearly) optimal policy in the... | ['Yu Wang', 'Huazhong Yang', 'Wei-Wei Tu', 'Chao Yu', 'Yuxiang Yang', 'Yiwen Sun', 'Shiyu Huang', 'Xinyi Yang'] | 2023-02-08 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-2.14326575e-01 2.54033029e-01 -2.20030084e-01 2.72466838e-01
-8.30827773e-01 -4.94481117e-01 6.32604003e-01 1.51397675e-01
-5.67098200e-01 9.99392211e-01 8.36137533e-02 -4.55722958e-01
-4.16705638e-01 -9.94999588e-01 -7.48330355e-01 -7.75659084e-01
-5.62178671e-01 9.40707743e-01 2.59185374e-01 -5.55596650... | [3.8151705265045166, 1.7958526611328125] |
c4ba690d-7a1e-4673-b795-f119a63ba8fe | capturing-localized-image-artifacts-through-a | 1711.04945 | null | http://arxiv.org/abs/1711.04945v1 | http://arxiv.org/pdf/1711.04945v1.pdf | Capturing Localized Image Artifacts through a CNN-based Hyper-image Representation | Training deep CNNs to capture localized image artifacts on a relatively small
dataset is a challenging task. With enough images at hand, one can hope that a
deep CNN characterizes localized artifacts over the entire data and their
effect on the output. However, on smaller datasets, such deep CNNs may overfit
and shallo... | ['Parag Shridhar Chandakkar', 'Baoxin Li'] | 2017-11-14 | null | null | null | null | ['image-quality-estimation'] | ['computer-vision'] | [ 4.78555471e-01 -1.52185351e-01 2.26750627e-01 -9.28528905e-02
-9.97717857e-01 -3.70377481e-01 3.67575198e-01 1.88912243e-01
-3.60151343e-02 5.04813373e-01 1.21679366e-01 6.04564324e-02
2.27513716e-01 -7.84842193e-01 -1.31793559e+00 -8.76887858e-01
2.21356809e-01 -2.75896490e-01 4.55065817e-01 -6.42368272... | [11.511083602905273, -1.8133447170257568] |
f1d04e16-c69e-45c9-b3f5-ffc2a4d0fb73 | explicit-syntactic-guidance-for-neural-text | 2306.11485 | null | https://arxiv.org/abs/2306.11485v2 | https://arxiv.org/pdf/2306.11485v2.pdf | Explicit Syntactic Guidance for Neural Text Generation | Most existing text generation models follow the sequence-to-sequence paradigm. Generative Grammar suggests that humans generate natural language texts by learning language grammar. We propose a syntax-guided generation schema, which generates the sequence guided by a constituency parse tree in a top-down direction. The... | ['Yongjing Yin', 'Yue Zhang', 'Shuming Shi', 'Wei Bi', 'Jianhao Yan', 'Leyang Cui', 'Yafu Li'] | 2023-06-20 | null | null | null | null | ['paraphrase-generation', 'text-generation', 'machine-translation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 6.95005059e-01 5.16376913e-01 -1.42641783e-01 -5.09923041e-01
-8.08406234e-01 -7.59455144e-01 8.28981221e-01 -7.03420863e-03
1.63843334e-01 9.72777486e-01 9.24629748e-01 -7.56883085e-01
3.01626086e-01 -1.13408518e+00 -6.25759184e-01 -3.96730810e-01
3.67126077e-01 7.33229756e-01 -2.39521340e-01 -4.55734640... | [11.719158172607422, 9.001250267028809] |
826511c3-1d52-487d-90e8-5627bf2fc264 | efficient-conditional-gan-transfer-with | 2102.06696 | null | https://arxiv.org/abs/2102.06696v2 | https://arxiv.org/pdf/2102.06696v2.pdf | Efficient Conditional GAN Transfer with Knowledge Propagation across Classes | Generative adversarial networks (GANs) have shown impressive results in both unconditional and conditional image generation. In recent literature, it is shown that pre-trained GANs, on a different dataset, can be transferred to improve the image generation from a small target data. The same, however, has not been well-... | ['Luc van Gool', 'Ajad Chhatkuli', 'Danda Pani Paudel', 'Zhiwu Huang', 'Mohamad Shahbazi'] | 2021-02-12 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Shahbazi_Efficient_Conditional_GAN_Transfer_With_Knowledge_Propagation_Across_Classes_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Shahbazi_Efficient_Conditional_GAN_Transfer_With_Knowledge_Propagation_Across_Classes_CVPR_2021_paper.pdf | cvpr-2021-1 | ['conditional-image-generation'] | ['computer-vision'] | [ 4.16248888e-01 2.87899494e-01 -1.64119169e-01 -2.08230987e-01
-6.86294198e-01 -5.84083021e-01 7.00639307e-01 -1.81393087e-01
-2.47393742e-01 1.27613461e+00 9.48824957e-02 9.87397805e-02
2.14212656e-01 -1.14793277e+00 -9.27442670e-01 -1.31996381e+00
4.29774612e-01 4.53961730e-01 6.70472160e-02 -2.03987658... | [11.625799179077148, -0.2488868087530136] |
028a55e5-ebef-4396-a016-d35409458c87 | learning-to-model-multimodal-semantic | 2211.07289 | null | https://arxiv.org/abs/2211.07289v1 | https://arxiv.org/pdf/2211.07289v1.pdf | Learning to Model Multimodal Semantic Alignment for Story Visualization | Story visualization aims to generate a sequence of images to narrate each sentence in a multi-sentence story, where the images should be realistic and keep global consistency across dynamic scenes and characters. Current works face the problem of semantic misalignment because of their fixed architecture and diversity o... | ['Thomas Lukasiewicz', 'Bowen Li'] | 2022-11-14 | null | null | null | null | ['story-visualization'] | ['computer-vision'] | [ 5.00668526e-01 2.08636925e-01 1.97694421e-01 -4.11558062e-01
-5.85860312e-01 -6.02073908e-01 8.65786731e-01 -4.34240043e-01
6.09653443e-02 6.69338286e-01 3.78290802e-01 2.63193429e-01
2.29594082e-01 -7.82595098e-01 -7.86605477e-01 -6.11161947e-01
7.12202907e-01 4.03647095e-01 4.22573984e-01 -3.28365415... | [11.176928520202637, 0.4622081518173218] |
976ccc4e-106a-4820-910f-d25795f45428 | towards-hiding-adversarial-examples-from | 1812.02843 | null | https://arxiv.org/abs/1812.02843v2 | https://arxiv.org/pdf/1812.02843v2.pdf | Fooling Network Interpretation in Image Classification | Deep neural networks have been shown to be fooled rather easily using adversarial attack algorithms. Practical methods such as adversarial patches have been shown to be extremely effective in causing misclassification. However, these patches are highlighted using standard network interpretation algorithms, thus reveali... | ['Hamed Pirsiavash', 'Vipin Pillai', 'Akshayvarun Subramanya'] | 2018-12-06 | fooling-network-interpretation-in-image | http://openaccess.thecvf.com/content_ICCV_2019/html/Subramanya_Fooling_Network_Interpretation_in_Image_Classification_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Subramanya_Fooling_Network_Interpretation_in_Image_Classification_ICCV_2019_paper.pdf | iccv-2019-10 | ['network-interpretation'] | ['computer-vision'] | [ 7.55555451e-01 9.06868815e-01 1.35426521e-01 -2.79957503e-01
-5.47688544e-01 -1.17944133e+00 4.87619609e-01 -3.39417815e-01
-1.47763163e-01 7.17440546e-01 -3.29530478e-01 -8.23977590e-01
2.43413046e-01 -8.65157366e-01 -1.27489471e+00 -6.58002913e-01
-1.22348122e-01 3.80419016e-01 1.50227189e-01 -4.46017608... | [5.732962608337402, 7.822439670562744] |
96602cf3-d1dc-4faa-9846-710690724cbe | nuno-a-general-framework-for-learning | 2305.18694 | null | https://arxiv.org/abs/2305.18694v2 | https://arxiv.org/pdf/2305.18694v2.pdf | NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data | The neural operator has emerged as a powerful tool in learning mappings between function spaces in PDEs. However, when faced with real-world physical data, which are often highly non-uniformly distributed, it is challenging to use mesh-based techniques such as the FFT. To address this, we introduce the Non-Uniform Neur... | ['Jun Zhu', 'Ze Cheng', 'Hang Su', 'Chengyang Ying', 'Zhongkai Hao', 'Songming Liu'] | 2023-05-30 | null | null | null | null | ['operator-learning'] | ['miscellaneous'] | [-2.38446400e-01 -4.74785298e-01 7.61291683e-02 2.37191096e-01
-7.43548572e-01 -4.51690584e-01 5.38695678e-02 2.26989537e-01
-2.37007454e-01 1.02860308e+00 -6.50282651e-02 -6.13746464e-01
-8.58122557e-02 -8.78730178e-01 -7.03218281e-01 -5.93745708e-01
-4.93114889e-01 4.58068013e-01 -4.21498157e-02 2.37018277... | [6.509576797485352, 3.408738136291504] |
07a778e1-f29e-4bde-a25b-029c0a201f07 | a-bayesian-model-for-generative-transition | 1506.04334 | null | http://arxiv.org/abs/1506.04334v2 | http://arxiv.org/pdf/1506.04334v2.pdf | A Bayesian Model for Generative Transition-based Dependency Parsing | We propose a simple, scalable, fully generative model for transition-based
dependency parsing with high accuracy. The model, parameterized by Hierarchical
Pitman-Yor Processes, overcomes the limitations of previous generative models
by allowing fast and accurate inference. We propose an efficient decoding
algorithm bas... | ['Jan Buys', 'Phil Blunsom'] | 2015-06-13 | a-bayesian-model-for-generative-transition-1 | https://aclanthology.org/W15-2108 | https://aclanthology.org/W15-2108.pdf | ws-2015-8 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [ 2.24076211e-01 6.76047206e-01 -7.81571418e-02 -6.09215021e-01
-1.55825174e+00 -6.68183446e-01 7.61223018e-01 9.73847806e-02
-2.94890046e-01 9.72672999e-01 6.49917006e-01 -6.93515122e-01
2.33034521e-01 -9.32216883e-01 -7.49540031e-01 -7.51226962e-01
1.08689800e-01 1.30511057e+00 3.60307425e-01 -1.58093825... | [10.388066291809082, 9.625964164733887] |
f9dd149b-9aad-4732-bb3b-7aef34f364d1 | learning-embedding-of-3d-models-with-quadric | 1907.10250 | null | https://arxiv.org/abs/1907.10250v1 | https://arxiv.org/pdf/1907.10250v1.pdf | Learning Embedding of 3D models with Quadric Loss | Sharp features such as edges and corners play an important role in the perception of 3D models. In order to capture them better, we propose quadric loss, a point-surface loss function, which minimizes the quadric error between the reconstructed points and the input surface. Computation of Quadric loss is easy, efficien... | ['Sung-Eui Yoon', 'Nitin Agarwal', 'M Gopi'] | 2019-07-24 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [-2.78702229e-01 4.57543842e-02 -1.05748810e-01 -2.44661033e-01
-7.93358922e-01 -1.84997067e-01 6.01374447e-01 2.35132668e-02
-3.39508168e-02 3.61680329e-01 -1.01471663e-01 7.73407072e-02
-1.52534455e-01 -8.82649839e-01 -1.01826155e+00 -1.91103190e-01
-2.78939337e-01 5.55403650e-01 4.83521312e-01 -2.33089045... | [8.528017044067383, -3.539512872695923] |
255b9171-e802-41ee-a113-582b69d5a7d1 | improving-sentence-similarity-estimation-for | 2302.12490 | null | https://arxiv.org/abs/2302.12490v1 | https://arxiv.org/pdf/2302.12490v1.pdf | Improving Sentence Similarity Estimation for Unsupervised Extractive Summarization | Unsupervised extractive summarization aims to extract salient sentences from a document as the summary without labeled data. Recent literatures mostly research how to leverage sentence similarity to rank sentences in the order of salience. However, sentence similarity estimation using pre-trained language models mostly... | ['Sujian Li', 'Wenjie Li', 'Ruifeng Yuan', 'Shichao Sun'] | 2023-02-24 | null | null | null | null | ['unsupervised-extractive-summarization', 'extractive-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.89021683e-01 2.28734180e-01 -5.26749790e-01 -4.99619335e-01
-1.00304961e+00 -3.19855303e-01 5.83650410e-01 8.42012048e-01
-4.92548615e-01 6.78440213e-01 1.06237924e+00 2.93538153e-01
-2.71521658e-01 -5.11596262e-01 -3.17210346e-01 -4.78950411e-01
1.77950606e-01 -2.46293783e-01 9.24451277e-02 -1.95173562... | [12.572571754455566, 9.54356575012207] |
91d1c8e6-fa91-4aac-955b-3f33f856ef80 | processing-and-normalizing-hashtags | null | null | https://aclanthology.org/R15-1015 | https://aclanthology.org/R15-1015.pdf | Processing and Normalizing Hashtags | null | ['Thierry Declerck', 'Piroska Lendvai'] | 2015-09-01 | processing-and-normalizing-hashtags-1 | https://aclanthology.org/R15-1015 | https://aclanthology.org/R15-1015.pdf | ranlp-2015-9 | ['rumour-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.206403732299805, 3.7356035709381104] |
a0079327-7ad3-47c6-9c40-ccd8e35163b6 | xeroalign-zero-shot-cross-lingual-transformer | 2105.02472 | null | https://arxiv.org/abs/2105.02472v2 | https://arxiv.org/pdf/2105.02472v2.pdf | XeroAlign: Zero-Shot Cross-lingual Transformer Alignment | The introduction of pretrained cross-lingual language models brought decisive improvements to multilingual NLP tasks. However, the lack of labelled task data necessitates a variety of methods aiming to close the gap to high-resource languages. Zero-shot methods in particular, often use translated task data as a trainin... | ['Ignacio Iacobacci', 'Milan Gritta'] | 2021-05-06 | null | https://aclanthology.org/2021.findings-acl.32 | https://aclanthology.org/2021.findings-acl.32.pdf | findings-acl-2021-8 | ['multilingual-nlp'] | ['natural-language-processing'] | [ 1.28577694e-01 2.64059603e-01 -4.04470623e-01 -4.69168365e-01
-1.47997689e+00 -7.56769419e-01 1.00969768e+00 -1.29320785e-01
-6.34115994e-01 9.14648592e-01 4.95291680e-01 -6.95408106e-01
5.75922549e-01 -4.89336193e-01 -1.02872777e+00 -1.18506044e-01
6.88153267e-01 1.04027224e+00 -2.94304490e-01 -7.40149856... | [11.165385246276855, 9.96558952331543] |
4dc818ac-424c-4faf-b769-bba1f1a70936 | deblursr-event-based-motion-deblurring-under | 2303.08977 | null | https://arxiv.org/abs/2303.08977v1 | https://arxiv.org/pdf/2303.08977v1.pdf | DeblurSR: Event-Based Motion Deblurring Under the Spiking Representation | We present DeblurSR, a novel motion deblurring approach that converts a blurry image into a sharp video. DeblurSR utilizes event data to compensate for motion ambiguities and exploits the spiking representation to parameterize the sharp output video as a mapping from time to intensity. Our key contribution, the Spiking... | ['QiXing Huang', 'Chandrajit Bajaj', 'Chen Song'] | 2023-03-15 | null | null | null | null | ['deblurring', 'video-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 5.69309831e-01 -5.16904175e-01 3.77678454e-01 4.60251160e-02
-1.92220181e-01 -7.59821951e-01 6.48969889e-01 -5.64745009e-01
-5.50582707e-01 1.07856905e+00 4.58481580e-01 3.58317673e-01
2.58690536e-01 -6.05061173e-01 -8.94090354e-01 -8.32415283e-01
1.44655496e-01 -2.36189365e-01 6.55651867e-01 -1.07922377... | [8.737509727478027, -1.2484973669052124] |
8e415248-09cf-4a5c-9eb0-7b031b508c21 | bilinear-value-networks | 2204.13695 | null | https://arxiv.org/abs/2204.13695v3 | https://arxiv.org/pdf/2204.13695v3.pdf | Bilinear value networks | The dominant framework for off-policy multi-goal reinforcement learning involves estimating goal conditioned Q-value function. When learning to achieve multiple goals, data efficiency is intimately connected with the generalization of the Q-function to new goals. The de-facto paradigm is to approximate Q(s, a, g) using... | ['Pulkit Agrawal', 'Ge Yang', 'Zhang-Wei Hong'] | 2022-04-28 | null | null | null | null | ['multi-goal-reinforcement-learning'] | ['methodology'] | [-5.90132624e-02 -9.21132639e-02 -2.32992813e-01 -7.85396770e-02
-8.43826234e-01 -5.13225496e-01 3.46185923e-01 1.77959517e-01
-5.27446508e-01 8.49319160e-01 4.17733103e-01 -1.24170348e-01
-5.26121259e-01 -5.46123505e-01 -9.16124165e-01 -7.85343945e-01
-5.32030404e-01 4.79546100e-01 -1.35059893e-01 -5.70498645... | [4.2092742919921875, 1.9114960432052612] |
2fe62cde-7e4e-4224-b447-2b09c2bba98e | adaptive-multi-view-subspace-clustering-for | null | null | https://ui.adsabs.harvard.edu/abs/2020PaReL.130..299Y/abstract | https://ui.adsabs.harvard.edu/abs/2020PaReL.130..299Y/abstract | Adaptive multi-view subspace clustering for high-dimensional data, | With the rapid development of multimedia technologies, we frequently confront with high-dimensional data and multi-view data, which usually contain redundant features and distinct types of features. How to efficiently cluster such kinds of data is still a great challenge. Traditional multi-view subspace clustering aims... | ['Yan Fei ; Wang Xiaodong ; Zeng Zhiqiang ; Hong Chaoqun'] | 2020-02-01 | null | null | null | pattern-recognition-letters-2020-2 | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-4.58139956e-01 -9.20591354e-01 1.25320880e-02 -1.52557909e-01
-4.67721164e-01 -7.85200059e-01 2.23468721e-01 -2.33392134e-01
-2.73677021e-01 5.46183996e-02 4.78088468e-01 8.85025412e-02
-5.40312529e-01 -5.31999707e-01 -3.75173390e-02 -1.01513314e+00
1.85156748e-01 5.63712418e-01 1.01703823e-01 1.31408364... | [8.151823997497559, 4.589442729949951] |
945ae92e-6395-4412-aeb1-4e6b564d7d58 | self-move-and-other-move-quantum-categorical | 2210.04451 | null | https://arxiv.org/abs/2210.04451v1 | https://arxiv.org/pdf/2210.04451v1.pdf | Self-move and Other-move: Quantum Categorical Foundations of Japanese | The purpose of this work is to contribute toward the larger goal of creating a Quantum Natural Language Processing (QNLP) translator program. This work contributes original diagrammatic representations of the Japanese language based on prior work that accomplished on the English language based on category theory. The g... | ['Ryder Dale Walton'] | 2022-10-10 | null | null | null | null | ['culture'] | ['speech'] | [-1.40431687e-01 -7.77310319e-03 1.59052275e-02 -1.29397318e-01
-1.70204222e-01 -7.59107172e-01 8.34929407e-01 2.81126827e-01
-4.40326482e-01 7.41746068e-01 5.48606694e-01 -7.18150020e-01
1.03892334e-01 -1.00782156e+00 -1.44925252e-01 -2.32085496e-01
1.72695085e-01 3.23572129e-01 -1.41589507e-01 -1.03495908... | [10.449748992919922, 9.936261177062988] |
d354314f-fe9d-423f-9ed5-6fdc91172ed5 | tvlt-textless-vision-language-transformer | 2209.14156 | null | https://arxiv.org/abs/2209.14156v2 | https://arxiv.org/pdf/2209.14156v2.pdf | TVLT: Textless Vision-Language Transformer | In this work, we present the Textless Vision-Language Transformer (TVLT), where homogeneous transformer blocks take raw visual and audio inputs for vision-and-language representation learning with minimal modality-specific design, and do not use text-specific modules such as tokenization or automatic speech recognition... | ['Mohit Bansal', 'Yixin Nie', 'Jaemin Cho', 'Zineng Tang'] | 2022-09-28 | null | null | null | null | ['multimodal-sentiment-analysis', 'multimodal-sentiment-analysis'] | ['computer-vision', 'natural-language-processing'] | [ 2.05138221e-01 -1.11417770e-01 -3.72416556e-01 -3.66455227e-01
-1.31049335e+00 -6.32714689e-01 7.78456748e-01 1.20717995e-01
-5.48636079e-01 3.07133704e-01 3.68420869e-01 -4.83865976e-01
3.99110466e-01 -1.49954319e-01 -8.90970230e-01 -4.80336905e-01
2.87765712e-01 -3.18466611e-02 -1.10949971e-01 1.25763074... | [10.39376163482666, 1.1021649837493896] |
29b3c3f0-6d09-480f-99c0-45e7f1772807 | what-does-the-failure-to-reason-with | 2305.19597 | null | https://arxiv.org/abs/2305.19597v1 | https://arxiv.org/pdf/2305.19597v1.pdf | What does the Failure to Reason with "Respectively" in Zero/Few-Shot Settings Tell Us about Language Models? | Humans can effortlessly understand the coordinate structure of sentences such as "Niels Bohr and Kurt Cobain were born in Copenhagen and Seattle, respectively". In the context of natural language inference (NLI), we examine how language models (LMs) reason with respective readings (Gawron and Kehler, 2004) from two per... | ['Anders Søgaard', 'Daniel Hershcovich', 'Seolhwa Lee', 'Ruixiang Cui'] | 2023-05-31 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 2.62631088e-01 6.59820676e-01 -3.13981213e-02 -5.99729896e-01
-6.23496473e-01 -6.52121663e-01 1.09433687e+00 4.68022436e-01
-4.60748732e-01 9.49256361e-01 6.22502327e-01 -6.74554527e-01
-2.16289967e-01 -9.16546285e-01 -7.18516648e-01 -1.55872285e-01
2.43089750e-01 7.32881129e-01 1.57716200e-01 -4.77497876... | [10.197457313537598, 8.371746063232422] |
346ccbd4-5121-4c24-92ec-f08ec26cec42 | fea2fea-exploring-structural-feature | 2106.13061 | null | https://arxiv.org/abs/2106.13061v4 | https://arxiv.org/pdf/2106.13061v4.pdf | Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks | Structural features are important features in a geometrical graph. Although there are some correlation analysis of features based on covariance, there is no relevant research on structural feature correlation analysis with graph neural networks. In this paper, we introuduce graph feature to feature (Fea2Fea) prediction... | ['Rex Ying', 'Jiaqing Xie'] | 2021-06-24 | null | null | null | null | ['structual-feature-correlation'] | ['graphs'] | [-2.20002308e-01 3.23007107e-01 8.98050442e-02 -3.83871853e-01
3.13218981e-01 -3.55353206e-01 5.56149721e-01 4.71223086e-01
4.99859415e-02 2.47661546e-01 3.53653550e-01 -4.21854138e-01
-7.47849047e-01 -1.31782472e+00 -2.17858016e-01 -4.76157099e-01
-8.33993018e-01 2.48453692e-01 1.13253914e-01 -7.09364831... | [6.999556541442871, 6.104860782623291] |
6ad2b708-0a81-4c4f-907c-bc83f4daa973 | benchmarking-evaluation-metrics-for-code | 2211.16319 | null | https://arxiv.org/abs/2211.16319v1 | https://arxiv.org/pdf/2211.16319v1.pdf | Benchmarking Evaluation Metrics for Code-Switching Automatic Speech Recognition | Code-switching poses a number of challenges and opportunities for multilingual automatic speech recognition. In this paper, we focus on the question of robust and fair evaluation metrics. To that end, we develop a reference benchmark data set of code-switching speech recognition hypotheses with human judgments. We defi... | ['Ahmed Ali', 'Nizar Habash', 'Sunayana Sitaram', 'Hamdy Mubarak', 'Shammur Chowdhury', 'Oumnia Chellah', 'Amir Hussein', 'Injy Hamed'] | 2022-11-22 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [ 1.98997438e-01 9.58868023e-03 3.94210145e-02 -6.53759539e-01
-9.80677426e-01 -8.72031271e-01 6.76041245e-01 1.15080558e-01
-4.43199337e-01 3.81431580e-01 4.04851377e-01 -7.21396923e-01
1.27944410e-01 7.54361600e-02 -2.66972691e-01 -2.84276128e-01
2.65066892e-01 4.37922627e-01 2.04359040e-01 -4.75952983... | [14.369465827941895, 7.030169486999512] |
13dad4df-317c-49aa-afde-d41acad70026 | segment-phrase-table-for-semantic | 1509.08075 | null | http://arxiv.org/abs/1509.08075v1 | http://arxiv.org/pdf/1509.08075v1.pdf | Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing | We introduce Segment-Phrase Table (SPT), a large collection of bijective
associations between textual phrases and their corresponding segmentations.
Leveraging recent progress in object recognition and natural language
semantics, we show how we can successfully build a high-quality segment-phrase
table using minimal hu... | ['Yejin Choi', 'Ali Farhadi', 'Santosh Kumar Divvala', 'Fereshteh Sadeghi', 'Hamid Izadinia'] | 2015-09-27 | segment-phrase-table-for-semantic-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Izadinia_Segment-Phrase_Table_for_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Izadinia_Segment-Phrase_Table_for_ICCV_2015_paper.pdf | iccv-2015-12 | ['visual-entailment'] | ['reasoning'] | [ 5.89507520e-01 1.77361548e-01 -5.90444744e-01 -5.91041923e-01
-1.04517913e+00 -1.06114912e+00 6.87468171e-01 3.47315758e-01
-1.39720112e-01 5.17362058e-01 4.08412784e-01 -2.81037122e-01
1.81235701e-01 -5.01772285e-01 -1.13566828e+00 -2.20509991e-01
1.78430483e-01 4.95135963e-01 4.72834736e-01 1.43464198... | [10.547661781311035, 1.4418561458587646] |
73e13ed0-3360-4936-828f-6b6d5f181afd | a-knowledge-augmented-neural-network-model | null | null | https://aclanthology.org/C18-1049 | https://aclanthology.org/C18-1049.pdf | A Knowledge-Augmented Neural Network Model for Implicit Discourse Relation Classification | Identifying discourse relations that are not overtly marked with discourse connectives remains a challenging problem. The absence of explicit clues indicates a need for the combination of world knowledge and weak contextual clues, which can hardly be learned from a small amount of manually annotated data. In this paper... | ['Yugo Murawaki', 'Yudai Kishimoto', 'Sadao Kurohashi'] | 2018-08-01 | a-knowledge-augmented-neural-network-model-1 | https://aclanthology.org/C18-1049 | https://aclanthology.org/C18-1049.pdf | coling-2018-8 | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [ 2.49598205e-01 8.92884791e-01 -4.23994929e-01 -3.37336898e-01
-3.44661504e-01 -6.71788335e-01 9.29001272e-01 5.58691740e-01
-5.30834556e-01 1.35206103e+00 5.95939159e-01 -5.39083302e-01
-7.52291083e-02 -8.69937778e-01 -4.14985657e-01 -2.35053390e-01
-3.44578959e-02 4.29179370e-01 5.87611377e-01 -5.92338145... | [10.803590774536133, 9.261039733886719] |
7b6c1e47-03c2-4f81-b34f-127c0ba02662 | joint-graphical-models-for-date-selection-in | null | null | https://aclanthology.org/P15-1154 | https://aclanthology.org/P15-1154.pdf | Joint Graphical Models for Date Selection in Timeline Summarization | null | ['Giang Tran', 'Katja Markert', 'Eelco Herder'] | 2015-07-01 | joint-graphical-models-for-date-selection-in-1 | https://aclanthology.org/P15-1154 | https://aclanthology.org/P15-1154.pdf | ijcnlp-2015-7 | ['timeline-summarization'] | ['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 -6.93831444... | [-7.446542739868164, 3.658743143081665] |
6d962044-75a0-46fb-b912-188cd955fb23 | training-graph-neural-networks-by-graphon | 2109.01918 | null | https://arxiv.org/abs/2109.01918v1 | https://arxiv.org/pdf/2109.01918v1.pdf | Training Graph Neural Networks by Graphon Estimation | In this work, we propose to train a graph neural network via resampling from a graphon estimate obtained from the underlying network data. More specifically, the graphon or the link probability matrix of the underlying network is first obtained from which a new network will be resampled and used during the training pro... | ['Lizhen Lin', 'Yihao Fang', 'Ziqing Hu'] | 2021-09-04 | null | null | null | null | ['graphon-estimation'] | ['graphs'] | [ 3.78861219e-01 8.22859168e-01 -3.43524843e-01 -1.77333638e-01
1.97000112e-02 -3.05902928e-01 6.07536137e-01 8.03977996e-02
-3.45667481e-01 9.53260779e-01 -1.99985921e-01 -5.20147860e-01
-3.79379034e-01 -1.12612486e+00 -1.17688727e+00 -5.63502371e-01
-2.94922411e-01 4.52523410e-01 3.98566537e-02 9.83330160... | [6.9846272468566895, 6.060612678527832] |
4bfa7494-80bb-4a01-b5a0-98a64e6c7f74 | nir-prompt-a-multi-task-generalized-neural | 2212.00229 | null | https://arxiv.org/abs/2212.00229v2 | https://arxiv.org/pdf/2212.00229v2.pdf | NIR-Prompt: A Multi-task Generalized Neural Information Retrieval Training Framework | Information retrieval aims to find information that meets users' needs from the corpus. Different needs correspond to different IR tasks such as document retrieval, open-domain question answering, retrieval-based dialogue, etc., while they share the same schema to estimate the relationship between texts. It indicates t... | ['Xueqi Cheng', 'HuaWei Shen', 'Liang Pang', 'Shicheng Xu'] | 2022-12-01 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [ 1.80955499e-01 -3.01740915e-01 -4.04716820e-01 -3.61612886e-01
-1.08111465e+00 -4.61001337e-01 8.42079639e-01 -1.29926145e-01
-4.29568082e-01 1.64336249e-01 3.43157560e-01 1.38267577e-01
-4.66090739e-01 -2.19620377e-01 -1.69176117e-01 -2.83232689e-01
4.46683288e-01 6.73894227e-01 5.95599115e-02 -7.49820888... | [11.220016479492188, 7.920525550842285] |
72590d73-fdb7-4fa5-b900-a29482571ebd | safe-policy-optimization-with-local | 2111.04894 | null | https://arxiv.org/abs/2111.04894v1 | https://arxiv.org/pdf/2111.04894v1.pdf | Safe Policy Optimization with Local Generalized Linear Function Approximations | Safe exploration is a key to applying reinforcement learning (RL) in safety-critical systems. Existing safe exploration methods guaranteed safety under the assumption of regularity, and it has been difficult to apply them to large-scale real problems. We propose a novel algorithm, SPO-LF, that optimizes an agent's poli... | ['Yanan Sui', 'Yunyue Wei', 'Akifumi Wachi'] | 2021-11-09 | null | http://proceedings.neurips.cc/paper/2021/hash/adf7e293599134777339fdc40ddfa818-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/adf7e293599134777339fdc40ddfa818-Paper.pdf | neurips-2021-12 | ['safe-exploration'] | ['robots'] | [-1.52079269e-01 3.79935086e-01 -6.92321897e-01 2.08197087e-01
-9.92179751e-01 -4.40652966e-01 4.26032543e-01 2.73199081e-01
-6.39020741e-01 1.30574298e+00 8.02930631e-03 -5.27109921e-01
-2.87112862e-01 -7.03550220e-01 -9.80514109e-01 -7.84408748e-01
-9.47754681e-01 1.87603265e-01 1.58423617e-01 -2.11804941... | [4.527355194091797, 2.1679186820983887] |
897840a7-5978-4051-8556-059786a03462 | language-agnostic-code-switching-in-end-to | 2210.08992 | null | https://arxiv.org/abs/2210.08992v2 | https://arxiv.org/pdf/2210.08992v2.pdf | Language-agnostic Code-Switching in Sequence-To-Sequence Speech Recognition | Code-Switching (CS) is referred to the phenomenon of alternately using words and phrases from different languages. While today's neural end-to-end (E2E) models deliver state-of-the-art performances on the task of automatic speech recognition (ASR) it is commonly known that these systems are very data-intensive. However... | ['Alexander Waibel', 'Juan Hussain', 'Christian Huber', 'Enes Yavuz Ugan'] | 2022-10-17 | null | null | null | null | ['sequence-to-sequence-speech-recognition'] | ['speech'] | [ 3.52748662e-01 1.61774885e-02 -7.34912157e-02 -3.76995772e-01
-1.31010854e+00 -8.52390051e-01 6.81894600e-01 -4.59664539e-02
-6.63203955e-01 6.13789380e-01 3.13943416e-01 -9.02935445e-01
6.01120889e-01 -5.17705129e-03 -8.53626490e-01 -3.15394431e-01
1.38441578e-01 6.80248559e-01 1.35609612e-01 -5.94652772... | [14.365903854370117, 6.939558982849121] |
f364a510-76af-4d4b-895a-d539a7adaa10 | stochastically-dominant-distributional | 1905.07318 | null | https://arxiv.org/abs/1905.07318v4 | https://arxiv.org/pdf/1905.07318v4.pdf | Stochastically Dominant Distributional Reinforcement Learning | We describe a new approach for managing aleatoric uncertainty in the Reinforcement Learning (RL) paradigm. Instead of selecting actions according to a single statistic, we propose a distributional method based on the second-order stochastic dominance (SSD) relation. This compares the inherent dispersion of random retur... | ['Michal Lyskawinski', 'Xiaohu Li', 'John D. Martin', 'Brendan Englot'] | 2019-05-17 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/3862-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/3862-Paper.pdf | icml-2020-1 | ['distributional-reinforcement-learning'] | ['methodology'] | [-1.35047594e-02 2.59822041e-01 -1.55338660e-01 -2.20771909e-01
-1.01010036e+00 -4.95963961e-01 7.39723921e-01 2.08353609e-01
-9.09339964e-01 1.14248025e+00 1.79372475e-01 -3.54405969e-01
-8.43952417e-01 -7.37890959e-01 -5.92839718e-01 -1.07076442e+00
-2.17580438e-01 4.71625149e-01 7.81886652e-02 1.34475753... | [4.287740230560303, 2.633284091949463] |
777c8adb-6ceb-4a92-a15e-19a741d4e9db | classification-with-costly-features-as-a | 1909.02564 | null | https://arxiv.org/abs/1909.02564v1 | https://arxiv.org/pdf/1909.02564v1.pdf | Classification with Costly Features as a Sequential Decision-Making Problem | This work focuses on a specific classification problem, where the information about a sample is not readily available, but has to be acquired for a cost, and there is a per-sample budget. Inspired by real-world use-cases, we analyze average and hard variations of a directly specified budget. We postulate the problem in... | ['Viliam Lisý', 'Tomáš Pevný', 'Jaromír Janisch'] | 2019-09-05 | null | null | null | null | ['classification-with-costly-features'] | ['miscellaneous'] | [ 5.58732390e-01 2.04543591e-01 -7.33186126e-01 -4.83592033e-01
-9.47004437e-01 -5.88769674e-01 6.74373269e-01 -2.21111532e-02
-7.06459224e-01 1.29338813e+00 -1.75198227e-01 3.54495384e-02
-5.37099957e-01 -6.32885277e-01 -8.77653658e-01 -7.72357702e-01
-3.70382890e-02 1.00093460e+00 -2.60991633e-01 -1.34350270... | [4.155403137207031, 2.206609010696411] |
675af2f5-1f8e-4c75-a9bf-86d32fd5f544 | cad-pu-a-curvature-adaptive-deep-learning | 2009.04660 | null | https://arxiv.org/abs/2009.04660v1 | https://arxiv.org/pdf/2009.04660v1.pdf | CAD-PU: A Curvature-Adaptive Deep Learning Solution for Point Set Upsampling | Point set is arguably the most direct approximation of an object or scene surface, yet its practical acquisition often suffers from the shortcoming of being noisy, sparse, and possibly incomplete, which restricts its use for a high-quality surface recovery. Point set upsampling aims to increase its density and regulari... | ['Yuan Gao', 'Ke Chen', 'Kui Jia', 'Xian Shi', 'Jiehong Lin'] | 2020-09-10 | null | null | null | null | ['point-set-upsampling'] | ['computer-vision'] | [ 9.05622989e-02 2.28717849e-01 -1.40990287e-01 -2.38993652e-02
-1.00690162e+00 -2.53789216e-01 3.42396945e-01 -1.00539990e-01
6.09497055e-02 4.20519799e-01 6.59955069e-02 -2.83779670e-02
-1.81362350e-02 -1.11687005e+00 -1.03775382e+00 -4.78633970e-01
-1.08553153e-02 2.78036028e-01 1.12303503e-01 -3.54405761... | [8.373880386352539, -3.5390121936798096] |
70468d1b-d7a5-4dad-8618-14608c9e0c11 | source-class-selection-with-label-propagation | null | null | https://breckon.org/toby/publications/papers/wang21pda.pdf | https://breckon.org/toby/publications/papers/wang21pda.pdf | Source Class Selection with Label Propagation for Partial Domain Adaptation | In traditional unsupervised domain adaptation problems, the target domain is assumed to share the same set of classes as the source domain. In practice, there exist situations where target-domain data are from only a subset of source-domain classes and it is not known which classes the target-domain data belong to sinc... | ['Toby P.', 'Qian; Breckon', 'Wang'] | 2021-09-01 | null | null | null | icip-2021-9 | ['partial-domain-adaptation'] | ['methodology'] | [ 5.00032008e-01 4.25034910e-02 -2.61016488e-01 -4.15014774e-01
-7.91163206e-01 -5.63345909e-01 3.60320151e-01 1.49369732e-01
-3.03530306e-01 1.08552659e+00 -4.70340699e-02 2.46644959e-01
-8.66286457e-02 -5.62486529e-01 -5.16280472e-01 -1.04911566e+00
2.81262010e-01 7.54131317e-01 4.68285024e-01 7.10131088... | [10.369848251342773, 3.0295941829681396] |
339f1b32-d90e-4f2f-8854-b51d38c420e6 | open-vocabulary-panoptic-segmentation-with-1 | 2303.04803 | null | https://arxiv.org/abs/2303.04803v4 | https://arxiv.org/pdf/2303.04803v4.pdf | Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models | We present ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation, which unifies pre-trained text-image diffusion and discriminative models to perform open-vocabulary panoptic segmentation. Text-to-image diffusion models have the remarkable ability to generate high-quality images with diverse open-vocabulary lang... | ['Shalini De Mello', 'Xiaolong Wang', 'Wonmin Byeon', 'Arash Vahdat', 'Sifei Liu', 'Jiarui Xu'] | 2023-03-08 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_Open-Vocabulary_Panoptic_Segmentation_With_Text-to-Image_Diffusion_Models_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_Open-Vocabulary_Panoptic_Segmentation_With_Text-to-Image_Diffusion_Models_CVPR_2023_paper.pdf | cvpr-2023-1 | ['panoptic-segmentation', 'open-vocabulary-panoptic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 7.21310526e-02 -5.53076081e-02 -3.31153989e-01 -8.06735530e-02
-1.14828157e+00 -1.17108202e+00 9.18044627e-01 -2.19443694e-01
-2.35728219e-01 1.64697900e-01 2.43601918e-01 -2.62934536e-01
1.94210649e-01 -8.42450619e-01 -6.12185121e-01 -6.54591024e-01
1.84725881e-01 9.24339712e-01 3.79396856e-01 -1.78789526... | [9.692018508911133, 0.7392117977142334] |
91cc9392-b63f-4f3f-aacc-bc247dc6329e | pixel-by-pixel-mean-opinion-score-pmos-for-no | 2206.06541 | null | https://arxiv.org/abs/2206.06541v1 | https://arxiv.org/pdf/2206.06541v1.pdf | Pixel-by-pixel Mean Opinion Score (pMOS) for No-Reference Image Quality Assessment | Deep-learning based techniques have contributed to the remarkable progress in the field of automatic image quality assessment (IQA). Existing IQA methods are designed to measure the quality of an image in terms of Mean Opinion Score (MOS) at the image-level (i.e. the whole image) or at the patch-level (dividing the ima... | ['Jayoon Koo', 'Ilhyun Cho', 'Namuk Kim', 'Anant Baijal', 'Cheul-hee Hahm', 'Wook-Hyung Kim'] | 2022-06-14 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 3.46019149e-01 -3.25803310e-01 -5.15524447e-02 -2.38087326e-01
-7.57160068e-01 -1.43750787e-01 1.96616605e-01 4.34953034e-01
-2.59036362e-01 4.20351416e-01 1.27323449e-01 5.94617836e-02
-1.54144779e-01 -1.25242186e+00 -3.91363025e-01 -9.58499670e-01
5.81608228e-02 -4.82322663e-01 3.77179921e-01 -1.05839847... | [11.736309051513672, -1.935028076171875] |
de20d434-667c-43e0-8ee5-45ac130dee1e | domain-concretization-from-examples | 2011.09034 | null | https://arxiv.org/abs/2011.09034v1 | https://arxiv.org/pdf/2011.09034v1.pdf | Domain Concretization from Examples: Addressing Missing Domain Knowledge via Robust Planning | The assumption of complete domain knowledge is not warranted for robot planning and decision-making in the real world. It could be due to design flaws or arise from domain ramifications or qualifications. In such cases, existing planning and learning algorithms could produce highly undesirable behaviors. This problem i... | ['Yu Zhang', 'Piyush Rajesh Medikeri', 'Akshay Sharma'] | 2020-11-18 | null | null | null | null | ['known-unknowns'] | ['miscellaneous'] | [ 4.75965410e-01 9.91305590e-01 -2.28513449e-01 -1.39518201e-01
-4.72075343e-01 -5.59270263e-01 4.89310592e-01 2.00091861e-02
-1.91733599e-01 1.09312534e+00 -3.04682758e-02 -3.21134478e-01
-7.00137794e-01 -8.58156025e-01 -7.51829207e-01 -3.03359985e-01
1.10721968e-01 1.04149354e+00 5.73713422e-01 -2.40503088... | [4.447085380554199, 1.3993568420410156] |
769bdcc3-0cd0-4820-8950-60de8c8a5cb6 | universal-embeddings-for-spatio-temporal | 2011.06165 | null | https://arxiv.org/abs/2011.06165v1 | https://arxiv.org/pdf/2011.06165v1.pdf | Universal Embeddings for Spatio-Temporal Tagging of Self-Driving Logs | In this paper, we tackle the problem of spatio-temporal tagging of self-driving scenes from raw sensor data. Our approach learns a universal embedding for all tags, enabling efficient tagging of many attributes and faster learning of new attributes with limited data. Importantly, the embedding is spatio-temporally awar... | ['Raquel Urtasun', 'Ersin Yumer', 'Abbas Sadat', 'Wenjie Luo', 'Eric Kee', 'Sean Segal'] | 2020-11-12 | null | null | null | null | ['temporal-tagging'] | ['natural-language-processing'] | [-2.15264305e-01 6.72535077e-02 -3.04001898e-01 -6.45546913e-01
-5.31422079e-01 -5.97705245e-01 7.13701725e-01 7.38271594e-01
-7.92475581e-01 6.53533459e-01 3.76136988e-01 -3.12472403e-01
-1.17997393e-01 -1.28645635e+00 -7.62344718e-01 -6.22082353e-01
-5.04686058e-01 4.63703781e-01 7.94683158e-01 4.57964242... | [6.268782615661621, 0.6191445589065552] |
52079f47-befd-439d-9bd2-bd4abd8702fc | parsing-r-cnn-for-instance-level-human | 1811.12596 | null | http://arxiv.org/abs/1811.12596v1 | http://arxiv.org/pdf/1811.12596v1.pdf | Parsing R-CNN for Instance-Level Human Analysis | Instance-level human analysis is common in real-life scenarios and has
multiple manifestations, such as human part segmentation, dense pose
estimation, human-object interactions, etc. Models need to distinguish
different human instances in the image panel and learn rich features to
represent the details of each instanc... | ['Lu Yang', 'Qing Song', 'Zhihui Wang', 'Ming Jiang'] | 2018-11-30 | parsing-r-cnn-for-instance-level-human-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Yang_Parsing_R-CNN_for_Instance-Level_Human_Analysis_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_Parsing_R-CNN_for_Instance-Level_Human_Analysis_CVPR_2019_paper.pdf | cvpr-2019-6 | ['multi-human-parsing', 'human-part-segmentation', 'human-parsing'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.51756853e-02 3.84702206e-01 3.05451900e-01 -4.58943576e-01
-7.95325935e-01 -3.11318785e-01 2.38177687e-01 7.98552670e-03
-3.64867806e-01 4.00910884e-01 2.01077729e-01 4.77254033e-01
3.23118061e-01 -5.83203852e-01 -1.06722999e+00 -3.02327812e-01
-1.91341117e-02 1.22962880e+00 5.05620718e-01 -3.33870381... | [8.38000774383545, -0.1571657955646515] |
3bf0969e-26e5-4f2e-bc3b-1f5e3f1d4056 | object-based-bayesian-full-waveform-inversion | 2305.06646 | null | https://arxiv.org/abs/2305.06646v1 | https://arxiv.org/pdf/2305.06646v1.pdf | Object based Bayesian full-waveform inversion for shear elastography | We develop a computational framework to quantify uncertainty in shear elastography imaging of anomalies in tissues. We adopt a Bayesian inference formulation. Given the observed data, a forward model and their uncertainties, we find the posterior probability of parameter fields representing the geometry of the anomalie... | ['Andrea Gutierrez', 'Elena Cebrian', 'Ana Carpio'] | 2023-05-11 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 2.03823462e-01 7.24388883e-02 2.35192135e-01 -1.02125645e-01
-7.97573507e-01 -4.29697245e-01 7.11482882e-01 9.36319679e-02
-2.51997828e-01 6.76641762e-01 8.18554834e-02 -1.47887051e-01
-4.15622264e-01 -7.56521106e-01 -5.49713492e-01 -1.03813601e+00
-3.20343524e-01 8.74386251e-01 4.05592978e-01 2.75683761... | [6.807473659515381, 3.765434503555298] |
44b21945-438d-4dee-a12a-bb1d1b890aec | wide-context-semantic-image-extrapolation | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Wide-Context_Semantic_Image_Extrapolation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Wide-Context_Semantic_Image_Extrapolation_CVPR_2019_paper.pdf | Wide-Context Semantic Image Extrapolation | This paper studies the fundamental problem of extrapolating visual context using deep generative models, i.e., extending image borders with plausible structure and details. This seemingly easy task actually faces many crucial technical challenges and has its unique properties. The two major issues are size expansion an... | [' Jiaya Jia', ' Xiaoyong Shen', ' Xin Tao', 'Yi Wang'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['seeing-beyond-the-visible', 'image-outpainting'] | ['computer-vision', 'computer-vision'] | [ 3.85381222e-01 1.17808543e-01 -1.78201690e-01 -3.56970251e-01
-3.79014701e-01 -3.66250873e-01 6.42780423e-01 -6.86377823e-01
1.88165065e-02 1.01434779e+00 2.46305808e-01 -1.50689408e-01
-6.48288289e-04 -8.43149006e-01 -6.44519985e-01 -7.31601357e-01
1.97787985e-01 7.83063248e-02 5.85980356e-01 -3.74683201... | [11.327943801879883, -0.8418619632720947] |
a2a9869f-42cc-4706-b295-f6e5f622036b | ssl4eo-l-datasets-and-foundation-models-for | 2306.09424 | null | https://arxiv.org/abs/2306.09424v1 | https://arxiv.org/pdf/2306.09424v1.pdf | SSL4EO-L: Datasets and Foundation Models for Landsat Imagery | The Landsat program is the longest-running Earth observation program in history, with 50+ years of data acquisition by 8 satellites. The multispectral imagery captured by sensors onboard these satellites is critical for a wide range of scientific fields. Despite the increasing popularity of deep learning and remote sen... | ['Arindam Banerjee', 'Caleb Robinson', 'Shradha Sehgal', 'Nassim Ait Ali Braham', 'Yi-Chia Chang', 'Yi Wang', 'Isaac A. Corley', 'Nils Lehmann', 'Adam J. Stewart'] | 2023-06-15 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 3.15506130e-01 -4.86252993e-01 -3.74401748e-01 -4.78047252e-01
-7.14746952e-01 -7.29717970e-01 3.69225621e-01 -1.95948809e-01
-4.79315072e-01 5.71394205e-01 -3.57182711e-01 -8.00738454e-01
-9.91381854e-02 -1.12511098e+00 -6.25321627e-01 -7.48301685e-01
-5.58695734e-01 4.34647292e-01 1.30717143e-01 -1.42795131... | [9.459781646728516, -1.5035566091537476] |
1808cafe-c455-4c31-b64b-c08dc5f75845 | rsca-real-time-segmentation-based-context | 2105.12789 | null | https://arxiv.org/abs/2105.12789v1 | https://arxiv.org/pdf/2105.12789v1.pdf | RSCA: Real-time Segmentation-based Context-Aware Scene Text Detection | Segmentation-based scene text detection methods have been widely adopted for arbitrary-shaped text detection recently, since they make accurate pixel-level predictions on curved text instances and can facilitate real-time inference without time-consuming processing on anchors. However, current segmentation-based models... | ['Humphrey Shi', 'Chiu Man Ho', 'Rongrong Liu', 'Yuan Lin', 'Jiachen Li'] | 2021-05-26 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 4.94588405e-01 -4.50114965e-01 -1.14377141e-01 -3.77994567e-01
-9.72405195e-01 -4.74811614e-01 5.38501441e-01 2.79086620e-01
-4.44227606e-01 -8.07868689e-02 -1.40941754e-01 -4.28810507e-01
5.98503411e-01 -5.96963108e-01 -6.76241994e-01 -4.21262085e-01
5.16237438e-01 5.67845583e-01 1.16057622e+00 4.81858253... | [12.06397819519043, 2.2634410858154297] |
26fef950-60e3-4db6-9a2c-d8c12ae5d853 | multimodal-short-video-rumor-detection-system | 2304.08401 | null | https://arxiv.org/abs/2304.08401v3 | https://arxiv.org/pdf/2304.08401v3.pdf | Multimodal Short Video Rumor Detection System Based on Contrastive Learning | With the rise of short video platforms as prominent channels for news dissemination, major platforms in China have gradually evolved into fertile grounds for the proliferation of fake news. However, distinguishing short video rumors poses a significant challenge due to the substantial amount of information and shared f... | ['Haizhou Wang', 'Pengchao Wang', 'Xiangyu Min', 'Siyi Wang', 'Junhao Zhao', 'Yuxing Yang'] | 2023-04-17 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 1.81643322e-01 -4.74777162e-01 -5.72753131e-01 1.21803224e-01
-6.65798724e-01 -3.93452644e-01 7.86660910e-01 -2.03672498e-02
-2.82115161e-01 4.68878657e-01 4.90860552e-01 -2.46707022e-01
5.95383011e-02 -5.95208943e-01 -4.10694152e-01 -6.22749746e-01
1.53688937e-01 -3.06545943e-01 1.87117562e-01 -3.90604407... | [8.155094146728516, 10.282129287719727] |
11178401-0693-44c0-b0b1-4971e3d42012 | distilling-relation-embeddings-from | null | null | https://aclanthology.org/2021.emnlp-main.712 | https://aclanthology.org/2021.emnlp-main.712.pdf | Distilling Relation Embeddings from Pretrained Language Models | Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models.... | ['Steven Schockaert', 'Jose Camacho-Collados', 'Asahi Ushio'] | null | null | null | null | emnlp-2021-11 | ['relation-classification'] | ['natural-language-processing'] | [-1.70399100e-01 2.60180295e-01 -4.74898070e-01 -3.67900699e-01
-4.25776452e-01 -8.27641249e-01 9.84425366e-01 7.74009526e-01
-4.55674440e-01 6.30485356e-01 5.86607158e-01 -5.50465584e-01
-1.61124021e-01 -1.17331791e+00 -4.43389088e-01 -2.71822691e-01
-2.67686937e-02 5.63321769e-01 1.19037829e-01 -5.07847607... | [10.054581642150879, 8.73697280883789] |
f90b9e29-e599-43ee-897a-d8539823ebaf | lamv-learning-to-align-and-match-videos-with | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Baraldi_LAMV_Learning_to_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Baraldi_LAMV_Learning_to_CVPR_2018_paper.pdf | LAMV: Learning to Align and Match Videos With Kernelized Temporal Layers | This paper considers a learnable approach for comparing and aligning videos. Our architecture builds upon and revisits temporal match kernels within neural networks: we propose a new temporal layer that finds temporal alignments by maximizing the scores between two sequences of vectors, according to a time-sensitive si... | ['Hervé Jégou', 'Rita Cucchiara', 'Matthijs Douze', 'Lorenzo Baraldi'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['video-alignment'] | ['computer-vision'] | [ 2.19010621e-01 -5.48916280e-01 -4.18369710e-01 -2.68217534e-01
-1.08669353e+00 -5.41255474e-01 8.68771136e-01 2.66205758e-01
-7.85913825e-01 1.37194246e-01 2.04840615e-01 3.65243793e-01
-4.50232327e-01 -2.59245157e-01 -1.06408155e+00 -6.14527881e-01
-6.64636731e-01 2.08726674e-01 4.93657291e-01 2.70695478... | [9.162869453430176, 0.576154351234436] |
5c3963af-78ed-49bf-a631-b350aa9fbda4 | extracting-topics-with-simultaneous-word-co | null | null | https://aclanthology.org/2021.findings-emnlp.2 | https://aclanthology.org/2021.findings-emnlp.2.pdf | Extracting Topics with Simultaneous Word Co-occurrence and Semantic Correlation Graphs: Neural Topic Modeling for Short Texts | Short text nowadays has become a more fashionable form of text data, e.g., Twitter posts, news titles, and product reviews. Extracting semantic topics from short texts plays a significant role in a wide spectrum of NLP applications, and neural topic modeling is now a major tool to achieve it. Motivated by learning more... | ['Jihong Ouyang', 'Xiaotang Zhou', 'Ximing Li', 'Yiming Wang'] | null | null | null | null | findings-emnlp-2021-11 | ['topic-models'] | ['natural-language-processing'] | [-1.68892086e-01 3.09645534e-01 -6.76777065e-01 -2.76540518e-01
-3.95723224e-01 -1.30764246e-01 9.12292898e-01 1.92540437e-01
-8.89122337e-02 5.61313510e-01 8.04095030e-01 -1.13446020e-01
-2.16246858e-01 -1.24277747e+00 -6.69296384e-01 -7.11500347e-01
1.46311998e-01 4.77253050e-01 -1.34584621e-01 -1.48262993... | [10.38088321685791, 6.942087173461914] |
a4e0adae-eaaa-4497-b402-9fc26a20e10c | affordance-detection-with-dynamic-tree | 2211.05200 | null | https://arxiv.org/abs/2211.05200v1 | https://arxiv.org/pdf/2211.05200v1.pdf | Affordance detection with Dynamic-Tree Capsule Networks | Affordance detection from visual input is a fundamental step in autonomous robotic manipulation. Existing solutions to the problem of affordance detection rely on convolutional neural networks. However, these networks do not consider the spatial arrangement of the input data and miss parts-to-whole relationships. There... | ['Matteo Saveriano', 'Jakob Mittelberger', 'Chris Engelhardt', 'David Peer', 'Simon Haller-Seeber', 'Antonio Rodríguez-Sánchez'] | 2022-11-09 | null | null | null | null | ['affordance-detection'] | ['computer-vision'] | [-6.69275522e-02 1.71728302e-02 -5.56891523e-02 -1.74776614e-01
-3.07083223e-02 -7.68818438e-01 4.07133847e-01 8.03205296e-02
-3.86905432e-01 2.76123226e-01 2.36024503e-02 1.06604006e-02
-3.89996558e-01 -3.39397162e-01 -9.95767832e-01 -2.75724679e-01
-4.80170101e-01 5.79523325e-01 4.57610518e-01 -2.90761203... | [5.294111251831055, -0.26682034134864807] |
ddb99ed6-d1c5-41af-b6fe-2c6cdb295549 | subspace-clustering-with-active-learning | 1911.03299 | null | https://arxiv.org/abs/1911.03299v2 | https://arxiv.org/pdf/1911.03299v2.pdf | Subspace Clustering with Active Learning | Subspace clustering is a growing field of unsupervised learning that has gained much popularity in the computer vision community. Applications can be found in areas such as motion segmentation and face clustering. It assumes that data originate from a union of subspaces, and clusters the data depending on the correspon... | ['Nicos G. Pavlidis', 'Hankui Peng'] | 2019-11-08 | null | null | null | null | ['motion-segmentation', 'face-clustering'] | ['computer-vision', 'computer-vision'] | [ 2.31744215e-01 -1.50321811e-01 -2.53447980e-01 -3.89658034e-01
-7.44436204e-01 -6.56491995e-01 3.60164285e-01 -1.10631078e-01
-4.27602828e-01 3.71772021e-01 2.14193370e-02 1.02201141e-01
-3.10750991e-01 -4.56843138e-01 -3.51990312e-01 -1.25511634e+00
-9.85882804e-03 6.64427400e-01 2.53332257e-01 4.58827227... | [7.72127103805542, 4.447030544281006] |
16f29791-d532-40e0-9244-31b3d02f30f6 | risclip-referring-image-segmentation | 2306.08498 | null | https://arxiv.org/abs/2306.08498v1 | https://arxiv.org/pdf/2306.08498v1.pdf | RISCLIP: Referring Image Segmentation Framework using CLIP | Recent advances in computer vision and natural language processing have naturally led to active research in multi-modal tasks, including Referring Image Segmentation (RIS). Recent approaches have advanced the frontier of RIS by impressive margins, but they require an additional pretraining stage on external visual grou... | ['Jaesik Park', 'Minguk Kang', 'Seoyeon Kim'] | 2023-06-14 | null | null | null | null | ['visual-grounding'] | ['computer-vision'] | [ 7.25866914e-01 2.00475469e-01 -1.58582017e-01 -4.67376530e-01
-1.05299723e+00 -5.80595851e-01 7.37806320e-01 1.85279161e-01
-7.60319948e-01 2.28110313e-01 1.19619161e-01 -2.97323018e-01
1.72753409e-01 -4.17463392e-01 -8.72380674e-01 -5.21564901e-01
2.20439777e-01 6.57032371e-01 4.99931306e-01 -4.04882401... | [10.264892578125, 1.3371589183807373] |
384d8dd6-1f02-4c5b-89b9-a7046c1f3941 | densepoint-learning-densely-contextual | 1909.03669 | null | https://arxiv.org/abs/1909.03669v1 | https://arxiv.org/pdf/1909.03669v1.pdf | DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing | Point cloud processing is very challenging, as the diverse shapes formed by irregular points are often indistinguishable. A thorough grasp of the elusive shape requires sufficiently contextual semantic information, yet few works devote to this. Here we propose DensePoint, a general architecture to learn densely context... | ['Jiwen Lu', 'Yongcheng Liu', 'Shiming Xiang', 'Gaofeng Meng', 'Chunhong Pan', 'Bin Fan'] | 2019-09-09 | densepoint-learning-densely-contextual-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Liu_DensePoint_Learning_Densely_Contextual_Representation_for_Efficient_Point_Cloud_Processing_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_DensePoint_Learning_Densely_Contextual_Representation_for_Efficient_Point_Cloud_Processing_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-part-segmentation'] | ['computer-vision'] | [-1.23635024e-01 -2.67248720e-01 -5.58620207e-02 -3.66086721e-01
-4.63326365e-01 -6.96382999e-01 7.40316391e-01 4.14826840e-01
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-2.39126787e-01 -1.16312933e+00 -1.12778294e+00 -5.96496284e-01
-2.45677993e-01 4.50162649e-01 2.41116062e-01 -3.75007719... | [7.956679821014404, -3.601789712905884] |
0d9de8da-1873-4479-a4d5-cfac003f48af | accurate-and-interpretable-evaluation-of | 1908.07319 | null | https://arxiv.org/abs/1908.07319v1 | https://arxiv.org/pdf/1908.07319v1.pdf | Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks | Purpose: Manual feedback from senior surgeons observing less experienced trainees is a laborious task that is very expensive, time-consuming and prone to subjectivity. With the number of surgical procedures increasing annually, there is an unprecedented need to provide an accurate, objective and automatic evaluation of... | ['Pierre-Alain Muller', 'Jonathan Weber', 'Hassan Ismail Fawaz', 'Germain Forestier', 'Lhassane Idoumghar'] | 2019-08-20 | null | null | null | null | ['skills-evaluation', 'surgical-skills-evaluation'] | ['computer-vision', 'medical'] | [ 3.07755861e-02 5.43934703e-01 -3.85975510e-01 -4.45025802e-01
-4.21474695e-01 -6.49000168e-01 2.15257946e-02 2.99402535e-01
-7.52772927e-01 3.43945771e-01 3.90131831e-01 -7.22030640e-01
-4.47074145e-01 -3.23643327e-01 -5.91384470e-01 -4.97650027e-01
-8.37703049e-02 2.61150390e-01 -1.47979885e-01 -4.46552962... | [14.084404945373535, -3.363438844680786] |
49f09090-764c-49a3-b039-ed9049e5de65 | enhancing-llm-with-evolutionary-fine-tuning | 2307.02839 | null | https://arxiv.org/abs/2307.02839v1 | https://arxiv.org/pdf/2307.02839v1.pdf | Enhancing LLM with Evolutionary Fine Tuning for News Summary Generation | News summary generation is an important task in the field of intelligence analysis, which can provide accurate and comprehensive information to help people better understand and respond to complex real-world events. However, traditional news summary generation methods face some challenges, which are limited by the mode... | ['Xiaolin Chen', 'Le Xiao'] | 2023-07-06 | null | null | null | null | ['natural-language-understanding'] | ['natural-language-processing'] | [ 4.15014774e-01 -5.46755530e-02 -1.54228851e-01 -1.85898572e-01
-5.16888976e-01 -2.75295794e-01 8.75184715e-01 4.96423304e-01
-5.17972596e-02 1.23434937e+00 7.51969576e-01 2.24951237e-01
-1.24300011e-01 -1.22532833e+00 -3.43086153e-01 -5.16254365e-01
-1.61967184e-02 6.93408906e-01 2.17930689e-01 -3.29654843... | [12.464227676391602, 9.375334739685059] |
ba2dd3d6-dc0c-4953-9f96-585edb2b7526 | bed-a-real-time-object-detection-system-for | 2202.07503 | null | https://arxiv.org/abs/2202.07503v4 | https://arxiv.org/pdf/2202.07503v4.pdf | BED: A Real-Time Object Detection System for Edge Devices | Deploying deep neural networks~(DNNs) on edge devices provides efficient and effective solutions for the real-world tasks. Edge devices have been used for collecting a large volume of data efficiently in different domains. DNNs have been an effective tool for data processing and analysis. However, designing DNNs on edg... | ['Xuanting Cai', 'Xia Hu', 'Erman Okman', 'Gorkem Ulkar', 'Afshin Niktash', 'Alfredo Costilla Reyes', 'Daochen Zha', 'Yi-Wei Chen', 'Zhimeng Jiang', 'Zaid Pervaiz Bhat', 'Guanchu Wang'] | 2022-02-14 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-3.12809259e-01 -5.26580930e-01 -3.17861676e-01 -3.61129552e-01
-2.69329417e-02 -3.53602737e-01 -1.15651023e-02 -6.25701696e-02
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4.06517118e-01 -9.27092373e-01 -4.82114702e-01 -6.25106275e-01
2.95388013e-01 2.48143062e-01 3.89006495e-01 1.37478337... | [8.425315856933594, 2.8930511474609375] |
66059e6c-ce70-40bb-a32d-12413c4b2d8c | active-anomaly-detection-via-ensembles-1 | 1901.08930 | null | http://arxiv.org/abs/1901.08930v1 | http://arxiv.org/pdf/1901.08930v1.pdf | Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability | Anomaly detection (AD) task corresponds to identifying the true anomalies
from a given set of data instances. AD algorithms score the data instances and
produce a ranked list of candidate anomalies, which are then analyzed by a
human to discover the true anomalies. However, this process can be laborious
for the human a... | ['Md. Rakibul Islam', 'Nitthilan Kannappan Jayakodi', 'Janardhan Rao Doppa', 'Shubhomoy Das'] | 2019-01-23 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 2.96492249e-01 6.70432225e-02 -1.30857127e-02 -5.48369765e-01
-9.19311047e-01 -5.91905832e-01 3.56349528e-01 6.16510630e-01
-1.29984006e-01 2.63155431e-01 -1.54511020e-01 -4.55313683e-01
-3.23878765e-01 -7.99978971e-01 -3.90049875e-01 -6.49713874e-01
-6.93226814e-01 7.09499061e-01 2.86918700e-01 -5.33282794... | [7.57821798324585, 2.5090701580047607] |
4a459d07-45ed-4a2a-b188-f01f0ff51982 | deep-recurrent-neural-network-for-protein | 1701.08318 | null | http://arxiv.org/abs/1701.08318v1 | http://arxiv.org/pdf/1701.08318v1.pdf | Deep Recurrent Neural Network for Protein Function Prediction from Sequence | As high-throughput biological sequencing becomes faster and cheaper, the need
to extract useful information from sequencing becomes ever more paramount,
often limited by low-throughput experimental characterizations. For proteins,
accurate prediction of their functions directly from their primary amino-acid
sequences h... | ['Xueliang Liu'] | 2017-01-28 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 7.84013271e-01 -1.72838867e-02 8.35020617e-02 -3.91324818e-01
-6.17347658e-01 -5.83756983e-01 1.25079960e-01 4.73620981e-01
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-1.08678877e-01 -3.84145141e-01 -7.62731612e-01 -9.67073917e-01
-2.84613073e-01 6.88064754e-01 2.46605694e-01 -2.77949095... | [4.733170509338379, 5.578851699829102] |
a97f9595-d2b4-4713-986a-d18fb27c4099 | combining-discrete-and-continuous-features | null | null | https://aclanthology.org/D15-1153 | https://aclanthology.org/D15-1153.pdf | Combining Discrete and Continuous Features for Deterministic Transition-based Dependency Parsing | null | ['Meishan Zhang', 'Yue Zhang'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['transition-based-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 -6.93831444... | [-7.230048179626465, 3.805534601211548] |
a064128a-b055-4660-aee7-b2920e706fbf | towards-unsupervised-object-detection-from | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Towards_Unsupervised_Object_Detection_From_LiDAR_Point_Clouds_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Towards_Unsupervised_Object_Detection_From_LiDAR_Point_Clouds_CVPR_2023_paper.pdf | Towards Unsupervised Object Detection From LiDAR Point Clouds | In this paper, we study the problem of unsupervised object detection from 3D point clouds in self-driving scenes. We present a simple yet effective method that exploits (i) point clustering in near-range areas where the point clouds are dense, (ii) temporal consistency to filter out noisy unsupervised detections, (... | ['Raquel Urtasun', 'Mengye Ren', 'Bin Yang', 'Sergio Casas', 'Yuwen Xiong', 'Anqi Joyce Yang', 'Lunjun Zhang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['object-discovery'] | ['computer-vision'] | [ 1.37712330e-01 1.52546942e-01 9.34074540e-03 -4.07335699e-01
-6.83895290e-01 -6.57035828e-01 7.08870709e-01 3.77270103e-01
-6.41216815e-01 2.34729603e-01 -1.85796648e-01 7.35083073e-02
-2.31657743e-01 -5.92988312e-01 -1.12928796e+00 -4.39211398e-01
-3.92979413e-01 1.01099384e+00 9.09852266e-01 -7.98697323... | [7.754405975341797, -2.6245453357696533] |
fd7a5d84-17e7-4402-93f0-4b02c7c8d8f2 | extra-explanation-ranking-datasets-for | 2102.10315 | null | https://arxiv.org/abs/2102.10315v3 | https://arxiv.org/pdf/2102.10315v3.pdf | EXTRA: Explanation Ranking Datasets for Explainable Recommendation | Recently, research on explainable recommender systems has drawn much attention from both academia and industry, resulting in a variety of explainable models. As a consequence, their evaluation approaches vary from model to model, which makes it quite difficult to compare the explainability of different models. To achie... | ['Li Chen', 'Yongfeng Zhang', 'Lei LI'] | 2021-02-20 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 3.85533534e-02 7.03598708e-02 -2.61931449e-01 -6.40576720e-01
-8.34847271e-01 -6.55716002e-01 3.02537531e-01 2.23865017e-01
2.73552239e-01 5.18100202e-01 5.09113431e-01 -3.82546335e-01
-3.14277947e-01 -6.14143312e-01 -5.63875616e-01 -4.26616162e-01
1.55439332e-01 4.22774106e-01 2.46217892e-01 -2.01613843... | [10.00525951385498, 5.74029541015625] |
88c7c2ef-a9a4-460a-bcce-07329a387a53 | a-neural-multi-task-learning-framework-to | 1812.06081 | null | http://arxiv.org/abs/1812.06081v1 | http://arxiv.org/pdf/1812.06081v1.pdf | A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization | State-of-the-art studies have demonstrated the superiority of joint modelling
over pipeline implementation for medical named entity recognition and
normalization due to the mutual benefits between the two processes. To exploit
these benefits in a more sophisticated way, we propose a novel deep neural
multi-task learnin... | ['Ting Liu', 'Fei Wang', 'Sicheng Zhao', 'Sendong Zhao'] | 2018-12-14 | null | null | null | null | ['medical-named-entity-recognition'] | ['natural-language-processing'] | [ 1.13474257e-01 9.51814428e-02 -3.01147223e-01 -5.73018193e-01
-9.79182184e-01 9.27924886e-02 5.11815846e-01 3.65489990e-01
-7.31091142e-01 6.95143342e-01 4.36293632e-01 6.46822453e-02
-2.10391968e-01 -4.49167371e-01 -5.44619143e-01 -5.08740067e-01
2.14020178e-01 3.80602211e-01 1.50011286e-01 4.08226997... | [8.669329643249512, 8.877745628356934] |
c229571f-d7de-4b38-bdf7-e5c66a7f5a7f | iris-segmentation-techniques-to-recognize-the | 2005.02450 | null | https://arxiv.org/abs/2005.02450v1 | https://arxiv.org/pdf/2005.02450v1.pdf | Iris segmentation techniques to recognize the behavior of a vigilant driver | In this paper, we clarify how to recognize different levels of vigilance for vehicle drivers. In order to avoid the classical problems of crisp logic, we preferred to employ a fuzzy logic-based system that depends on two variables to make the final decision. Two iris segmentation techniques are well illustrated. A new ... | ['Abdullatif Baba'] | 2020-05-05 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [-2.15846766e-02 1.30397841e-01 -8.02360401e-02 -6.64401114e-01
2.11739108e-01 -3.89735162e-01 2.38794789e-01 3.22337717e-01
-9.20498908e-01 9.41102803e-01 -4.65652108e-01 -8.22953999e-01
-6.86279356e-01 -7.02388167e-01 -1.23306364e-02 -6.88747168e-01
5.16801953e-01 3.74590814e-01 1.89153254e-01 -3.49626929... | [13.396288871765137, 3.0665299892425537] |
4a61257b-89f7-41e1-b009-1e7f926dc454 | correspondence-insertion-for-as-projective-as | 1608.07997 | null | http://arxiv.org/abs/1608.07997v1 | http://arxiv.org/pdf/1608.07997v1.pdf | Correspondence Insertion for As-Projective-As-Possible Image Stitching | Spatially varying warps are increasingly popular for image alignment. In
particular, as-projective-as-possible (APAP) warps have been proven effective
for accurate panoramic stitching, especially in cases with significant depth
parallax that defeat standard homographic warps. However, estimating spatially
varying warps... | ['Tat-Jun Chin', 'William X. Liu'] | 2016-08-29 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 5.38040400e-01 -3.14041972e-01 -9.15662721e-02 2.04982594e-01
-7.17297077e-01 -8.71690035e-01 6.58847153e-01 -2.88716912e-01
-5.83320521e-02 3.12339604e-01 3.19680870e-01 8.88768584e-02
-1.62337616e-01 -3.76691222e-01 -5.73456287e-01 -9.23638701e-01
1.91000253e-02 4.26451266e-01 4.90107000e-01 -4.25569475... | [9.405171394348145, -2.365860939025879] |
b85c5bdf-4284-4918-8a23-e412c7c13863 | data-generation-using-pass-phrase-dependent | 2102.02074 | null | https://arxiv.org/abs/2102.02074v1 | https://arxiv.org/pdf/2102.02074v1.pdf | Data Generation Using Pass-phrase-dependent Deep Auto-encoders for Text-Dependent Speaker Verification | In this paper, we propose a novel method that trains pass-phrase specific deep neural network (PP-DNN) based auto-encoders for creating augmented data for text-dependent speaker verification (TD-SV). Each PP-DNN auto-encoder is trained using the utterances of a particular pass-phrase available in the target enrollment ... | ['Zheng-Hua Tan', 'Md Sahidullah', 'Achintya Kumar Sarkar'] | 2021-02-03 | null | null | null | null | ['text-dependent-speaker-verification'] | ['speech'] | [ 7.37451464e-02 -1.26706421e-01 1.67568877e-01 -6.41379237e-01
-1.22642124e+00 -3.83997679e-01 6.17810905e-01 -2.03062609e-01
-4.24737424e-01 5.36922336e-01 5.33387005e-01 -4.51684207e-01
5.29299796e-01 -4.99408007e-01 -5.76030672e-01 -9.07211840e-01
2.81581938e-01 4.29456264e-01 2.43981052e-02 -1.85897782... | [14.37320327758789, 6.1202239990234375] |
be2a4f25-ba68-4e11-afab-e28731fcab4b | causal-explanation-of-convolutional-neural | null | null | https://2021.ecmlpkdd.org/wp-content/uploads/2021/07/sub_287.pdf | https://2021.ecmlpkdd.org/wp-content/uploads/2021/07/sub_287.pdf | Causal Explanation of Convolutional Neural Networks | In this paper we introduce an explanation technique for Convolutional Neural Networks (CNNs) based on the theory of causality by Halpern and Pearl [12]. The causal explanation technique (CexCNN) is based on measuring the filter importance to a CNN decision, which is measured through counterfactual reasoning. In additio... | ['Hichem Debbi'] | 2021-09-13 | null | null | null | ecml-2021-9 | ['counterfactual-explanation'] | ['miscellaneous'] | [ 1.57572120e-01 5.38115144e-01 -1.33488357e-01 -4.23543483e-01
4.10050213e-01 -3.58658403e-01 8.21657777e-01 2.14973152e-01
-4.27013844e-01 7.44307101e-01 7.40056634e-01 -5.81296802e-01
-5.48398077e-01 -8.89249265e-01 -9.74794865e-01 -5.36032498e-01
-3.36066127e-01 -2.47595564e-01 1.65239885e-01 4.50780615... | [8.925890922546387, 5.551112174987793] |
a5a5151c-319f-4333-a6ba-a12e913f8b82 | training-a-first-order-theorem-prover-from | 2103.03798 | null | https://arxiv.org/abs/2103.03798v2 | https://arxiv.org/pdf/2103.03798v2.pdf | Training a First-Order Theorem Prover from Synthetic Data | A major challenge in applying machine learning to automated theorem proving is the scarcity of training data, which is a key ingredient in training successful deep learning models. To tackle this problem, we propose an approach that relies on training purely with synthetically generated theorems, without any human data... | ['Shibl Mourad', 'Doina Precup', 'Lei Zhang', 'Laurent Orseau', 'Xavier Glorot', 'Zafarali Ahmed', 'Ankit Anand', 'Eser Aygun', 'Vlad Firoiu'] | 2021-03-05 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 1.90846950e-01 6.24565899e-01 -5.80996946e-02 -1.09815402e-02
-8.32921982e-01 -6.79164827e-01 5.71481049e-01 3.13977264e-02
-2.02958420e-01 1.17701960e+00 -4.71851498e-01 -1.24404371e+00
-3.00143044e-02 -1.11386132e+00 -1.39569211e+00 1.98913172e-01
-1.63318932e-01 8.88609946e-01 3.98993611e-01 -6.06670320... | [8.925095558166504, 7.064830780029297] |
c01fefbc-c546-4ecf-9fd0-3cf6af0401f8 | covid-ct-mask-net-prediction-of-covid-19-from | null | null | https://www.medrxiv.org/content/10.1101/2020.10.11.20211052v1 | https://www.medrxiv.org/content/10.1101/2020.10.11.20211052v1.full.pdf | COVID-CT-Mask-Net: Prediction of COVID-19 from CT Scans Using Regional Features | We present COVID-CT-Mask-Net model that predicts COVID-19 from CT scans. The model works in two stages: first, it detects the instances of ground glass opacity and consolidation in CT scans, then predicts the condition from the ranked bounding box detections. To develop the solution for the three-class problem (COVID, ... | ['Aram Ter-Sarkisov'] | 2020-10-14 | null | null | null | null | ['covid-19-image-segmentation'] | ['computer-vision'] | [ 2.09186047e-01 -1.46543071e-01 -1.98695555e-01 -6.17636800e-01
-8.36276948e-01 -3.00024033e-01 1.07528001e-01 5.18279821e-02
-5.30623794e-01 5.97774625e-01 -5.78255355e-02 -3.95103127e-01
-2.34782901e-02 -8.29431653e-01 -6.37711406e-01 -7.81813025e-01
-2.04537615e-01 8.58132601e-01 6.45804048e-01 3.90725791... | [15.426780700683594, -1.87602961063385] |
59ad6309-eb13-444d-976d-9045ddd1605d | semantic-segmentation-of-skin-lesions-using-a | 1910.10534 | null | https://arxiv.org/abs/1910.10534v1 | https://arxiv.org/pdf/1910.10534v1.pdf | Semantic Segmentation of Skin Lesions using a Small Data Set | Early detection of melanoma is difficult for the human eye but a crucial step towards reducing its death rate. Computerized detection of these melanoma and other skin lesions is necessary. The central research question in this paper is "How to segment skin lesion images using a neural network with low available data?".... | ['Beril Sirmacek', 'Max Kivits'] | 2019-10-23 | null | null | null | null | ['small-data', 'skin-lesion-segmentation'] | ['computer-vision', 'medical'] | [ 6.14253163e-01 2.22562313e-01 -4.12714332e-01 -2.54815012e-01
-2.75759220e-01 -2.03774661e-01 9.94080231e-02 -1.77904248e-01
-8.10480714e-01 8.27968180e-01 -1.29188105e-01 -5.37681997e-01
-2.47911766e-01 -7.11311817e-01 -2.18801022e-01 -7.71872222e-01
1.47379145e-01 -2.84977406e-01 4.15875018e-01 -1.33048519... | [15.639915466308594, -3.009631872177124] |
6a1da082-21da-4143-bba6-7639fc26f685 | multi-branch-deep-radial-basis-function | 2109.03336 | null | https://arxiv.org/abs/2109.03336v1 | https://arxiv.org/pdf/2109.03336v1.pdf | Multi-Branch Deep Radial Basis Function Networks for Facial Emotion Recognition | Emotion recognition (ER) from facial images is one of the landmark tasks in affective computing with major developments in the last decade. Initial efforts on ER relied on handcrafted features that were used to characterize facial images and then feed to standard predictive models. Recent methodologies comprise end-to-... | ['Hugo Jair Escalante', 'Fernanda Hernández-Luquin'] | 2021-09-07 | null | null | null | null | ['facial-emotion-recognition', 'face-model'] | ['computer-vision', 'computer-vision'] | [ 8.33904594e-02 2.97743499e-01 -9.66631696e-02 -7.50843585e-01
-2.93906182e-01 -9.61779803e-02 7.43234754e-01 -8.43272917e-03
-3.23608696e-01 5.10196447e-01 2.28207961e-01 3.64689916e-01
-2.06008688e-01 -5.99805772e-01 -6.40644848e-01 -7.33484149e-01
-1.47394225e-01 2.10803226e-01 -2.80469686e-01 -4.05405253... | [13.563758850097656, 1.8396761417388916] |
407df860-dde2-4e2a-a03d-9fd05af90489 | regionclip-region-based-language-image | 2112.09106 | null | https://arxiv.org/abs/2112.09106v1 | https://arxiv.org/pdf/2112.09106v1.pdf | RegionCLIP: Region-based Language-Image Pretraining | Contrastive language-image pretraining (CLIP) using image-text pairs has achieved impressive results on image classification in both zero-shot and transfer learning settings. However, we show that directly applying such models to recognize image regions for object detection leads to poor performance due to a domain shi... | ['Jianfeng Gao', 'Yin Li', 'Lu Yuan', 'Xiyang Dai', 'Luowei Zhou', 'Liunian Harold Li', 'Noel Codella', 'Chunyuan Li', 'Pengchuan Zhang', 'Jianwei Yang', 'Yiwu Zhong'] | 2021-12-16 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhong_RegionCLIP_Region-Based_Language-Image_Pretraining_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhong_RegionCLIP_Region-Based_Language-Image_Pretraining_CVPR_2022_paper.pdf | cvpr-2022-1 | ['open-vocabulary-object-detection'] | ['computer-vision'] | [ 3.95766526e-01 -1.07444286e-01 -5.26862979e-01 -3.85684490e-01
-1.31905448e+00 -7.33796358e-01 9.08971906e-01 6.53043911e-02
-5.44198871e-01 1.78162843e-01 4.53567393e-02 2.72090454e-02
4.56494898e-01 -4.27902460e-01 -1.23325098e+00 -4.46915030e-01
3.87172312e-01 3.11884105e-01 3.76856625e-01 1.26877442... | [9.992239952087402, 1.6455931663513184] |
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