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
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5930e1af-7116-49fd-b5cd-76a214a2ecc1 | identifiable-causal-inference-with-noisy | 2306.10614 | null | https://arxiv.org/abs/2306.10614v1 | https://arxiv.org/pdf/2306.10614v1.pdf | Identifiable causal inference with noisy treatment and no side information | In some causal inference scenarios, the treatment (i.e. cause) variable is measured inaccurately, for instance in epidemiology or econometrics. Failure to correct for the effect of this measurement error can lead to biased causal effect estimates. Previous research has not studied methods that address this issue from a... | ['Pekka Marttinen', 'Antti Pöllänen'] | 2023-06-18 | null | null | null | null | ['causal-inference', 'epidemiology', 'econometrics', 'causal-inference'] | ['knowledge-base', 'medical', 'miscellaneous', 'miscellaneous'] | [ 5.46208978e-01 4.28083718e-01 -7.39158452e-01 -5.60808063e-01
-9.07618165e-01 -2.57201463e-01 6.56448901e-01 4.75747138e-03
-2.96909958e-01 1.07965040e+00 5.49425721e-01 -5.86446166e-01
-3.39576572e-01 -9.28559244e-01 -1.15353096e+00 -6.63338244e-01
5.15145846e-02 5.23755074e-01 -3.89410198e-01 4.64523017... | [8.04196548461914, 5.312739849090576] |
9772a007-01c6-4b1c-8c5b-fa7aaa66211c | piece-wise-matching-layer-in-representation | 2010.06510 | null | https://arxiv.org/abs/2010.06510v1 | https://arxiv.org/pdf/2010.06510v1.pdf | Piece-wise Matching Layer in Representation Learning for ECG Classification | This paper proposes piece-wise matching layer as a novel layer in representation learning methods for electrocardiogram (ECG) classification. Despite the remarkable performance of representation learning methods in the analysis of time series, there are still several challenges associated with these methods ranging fro... | ['Sixian Zhang', 'Fatemeh Afghah', 'Behzad Ghazanfari'] | 2020-09-26 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 3.70328546e-01 -3.13866884e-01 9.57377180e-02 -2.70822674e-01
-5.70552111e-01 -5.81510007e-01 4.25886065e-01 5.66857994e-01
-4.88033801e-01 6.01186216e-01 -4.02215235e-02 -3.59647214e-01
-7.93548465e-01 -6.30502820e-01 -2.88893998e-01 -6.55380070e-01
-5.93884766e-01 6.43417761e-02 1.61024585e-01 -2.54123658... | [14.288241386413574, 3.2783215045928955] |
a0ef53c1-719c-4ee9-89b9-0098669f21c6 | aware-of-the-history-trajectory-forecasting | 2207.09646 | null | https://arxiv.org/abs/2207.09646v1 | https://arxiv.org/pdf/2207.09646v1.pdf | Aware of the History: Trajectory Forecasting with the Local Behavior Data | The historical trajectories previously passing through a location may help infer the future trajectory of an agent currently at this location. Despite great improvements in trajectory forecasting with the guidance of high-definition maps, only a few works have explored such local historical information. In this work, w... | ['Ulrich Neumann', 'Siheng Chen', 'Zhenyang Ni', 'Yiqi Zhong'] | 2022-07-20 | null | null | null | null | ['trajectory-forecasting'] | ['computer-vision'] | [-4.86954600e-01 -3.22613388e-01 -5.30009568e-01 -5.52634358e-01
-5.27423024e-01 -5.49399495e-01 1.02824616e+00 4.21863705e-01
-2.89064646e-01 7.96681583e-01 7.35945940e-01 -4.41073120e-01
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-3.65152985e-01 4.12795335e-01 7.87906170e-01 -3.60758156... | [5.99837589263916, 0.9942309260368347] |
e3269aa8-cfd3-4f2d-a193-5e2d7304ac6f | a-fast-and-accurate-system-for-face-detection | 1809.07586 | null | http://arxiv.org/abs/1809.07586v1 | http://arxiv.org/pdf/1809.07586v1.pdf | A Fast and Accurate System for Face Detection, Identification, and Verification | The availability of large annotated datasets and affordable computation power
have led to impressive improvements in the performance of CNNs on various
object detection and recognition benchmarks. These, along with a better
understanding of deep learning methods, have also led to improved capabilities
of machine unders... | ['Jun-Cheng Chen', 'Rajeev Ranjan', 'Joshua Gleason', 'Jingxiao Zheng', 'Ankan Bansal', 'Anirudh Nanduri', 'Rama Chellappa', 'Hongyu Xu', 'Carlos D. Castillo', 'Boyu Lu'] | 2018-09-20 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [-2.07179338e-01 -4.31714833e-01 3.41781527e-02 -6.75339699e-01
-6.43258274e-01 -5.64302862e-01 5.75397789e-01 -6.95128202e-01
-1.99751899e-01 1.93110451e-01 -2.16681838e-01 1.93182215e-01
2.72694111e-01 -4.09936011e-01 -7.15730548e-01 -6.30169392e-01
-3.59904766e-01 3.93830329e-01 -8.83292258e-02 1.81270346... | [13.335994720458984, 0.7265997529029846] |
4fe189b6-126c-4e6e-a30e-6b06c7a6f24a | human-body-model-fitting-by-learned-gradient | 2008.08474 | null | https://arxiv.org/abs/2008.08474v1 | https://arxiv.org/pdf/2008.08474v1.pdf | Human Body Model Fitting by Learned Gradient Descent | We propose a novel algorithm for the fitting of 3D human shape to images. Combining the accuracy and refinement capabilities of iterative gradient-based optimization techniques with the robustness of deep neural networks, we propose a gradient descent algorithm that leverages a neural network to predict the parameter u... | ['Xu Chen', 'Otmar Hilliges', 'Jie Song'] | 2020-08-19 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3610_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123650732.pdf | eccv-2020-8 | ['image-to-3d'] | ['computer-vision'] | [-2.57768720e-01 1.11263327e-01 3.76669765e-02 -4.81608659e-01
-7.40831137e-01 -5.21052718e-01 5.50499141e-01 -1.71751246e-01
-9.51684415e-01 3.65961075e-01 2.69755185e-01 1.49129197e-01
9.55729559e-02 -1.89268529e-01 -1.01909089e+00 -3.31419468e-01
7.75252730e-02 1.25529432e+00 1.46661803e-01 -2.03976005... | [7.048025131225586, -0.9460397958755493] |
7bb85593-1c84-4d42-bf69-3c49a1b6fdee | integrating-knowledge-supported-search-into | null | null | https://aclanthology.org/D18-2022 | https://aclanthology.org/D18-2022.pdf | Integrating Knowledge-Supported Search into the INCEpTION Annotation Platform | Annotating entity mentions and linking them to a knowledge resource are essential tasks in many domains. It disambiguates mentions, introduces cross-document coreferences, and the resources contribute extra information, e.g. taxonomic relations. Such tasks benefit from text annotation tools that integrate a search whic... | ['Jan-Christoph Klie', 'Iryna Gurevych', 'Beto Boullosa', 'Naveen Kumar', 'Richard Eckart de Castilho'] | 2018-11-01 | null | null | null | emnlp-2018-11 | ['text-annotation'] | ['natural-language-processing'] | [-1.80607036e-01 6.45402789e-01 -5.59403539e-01 -8.15200359e-02
-6.33443594e-01 -1.03492725e+00 6.03145778e-01 1.06676042e+00
-5.99743366e-01 9.31805193e-01 5.13838828e-01 -1.17641836e-01
-3.89358521e-01 -8.31930339e-01 -1.53516039e-01 9.38982219e-02
1.70061544e-01 8.81472528e-01 8.49725008e-01 -2.67270476... | [9.333934783935547, 8.68156909942627] |
7bbf0a80-0fa2-40df-b9ef-d99f35fc9b14 | perceptual-image-restoration-with-high | 2103.03010 | null | https://arxiv.org/abs/2103.03010v1 | https://arxiv.org/pdf/2103.03010v1.pdf | Perceptual Image Restoration with High-Quality Priori and Degradation Learning | Perceptual image restoration seeks for high-fidelity images that most likely degrade to given images. For better visual quality, previous work proposed to search for solutions within the natural image manifold, by exploiting the latent space of a generative model. However, the quality of generated images are only guara... | ['Jianhua Lu', 'Xiaoming Tao', 'Yiping Duan', 'Chaoyi Han'] | 2021-03-04 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 4.38694566e-01 -4.63411398e-02 1.00156851e-01 -2.11003542e-01
-1.01573002e+00 -2.59668291e-01 3.05729717e-01 -4.08776969e-01
-1.15893222e-01 7.35600233e-01 4.00579214e-01 1.26257213e-02
-3.37853819e-01 -6.30085528e-01 -6.86639547e-01 -1.07808304e+00
1.03787921e-01 -2.67434835e-01 -2.17970580e-01 3.88356261... | [11.44950008392334, -2.1646368503570557] |
87f7b56c-d382-42f2-84e1-208b26f28e12 | multi-channel-speech-separation-using | 2304.12023 | null | https://arxiv.org/abs/2304.12023v1 | https://arxiv.org/pdf/2304.12023v1.pdf | Multi-channel Speech Separation Using Spatially Selective Deep Non-linear Filters | In a multi-channel separation task with multiple speakers, we aim to recover all individual speech signals from the mixture. In contrast to single-channel approaches, which rely on the different spectro-temporal characteristics of the speech signals, multi-channel approaches should additionally utilize the different sp... | ['Timo Gerkmann', 'Kristina Tesch'] | 2023-04-24 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 3.78979295e-01 -1.51575908e-01 2.12480739e-01 -1.64488867e-01
-9.85218883e-01 -5.75318635e-01 5.24547160e-01 -3.75877529e-01
-3.69712472e-01 5.94126344e-01 6.24589860e-01 -1.74246624e-01
-5.30052781e-01 -2.34482422e-01 -6.40806377e-01 -1.02475238e+00
-8.29208940e-02 1.23761021e-01 2.16471329e-01 -1.13548629... | [15.085515022277832, 5.770382404327393] |
b677dbdf-ec85-483d-b992-cf9a37bdaca7 | between-homomorphic-signal-processing-and | 1706.08231 | null | http://arxiv.org/abs/1706.08231v1 | http://arxiv.org/pdf/1706.08231v1.pdf | Between Homomorphic Signal Processing and Deep Neural Networks: Constructing Deep Algorithms for Polyphonic Music Transcription | This paper presents a new approach in understanding how deep neural networks
(DNNs) work by applying homomorphic signal processing techniques. Focusing on
the task of multi-pitch estimation (MPE), this paper demonstrates the
equivalence relation between a generalized cepstrum and a DNN in terms of their
structures and ... | ['Li Su'] | 2017-06-26 | null | null | null | null | ['music-transcription'] | ['music'] | [ 4.47523594e-02 1.08349502e-01 2.75915086e-01 5.63403107e-02
-1.30843684e-01 -4.62975472e-01 6.02142572e-01 2.33903736e-01
-6.42869174e-01 5.17766416e-01 4.08286840e-01 -2.73938160e-02
-4.30149019e-01 -5.37261486e-01 -4.83019412e-01 -9.35897768e-01
-1.63483173e-01 -2.71819592e-01 2.47401014e-01 -4.47264433... | [15.222196578979492, 5.518536567687988] |
ec27750f-7431-4625-9f6d-63100c8256fe | vitag-online-wifi-fine-time-measurements | null | null | https://www.winlab.rutgers.edu/~hansiiii/papers/ViTag_SECON2022_camera_ready_v6.pdf | https://www.winlab.rutgers.edu/~hansiiii/papers/ViTag_SECON2022_camera_ready_v6.pdf | ViTag: Online WiFi Fine Time Measurements Aided Vision-Motion Identity Association in Multi-person Environments | In this paper, we present ViTag to associate user identities across multimodal data, particularly those obtained from cameras and smartphones. ViTag associates a sequence of vision tracker generated bounding boxes with Inertial Measurement Unit (IMU) data and Wi-Fi Fine Time Measurements (FTM) from smartphones. We form... | ['Shubham Jain', 'Ashwin Ashok', 'Kristin Dana', 'Marco Gruteser', 'Nicholas Meegan', 'Hansi Liu', 'Abrar Alali', 'Bryan Bo Cao'] | 2022-09-20 | null | null | null | ieee-international-conference-on-sensing | ['multimodal-association'] | ['time-series'] | [ 5.00546753e-01 -2.84933299e-01 -1.53588196e-02 -2.70149767e-01
-9.86567318e-01 -7.30383694e-01 8.03484797e-01 -3.72057527e-01
-4.87909526e-01 8.92498791e-01 9.63507220e-02 5.85717149e-02
1.82940468e-01 -3.63729924e-01 -1.08275068e+00 -3.52238625e-01
1.51182443e-01 4.60635632e-01 -3.53407800e-01 2.32841194... | [14.53492259979248, 1.0206551551818848] |
f5b948a5-213c-4974-a9ef-cba40599a621 | specializing-joint-representations-for-the | 1706.07625 | null | http://arxiv.org/abs/1706.07625v2 | http://arxiv.org/pdf/1706.07625v2.pdf | Specializing Joint Representations for the task of Product Recommendation | We propose a unified product embedded representation that is optimized for
the task of retrieval-based product recommendation. To this end, we introduce a
new way to fuse modality-specific product embeddings into a joint product
embedding, in order to leverage both product content information, such as
textual descripti... | ['Vasile Flavian', 'Smirnova Elena', 'Nedelec Thomas'] | 2017-07-18 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-3.88430990e-02 -2.62155503e-01 -2.57419765e-01 -5.74247181e-01
-5.60291767e-01 -8.59074712e-01 5.64603388e-01 1.89635128e-01
-2.49736100e-01 -9.18689072e-02 4.02300328e-01 -2.53158063e-01
-1.99893340e-01 -6.92034483e-01 -4.98644948e-01 -4.25646514e-01
2.05155507e-01 4.19044763e-01 1.06971823e-01 -5.09181321... | [10.114974975585938, 5.715418815612793] |
f8832991-014d-4481-9b7a-86204c4b5863 | comae-a-multi-factor-hierarchical-framework | 2105.08316 | null | https://arxiv.org/abs/2105.08316v3 | https://arxiv.org/pdf/2105.08316v3.pdf | CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation | The capacity of empathy is crucial to the success of open-domain dialog systems. Due to its nature of multi-dimensionality, there are various factors that relate to empathy expression, such as communication mechanism, dialog act and emotion. However, existing methods for empathetic response generation usually either co... | ['Minlie Huang', 'Yongcai Leng', 'Wei Chen', 'Yong liu', 'Chujie Zheng'] | 2021-05-18 | null | https://aclanthology.org/2021.findings-acl.72 | https://aclanthology.org/2021.findings-acl.72.pdf | findings-acl-2021-8 | ['empathetic-response-generation', 'open-domain-dialog'] | ['natural-language-processing', 'natural-language-processing'] | [-6.78010225e-01 -5.37738483e-03 -7.92870224e-02 -4.21500325e-01
-1.54015750e-01 -3.43372375e-01 4.17943865e-01 -2.33857259e-01
-2.00455070e-01 8.12787294e-01 7.93670952e-01 5.77837527e-02
-2.25805834e-01 -5.67766726e-01 2.92667061e-01 -3.80551815e-01
4.01917815e-01 4.39768195e-01 -2.22350955e-01 -6.91129029... | [13.198698043823242, 7.582991123199463] |
e58600bf-94cc-40e5-9fcc-8ce35e74dc29 | comparing-a-composite-model-versus-chained | 2306.01551 | null | https://arxiv.org/abs/2306.01551v1 | https://arxiv.org/pdf/2306.01551v1.pdf | Comparing a composite model versus chained models to locate a nearest visual object | Extracting information from geographic images and text is crucial for autonomous vehicles to determine in advance the best cell stations to connect to along their future path. Multiple artificial neural network models can address this challenge; however, there is no definitive guidance on the selection of an appropriat... | ['Tayeb Lemlouma', 'Fanny Parzysz', 'Xavier Marjou', 'Antoine Le Borgne'] | 2023-06-02 | null | null | null | null | ['autonomous-vehicles'] | ['computer-vision'] | [-9.46299806e-02 2.13156849e-01 -3.31532687e-01 -4.42601830e-01
-6.21134818e-01 -4.27970469e-01 5.58696568e-01 -7.71459788e-02
-4.74873364e-01 8.29303145e-01 4.22927067e-02 -9.81348753e-01
-1.70910150e-01 -6.20811880e-01 -4.89272863e-01 -6.81479752e-01
6.19394258e-02 4.25446719e-01 -1.87662542e-02 1.75325554... | [6.5575971603393555, 1.9241260290145874] |
e5113f1b-c83d-433f-8255-434ea1f58dd6 | an-empirical-study-on-neural-keyphrase | 2009.10229 | null | https://arxiv.org/abs/2009.10229v3 | https://arxiv.org/pdf/2009.10229v3.pdf | An Empirical Study on Neural Keyphrase Generation | Recent years have seen a flourishing of neural keyphrase generation (KPG) works, including the release of several large-scale datasets and a host of new models to tackle them. Model performance on KPG tasks has increased significantly with evolving deep learning research. However, there lacks a comprehensive comparison... | ['Adam Trischler', 'Tong Wang', 'Xingdi Yuan', 'Sanqiang Zhao', 'Rui Meng', 'Daqing He'] | 2020-09-22 | null | https://aclanthology.org/2021.naacl-main.396 | https://aclanthology.org/2021.naacl-main.396.pdf | naacl-2021-4 | ['keyphrase-generation'] | ['natural-language-processing'] | [-1.75923601e-01 -2.20394596e-01 -4.60435778e-01 -6.55751005e-02
-5.35383403e-01 -3.64254981e-01 9.02144551e-01 5.87393828e-02
-5.22784472e-01 7.84806609e-01 4.82427329e-01 -6.53830290e-01
-3.14513743e-01 -8.09701443e-01 -7.32562304e-01 -5.28051972e-01
-1.19351700e-01 -1.67748276e-02 7.84050301e-03 -2.32320771... | [10.756481170654297, 8.282617568969727] |
1293a324-f525-46e7-8816-9274b057b14a | knowledge-prompted-estimator-a-novel-approach | 2306.07486 | null | https://arxiv.org/abs/2306.07486v1 | https://arxiv.org/pdf/2306.07486v1.pdf | Knowledge-Prompted Estimator: A Novel Approach to Explainable Machine Translation Assessment | Cross-lingual Machine Translation (MT) quality estimation plays a crucial role in evaluating translation performance. GEMBA, the first MT quality assessment metric based on Large Language Models (LLMs), employs one-step prompting to achieve state-of-the-art (SOTA) in system-level MT quality estimation; however, it lack... | ['Yanfei Jiang', 'Daimeng Wei', 'Minghan Wang', 'Shimin Tao', 'Min Zhang', 'Hao Yang'] | 2023-06-13 | null | null | null | null | ['machine-translation'] | ['natural-language-processing'] | [ 1.59980040e-02 -1.02405213e-01 -6.37439013e-01 -3.60079199e-01
-1.81037366e+00 -6.11607492e-01 8.46759677e-01 3.04918498e-01
-3.24987769e-01 7.28657842e-01 5.70201039e-01 -7.56189287e-01
5.73835671e-02 -3.51079106e-01 -7.80089974e-01 -1.18554980e-01
4.69084769e-01 7.54744768e-01 -2.08241329e-01 -3.95350784... | [11.666573524475098, 10.285221099853516] |
28017177-05b1-45e5-8303-c6c6a87b5339 | sparse-message-passing-network-with-feature | 2212.02992 | null | https://arxiv.org/abs/2212.02992v1 | https://arxiv.org/pdf/2212.02992v1.pdf | Sparse Message Passing Network with Feature Integration for Online Multiple Object Tracking | Existing Multiple Object Tracking (MOT) methods design complex architectures for better tracking performance. However, without a proper organization of input information, they still fail to perform tracking robustly and suffer from frequent identity switches. In this paper, we propose two novel methods together with a ... | ['Guo Cao', 'Horst Bischof', 'Horst Possegger', 'Bisheng Wang'] | 2022-12-06 | null | null | null | null | ['multiple-object-tracking'] | ['computer-vision'] | [-1.71216547e-01 -2.92224914e-01 -3.59606683e-01 1.42417746e-02
-3.56809318e-01 -5.72716773e-01 6.43275619e-01 1.26726687e-01
-4.31293577e-01 6.82345688e-01 -1.85467660e-01 -8.45109895e-02
-1.11039191e-01 -7.44793594e-01 -8.20079446e-01 -5.98830402e-01
-4.65313047e-01 6.09808564e-01 9.06174064e-01 4.63432372... | [6.310306549072266, -2.079286813735962] |
7bc66d44-fa3a-4487-9ca0-f7b6e284a9fb | constructing-topological-maps-using-markov | null | null | http://papers.nips.cc/paper/3861-constructing-topological-maps-using-markov-random-fields-and-loop-closure-detection | http://papers.nips.cc/paper/3861-constructing-topological-maps-using-markov-random-fields-and-loop-closure-detection.pdf | Constructing Topological Maps using Markov Random Fields and Loop-Closure Detection | We present a system which constructs a topological map of an environment given a sequence of images. This system includes a novel image similarity score which uses dynamic programming to match images using both the appearance and relative positions of local features simultaneously. Additionally an MRF is constructed to... | ['Roy Anati', 'Kostas Daniilidis'] | 2009-12-01 | null | null | null | neurips-2009-12 | ['loop-closure-detection'] | ['computer-vision'] | [ 3.98044974e-01 -1.20682217e-01 3.13259698e-02 -8.11816931e-01
-7.25176513e-01 -5.00761271e-01 7.85593390e-01 4.75622296e-01
-2.47127026e-01 3.23352277e-01 1.35372654e-02 -2.28388920e-01
-4.30532247e-02 -8.06366444e-01 -9.21049833e-01 -2.43720114e-01
-4.75092769e-01 4.97019082e-01 9.13460553e-01 -2.72676855... | [7.556637287139893, -2.052532911300659] |
05c06649-0d35-446f-a3c0-f35284d650dd | detection-and-tracking-of-fingertips-for | null | null | https://ieeexplore.ieee.org/document/9035256 | https://ieeexplore.ieee.org/document/9035256 | Detection and tracking of fingertips for geometric transformation of objects in virtual environment | This paper presents an approach of two-stage convolutional neural network (CNN) for detection of fingertips so that an interaction of the fingertips with a 3D object in the virtual environment (VR) can be established. The first-stage CNN is assigned to detect and locate the hand. Subsequently, the detected hand is crop... | ['S. M. Mahbubur Rahman', 'Mohammad Mahmudul Alam'] | 2020-03-16 | null | null | null | null | ['hand-detection', 'fingertip-detection'] | ['computer-vision', 'computer-vision'] | [-7.27476925e-02 -5.11153638e-01 1.97114497e-01 6.48712516e-02
7.27698207e-02 -7.38960147e-01 3.85744214e-01 -4.85963702e-01
-5.01980543e-01 -9.36711952e-02 -3.60108197e-01 -1.72448635e-01
-3.00641265e-03 -5.11369944e-01 -4.30032104e-01 -3.59283894e-01
3.44233550e-02 -4.59905155e-02 4.42183167e-01 -7.32447580... | [6.476024627685547, -0.40534862875938416] |
f7bb8058-721b-49ac-b9c4-f63709270769 | improving-robustness-and-accuracy-via | 2107.13994 | null | https://arxiv.org/abs/2107.13994v1 | https://arxiv.org/pdf/2107.13994v1.pdf | Improving Robustness and Accuracy via Relative Information Encoding in 3D Human Pose Estimation | Most of the existing 3D human pose estimation approaches mainly focus on predicting 3D positional relationships between the root joint and other human joints (local motion) instead of the overall trajectory of the human body (global motion). Despite the great progress achieved by these approaches, they are not robust t... | ['Wen Gao', 'Xinfeng Zhang', 'Shanshe Wang', 'Haopeng Lu', 'Wenkang Shan'] | 2021-07-29 | null | null | null | null | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-9.63815376e-02 -2.72963941e-01 -3.74867648e-01 -1.90284416e-01
-3.80946904e-01 -2.27122039e-01 3.75070602e-01 -1.87159926e-01
-3.22147489e-01 5.08708775e-01 6.06637955e-01 4.45968568e-01
-1.38090760e-01 -6.77935481e-01 -5.01796842e-01 -5.49202323e-01
-1.79600000e-01 4.00748253e-01 3.71898234e-01 -3.86858702... | [7.135378837585449, -0.6709673404693604] |
f4ea8df5-5798-4443-8072-e8deeba4c6eb | data-augmentation-for-opcode-sequence-based | 2106.11821 | null | https://arxiv.org/abs/2106.11821v2 | https://arxiv.org/pdf/2106.11821v2.pdf | Data Augmentation for Opcode Sequence Based Malware Detection | In this paper we study data augmentation for opcode sequence based Android malware detection. Data augmentation has been successfully used in many areas of deep-learning to significantly improve model performance. Typically, data augmentation simulates realistic variations in data to increase the apparent diversity of ... | ['Jesus Martinez del Rincon', 'Niall McLaughlin'] | 2021-06-22 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 7.71313608e-01 1.29472762e-01 -4.20043945e-01 -3.01660925e-01
-1.54539898e-01 -3.37021589e-01 7.17329681e-01 2.15708539e-01
-4.14730906e-01 3.42129588e-01 -7.39101917e-02 -8.86810958e-01
4.27093983e-01 -6.20103240e-01 -5.93755066e-01 -4.77445632e-01
-3.93842727e-01 5.05958915e-01 1.72561333e-01 -4.64438498... | [14.423800468444824, 9.681509017944336] |
c3a6c01e-5312-4e84-8381-29fecb666ac8 | le-trading-algorithmique | 0810.4000 | null | https://arxiv.org/abs/0810.4000v2 | https://arxiv.org/pdf/0810.4000v2.pdf | Le trading algorithmique | The algorithmic trading comes from digitalisation of the processing of trading assets on financial markets. Since 1980 the computerization of the stock market offers real time processing of financial information. This technological revolution has offered processes and mathematic methods to identify best return on trans... | ['Victor Lebreton'] | 2008-10-22 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-5.40418863e-01 2.16584504e-01 2.92495620e-02 -8.57871696e-02
4.21808571e-01 -9.66482341e-01 9.21952963e-01 -1.36829719e-01
-6.99014485e-01 7.47455001e-01 -3.02030712e-01 -5.12192786e-01
-1.52119160e-01 -1.37894392e+00 -8.57786164e-02 -5.27300477e-01
-6.11650586e-01 1.23882723e+00 5.25638461e-01 -7.38119364... | [4.570631980895996, 4.062162399291992] |
ae80feac-fbae-4250-87ea-ef22f80cf83e | android-malware-detection-using-deep-learning | 1712.08996 | null | http://arxiv.org/abs/1712.08996v1 | http://arxiv.org/pdf/1712.08996v1.pdf | Android Malware Detection using Deep Learning on API Method Sequences | Android OS experiences a blazing popularity since the last few years. This
predominant platform has established itself not only in the mobile world but
also in the Internet of Things (IoT) devices. This popularity, however, comes
at the expense of security, as it has become a tempting target of malicious
apps. Hence, t... | ['Mourad Debbabi', 'ElMouatez Billah Karbab', 'Abdelouahid Derhab', 'Djedjiga Mouheb'] | 2017-12-25 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 7.37318397e-02 -3.98748726e-01 -5.92585683e-01 8.37198347e-02
-3.86849195e-01 -7.99799681e-01 5.15797257e-01 -1.45474896e-01
8.81229714e-02 3.80220503e-01 -3.19177628e-01 -6.48182929e-01
2.69629300e-01 -6.16149724e-01 -4.59447622e-01 -5.92082381e-01
-1.11510769e-01 1.74449176e-01 5.47361493e-01 9.25152898... | [14.42257022857666, 9.681090354919434] |
0b9fccb0-c16b-4671-ab25-9689325e9739 | distortion-adaptive-grape-bunch-counting-for | 2008.12511 | null | https://arxiv.org/abs/2008.12511v1 | https://arxiv.org/pdf/2008.12511v1.pdf | Distortion-Adaptive Grape Bunch Counting for Omnidirectional Images | This paper proposes the first object counting method for omnidirectional images. Because conventional object counting methods cannot handle the distortion of omnidirectional images, we propose to process them using stereographic projection, which enables conventional methods to obtain a good approximation of the densit... | ['Yuzuko Utsumi', 'Ryota Akai', 'Yuka Miwa', 'Koichi Kise', 'Masakazu Iwamura'] | 2020-08-28 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 1.71748862e-01 -1.42126098e-01 3.19949090e-01 -4.22578186e-01
-1.12931654e-01 -4.55990136e-01 8.93510342e-01 -1.80886641e-01
-7.60602832e-01 5.86631179e-01 1.72512993e-01 -3.28639567e-01
1.64490685e-01 -1.00015414e+00 -5.59813619e-01 -5.16495585e-01
3.65503311e-01 5.33708632e-01 2.47227296e-01 4.11167383... | [8.895583152770996, -2.49094295501709] |
63011ae4-7a66-46f0-897a-66a86dcfa4a8 | informative-visual-storytelling-with-cross | 1907.03240 | null | https://arxiv.org/abs/1907.03240v2 | https://arxiv.org/pdf/1907.03240v2.pdf | Informative Visual Storytelling with Cross-modal Rules | Existing methods in the Visual Storytelling field often suffer from the problem of generating general descriptions, while the image contains a lot of meaningful contents remaining unnoticed. The failure of informative story generation can be concluded to the model's incompetence of capturing enough meaningful concepts.... | ['Siliang Tang', 'Jiacheng Li', 'Haizhou Shi', 'Yueting Zhuang', 'Fei Wu'] | 2019-07-07 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [ 2.68071234e-01 1.83704510e-01 -1.84967905e-01 -3.18925172e-01
-6.46986485e-01 -3.61190081e-01 8.43418539e-01 1.42377377e-01
-1.58801824e-01 8.40457559e-01 7.49002814e-01 -1.38433486e-01
6.93465322e-02 -8.20930123e-01 -8.60658646e-01 -5.67862570e-01
-2.26509944e-02 3.06866527e-01 1.26781538e-01 -3.57422471... | [11.090639114379883, 0.7280737161636353] |
857716f6-12d5-455a-af37-b9c338da47e4 | statistical-spatial-analysis-for-cryo | 2107.06738 | null | https://arxiv.org/abs/2107.06738v1 | https://arxiv.org/pdf/2107.06738v1.pdf | Statistical spatial analysis for cryo-electron tomography | Cryo-electron tomography (cryo-ET) is uniquely suited to precisely localize macromolecular complexes in situ, that is in a close-to-native state within their cellular compartments, in three-dimensions at high resolution. Point pattern analysis (PPA) allows quantitative characterization of the spatial organization of pa... | ['Vladan Lučić', 'Wolfgang Baumeister', 'Antonio Martinez-Sanchez'] | 2021-07-14 | null | null | null | null | ['electron-tomography'] | ['medical'] | [-1.54563963e-01 -5.95302343e-01 6.12153471e-01 8.97763595e-02
-2.95375437e-01 -6.57396793e-01 5.57093561e-01 5.84061861e-01
-7.46324718e-01 9.33795452e-01 -4.65680212e-01 -3.26908857e-01
-3.62486869e-01 -9.18972552e-01 -5.05613863e-01 -1.13151455e+00
-2.18786463e-01 1.18987215e+00 4.66316760e-01 1.35507479... | [13.416158676147461, -3.05718731880188] |
a60765b2-846b-4227-9f67-41162584ba84 | starvqa-co-training-space-time-attention-for | 2306.12298 | null | https://arxiv.org/abs/2306.12298v1 | https://arxiv.org/pdf/2306.12298v1.pdf | StarVQA+: Co-training Space-Time Attention for Video Quality Assessment | Self-attention based Transformer has achieved great success in many computer vision tasks. However, its application to video quality assessment (VQA) has not been satisfactory so far. Evaluating the quality of in-the-wild videos is challenging due to the unknown of pristine reference and shooting distortion. This paper... | ['Sam Kwong', 'Guopu Zhu', 'Weixuan Tang', 'Yuan-Gen Wang', 'Fengchuang Xing'] | 2023-06-21 | null | null | null | null | ['video-quality-assessment', 'video-quality-assessment'] | ['computer-vision', 'time-series'] | [-3.24333876e-01 -5.77918589e-01 1.80278942e-01 -4.21484828e-01
-9.87496018e-01 -8.10351744e-02 9.93511230e-02 -1.73069745e-01
-3.21723491e-01 4.56153661e-01 4.52893823e-01 -7.35342205e-02
-4.67322730e-02 -5.02878487e-01 -6.76685035e-01 -5.99166095e-01
-5.73594794e-02 -7.50276670e-02 6.76056370e-02 -2.21828625... | [11.662569999694824, -1.7746471166610718] |
ff2594be-e9b1-48f3-8370-6dcfc2412787 | positive-and-unlabeled-learning-through | 1805.07331 | null | http://arxiv.org/abs/1805.07331v2 | http://arxiv.org/pdf/1805.07331v2.pdf | Positive and Unlabeled Learning through Negative Selection and Imbalance-aware Classification | Motivated by applications in protein function prediction, we consider a
challenging supervised classification setting in which positive labels are
scarce and there are no explicit negative labels. The learning algorithm must
thus select which unlabeled examples to use as negative training points,
possibly ending up wit... | ['Nicolò Cesa-Bianchi', 'Marco Frasca'] | 2018-05-18 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 7.34079778e-01 4.41512942e-01 -8.25423300e-01 -5.23745298e-01
-9.77209032e-01 -6.38344824e-01 1.70327917e-01 7.80457556e-01
-5.40590465e-01 1.21833444e+00 -2.53988028e-01 -3.65445048e-01
-2.02363580e-02 -5.39624453e-01 -6.65290594e-01 -9.69344020e-01
-1.29676670e-01 9.61068988e-01 2.76094079e-01 1.04442909... | [9.504339218139648, 4.015408992767334] |
2dbb871d-8143-4eed-b679-5b7ae321ea1a | eventbert-a-pre-trained-model-for-event | 2110.06533 | null | https://arxiv.org/abs/2110.06533v1 | https://arxiv.org/pdf/2110.06533v1.pdf | EventBERT: A Pre-Trained Model for Event Correlation Reasoning | Event correlation reasoning infers whether a natural language paragraph containing multiple events conforms to human common sense. For example, "Andrew was very drowsy, so he took a long nap, and now he is very alert" is sound and reasonable. In contrast, "Andrew was very drowsy, so he stayed up a long time, now he is ... | ['Daxin Jiang', 'Guodong Long', 'Tao Shen', 'Xiubo Geng', 'Yucheng Zhou'] | 2021-10-13 | null | null | null | null | ['cloze-test'] | ['natural-language-processing'] | [ 1.39928460e-01 -9.22688842e-02 -2.57450968e-01 -2.88390428e-01
-8.36350739e-01 -6.43916845e-01 8.56407106e-01 5.24388611e-01
-3.60788316e-01 9.50641155e-01 7.32030571e-01 -3.56807381e-01
-2.62424886e-01 -9.02108371e-01 -6.10888541e-01 -2.25563258e-01
1.36673242e-01 5.70124209e-01 4.35140580e-01 -3.09383452... | [11.100076675415039, 8.878588676452637] |
a4efd1be-5f4e-4238-a4f2-fd43fedbd321 | hat-hierarchical-aggregation-transformers-for | 2107.05946 | null | https://arxiv.org/abs/2107.05946v2 | https://arxiv.org/pdf/2107.05946v2.pdf | HAT: Hierarchical Aggregation Transformers for Person Re-identification | Recently, with the advance of deep Convolutional Neural Networks (CNNs), person Re-Identification (Re-ID) has witnessed great success in various applications. However, with limited receptive fields of CNNs, it is still challenging to extract discriminative representations in a global view for persons under non-overlapp... | ['Huchuan Lu', 'Jinqing Qi', 'Pingping Zhang', 'Guowen Zhang'] | 2021-07-13 | null | null | null | null | ['person-retrieval'] | ['computer-vision'] | [-2.10889757e-01 -5.94884753e-01 -5.03647700e-03 -5.38969278e-01
-4.62772340e-01 -2.23360047e-01 6.16066754e-01 -7.22076232e-03
-4.32174295e-01 5.19054472e-01 4.73186821e-01 3.72620940e-01
-1.67003527e-01 -8.61141145e-01 -4.67177540e-01 -5.89130282e-01
2.80473202e-01 2.95341402e-01 7.52333626e-02 -1.76230967... | [14.714652061462402, 0.9342683553695679] |
f974b709-f7cf-4197-aa1e-44a65a989fdb | flexible-and-scalable-state-tracking | 1811.12891 | null | http://arxiv.org/abs/1811.12891v1 | http://arxiv.org/pdf/1811.12891v1.pdf | Flexible and Scalable State Tracking Framework for Goal-Oriented Dialogue Systems | Goal-oriented dialogue systems typically rely on components specifically
developed for a single task or domain. This limits such systems in two
different ways: If there is an update in the task domain, the dialogue system
usually needs to be updated or completely re-trained. It is also harder to
extend such dialogue sy... | ['Dilek Hakkani-Tur', 'Tagyoung Chung', 'Shachi Paul', 'Jeremie Lecomte', 'Arindam Mandal', 'Rahul Goel'] | 2018-11-30 | null | null | null | null | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [-5.55890650e-02 3.04124862e-01 -3.04278851e-01 -4.89340305e-01
-3.94319266e-01 -1.01992369e+00 9.98858213e-01 1.81732073e-01
-5.62055588e-01 9.21375632e-01 2.35625833e-01 -3.75735015e-01
2.33835146e-01 -7.57822812e-01 -1.08883873e-01 -2.54524380e-01
8.22378173e-02 8.68224919e-01 6.74534082e-01 -8.78316641... | [12.881855010986328, 7.875612735748291] |
e637d8a0-c4da-49c4-8867-829037fb308b | dcdetector-dual-attention-contrastive | 2306.10347 | null | https://arxiv.org/abs/2306.10347v1 | https://arxiv.org/pdf/2306.10347v1.pdf | DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection | Time series anomaly detection is critical for a wide range of applications. It aims to identify deviant samples from the normal sample distribution in time series. The most fundamental challenge for this task is to learn a representation map that enables effective discrimination of anomalies. Reconstruction-based metho... | ['Liang Sun', 'Qingsong Wen', 'Tian Zhou', 'Chaoli Zhang', 'Yiyuan Yang'] | 2023-06-17 | null | null | null | null | ['contrastive-learning', 'contrastive-learning', 'anomaly-detection', 'time-series-anomaly-detection'] | ['computer-vision', 'methodology', 'methodology', 'time-series'] | [ 8.45155269e-02 -7.39427567e-01 1.82928324e-01 -2.56309420e-01
-7.89424241e-01 -4.62176800e-01 5.20602584e-01 3.35468739e-01
-7.46761113e-02 2.26090342e-01 6.58573806e-02 -3.52236778e-01
-2.74091065e-01 -7.31642663e-01 -5.56922317e-01 -9.51840758e-01
-4.81602758e-01 3.06345284e-01 -8.98137987e-02 -3.34549189... | [7.562896728515625, 2.37819242477417] |
3865edd1-ec5d-4c51-9771-757661f24039 | multi-omic-data-integration-and-feature | 2206.10699 | null | https://arxiv.org/abs/2206.10699v2 | https://arxiv.org/pdf/2206.10699v2.pdf | Multi-Omic Data Integration and Feature Selection for Survival-based Patient Stratification via Supervised Concrete Autoencoders | Cancer is a complex disease with significant social and economic impact. Advancements in high-throughput molecular assays and the reduced cost for performing high-quality multi-omics measurements have fuelled insights through machine learning . Previous studies have shown promise on using multiple omic layers to predic... | ['Sophia Tsoka', 'Min Wu', 'Sophia Karagiannis', 'Roman Laddach', 'Pedro Henrique da Costa Avelar'] | 2022-06-21 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-1.12078495e-01 -2.44539678e-01 -2.95068383e-01 -3.43683690e-01
-4.79717195e-01 -1.81501403e-01 5.99227428e-01 7.17651367e-01
-3.63688260e-01 7.46593893e-01 5.91266453e-01 -3.52033198e-01
-4.64777648e-01 -7.81369567e-01 -3.80787432e-01 -9.16778207e-01
-2.68321067e-01 5.56695938e-01 -3.94712240e-01 -8.35160539... | [6.07305383682251, 5.680384635925293] |
c5063656-d65f-4545-b73e-938e70aeea54 | hyperspectral-image-reconstruction-from | 2209.07891 | null | https://arxiv.org/abs/2209.07891v1 | https://arxiv.org/pdf/2209.07891v1.pdf | Hyperspectral Image Reconstruction from Multispectral Images Using Non-Local Filtering | Using light spectra is an essential element in many applications, for example, in material classification. Often this information is acquired by using a hyperspectral camera. Unfortunately, these cameras have some major disadvantages like not being able to record videos. Therefore, multispectral cameras with wide-band ... | ['André Kaup', 'Jürgen Seiler', 'Frank Sippel'] | 2022-09-16 | null | null | null | null | ['spectral-reconstruction', 'material-classification'] | ['computer-vision', 'computer-vision'] | [ 8.60411525e-01 -6.70581579e-01 1.63995996e-01 4.09064591e-02
-6.76004827e-01 -4.99711454e-01 3.58010054e-01 1.95263565e-01
-5.32538295e-01 8.04821014e-01 -3.11159819e-01 4.56607901e-02
-4.49933171e-01 -1.04770029e+00 -5.70527732e-01 -1.23148465e+00
4.23771054e-01 -2.25944072e-01 2.43130714e-01 1.73220485... | [10.182194709777832, -2.2293944358825684] |
1931b48a-0ce0-45bf-8049-19ca54d3b039 | dsamnet-a-deeply-supervised-attention-metric | null | null | https://ieeexplore.ieee.org/document/9555146 | https://ieeexplore.ieee.org/document/9555146 | DSAMNet: A Deeply Supervised Attention Metric Based Network for Change Detection of High-Resolution Images | In view of the insufficient of current change detection, we propose a deeply-supervised attention metric-based network (DSAMNet) for bi-temporal image change detection. The DSAMNet contains a CBAM integrated change decision module to learn a change map directly from features from feature extractor, and an auxiliary dee... | ['Qian Shi', 'Mengxi Liu'] | 2021-10-12 | null | null | null | ieee-international-geoscience-and-remote | ['change-detection', 'change-detection-for-remote-sensing-images'] | ['computer-vision', 'miscellaneous'] | [ 1.26891583e-01 -5.43264985e-01 1.41552195e-01 -5.21236658e-01
-4.36526656e-01 -4.32092696e-03 6.30014956e-01 -3.77900660e-01
-4.74581122e-01 5.31005323e-01 1.13952383e-01 -4.97060567e-02
2.65019476e-01 -9.00308013e-01 -6.65280581e-01 -6.91018999e-01
-2.56904006e-01 -7.95290992e-02 4.60447252e-01 -3.07400465... | [9.64319133758545, -1.1186565160751343] |
b2a3cc6a-8328-458b-94e3-7a9fe38085f6 | is-someone-speaking-exploring-long-term | 2107.06592 | null | https://arxiv.org/abs/2107.06592v2 | https://arxiv.org/pdf/2107.06592v2.pdf | Is Someone Speaking? Exploring Long-term Temporal Features for Audio-visual Active Speaker Detection | Active speaker detection (ASD) seeks to detect who is speaking in a visual scene of one or more speakers. The successful ASD depends on accurate interpretation of short-term and long-term audio and visual information, as well as audio-visual interaction. Unlike the prior work where systems make decision instantaneously... | ['Haizhou Li', 'Mike Zheng Shou', 'Xinyuan Qian', 'Rohan Kumar Das', 'Zexu Pan', 'Ruijie Tao'] | 2021-07-14 | null | null | null | null | ['audio-visual-active-speaker-detection'] | ['computer-vision'] | [-2.04140827e-01 -9.51256081e-02 1.05305739e-01 -6.75043046e-01
-1.23605275e+00 -5.94616055e-01 7.06108391e-01 -1.32557884e-01
-1.89571828e-01 2.46576980e-01 6.65418625e-01 -2.10639253e-01
2.61887193e-01 -2.27078304e-01 -5.07397234e-01 -6.46951795e-01
-1.14339896e-01 5.57515472e-02 2.58217782e-01 2.84614153... | [14.432462692260742, 5.13247013092041] |
2888ea73-2955-4264-ac26-30df8a6a8b1c | iov-scenario-implementation-of-a-bandwidth | 2202.03488 | null | https://arxiv.org/abs/2202.03488v1 | https://arxiv.org/pdf/2202.03488v1.pdf | IoV Scenario: Implementation of a Bandwidth Aware Algorithm in Wireless Network Communication Mode | The wireless network communication mode represented by the Internet of vehicles (IoV) has been widely used. However, due to the limitations of traditional network architecture, resource scheduling in wireless network environment is still facing great challenges. This paper focuses on the allocation of bandwidth resourc... | ['Mohsen Guizani', 'Neeraj Kumar', 'Gagangeet Singh Aujla', 'Chao Wang', 'Peiying Zhang'] | 2022-02-03 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-2.81937480e-01 -2.27221712e-01 -4.85360801e-01 1.56211793e-01
5.24561703e-01 -9.20273960e-02 1.22464746e-01 -3.35860193e-01
-4.85864580e-01 1.06631839e+00 -3.09392303e-01 -5.32105029e-01
-6.77910089e-01 -1.21934462e+00 4.96810935e-02 -5.85935354e-01
-1.96734145e-01 4.89930600e-01 4.22754616e-01 -3.28012228... | [5.8687872886657715, 1.7084227800369263] |
ad786363-5292-4684-b5ac-d32a686fedaf | human-skeletons-and-change-detection-for | null | null | https://www.sciencedirect.com/science/article/pii/S1077314223001194 | https://www.sciencedirect.com/science/article/pii/S1077314223001194 | Human skeletons and change detection for efficient violence detection in surveillance videos | In our constantly monitored world, surveillance cameras play a crucial role in curbing crime and violence in public spaces by serving as a deterrent. To enhance their effectiveness, there is a growing need for automated tools that can detect crimes in real time. In this paper, we propose a novel deep learning architect... | ['Juan C. San Miguel', 'Guillermo Garcia-Cobo'] | 2023-05-20 | null | null | null | computer-vision-and-image-understanding-2023 | ['change-detection'] | ['computer-vision'] | [ 2.12965161e-01 -2.94641376e-01 1.02650076e-01 -1.09617390e-01
-4.10349399e-01 -5.13216496e-01 6.18469238e-01 9.56927985e-03
-8.06366861e-01 4.69989061e-01 -5.11710858e-03 -1.49186850e-01
1.10785484e-01 -9.20228124e-01 -7.53467739e-01 -7.62804210e-01
1.21785803e-02 -1.67634189e-01 5.59138417e-01 -1.76751286... | [7.9570512771606445, 0.7918953895568848] |
30941d1a-ee1f-4afb-8400-e7a268724b6c | robust-structured-declarative-classifiers-for | 2203.15245 | null | https://arxiv.org/abs/2203.15245v1 | https://arxiv.org/pdf/2203.15245v1.pdf | Robust Structured Declarative Classifiers for 3D Point Clouds: Defending Adversarial Attacks with Implicit Gradients | Deep neural networks for 3D point cloud classification, such as PointNet, have been demonstrated to be vulnerable to adversarial attacks. Current adversarial defenders often learn to denoise the (attacked) point clouds by reconstruction, and then feed them to the classifiers as input. In contrast to the literature, we ... | ['Guanghui Wang', 'Cuncong Zhong', 'Ziming Zhang', 'Kaidong Li'] | 2022-03-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Robust_Structured_Declarative_Classifiers_for_3D_Point_Clouds_Defending_Adversarial_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Robust_Structured_Declarative_Classifiers_for_3D_Point_Clouds_Defending_Adversarial_CVPR_2022_paper.pdf | cvpr-2022-1 | ['point-cloud-classification'] | ['computer-vision'] | [-2.91121691e-01 -2.38067936e-02 6.48805723e-02 -2.60363847e-01
-9.19715345e-01 -1.23192918e+00 6.48197412e-01 -1.92208529e-01
-2.61910796e-01 2.27674380e-01 -3.12935829e-01 -6.09755278e-01
-2.21669860e-02 -9.38813448e-01 -1.39292872e+00 -6.19305193e-01
-5.73567688e-01 5.35634041e-01 1.07253045e-01 -3.72470438... | [7.691714286804199, -4.482879638671875] |
9568df1d-72e8-443c-a1c3-f5a085316396 | signal-level-deep-metric-learning-for | 2004.11085 | null | https://arxiv.org/abs/2004.11085v4 | https://arxiv.org/pdf/2004.11085v4.pdf | SL-DML: Signal Level Deep Metric Learning for Multimodal One-Shot Action Recognition | Recognizing an activity with a single reference sample using metric learning approaches is a promising research field. The majority of few-shot methods focus on object recognition or face-identification. We propose a metric learning approach to reduce the action recognition problem to a nearest neighbor search in embed... | ['Dietrich Paulus', 'Raphael Memmesheimer', 'Nick Theisen'] | 2020-04-23 | null | null | null | null | ['one-shot-3d-action-recognition'] | ['computer-vision'] | [ 6.08373702e-01 -2.03599438e-01 -5.04893005e-01 -6.34777606e-01
-1.34590876e+00 -1.24879442e-01 7.47524858e-01 -3.99295837e-01
-6.61263466e-01 4.76572782e-01 6.52853966e-01 4.30282533e-01
-2.51995981e-01 -5.27882516e-01 -6.82828844e-01 -6.00035906e-01
-2.76493132e-01 1.79324493e-01 8.61599743e-02 6.14146590... | [7.925797462463379, 0.5926263332366943] |
62e3f441-6164-427f-aa54-5b07976cf174 | the-adaptive-multi-factor-model-and-the | 2107.14410 | null | https://arxiv.org/abs/2107.14410v2 | https://arxiv.org/pdf/2107.14410v2.pdf | The Adaptive Multi-Factor Model and the Financial Market | Modern evolvements of the technologies have been leading to a profound influence on the financial market. The introduction of constituents like Exchange-Traded Funds, and the wide-use of advanced technologies such as algorithmic trading, results in a boom of the data which provides more opportunities to reveal deeper i... | ['Liao Zhu'] | 2021-07-30 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-6.84432626e-01 -3.98723125e-01 -1.70066699e-01 -3.87823313e-01
8.95447806e-02 -7.09725738e-01 6.17792010e-01 -2.04914182e-01
3.75466049e-02 9.28589106e-01 2.76510179e-01 -5.15715480e-01
-2.69131660e-01 -8.58729541e-01 -1.11986116e-01 -4.43781406e-01
-3.42055410e-01 2.87640452e-01 1.04644842e-01 -2.89869219... | [4.605595111846924, 4.1562323570251465] |
eeb7d4fe-8e11-40c0-94fc-73c53a0d75d3 | ftgan-a-fully-trained-generative-adversarial | 1904.05729 | null | http://arxiv.org/abs/1904.05729v1 | http://arxiv.org/pdf/1904.05729v1.pdf | FTGAN: A Fully-trained Generative Adversarial Networks for Text to Face Generation | As a sub-domain of text-to-image synthesis, text-to-face generation has huge
potentials in public safety domain. With lack of dataset, there are almost no
related research focusing on text-to-face synthesis. In this paper, we propose
a fully-trained Generative Adversarial Network (FTGAN) that trains the text
encoder an... | ['Yining Xu', 'Lingbo Qing', 'Xiaohai He', 'Xiaodong Luo', 'Xiang Chen'] | 2019-04-11 | null | null | null | null | ['text-to-face-generation'] | ['computer-vision'] | [ 3.87074828e-01 4.74806279e-01 1.48771197e-01 -4.01156604e-01
-7.89966404e-01 -3.04314673e-01 9.16730940e-01 -1.11728489e+00
4.17355299e-01 8.51252854e-01 3.22081864e-01 -5.80468029e-02
6.56136692e-01 -1.06228900e+00 -1.11036038e+00 -7.52800643e-01
7.28193760e-01 3.17836076e-01 -3.01266134e-01 -3.01125914... | [12.33445930480957, -0.16620200872421265] |
6e4a7293-4283-404e-9006-de23eebab6e9 | quantizable-transformers-removing-outliers-by | 2306.12929 | null | https://arxiv.org/abs/2306.12929v1 | https://arxiv.org/pdf/2306.12929v1.pdf | Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing | Transformer models have been widely adopted in various domains over the last years, and especially large language models have advanced the field of AI significantly. Due to their size, the capability of these networks has increased tremendously, but this has come at the cost of a significant increase in necessary compu... | ['Tijmen Blankevoort', 'Markus Nagel', 'Yelysei Bondarenko'] | 2023-06-22 | null | null | null | null | ['quantization'] | ['methodology'] | [ 4.11557630e-02 8.03866461e-02 1.08202726e-01 -4.90898907e-01
-6.74422681e-01 -2.00403169e-01 3.04783911e-01 1.91091418e-01
-6.86586320e-01 5.98118842e-01 -1.69673890e-01 -3.26368093e-01
1.06447026e-01 -6.38389707e-01 -9.82406259e-01 -6.75425589e-01
-2.83299237e-02 4.65178758e-01 3.87529433e-01 -2.49802917... | [8.568939208984375, 3.2344563007354736] |
ea8e7d60-3e5a-4f9c-9807-290d62d24bf6 | meta-analysis-of-transfer-learning-for | 2306.11714 | null | https://arxiv.org/abs/2306.11714v1 | https://arxiv.org/pdf/2306.11714v1.pdf | Meta-Analysis of Transfer Learning for Segmentation of Brain Lesions | A major challenge in stroke research and stroke recovery predictions is the determination of a stroke lesion's extent and its impact on relevant brain systems. Manual segmentation of stroke lesions from 3D magnetic resonance (MR) imaging volumes, the current gold standard, is not only very time-consuming, but its accur... | ['Gottfried Schlaug', 'Sirisha Nouduri', 'Aleksei Rutkovskii', 'Anant Shinde', 'Advait Gosai', 'Sovesh Mohapatra'] | 2023-06-20 | null | null | null | null | ['lesion-segmentation'] | ['medical'] | [ 2.27448002e-01 -2.53992379e-01 -2.32258201e-01 -1.06952138e-01
-1.05764520e+00 -6.22005761e-01 5.34253061e-01 9.95582864e-02
-6.22041941e-01 7.22326219e-01 6.98816895e-01 -4.71168548e-01
-3.47287744e-01 -5.82223833e-01 -3.11597586e-01 -5.66388011e-01
-1.98418051e-01 9.32721794e-01 7.26028323e-01 -7.21545815... | [14.219627380371094, -2.0566253662109375] |
a70e7113-0f4c-48a4-9543-4b5821fd3571 | fair-multilingual-vandalism-detection-system | 2306.01650 | null | https://arxiv.org/abs/2306.01650v1 | https://arxiv.org/pdf/2306.01650v1.pdf | Fair multilingual vandalism detection system for Wikipedia | This paper presents a novel design of the system aimed at supporting the Wikipedia community in addressing vandalism on the platform. To achieve this, we collected a massive dataset of 47 languages, and applied advanced filtering and feature engineering techniques, including multilingual masked language modeling to bui... | ['Diego Saez-Trumper', 'Ricardo Baeza-Yates', 'Ai-Jou Chou', 'Muniza Aslam', 'Mykola Trokhymovych'] | 2023-06-02 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-6.61947131e-01 7.36948550e-02 -3.42669152e-02 1.37185663e-01
-6.85126424e-01 -6.72756314e-01 6.76813006e-01 7.40835309e-01
-7.24720418e-01 7.54975557e-01 4.19624001e-01 -2.05747381e-01
1.29649565e-01 -9.93941844e-01 -6.10902190e-01 2.69242913e-01
1.19720429e-01 2.98494220e-01 2.84718871e-01 -6.84727967... | [9.736517906188965, 9.806747436523438] |
acd5a114-ede7-43b4-8b1c-af03570c9256 | attention-over-parameters-for-dialogue | 2001.01871 | null | https://arxiv.org/abs/2001.01871v2 | https://arxiv.org/pdf/2001.01871v2.pdf | Attention over Parameters for Dialogue Systems | Dialogue systems require a great deal of different but complementary expertise to assist, inform, and entertain humans. For example, different domains (e.g., restaurant reservation, train ticket booking) of goal-oriented dialogue systems can be viewed as different skills, and so does ordinary chatting abilities of chit... | ['Chien-Sheng Wu', 'Pascale Fung', 'Jamin Shin', 'Andrea Madotto', 'Zhaojiang Lin'] | 2020-01-07 | null | null | null | null | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 3.49915959e-02 4.48810309e-01 1.58631012e-01 -5.85283160e-01
-7.68467963e-01 -8.12426925e-01 5.84662020e-01 -1.07174769e-01
-6.28952265e-01 1.05450833e+00 3.46327901e-01 -2.77221203e-01
-1.66960657e-02 -5.39155066e-01 5.06664217e-02 -4.99209881e-01
3.18566322e-01 1.26481962e+00 4.59530264e-01 -1.03748858... | [12.851606369018555, 8.053685188293457] |
b59c7272-8102-4908-aee3-9e9b3ce4692a | multimodal-neural-databases | 2305.01447 | null | https://arxiv.org/abs/2305.01447v1 | https://arxiv.org/pdf/2305.01447v1.pdf | Multimodal Neural Databases | The rise in loosely-structured data available through text, images, and other modalities has called for new ways of querying them. Multimedia Information Retrieval has filled this gap and has witnessed exciting progress in recent years. Tasks such as search and retrieval of extensive multimedia archives have undergone ... | ['Fabrizio Silvestri', 'Alon Halevy', 'Emanuele Rodolà', 'Andrea Santilli', 'Giovanni Trappolini'] | 2023-05-02 | null | null | null | null | ['multimodal-deep-learning'] | ['natural-language-processing'] | [-1.38639733e-01 -2.11605072e-01 -9.68089104e-02 -4.27693248e-01
-9.99538004e-01 -7.66582012e-01 9.49449062e-01 3.48160774e-01
-7.96255887e-01 6.47172034e-01 3.72470021e-01 3.50708701e-02
-1.24978721e-01 -8.16862106e-01 -5.96007407e-01 -4.59835559e-01
9.07976851e-02 8.02098513e-01 4.93044764e-01 -4.48919207... | [10.706006050109863, 1.4991449117660522] |
e7b16f0d-0875-4e03-b36b-79f02803f291 | research-on-discourse-parsing-from-the | null | null | https://aclanthology.org/2020.iwdp-1.1 | https://aclanthology.org/2020.iwdp-1.1.pdf | Research on Discourse Parsing: from the Dependency View | Discourse parsing aims to comprehensively acquire the logical structure of the whole text which may be helpful to some downstream applications such as summarization, reading comprehension, QA and so on. One important issue behind discourse parsing is the representation of discourse structure. Up to now, many discourse ... | ['Sujian Li'] | null | null | null | null | aacl-iwdp-2020-12 | ['discourse-parsing'] | ['natural-language-processing'] | [ 4.92599338e-01 8.73548269e-01 -4.13471460e-01 -4.18107539e-01
-5.63357949e-01 -5.65667033e-01 8.97437990e-01 7.16495812e-01
4.87593077e-02 1.11651421e+00 1.17962968e+00 -6.60362542e-01
7.60622099e-02 -8.49917948e-01 -1.56615108e-01 -2.93378919e-01
5.89888990e-02 1.38749629e-01 5.75212836e-01 -5.67647934... | [10.805436134338379, 9.485799789428711] |
758b13ef-12d6-484c-a54e-cf7701b1b0bb | s-page-a-speaker-and-position-aware-graph | 2112.12389 | null | https://arxiv.org/abs/2112.12389v1 | https://arxiv.org/pdf/2112.12389v1.pdf | S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation | Emotion recognition in conversation (ERC) has attracted much attention in recent years for its necessity in widespread applications. Existing ERC methods mostly model the self and inter-speaker context separately, posing a major issue for lacking enough interaction between them. In this paper, we propose a novel Speake... | ['Yang Dong', 'Yongliang Wang', 'Juyang Huang', 'Jing Xu', 'Chong Yang', 'Chen Liang'] | 2021-12-23 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 2.53060043e-01 1.80671394e-01 2.63756990e-01 -7.54984856e-01
-5.83042324e-01 -1.38154492e-01 6.73826694e-01 1.21690616e-01
-2.11688995e-01 3.48367602e-01 6.42306089e-01 -1.88504755e-01
1.97118819e-01 -7.45014250e-01 -2.74636149e-01 -6.85486794e-01
-7.14032948e-02 2.95224134e-02 9.70808864e-02 -4.57337260... | [13.004432678222656, 6.1357808113098145] |
335ff0b8-6c68-4692-a22b-cec73e2240e3 | seeing-deeply-and-bidirectionally-a-deep | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Jie_Yang_Seeing_Deeply_and_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Jie_Yang_Seeing_Deeply_and_ECCV_2018_paper.pdf | Seeing Deeply and Bidirectionally: A Deep Learning Approach for Single Image Reflection Removal | Reflections often obstruct the desired scene when taking photos through glass panels. Removing unwanted reflection automatically from the photos is highly desirable. Traditional methods often impose certain priors or assumptions to target particular type(s) of reflection such as shifted double reflection, thus have dif... | ['Dong Gong', 'Qinfeng Shi', 'Lingqiao Liu', 'Jie Yang'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['reflection-removal'] | ['computer-vision'] | [ 8.81846488e-01 1.37615144e-01 5.69631398e-01 -3.20322722e-01
-5.70394158e-01 -3.77885610e-01 6.11173570e-01 -8.26806366e-01
-1.17787123e-01 4.77549523e-01 2.67051786e-01 -3.54105562e-01
6.34469569e-01 -9.01840389e-01 -9.22153354e-01 -1.13522398e+00
5.77476680e-01 -1.30461097e-01 2.53268749e-01 -2.21502990... | [10.58234977722168, -2.7873902320861816] |
dd852ce6-b89c-4e55-ac08-86a6651f6dfb | fake-news-detection-as-natural-language | 1907.07347 | null | https://arxiv.org/abs/1907.07347v1 | https://arxiv.org/pdf/1907.07347v1.pdf | Fake News Detection as Natural Language Inference | This report describes the entry by the Intelligent Knowledge Management (IKM) Lab in the WSDM 2019 Fake News Classification challenge. We treat the task as natural language inference (NLI). We individually train a number of the strongest NLI models as well as BERT. We ensemble these results and retrain with noisy label... | ['Hung-Yu Kao', 'Timothy Niven', 'Kai-Chou Yang'] | 2019-07-17 | null | null | null | null | ['news-classification'] | ['natural-language-processing'] | [-7.84198567e-03 6.48677707e-01 -8.15315783e-01 -6.24738634e-01
-9.52652812e-01 -7.46806204e-01 1.08225441e+00 1.65493429e-01
-4.31434125e-01 1.24097359e+00 1.81041077e-01 -5.68459988e-01
-2.14626253e-01 -5.84298909e-01 -9.17697251e-01 -1.06971152e-01
-9.44753736e-03 1.09721696e+00 3.38378012e-01 -2.47037306... | [9.637542724609375, 8.750138282775879] |
983868dd-054b-4594-8dda-9b4b7dc66b81 | efficient-compressed-ratio-estimation-using | 2211.04284 | null | https://arxiv.org/abs/2211.04284v3 | https://arxiv.org/pdf/2211.04284v3.pdf | Efficient Compressed Ratio Estimation Using Online Sequential Learning for Edge Computing | Owing to the widespread adoption of the Internet of Things, a vast amount of sensor information is being acquired in real time. Accordingly, the communication cost of data from edge devices is increasing. Compressed sensing (CS), a data compression method that can be used on edge devices, has been attracting attention ... | ['Noboru Koshizuka', 'Hangli Ge', 'Hiroki Oikawa'] | 2022-11-08 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 2.61811823e-01 -1.34694427e-01 -2.39207223e-01 -1.29666761e-01
-5.41782022e-01 2.44330466e-01 1.05539225e-02 1.84813917e-01
-3.83629173e-01 5.40652037e-01 8.28488022e-02 -8.78093578e-03
-2.17402935e-01 -7.95833290e-01 -5.83537340e-01 -6.82314515e-01
-3.36305462e-02 8.05795845e-03 -7.48189315e-02 1.08199030... | [11.288043022155762, -1.579903244972229] |
8dbfeac3-bf6c-4733-a23b-bfcafadf8f76 | fine-grained-visual-classification-of-plant | 2106.02141 | null | https://arxiv.org/abs/2106.02141v1 | https://arxiv.org/pdf/2106.02141v1.pdf | Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention | Plant species identification in the wild is a difficult problem in part due to the high variability of the input data, but also because of complications induced by the long-tail effects of the datasets distribution. Inspired by the most recent fine-grained visual classification approaches which are based on attention t... | ['Gianfranco Doretto', 'Donald A. Adjeroh', 'Cole Henderson', 'Meghana Kovur', 'Ram J. Zaveri', 'Matthew R. Keaton'] | 2021-06-03 | null | null | null | null | ['organ-detection'] | ['medical'] | [ 8.77901092e-02 -4.61594582e-01 3.89051326e-02 -5.07277548e-02
-8.61027986e-02 -1.20198500e+00 5.38471162e-01 6.71166003e-01
-1.06307037e-01 4.01517689e-01 9.93686449e-03 -3.50801140e-01
-2.09893733e-01 -8.86569977e-01 -7.12093413e-01 -5.86515427e-01
-2.57118400e-02 3.95337313e-01 5.05573273e-01 -7.90121034... | [9.583440780639648, 2.0053212642669678] |
33dc7e19-62c8-4c78-86d6-9364216d6008 | bayesian-optimisation-for-a-biologically | 2104.05989 | null | https://arxiv.org/abs/2104.05989v1 | https://arxiv.org/pdf/2104.05989v1.pdf | Bayesian Optimisation for a Biologically Inspired Population Neural Network | We have used Bayesian Optimisation (BO) to find hyper-parameters in an existing biologically plausible population neural network. The 8-dimensional optimal hyper-parameter combination should be such that the network dynamics simulate the resting state alpha rhythm (8 - 13 Hz rhythms in brain signals). Each combination ... | ['Basabdatta Sen Bhattacharya', 'Elham Zareian', 'Jun Chen', 'Swapna Sasi', 'Mahak Kothari'] | 2021-04-13 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 3.15032214e-01 3.27971429e-01 1.83717459e-01 2.53976971e-01
-3.92400064e-02 -2.62623280e-01 4.95806187e-01 -5.14126793e-02
-7.39360631e-01 1.13532960e+00 5.97535484e-02 -4.90798429e-02
-7.24306881e-01 -4.45038319e-01 -2.96418965e-01 -1.10766041e+00
-6.76272571e-01 3.84864300e-01 5.63356817e-01 -2.49919727... | [8.02729606628418, 2.8985331058502197] |
5458c3da-121f-441b-9127-7e3da46599d4 | biologically-constrained-graphs-for-global | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Matejek_Biologically-Constrained_Graphs_for_Global_Connectomics_Reconstruction_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Matejek_Biologically-Constrained_Graphs_for_Global_Connectomics_Reconstruction_CVPR_2019_paper.pdf | Biologically-Constrained Graphs for Global Connectomics Reconstruction | Most current state-of-the-art connectome reconstruction pipelines have two major steps: initial pixel-based segmentation with affinity prediction and watershed transform, and refined segmentation by merging over-segmented regions. These methods rely only on local context and are typically agnostic to the underlying bio... | [' Hanspeter Pfister', ' Toufiq Parag', ' Donglai Wei', ' Haidong Zhu', ' Daniel Haehn', 'Brian Matejek'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['electron-microscopy-image-segmentation'] | ['computer-vision'] | [ 4.98421669e-01 3.47646236e-01 1.80974424e-01 -4.11911219e-01
-6.35787487e-01 -7.88442612e-01 3.19160938e-01 5.63863814e-01
-5.45613706e-01 8.40753496e-01 -2.74724007e-01 -2.15421230e-01
1.18899412e-01 -8.05956185e-01 -1.01025224e+00 -5.14921725e-01
-3.36636081e-02 6.55308604e-01 6.33212149e-01 2.33766779... | [14.27199935913086, -3.127729892730713] |
1454e93b-6ae2-4e8e-b7e3-954b2a7e28b7 | 2305-14706 | 2305.14706 | null | https://arxiv.org/abs/2305.14706v1 | https://arxiv.org/pdf/2305.14706v1.pdf | PruMUX: Augmenting Data Multiplexing with Model Compression | As language models increase in size by the day, methods for efficient inference are critical to leveraging their capabilities for various applications. Prior work has investigated techniques like model pruning, knowledge distillation, and data multiplexing to increase model throughput without sacrificing accuracy. In t... | ['Kai Li', 'Karthik Narasimhan', 'Vishvak Murahari', 'Yushan Su'] | 2023-05-24 | null | null | null | null | ['model-compression'] | ['methodology'] | [-9.33425035e-03 3.25439535e-02 -6.55582190e-01 -3.70683402e-01
-1.07181323e+00 -3.21944177e-01 3.31110448e-01 2.93512821e-01
-4.33513224e-01 7.36049414e-01 2.08999515e-02 -1.00254154e+00
-1.38974532e-01 -7.54333079e-01 -5.06780624e-01 -2.61111185e-02
-2.37378120e-01 4.86144155e-01 2.94008285e-01 1.48032099... | [8.682157516479492, 3.458923816680908] |
3e3eff3f-8433-44a4-b4c3-8f339e0f9c5b | geo-defakehop-high-performance-geographic | 2110.09795 | null | https://arxiv.org/abs/2110.09795v1 | https://arxiv.org/pdf/2110.09795v1.pdf | Geo-DefakeHop: High-Performance Geographic Fake Image Detection | A robust fake satellite image detection method, called Geo-DefakeHop, is proposed in this work. Geo-DefakeHop is developed based on the parallel subspace learning (PSL) methodology. PSL maps the input image space into several feature subspaces using multiple filter banks. By exploring response differences of different ... | ['C. -C. Jay Kuo', 'Suya You', 'Shuowen Hu', 'Kaitai Zhang', 'Hong-Shuo Chen'] | 2021-10-19 | null | null | null | null | ['fake-image-detection'] | ['computer-vision'] | [-8.69552977e-03 -6.65479541e-01 -7.78365210e-02 1.49098998e-02
-6.90876245e-01 -4.55404937e-01 4.44019467e-01 -3.35607022e-01
-2.60324150e-01 4.13867205e-01 7.75416521e-03 -2.18605176e-01
3.23159695e-02 -5.15652537e-01 -3.23157907e-01 -1.07090795e+00
-4.67637360e-01 -9.70214531e-02 3.24011356e-01 -2.42263898... | [12.374902725219727, 0.7276913523674011] |
896e31a5-583c-42af-ab51-8df67773876e | psuedoprop-at-semeval-2020-task-11-propaganda | null | null | https://aclanthology.org/2020.semeval-1.233 | https://aclanthology.org/2020.semeval-1.233.pdf | PsuedoProp at SemEval-2020 Task 11: Propaganda Span Detection Using BERT-CRF and Ensemble Sentence Level Classifier | This paper explains our teams{'} submission to the Shared Task of Fine-Grained Propaganda Detection in which we propose a sequential BERT-CRF based Span Identification model where the fine-grained detection is carried out only on the articles that are flagged as containing propaganda by an ensemble SLC model. We propos... | ['Harshita Diddee', 'Aniruddha Chauhan'] | 2020-12-01 | null | null | null | semeval-2020 | ['propaganda-detection'] | ['natural-language-processing'] | [-1.65922374e-01 -1.30752966e-01 4.81436327e-02 4.77472991e-02
-7.29518056e-01 -7.34910309e-01 1.01207411e+00 4.06076878e-01
-8.05821180e-01 9.47425246e-01 4.70748663e-01 -5.05806684e-01
-3.27973962e-01 -6.76410973e-01 -3.48980099e-01 -4.83499408e-01
3.96427885e-02 6.69197619e-01 2.86832482e-01 -1.61319301... | [8.48582935333252, 10.735417366027832] |
7d7deb6e-7d8e-4250-8efc-ba9e0f6c4879 | show-attend-and-read-a-simple-and-strong | 1811.00751 | null | http://arxiv.org/abs/1811.00751v2 | http://arxiv.org/pdf/1811.00751v2.pdf | Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition | Recognizing irregular text in natural scene images is challenging due to the
large variance in text appearance, such as curvature, orientation and
distortion. Most existing approaches rely heavily on sophisticated model
designs and/or extra fine-grained annotations, which, to some extent, increase
the difficulty in alg... | ['Peng Wang', 'Chunhua Shen', 'Hui Li', 'Guyu Zhang'] | 2018-11-02 | null | null | null | null | ['irregular-text-recognition'] | ['computer-vision'] | [ 2.91797161e-01 -3.71461272e-01 2.88062636e-02 -4.70178515e-01
-6.72715425e-01 -3.63179326e-01 6.80170119e-01 1.45178726e-02
-3.61177772e-01 6.26307502e-02 2.32744738e-01 -3.15396219e-01
5.16602159e-01 -4.57922429e-01 -7.96318233e-01 -5.58629274e-01
5.91446102e-01 3.07573527e-01 2.24704996e-01 -6.92301244... | [11.890118598937988, 2.20296049118042] |
93efa047-bf69-4cbd-9a49-18e522ea7020 | semantic-nearest-neighbor-fields-monocular | 1904.00738 | null | http://arxiv.org/abs/1904.00738v1 | http://arxiv.org/pdf/1904.00738v1.pdf | Semantic Nearest Neighbor Fields Monocular Edge Visual-Odometry | Recent advances in deep learning for edge detection and segmentation opens up
a new path for semantic-edge-based ego-motion estimation. In this work, we
propose a robust monocular visual odometry (VO) framework using category-aware
semantic edges. It can reconstruct large-scale semantic maps in challenging
outdoor envi... | ['Assia Benbihi', 'Cedric Pradalier', 'Antoine Richard', 'Xiaolong Wu'] | 2019-04-01 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-4.85577613e-01 -3.93035114e-01 -1.09028399e-01 -3.92456651e-01
-1.93145126e-01 -5.61062753e-01 4.72191781e-01 -3.84458661e-01
-3.45153272e-01 6.16423011e-01 -4.00036313e-02 1.73278376e-01
1.74971133e-01 -8.87277424e-01 -9.28816319e-01 -3.85491788e-01
2.19031706e-01 4.34663385e-01 8.00229073e-01 -8.15560147... | [8.020857810974121, -2.216367721557617] |
eb941546-73f2-4c59-a0f5-1ab83e3212be | boosting-graph-neural-networks-via-adaptive | 2210.05920 | null | https://arxiv.org/abs/2210.05920v2 | https://arxiv.org/pdf/2210.05920v2.pdf | Boosting Graph Neural Networks via Adaptive Knowledge Distillation | Graph neural networks (GNNs) have shown remarkable performance on diverse graph mining tasks. Although different GNNs can be unified as the same message passing framework, they learn complementary knowledge from the same graph. Knowledge distillation (KD) is developed to combine the diverse knowledge from multiple mode... | ['Nitesh Chawla', 'Chuxu Zhang', 'Yijun Tian', 'Yujie Fan', 'Chunhui Zhang', 'Zhichun Guo'] | 2022-10-12 | null | null | null | null | ['graph-mining'] | ['graphs'] | [ 5.28222881e-02 2.59414643e-01 -4.47385550e-01 -1.74642861e-01
-1.44931421e-01 -4.50095147e-01 2.92444369e-03 4.62553412e-01
-3.33260566e-01 6.38083518e-01 -3.95011842e-01 -5.79275727e-01
-3.55634093e-01 -1.26886380e+00 -8.62614989e-01 -7.90787697e-01
1.54380966e-02 2.73294657e-01 6.84468925e-01 -2.98067570... | [9.496859550476074, 3.3726789951324463] |
f4d0916c-2d57-486b-b997-823a6ef7c860 | toch-spatio-temporal-object-correspondence-to | 2205.07982 | null | https://arxiv.org/abs/2205.07982v2 | https://arxiv.org/pdf/2205.07982v2.pdf | TOCH: Spatio-Temporal Object-to-Hand Correspondence for Motion Refinement | We present TOCH, a method for refining incorrect 3D hand-object interaction sequences using a data prior. Existing hand trackers, especially those that rely on very few cameras, often produce visually unrealistic results with hand-object intersection or missing contacts. Although correcting such errors requires reasoni... | ['Bharat Lal Bhatnagar', 'Gerard Pons-Moll', 'Jan Eric Lenssen', 'Keyang Zhou'] | 2022-05-16 | null | null | null | null | ['object-reconstruction'] | ['computer-vision'] | [ 2.55898871e-02 -2.40222275e-01 1.42653985e-02 -2.31941968e-01
-4.38501567e-01 -5.93612611e-01 3.52450341e-01 -1.97178096e-01
9.57925543e-02 1.59482121e-01 2.01488063e-01 2.43027866e-01
-8.79001990e-02 -2.86223948e-01 -1.17041171e+00 -3.96931648e-01
1.41227737e-01 9.12551582e-01 4.47220325e-01 -2.08761603... | [6.371459484100342, -1.0095680952072144] |
a2b0fe26-09dd-41ab-9be1-dbd81eb44e00 | leveraging-large-scale-uncurated-data-for | 1905.01278 | null | https://arxiv.org/abs/1905.01278v3 | https://arxiv.org/pdf/1905.01278v3.pdf | Unsupervised Pre-Training of Image Features on Non-Curated Data | Pre-training general-purpose visual features with convolutional neural networks without relying on annotations is a challenging and important task. Most recent efforts in unsupervised feature learning have focused on either small or highly curated datasets like ImageNet, whereas using uncurated raw datasets was found t... | ['Julien Mairal', 'Armand Joulin', 'Piotr Bojanowski', 'Mathilde Caron'] | 2019-05-03 | unsupervised-pre-training-of-image-features | http://openaccess.thecvf.com/content_ICCV_2019/html/Caron_Unsupervised_Pre-Training_of_Image_Features_on_Non-Curated_Data_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Caron_Unsupervised_Pre-Training_of_Image_Features_on_Non-Curated_Data_ICCV_2019_paper.pdf | iccv-2019-10 | ['self-supervised-image-classification'] | ['computer-vision'] | [-2.14022491e-02 5.47442324e-02 -2.17708394e-01 -4.24657166e-01
-7.44679511e-01 -4.63110894e-01 6.17146134e-01 1.44082159e-01
-7.85815001e-01 6.01023138e-01 9.50076655e-02 1.95929427e-02
-4.54838946e-02 -6.69628799e-01 -8.71001720e-01 -6.09520435e-01
-1.55519158e-01 2.43144855e-01 1.39844447e-01 -2.63276584... | [9.527772903442383, 2.4386377334594727] |
4810301e-d63f-45c8-af51-d80e012f693f | wordalchemy-a-transformer-based-reverse | 2204.10181 | null | https://arxiv.org/abs/2204.10181v1 | https://arxiv.org/pdf/2204.10181v1.pdf | WordAlchemy: A transformer-based Reverse Dictionary | A reverse dictionary takes a target word's description as input and returns the words that fit the description. Reverse Dictionaries are useful for new language learners, anomia patients, and for solving common tip-of-the-tongue problems (lethologica). Currently, there does not exist any Reverse Dictionary provider wit... | ['Pranav Sadavarte', 'Kanhaiya Madaswar', 'Harshal Patil', 'Dr. Sunil B. Mane'] | 2022-04-16 | null | null | null | null | ['reverse-dictionary'] | ['natural-language-processing'] | [-3.74447525e-01 -1.35365427e-02 -7.44138360e-01 -2.42917418e-01
-7.64550507e-01 -6.05565906e-01 5.68836808e-01 -5.16538210e-02
-5.34832001e-01 5.51096678e-01 4.08732027e-01 -9.06165600e-01
3.33372742e-01 -5.69752455e-01 -3.54864836e-01 -2.15659067e-01
4.95791644e-01 9.44537461e-01 -8.75895172e-02 -7.84552455... | [11.147696495056152, 9.98476505279541] |
a3927d5d-6026-4838-a95b-2ccc23c54d6f | acoustic-identification-of-ae-aegypti | 2306.10091 | null | https://arxiv.org/abs/2306.10091v1 | https://arxiv.org/pdf/2306.10091v1.pdf | Acoustic Identification of Ae. aegypti Mosquitoes using Smartphone Apps and Residual Convolutional Neural Networks | In this paper, we advocate in favor of smartphone apps as low-cost, easy-to-deploy solution for raising awareness among the population on the proliferation of Aedes aegypti mosquitoes. Nevertheless, devising such a smartphone app is challenging, for many reasons, including the required maturity level of techniques for ... | ['Weverton Cordeiro', 'Rodrigo Brandão Mansilha1', 'Mariana Recamonde-Mendoza', 'Ricardo Rohweder', 'Kayuã Oleques Paim'] | 2023-06-16 | null | null | null | null | ['benchmarking', 'benchmarking'] | ['miscellaneous', 'robots'] | [ 1.68039247e-01 -5.08415580e-01 1.15704119e-01 -3.49233568e-01
-3.05756062e-01 -7.99194753e-01 6.64729536e-01 1.37276563e-03
-4.29246783e-01 4.20681953e-01 9.01893601e-02 -6.15179121e-01
-1.59652740e-01 -7.95397818e-01 -4.61101145e-01 -6.15775943e-01
-6.02265537e-01 -1.02820478e-01 -2.31506959e-01 -3.04556400... | [13.311408996582031, 1.1460202932357788] |
0a6c8687-55f9-40f6-9cf1-e0269203b9f7 | masked-autoencoders-for-generic-event | 2206.08610 | null | https://arxiv.org/abs/2206.08610v1 | https://arxiv.org/pdf/2206.08610v1.pdf | Masked Autoencoders for Generic Event Boundary Detection CVPR'2022 Kinetics-GEBD Challenge | Generic Event Boundary Detection (GEBD) tasks aim at detecting generic, taxonomy-free event boundaries that segment a whole video into chunks. In this paper, we apply Masked Autoencoders to improve algorithm performance on the GEBD tasks. Our approach mainly adopted the ensemble of Masked Autoencoders fine-tuned on the... | ['Jie Tang', 'Xu Cheng', 'Feng Hu', 'Zuwei Huang', 'Youzeng Li', 'Yuanxi Sun', 'Rui He'] | 2022-06-17 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [-2.28306651e-01 1.53278381e-01 -4.95918579e-02 -3.15106064e-01
-9.98728395e-01 -4.66095507e-01 3.35552454e-01 2.59516425e-02
-7.65017271e-01 6.96992874e-01 1.42025769e-01 1.15171149e-01
4.49474156e-01 -6.36952639e-01 -1.07309306e+00 -6.74767971e-01
4.24157530e-02 5.30374110e-01 4.56422597e-01 1.06745206... | [8.704931259155273, 0.3719427287578583] |
5b61f5b3-8f5e-45d9-94a0-7dcf40c77d2d | robustness-and-risk-management-via | 2112.15430 | null | https://arxiv.org/abs/2112.15430v1 | https://arxiv.org/pdf/2112.15430v1.pdf | Robustness and risk management via distributional dynamic programming | In dynamic programming (DP) and reinforcement learning (RL), an agent learns to act optimally in terms of expected long-term return by sequentially interacting with its environment modeled by a Markov decision process (MDP). More generally in distributional reinforcement learning (DRL), the focus is on the whole distri... | ['Gergely Neu', 'Mastane Achab'] | 2021-12-28 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-2.29772162e-02 3.78494591e-01 -4.61763412e-01 -2.01128051e-01
-7.89183497e-01 -7.05626845e-01 8.07622075e-01 2.74691105e-01
-9.10991371e-01 1.20239663e+00 1.90198660e-01 -5.00286937e-01
-5.61222255e-01 -9.13789928e-01 -7.70984769e-01 -1.15868890e+00
-2.96991259e-01 8.53168309e-01 -1.71393052e-01 -9.52455215... | [4.247817039489746, 2.556037187576294] |
54708cce-9103-4e17-a825-ee30745a429a | modeling-syntactic-semantic-dependency | null | null | https://aclanthology.org/2022.acl-long.548 | https://aclanthology.org/2022.acl-long.548.pdf | Modeling Syntactic-Semantic Dependency Correlations in Semantic Role Labeling Using Mixture Models | In this paper, we propose a mixture model-based end-to-end method to model the syntactic-semantic dependency correlation in Semantic Role Labeling (SRL). Semantic dependencies in SRL are modeled as a distribution over semantic dependency labels conditioned on a predicate and an argument word.The semantic label distribu... | ['Yusuke Miyao', 'Xiangheng He', 'Junjie Chen'] | null | null | null | null | acl-2022-5 | ['semantic-role-labeling'] | ['natural-language-processing'] | [-2.49706302e-03 3.45730245e-01 -6.80363834e-01 -1.01546836e+00
-1.03197026e+00 -8.21814060e-01 4.93180513e-01 3.38981360e-01
-5.62604666e-01 4.16939199e-01 7.46028543e-01 -8.36984292e-02
-1.33998841e-01 -2.98000574e-01 -3.75674903e-01 -6.07928991e-01
4.59059887e-02 1.10818219e+00 5.67277431e-01 -1.25292823... | [10.400146484375, 9.455375671386719] |
f4a04052-f912-4206-b19f-b07e9ddbf5e0 | boosting-neural-networks-to-decompile | 2301.00969 | null | https://arxiv.org/abs/2301.00969v1 | https://arxiv.org/pdf/2301.00969v1.pdf | Boosting Neural Networks to Decompile Optimized Binaries | Decompilation aims to transform a low-level program language (LPL) (eg., binary file) into its functionally-equivalent high-level program language (HPL) (e.g., C/C++). It is a core technology in software security, especially in vulnerability discovery and malware analysis. In recent years, with the successful applicati... | ['Peiwei Hu', 'Kai Chen', 'Ruigang Liang', 'Ying Cao'] | 2023-01-03 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 1.62044272e-01 -1.70914501e-01 -7.35408664e-01 -1.61806002e-01
-4.75415826e-01 -6.76286221e-01 2.55594522e-01 3.18639696e-01
-7.89391175e-02 4.12276715e-01 -6.90865517e-02 -1.21341085e+00
5.11312008e-01 -1.22875428e+00 -1.18141484e+00 -9.30235609e-02
-4.95190434e-02 3.64067793e-01 1.44787595e-01 -3.96641850... | [7.172488689422607, 7.836678504943848] |
bcbecec0-fb4b-4950-9451-1d1352b30b5a | fairness-in-streaming-submodular-maximization | 2010.07431 | null | https://arxiv.org/abs/2010.07431v2 | https://arxiv.org/pdf/2010.07431v2.pdf | Fairness in Streaming Submodular Maximization: Algorithms and Hardness | Submodular maximization has become established as the method of choice for the task of selecting representative and diverse summaries of data. However, if datapoints have sensitive attributes such as gender or age, such machine learning algorithms, left unchecked, are known to exhibit bias: under- or over-representatio... | ['Jakub Tarnawski', 'Jakab Tardos', 'Ashkan Norouzi-Fard', 'Slobodan Mitrović', 'Marwa El Halabi'] | 2020-10-14 | null | http://proceedings.neurips.cc/paper/2020/hash/9d752cb08ef466fc480fba981cfa44a1-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/9d752cb08ef466fc480fba981cfa44a1-Paper.pdf | neurips-2020-12 | ['movie-recommendation'] | ['miscellaneous'] | [ 2.00876176e-01 5.13923049e-01 -9.33801830e-01 -4.98136491e-01
-7.62803555e-01 -4.72814381e-01 3.00849766e-01 6.89773381e-01
-3.44804943e-01 1.16522670e+00 6.00349307e-01 1.19054061e-03
-4.85041022e-01 -7.18528330e-01 -5.87708533e-01 -5.99178135e-01
-3.70229721e-01 6.32349908e-01 -3.95883083e-01 2.00362783... | [6.616864204406738, 4.949122428894043] |
1087ad39-87a9-47a5-a179-abcc6370b1ac | cloud-detection-from-rgb-color-remote-sensing | 1801.08706 | null | http://arxiv.org/abs/1801.08706v1 | http://arxiv.org/pdf/1801.08706v1.pdf | Cloud Detection From RGB Color Remote Sensing Images With Deep Pyramid Networks | Cloud detection from remotely observed data is a critical pre-processing step
for various remote sensing applications. In particular, this problem becomes
even harder for RGB color images, since there is no distinct spectral pattern
for clouds, which is directly separable from the Earth surface. In this paper,
we adapt... | ['Savas Ozkan', 'Mehmet Efendioglu', 'Caner Demirpolat'] | 2018-01-26 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 3.72598380e-01 -4.93504077e-01 3.13506484e-01 -4.26177174e-01
-5.66671669e-01 -6.81138575e-01 2.59153008e-01 -9.77868289e-02
-5.70034027e-01 7.25070357e-01 -7.00633585e-01 -4.98829514e-01
4.12309319e-02 -9.45525169e-01 -4.56632078e-01 -1.06387687e+00
-7.71744475e-02 1.75978646e-01 5.01817325e-03 -1.37908161... | [9.794560432434082, -1.724785327911377] |
715bbcca-057e-42e7-a236-9abee83e9ce5 | ultra-high-definition-low-light-image | 2212.11548 | null | https://arxiv.org/abs/2212.11548v1 | https://arxiv.org/pdf/2212.11548v1.pdf | Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method | As the quality of optical sensors improves, there is a need for processing large-scale images. In particular, the ability of devices to capture ultra-high definition (UHD) images and video places new demands on the image processing pipeline. In this paper, we consider the task of low-light image enhancement (LLIE) and ... | ['Tong Lu', 'Bjorn Stenger', 'Wenhan Luo', 'Tianrun Shen', 'Kaihao Zhang', 'Tao Wang'] | 2022-12-22 | null | null | null | null | ['face-detection', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 3.44675004e-01 -3.53358895e-01 2.52991915e-01 -4.60433334e-01
-1.00972438e+00 -1.14629515e-01 3.20159942e-01 -2.38032818e-01
-5.21345913e-01 3.09377372e-01 2.64347672e-01 -4.64693271e-03
3.36005628e-01 -6.58115685e-01 -7.78404653e-01 -7.00216711e-01
4.20285732e-01 -2.54743934e-01 1.39973521e-01 -7.14072376... | [10.807023048400879, -2.381856918334961] |
829405d0-9e18-4097-b571-ec2ee994bafd | learning-to-segment-every-referring-object | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Qu_Learning_To_Segment_Every_Referring_Object_Point_by_Point_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Qu_Learning_To_Segment_Every_Referring_Object_Point_by_Point_CVPR_2023_paper.pdf | Learning To Segment Every Referring Object Point by Point | Referring Expression Segmentation (RES) can facilitate pixel-level semantic alignment between vision and language. Most of the existing RES approaches require massive pixel-level annotations, which are expensive and exhaustive. In this paper, we propose a new partially supervised training paradigm for RES, i.e., tr... | ['Yao Zhao', 'Xiaodan Liang', 'Wu Liu', 'Yunchao Wei', 'Yu Wu', 'Mengxue Qu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['referring-expression', 'referring-expression-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.71453673e-01 1.71676472e-01 -3.75828207e-01 -5.07286191e-01
-8.80401134e-01 -5.19669592e-01 3.44511539e-01 -3.51237684e-01
-6.10615849e-01 5.40050209e-01 -1.63430318e-01 -1.58849359e-01
4.37610954e-01 -5.92135787e-01 -1.16593850e+00 -5.35769343e-01
5.14080763e-01 1.66895241e-01 5.41771054e-01 -1.18191965... | [9.73613452911377, 0.6888526678085327] |
64269269-52ba-4613-99d1-a8a33e0dad8b | recurrent-squeeze-and-excitation-context | 1807.05698 | null | http://arxiv.org/abs/1807.05698v2 | http://arxiv.org/pdf/1807.05698v2.pdf | Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining | Rain streaks can severely degrade the visibility, which causes many current
computer vision algorithms fail to work. So it is necessary to remove the rain
from images. We propose a novel deep network architecture based on deep
convolutional and recurrent neural networks for single image deraining. As
contextual informa... | ['Hongbin Zha', 'Hong Liu', 'Zhouchen Lin', 'Xia Li', 'Jianlong Wu'] | 2018-07-16 | recurrent-squeeze-and-excitation-context-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Xia_Li_Recurrent_Squeeze-and-Excitation_Context_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Xia_Li_Recurrent_Squeeze-and-Excitation_Context_ECCV_2018_paper.pdf | eccv-2018-9 | ['single-image-deraining'] | ['computer-vision'] | [-5.50030917e-02 -6.06310248e-01 3.84042472e-01 -5.15235484e-01
-7.61357620e-02 -3.54775995e-01 -2.07757577e-03 -4.44558740e-01
-3.46709371e-01 8.14538300e-01 1.29391700e-01 -3.42733115e-01
3.30875784e-01 -8.57145488e-01 -6.09810174e-01 -1.19950557e+00
1.26390412e-01 -2.75330633e-01 3.65238369e-01 -3.93401831... | [10.899057388305664, -3.265470266342163] |
20a03ed3-f4ff-415f-bf77-eda35c3e45d3 | dq-detr-dual-query-detection-transformer-for | 2211.15516 | null | https://arxiv.org/abs/2211.15516v2 | https://arxiv.org/pdf/2211.15516v2.pdf | DQ-DETR: Dual Query Detection Transformer for Phrase Extraction and Grounding | In this paper, we study the problem of visual grounding by considering both phrase extraction and grounding (PEG). In contrast to the previous phrase-known-at-test setting, PEG requires a model to extract phrases from text and locate objects from images simultaneously, which is a more practical setting in real applicat... | ['Lei Zhang', 'Jun Zhu', 'Hang Su', 'Hao Zhang', 'Shijia Huang', 'Feng Li', 'Yaoyuan Liang', 'Shilong Liu'] | 2022-11-28 | null | null | null | null | ['phrase-extraction-and-grounding-peg', 'phrase-grounding'] | ['computer-vision', 'natural-language-processing'] | [ 3.42428803e-01 6.07424751e-02 -3.98414820e-01 -1.61062434e-01
-1.29470921e+00 -6.50588930e-01 3.33052516e-01 -4.05350253e-02
-5.59140086e-01 4.31952745e-01 -1.78057656e-01 -4.30590153e-01
1.56119645e-01 -7.35999346e-01 -1.03863764e+00 -6.19049847e-01
3.59433502e-01 4.28789437e-01 4.34212565e-01 -9.74527374... | [10.032334327697754, 0.9985991716384888] |
2297052c-c30b-41b6-87ad-3489875f6a01 | generalized-bilinear-deep-convolutional | 1807.01298 | null | http://arxiv.org/abs/1807.01298v1 | http://arxiv.org/pdf/1807.01298v1.pdf | Generalized Bilinear Deep Convolutional Neural Networks for Multimodal Biometric Identification | In this paper, we propose to employ a bank of modality-dedicated
Convolutional Neural Networks (CNNs), fuse, train, and optimize them together
for person classification tasks. A modality-dedicated CNN is used for each
modality to extract modality-specific features. We demonstrate that, rather
than spatial fusion at the... | ['Sobhan Soleymani', 'Nasser M. Nasrabadi', 'Jeremy Dawson', 'Amirsina Torfi'] | 2018-07-03 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 2.19111234e-01 -4.99511570e-01 2.93732196e-01 -6.28766119e-01
-9.86852467e-01 -6.51929915e-01 6.37736857e-01 2.44600564e-01
-6.70811892e-01 7.66897619e-01 2.36088187e-01 6.11171052e-02
-8.80771428e-02 -5.67501068e-01 -6.58825934e-01 -7.26161897e-01
1.30997673e-01 1.08427256e-01 -4.64458436e-01 -1.11079589... | [14.612428665161133, 0.9948211908340454] |
c44a7729-0c26-48e4-877a-aa721843e11c | inor-net-incremental-3d-object-recognition | 2302.09886 | null | https://arxiv.org/abs/2302.09886v1 | https://arxiv.org/pdf/2302.09886v1.pdf | InOR-Net: Incremental 3D Object Recognition Network for Point Cloud Representation | 3D object recognition has successfully become an appealing research topic in the real-world. However, most existing recognition models unreasonably assume that the categories of 3D objects cannot change over time in the real-world. This unrealistic assumption may result in significant performance degradation for them t... | ['Ender Konukoglu', 'Jun Li', 'Lingjuan Lyu', 'Lixu Wang', 'Gan Sun', 'Yang Cong', 'Jiahua Dong'] | 2023-02-20 | null | null | null | null | ['3d-object-recognition', 'object-recognition'] | ['computer-vision', 'computer-vision'] | [ 3.61616388e-02 3.79892215e-02 5.86940348e-02 -4.90945518e-01
-1.57449022e-01 -3.44103813e-01 3.50731224e-01 4.55565900e-02
-2.32958108e-01 6.74182296e-01 -2.09351122e-01 -6.07464202e-02
-2.35561252e-01 -8.62689912e-01 -7.46222377e-01 -7.70361483e-01
8.54513496e-02 5.35645008e-01 2.42354408e-01 3.09554227... | [7.853485584259033, -3.2083256244659424] |
1f9a70ee-aa88-4544-a597-9fd09c529027 | content-based-image-retrieval-speedup | 1911.11379 | null | https://arxiv.org/abs/1911.11379v2 | https://arxiv.org/pdf/1911.11379v2.pdf | Content-based image retrieval speedup | Content-based image retrieval (CBIR) is a task of retrieving images from their contents. Since retrieval process is a time-consuming task in large image databases, acceleration methods can be very useful. This paper presents a novel method to speed up CBIR systems. In the proposed method, first Zernike moments are extr... | ['Sadegh Fadaei', 'Elyas Rashno', 'Abdolreza Rashno'] | 2019-11-26 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 3.69971931e-01 -9.06560719e-01 -1.55699342e-01 -1.62959948e-01
-8.11343610e-01 -5.75912058e-01 1.87690750e-01 4.13273603e-01
-7.60592699e-01 4.38206851e-01 -1.44131452e-01 5.89230582e-02
-5.77821791e-01 -9.56358016e-01 -7.08514825e-02 -6.94534123e-01
3.03267032e-01 2.97448277e-01 6.52235508e-01 -1.86573341... | [10.768134117126465, 0.08648086339235306] |
3321bdee-4a41-4989-ae64-e856f9f66a0e | video-summarization-using-keyframe-extraction | 1910.04792 | null | https://arxiv.org/abs/1910.04792v2 | https://arxiv.org/pdf/1910.04792v2.pdf | Unsupervised video summarization framework using keyframe extraction and video skimming | Video is one of the robust sources of information and the consumption of online and offline videos has reached an unprecedented level in the last few years. A fundamental challenge of extracting information from videos is a viewer has to go through the complete video to understand the context, as opposed to an image wh... | ['Shruti Jadon', 'Mahmood Jasim'] | 2019-10-10 | null | null | null | null | ['unsupervised-video-summarization'] | ['computer-vision'] | [ 2.75814980e-01 -1.30248843e-02 1.19117953e-01 -1.92340612e-01
-8.65725338e-01 -4.97062206e-01 3.11431497e-01 4.49462026e-01
-4.58885372e-01 6.31472707e-01 2.68060893e-01 6.13704957e-02
-3.07374418e-01 -5.75810194e-01 -7.48076320e-01 -5.39602339e-01
-1.41971350e-01 2.83538431e-01 3.62969756e-01 -2.05260664... | [8.412813186645508, 0.11022111028432846] |
ebdefc0f-17f2-46fa-a9a0-8273cb813c9e | deep-ppg-large-scale-heart-rate-estimation | null | null | https://doi.org/10.3390/s19143079 | https://www.mdpi.com/1424-8220/19/14/3079/pdf | Deep PPG: Large-Scale Heart Rate Estimation with Convolutional Neural Networks | Photoplethysmography (PPG)-based continuous heart rate monitoring is essential in a number of domains, e.g., for healthcare or fitness applications. Recently, methods based on time-frequency spectra emerged to address the challenges of motion artefact compensation. However, existing approaches are highly parametrised a... | ['Ina Indlekofer', 'Attila Reiss', 'Philip Schmidt', 'Kristof Van Laerhoven'] | 2019-07-12 | null | null | null | sensors-2019-7 | ['photoplethysmography-ppg', 'heart-rate-estimation'] | ['medical', 'medical'] | [ 1.77248687e-01 -1.17173716e-02 -6.17819354e-02 -2.44773954e-01
-7.52758443e-01 -1.72540873e-01 1.66213065e-01 -7.51426220e-02
-4.13514018e-01 7.65208125e-01 3.45157951e-01 1.00334145e-01
1.38612716e-02 -4.57623690e-01 -4.45547312e-01 -5.28216898e-01
-4.73175883e-01 -4.77992110e-02 -2.93421179e-01 -2.06138566... | [13.906702041625977, 2.9495062828063965] |
415b1386-6319-4c05-9220-0465c21f7a4f | bioem-gpu-accelerated-computing-of-bayesian | 1609.06634 | null | http://arxiv.org/abs/1609.06634v1 | http://arxiv.org/pdf/1609.06634v1.pdf | BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images | In cryo-electron microscopy (EM), molecular structures are determined from
large numbers of projection images of individual particles. To harness the full
power of this single-molecule information, we use the Bayesian inference of EM
(BioEM) formalism. By ranking structural models using posterior probabilities
calculat... | ['David Rohr', 'Pilar Cossio', 'Volker Lindenstruth', 'Markus Rampp', 'Fabio Baruffa', 'Gerhard Hummer'] | 2016-09-21 | null | null | null | null | ['electron-tomography'] | ['medical'] | [ 2.99581856e-01 -5.26702285e-01 4.50986326e-01 -1.08681999e-01
-7.27125347e-01 -5.60492635e-01 7.03554451e-01 1.15249649e-01
-9.26249266e-01 9.71311390e-01 -2.46462598e-01 -4.83902901e-01
-5.74941970e-02 -5.75946212e-01 -3.80834997e-01 -1.08540368e+00
1.25250682e-01 1.28439867e+00 5.88822126e-01 4.09518331... | [13.30691146850586, -3.0685536861419678] |
d3b917cc-9a6e-43d4-9cc8-fe72eec61bf8 | depth-guided-adaptive-meta-fusion-network-for | 2010.09982 | null | https://arxiv.org/abs/2010.09982v1 | https://arxiv.org/pdf/2010.09982v1.pdf | Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition | Humans can easily recognize actions with only a few examples given, while the existing video recognition models still heavily rely on the large-scale labeled data inputs. This observation has motivated an increasing interest in few-shot video action recognition, which aims at learning new actions with only very few lab... | ['Yu-Gang Jiang', 'Yanwei Fu', 'Junke Wang', 'Li Zhang', 'Yuqian Fu'] | 2020-10-20 | null | null | null | null | ['few-shot-action-recognition'] | ['computer-vision'] | [ 4.87188995e-01 -1.90533757e-01 -3.31820220e-01 -4.06277567e-01
-6.40588105e-01 6.89078718e-02 7.60638297e-01 -1.33792803e-01
-3.83433402e-01 4.14898396e-01 3.94414395e-01 3.95510972e-01
2.18742937e-01 -6.60768688e-01 -7.88675547e-01 -9.49564815e-01
3.91119063e-01 -1.25084355e-01 3.73125553e-01 -5.01175709... | [8.552201271057129, 0.7019497156143188] |
03db1423-a581-4091-a866-7d66c0fcef9a | a-dual-attention-learning-network-with-word | 2210.00220 | null | https://arxiv.org/abs/2210.00220v2 | https://arxiv.org/pdf/2210.00220v2.pdf | A Dual-Attention Learning Network with Word and Sentence Embedding for Medical Visual Question Answering | Research in medical visual question answering (MVQA) can contribute to the development of computeraided diagnosis. MVQA is a task that aims to predict accurate and convincing answers based on given medical images and associated natural language questions. This task requires extracting medical knowledge-rich feature con... | ['Hongfang Gong', 'Xiaofei Huang'] | 2022-10-01 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [-2.10960526e-02 3.44210684e-01 -3.04194778e-01 -4.42166328e-01
-9.30255949e-01 -2.75590569e-01 4.09673542e-01 2.15384647e-01
-3.53920847e-01 3.12105656e-01 6.55307412e-01 -4.62103814e-01
-1.14001125e-01 -8.07027996e-01 -4.80735064e-01 -5.29701054e-01
3.35683465e-01 3.94473255e-01 1.67626873e-01 -3.33116025... | [10.954687118530273, 1.697878360748291] |
803e383c-e834-4d2b-9021-3b27dd3c3ae0 | implicit-quantile-networks-for-distributional | 1806.06923 | null | http://arxiv.org/abs/1806.06923v1 | http://arxiv.org/pdf/1806.06923v1.pdf | Implicit Quantile Networks for Distributional Reinforcement Learning | In this work, we build on recent advances in distributional reinforcement
learning to give a generally applicable, flexible, and state-of-the-art
distributional variant of DQN. We achieve this by using quantile regression to
approximate the full quantile function for the state-action return
distribution. By reparameter... | ['Rémi Munos', 'David Silver', 'Georg Ostrovski', 'Will Dabney'] | 2018-06-14 | implicit-quantile-networks-for-distributional-1 | https://icml.cc/Conferences/2018/Schedule?showEvent=2450 | http://proceedings.mlr.press/v80/dabney18a/dabney18a.pdf | icml-2018-7 | ['distributional-reinforcement-learning'] | ['methodology'] | [-4.89231020e-01 -2.21126571e-01 -3.86763871e-01 -3.18323880e-01
-1.26700878e+00 -5.84158003e-01 6.21949792e-01 -2.30348483e-01
-8.47597182e-01 1.36393678e+00 1.98093146e-01 -6.33391738e-01
-3.71258497e-01 -9.44179893e-01 -6.48671091e-01 -7.82318950e-01
-4.04539824e-01 9.55845654e-01 2.34934166e-01 -4.68203634... | [4.080747604370117, 2.5523266792297363] |
6ac1e27c-341c-4577-a48c-4e6c67e549f8 | findings-of-the-third-shared-task-on | null | null | https://aclanthology.org/W18-6402 | https://aclanthology.org/W18-6402.pdf | Findings of the Third Shared Task on Multimodal Machine Translation | We present the results from the third shared task on multimodal machine translation. In this task a source sentence in English is supplemented by an image and participating systems are required to generate a translation for such a sentence into German, French or Czech. The image can be used in addition to (or instead o... | ['Chiraag Lala', 'Lo{\\"\\i}c Barrault', 'Lucia Specia', 'Stella Frank', 'Fethi Bougares', 'Desmond Elliott'] | 2018-10-01 | null | null | null | ws-2018-10 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 4.64245617e-01 1.28008336e-01 2.65583992e-01 -3.90489012e-01
-1.49249172e+00 -1.10487592e+00 1.06321883e+00 -2.01088652e-01
-8.38226140e-01 1.18196285e+00 1.00229762e-01 -3.03110540e-01
8.64085317e-01 -2.52825975e-01 -6.54442608e-01 -4.81059402e-01
6.22676015e-01 9.48409498e-01 1.54206872e-01 -4.87751544... | [11.495931625366211, 1.5251071453094482] |
ab52dfbc-3d61-4119-9350-59459c0e87d9 | hyperlink-induced-pre-training-for-passage-1 | 2203.06942 | null | https://arxiv.org/abs/2203.06942v2 | https://arxiv.org/pdf/2203.06942v2.pdf | Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering | To alleviate the data scarcity problem in training question answering systems, recent works propose additional intermediate pre-training for dense passage retrieval (DPR). However, there still remains a large discrepancy between the provided upstream signals and the downstream question-passage relevance, which leads to... | ['Lei Chen', 'Qun Liu', 'Xin Jiang', 'Fan Yu', 'Zhao Cao', 'Hao Jiang', 'Xinyu Zhang', 'Enrui Hu', 'Ke Zhan', 'Lan Luo', 'Lifeng Shang', 'Xiaoguang Li', 'Jiawei Zhou'] | 2022-03-14 | null | https://aclanthology.org/2022.acl-long.493 | https://aclanthology.org/2022.acl-long.493.pdf | acl-2022-5 | ['passage-retrieval'] | ['natural-language-processing'] | [-1.44091919e-01 -9.98344272e-02 -3.01171035e-01 -4.52974401e-02
-1.66011596e+00 -6.41363561e-01 6.96966529e-01 4.82081920e-01
-4.23366010e-01 7.68039286e-01 6.50810122e-01 -4.09760386e-01
-7.29528487e-01 -9.33339715e-01 -6.85924411e-01 -2.49178365e-01
-3.09493691e-02 6.66273594e-01 9.52007711e-01 -9.57884073... | [11.418475151062012, 7.7341837882995605] |
c13f0efe-db65-439e-8ee7-039d4fea86d7 | task-aware-monocular-depth-estimation-for-3d | 1909.07701 | null | https://arxiv.org/abs/1909.07701v2 | https://arxiv.org/pdf/1909.07701v2.pdf | Task-Aware Monocular Depth Estimation for 3D Object Detection | Monocular depth estimation enables 3D perception from a single 2D image, thus attracting much research attention for years. Almost all methods treat foreground and background regions ("things and stuff") in an image equally. However, not all pixels are equal. Depth of foreground objects plays a crucial role in 3D objec... | ['Lei LI', 'Wei Yin', 'Chunhua Shen', 'Yuning Jiang', 'Xinlong Wang', 'Tao Kong'] | 2019-09-17 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [ 4.67364609e-01 -1.18734725e-01 -2.26249143e-01 -3.13030005e-01
-4.92627114e-01 -5.46473086e-01 5.73283970e-01 -4.25607026e-01
-1.69707060e-01 5.24304628e-01 5.53807616e-02 -3.63929093e-01
6.88708663e-01 -6.49648845e-01 -6.31312370e-01 -9.64301884e-01
2.36491904e-01 1.99241340e-01 9.52454269e-01 5.86298525... | [7.958476543426514, -2.509626865386963] |
0d15964d-0e22-4f1e-979a-8616edd670dd | logiformer-a-two-branch-graph-transformer | 2205.00731 | null | https://arxiv.org/abs/2205.00731v2 | https://arxiv.org/pdf/2205.00731v2.pdf | Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning | Machine reading comprehension has aroused wide concerns, since it explores the potential of model for text understanding. To further equip the machine with the reasoning capability, the challenging task of logical reasoning is proposed. Previous works on logical reasoning have proposed some strategies to extract the lo... | ['Jun Liu', 'Lingling Zhang', 'Yudai Pan', 'Qika Lin', 'Fangzhi Xu'] | 2022-05-02 | null | null | null | null | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 1.96596727e-01 6.13478065e-01 -3.35113355e-03 -4.14727926e-01
1.36052683e-01 -4.40612018e-01 5.20593941e-01 4.82428163e-01
-5.14215007e-02 2.25589290e-01 6.30479991e-01 -7.48330057e-01
-3.40905637e-01 -1.26198006e+00 -7.22809970e-01 -3.55078250e-01
4.78230834e-01 3.96584123e-01 4.20130789e-01 -5.46990693... | [9.664746284484863, 7.643124103546143] |
258c0397-0fcb-4e61-97f2-afb3407b6c65 | on-the-robustness-of-self-supervised-1 | 2209.15483 | null | https://arxiv.org/abs/2209.15483v2 | https://arxiv.org/pdf/2209.15483v2.pdf | Augmentation Invariant Discrete Representation for Generative Spoken Language Modeling | Generative Spoken Language Modeling research focuses on optimizing speech Language Models (LMs) using raw audio recordings without accessing any textual supervision. Such speech LMs usually operate over discrete units obtained from quantizing internal representations of self-supervised models. Although such units show ... | ['Emmanuel Dupoux', 'Gabriel Synnaeve', 'Tu Anh Nguyen', 'Yossi Adi', 'Jade Copet', 'Ann Lee', 'Felix Kreuk', 'Itai Gat'] | 2022-09-30 | null | null | null | null | ['speech-to-speech-translation'] | ['speech'] | [ 4.41299319e-01 3.81303757e-01 7.17989430e-02 -7.55145073e-01
-1.49206412e+00 -5.60123146e-01 8.10589373e-01 -1.36298269e-01
-2.79405385e-01 5.15329182e-01 4.51980591e-01 -2.23156139e-01
3.09700072e-01 -4.75194097e-01 -9.41084325e-01 -6.38541341e-01
6.49328008e-02 3.21407586e-01 -1.02169029e-01 -2.12448090... | [14.698742866516113, 6.6308512687683105] |
a709ac4f-340e-469d-8b62-606f06a02336 | fine-grained-image-classification-by | 1512.02665 | null | http://arxiv.org/abs/1512.02665v2 | http://arxiv.org/pdf/1512.02665v2.pdf | Fine-grained Image Classification by Exploring Bipartite-Graph Labels | Given a food image, can a fine-grained object recognition engine tell "which
restaurant which dish" the food belongs to? Such ultra-fine grained image
recognition is the key for many applications like search by images, but it is
very challenging because it needs to discern subtle difference between classes
while dealin... | ['Yuanqing Lin', 'Feng Zhou'] | 2015-12-08 | fine-grained-image-classification-by-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Zhou_Fine-Grained_Image_Classification_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhou_Fine-Grained_Image_Classification_CVPR_2016_paper.pdf | cvpr-2016-6 | ['fine-grained-image-recognition'] | ['computer-vision'] | [ 1.91037625e-01 -2.04518288e-01 -3.72894019e-01 -7.78460681e-01
-5.41765630e-01 -5.51232457e-01 1.89486489e-01 6.08376086e-01
-1.93091586e-01 5.63330710e-01 2.67829746e-01 1.61765348e-02
-4.12173241e-01 -1.35277021e+00 -1.19229782e+00 -7.98341870e-01
-2.59667456e-01 4.14530665e-01 -2.29743466e-01 -9.46226418... | [11.55046558380127, 4.37186336517334] |
5d641f6f-6767-4ae8-ba49-f67c1275d734 | modeling-intra-relation-in-math-word-problems | null | null | https://aclanthology.org/P19-1619 | https://aclanthology.org/P19-1619.pdf | Modeling Intra-Relation in Math Word Problems with Different Functional Multi-Head Attentions | Several deep learning models have been proposed for solving math word problems (MWPs) automatically. Although these models have the ability to capture features without manual efforts, their approaches to capturing features are not specifically designed for MWPs. To utilize the merits of deep learning models with simult... | ['Lei Wang', 'Jipeng Zhang', 'Bing Tian Dai', 'Yan Wang', 'Jierui Li', 'Dongxiang Zhang'] | 2019-07-01 | null | null | null | acl-2019-7 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [-1.29025981e-01 -1.75424471e-01 -9.94059220e-02 -6.42493308e-01
-1.08164334e+00 -5.09015560e-01 2.09779233e-01 4.37025279e-01
-5.12226045e-01 8.17529023e-01 -1.16181597e-01 -3.80779266e-01
-7.30469346e-01 -1.16680670e+00 -6.74722612e-01 -3.62971783e-01
9.35695600e-03 4.05482471e-01 1.41386569e-01 -3.57593685... | [9.813572883605957, 7.50708532333374] |
a3a86349-28f8-4a20-b8e3-d687142feea2 | getting-the-most-out-of-amr-parsing | null | null | https://aclanthology.org/D17-1129 | https://aclanthology.org/D17-1129.pdf | Getting the Most out of AMR Parsing | This paper proposes to tackle the AMR parsing bottleneck by improving two components of an AMR parser: concept identification and alignment. We first build a Bidirectional LSTM based concept identifier that is able to incorporate richer contextual information to learn sparse AMR concept labels. We then extend an HMM-ba... | ['Chuan Wang', 'Nianwen Xue'] | 2017-09-01 | null | null | null | emnlp-2017-9 | ['concept-alignment'] | ['computer-vision'] | [ 5.13813257e-01 5.66318274e-01 -1.81648687e-01 -5.77974916e-01
-1.11932397e+00 -6.83713853e-01 4.79752839e-01 5.97419024e-01
-5.12028098e-01 3.11809599e-01 4.32848871e-01 -6.98471844e-01
3.13987851e-01 -7.50846803e-01 -6.58331752e-01 -1.56497568e-01
3.13798971e-02 8.72924030e-01 9.96316150e-02 -1.32743925... | [10.437275886535645, 9.21379566192627] |
26daa574-8cef-4797-a07e-2474c33f8527 | collect-and-distribute-transformer-for-3d | 2306.01257 | null | https://arxiv.org/abs/2306.01257v1 | https://arxiv.org/pdf/2306.01257v1.pdf | Collect-and-Distribute Transformer for 3D Point Cloud Analysis | Although remarkable advancements have been made recently in point cloud analysis through the exploration of transformer architecture, it remains challenging to effectively learn local and global structures within point clouds. In this paper, we propose a new transformer architecture equipped with a collect-and-distribu... | ['DaCheng Tao', 'Baosheng Yu', 'Haibo Qiu'] | 2023-06-02 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [-2.59491354e-01 -3.86873126e-01 3.04635037e-02 -6.20933890e-01
-8.21886420e-01 -5.98606706e-01 5.23080468e-01 2.45751992e-01
5.02346493e-02 4.15574908e-01 -2.18157724e-01 -1.51646465e-01
-3.42575043e-01 -9.82121766e-01 -1.07187939e+00 -6.37660384e-01
-2.03727573e-01 6.66898072e-01 5.34664750e-01 -3.13392989... | [7.950525283813477, -3.403618812561035] |
f78aa6be-3d92-4533-a5aa-eb59f9a1d578 | learning-based-natural-geometric-matching | 1807.05119 | null | http://arxiv.org/abs/1807.05119v1 | http://arxiv.org/pdf/1807.05119v1.pdf | Learning-based Natural Geometric Matching with Homography Prior | Geometric matching is a key step in computer vision tasks. Previous
learning-based methods for geometric matching concentrate more on improving
alignment quality, while we argue the importance of naturalness issue
simultaneously. To deal with this, firstly, Pearson correlation is applied to
handle large intra-class var... | ['Tianli Liao', 'Yifang Xu', 'Jing Chen'] | 2018-07-13 | null | null | null | null | ['geometric-matching'] | ['computer-vision'] | [-8.75000283e-02 -3.35226029e-01 -2.43519798e-01 -4.58956093e-01
-4.82189029e-01 -4.51907098e-01 4.68215227e-01 -2.07473353e-01
-3.70072007e-01 4.95943904e-01 1.44617930e-01 1.51077345e-01
-2.89595753e-01 -7.60817349e-01 -6.07824624e-01 -6.18901312e-01
1.82327051e-02 3.49378556e-01 1.59592539e-01 -1.23390496... | [8.611462593078613, -2.219709634780884] |
2096d23f-7f37-42c4-b106-3ce165f72e6f | towards-adversarial-robustness-of-bayesian | null | null | https://openreview.net/forum?id=Cue2ZEBf12 | https://openreview.net/pdf?id=Cue2ZEBf12 | Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical Variational Inference | Recent works have applied Bayesian Neural Network (BNN) to adversarial training, and shown the improvement of adversarial robustness via the BNN's strength of stochastic gradient defense. However, we have found that in general, the BNN loses its stochasticity after its training with the posterior. As a result, the lack... | ['Yong Man Ro', 'Youngjoon Yu', 'Byung-Kwan Lee'] | 2021-01-01 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [-3.3648911e-01 8.2967781e-02 2.9255390e-01 -1.7504673e-01
-6.1997163e-01 -7.0204914e-01 2.9274681e-01 -8.1284887e-01
-4.8372027e-01 9.6025741e-01 2.2508124e-02 -3.7315947e-01
-2.5545010e-01 -8.0930614e-01 -9.8704499e-01 -1.0727390e+00
-7.1039632e-02 -2.8301751e-02 2.9864624e-01 -2.6585037e-01
-1.2911531e-01... | [5.613193035125732, 7.874966621398926] |
66ec24d2-68c9-42c7-bc84-5844c7ee7f03 | crosspyramid-neural-ordinary-differential | 2212.03560 | null | https://arxiv.org/abs/2212.03560v1 | https://arxiv.org/pdf/2212.03560v1.pdf | CrossPyramid: Neural Ordinary Differential Equations Architecture for Partially-observed Time-series | Ordinary Differential Equations (ODE)-based models have become popular foundation models to solve many time-series problems. Combining neural ODEs with traditional RNN models has provided the best representation for irregular time series. However, ODE-based models require the trajectory of hidden states to be defined b... | ['Flora D. Salim', 'Yongli Ren', 'Hao Xue', 'Futoon M. Abushaqra'] | 2022-12-07 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [-1.43765181e-01 -5.06190300e-01 -3.96614403e-01 -2.05554098e-01
-3.11495394e-01 -3.93592805e-01 5.52840352e-01 -1.04738906e-01
2.57859752e-02 6.56420350e-01 2.66960025e-01 -3.03861916e-01
-2.67701685e-01 -6.03006363e-01 -5.47284305e-01 -9.15299952e-01
-4.29204553e-01 2.95265406e-01 -1.84131965e-01 -3.13685298... | [7.001523494720459, 3.1326916217803955] |
bc0448ca-0785-4d8f-b64f-87018ec9c982 | importance-weighted-structure-learning-for | 2205.07017 | null | https://arxiv.org/abs/2205.07017v1 | https://arxiv.org/pdf/2205.07017v1.pdf | Importance Weighted Structure Learning for Scene Graph Generation | Scene graph generation is a structured prediction task aiming to explicitly model objects and their relationships via constructing a visually-grounded scene graph for an input image. Currently, the message passing neural network based mean field variational Bayesian methodology is the ubiquitous solution for such a tas... | ['Josef Kittler', 'Miroslaw Bober', 'Daqi Liu'] | 2022-05-14 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 5.54370224e-01 4.64421958e-01 -5.81991561e-02 -3.23752195e-01
-1.06363094e+00 -5.11377603e-02 9.11091745e-01 1.15764081e-01
-2.66811609e-01 9.54451263e-01 2.11330906e-01 -3.13475169e-02
-1.55666798e-01 -7.52832711e-01 -1.06987965e+00 -9.42515850e-01
3.47125113e-01 6.97462440e-01 -8.76656845e-02 3.30407977... | [7.137632846832275, 3.6866416931152344] |
0ccc8f94-0e75-490f-bad9-a0073c48afad | an-instance-segmentation-dataset-of-yeast | 2304.07597 | null | https://arxiv.org/abs/2304.07597v2 | https://arxiv.org/pdf/2304.07597v2.pdf | An Instance Segmentation Dataset of Yeast Cells in Microstructures | Extracting single-cell information from microscopy data requires accurate instance-wise segmentations. Obtaining pixel-wise segmentations from microscopy imagery remains a challenging task, especially with the added complexity of microstructured environments. This paper presents a novel dataset for segmenting yeast cel... | ['Heinz Koeppl', 'André O. Françani', 'Tim Prangemeier', 'Christoph Reich'] | 2023-04-15 | null | null | null | null | ['panoptic-segmentation', 'cell-segmentation'] | ['computer-vision', 'medical'] | [ 4.36072826e-01 -2.91240066e-01 3.76962930e-01 -3.21170330e-01
-9.00962234e-01 -8.07608604e-01 2.04107776e-01 1.93326846e-01
-6.79838061e-01 1.10321772e+00 -6.79390371e-01 -4.87911329e-02
1.38880491e-01 -5.75314641e-01 -7.01241612e-01 -8.79903138e-01
1.90352678e-01 7.48545825e-01 3.41020823e-01 5.57339311... | [14.445114135742188, -3.1599833965301514] |
d6000174-cded-4a10-a209-95031103c292 | a-holistic-approach-to-polyphonic-music | 1910.12086 | null | https://arxiv.org/abs/1910.12086v1 | https://arxiv.org/pdf/1910.12086v1.pdf | A holistic approach to polyphonic music transcription with neural networks | We present a framework based on neural networks to extract music scores directly from polyphonic audio in an end-to-end fashion. Most previous Automatic Music Transcription (AMT) methods seek a piano-roll representation of the pitches, that can be further transformed into a score by incorporating tempo estimation, beat... | ['Jorge Calvo-Zaragoza', 'Miguel A. Román', 'Antonio Pertusa'] | 2019-10-26 | null | null | null | null | ['music-transcription'] | ['music'] | [ 5.48338711e-01 -9.13705006e-02 8.61951411e-02 -1.62872165e-01
-1.36833036e+00 -8.72013807e-01 3.88917774e-01 -3.53360698e-02
-2.42258862e-01 4.72999543e-01 4.67330039e-01 8.36267918e-02
-3.19825917e-01 -4.07580823e-01 -5.14911354e-01 -6.29721344e-01
-1.15184888e-01 3.70831668e-01 -1.74562424e-01 -2.03590795... | [15.852195739746094, 5.430830001831055] |
99273368-0ec8-49ae-be17-bdab606c37ef | efficient-multi-task-and-transfer | 2306.01839 | null | https://arxiv.org/abs/2306.01839v1 | https://arxiv.org/pdf/2306.01839v1.pdf | Efficient Multi-Task and Transfer Reinforcement Learning with Parameter-Compositional Framework | In this work, we investigate the potential of improving multi-task training and also leveraging it for transferring in the reinforcement learning setting. We identify several challenges towards this goal and propose a transferring approach with a parameter-compositional formulation. We investigate ways to improve the t... | ['Masayoshi Tomizuka', 'Wei Xu', 'Haichao Zhang', 'Lingfeng Sun'] | 2023-06-02 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [ 3.99907261e-01 -2.68640310e-01 -2.50253022e-01 -1.57097787e-01
-1.08097816e+00 -4.82101917e-01 3.69833708e-01 -4.38240111e-01
-6.90477610e-01 8.70790958e-01 -1.18047766e-01 -2.53176451e-01
-2.51601130e-01 -7.13334918e-01 -1.09776759e+00 -7.32853711e-01
-1.55334219e-01 3.88831705e-01 2.87480503e-01 -3.82477403... | [3.9742431640625, 1.7958205938339233] |
06daed84-34ea-44fd-80cc-3b2fd0e852ff | estan-enhanced-small-tumor-aware-network-for | 2009.12894 | null | https://arxiv.org/abs/2009.12894v1 | https://arxiv.org/pdf/2009.12894v1.pdf | ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation | Breast tumor segmentation is a critical task in computer-aided diagnosis (CAD) systems for breast cancer detection because accurate tumor size, shape and location are important for further tumor quantification and classification. However, segmenting small tumors in ultrasound images is challenging, due to the speckle n... | ['Alex Vakanski', 'Phoebe E. Freer', 'Min Xian', 'Bryar Shareef'] | 2020-09-27 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 4.09079939e-01 1.54765353e-01 -4.07808125e-01 -5.99441051e-01
-1.16785419e+00 -4.65747118e-02 5.01207681e-03 4.92098212e-01
-3.74583751e-01 3.00160199e-01 4.53271829e-02 -5.65416455e-01
7.06702173e-02 -5.90663970e-01 -4.97571290e-01 -8.58991146e-01
-1.70673132e-01 5.75882137e-01 4.46034133e-01 1.13524109... | [15.06545639038086, -2.544410467147827] |
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