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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0b0f7126-e41a-490c-8997-51766e5686ae | escaping-the-big-data-paradigm-with-compact | 2104.05704 | null | https://arxiv.org/abs/2104.05704v4 | https://arxiv.org/pdf/2104.05704v4.pdf | Escaping the Big Data Paradigm with Compact Transformers | With the rise of Transformers as the standard for language processing, and their advancements in computer vision, there has been a corresponding growth in parameter size and amounts of training data. Many have come to believe that because of this, transformers are not suitable for small sets of data. This trend leads t... | ['Humphrey Shi', 'Jiachen Li', 'Abulikemu Abuduweili', 'Nikhil Shah', 'Steven Walton', 'Ali Hassani'] | 2021-04-12 | null | null | null | null | ['superpixel-image-classification'] | ['computer-vision'] | [-9.49911550e-02 -2.32494473e-01 -2.68264562e-01 -1.96583480e-01
-8.66032779e-01 -6.88340068e-01 5.31839550e-01 -7.63165057e-02
-8.70618343e-01 4.33109432e-01 4.12919074e-02 -7.73513436e-01
2.05397159e-01 -8.66137922e-01 -7.72575259e-01 -3.56719136e-01
1.59872815e-01 4.61272478e-01 4.20276254e-01 -2.10812330... | [9.001587867736816, 2.613783836364746] |
1dcd195c-f5dc-4e6a-982a-705b132734ad | contrastive-learning-approach-for-semi | 2210.04776 | null | https://arxiv.org/abs/2210.04776v3 | https://arxiv.org/pdf/2210.04776v3.pdf | CONSS: Contrastive Learning Approach for Semi-Supervised Seismic Facies Classification | Recently, seismic facies classification based on convolutional neural networks (CNN) has garnered significant research interest. However, existing CNN-based supervised learning approaches necessitate massive labeled data. Labeling is laborious and time-consuming, particularly for 3D seismic data volumes. To overcome th... | ['Ruilin Jing', 'Hongjie Duan', 'Zhifeng Xu', 'YiMin Dou', 'Wenlong Liu', 'Kewen Li'] | 2022-10-10 | null | null | null | null | ['facies-classification'] | ['miscellaneous'] | [-7.24115744e-02 -2.21643656e-01 -1.59044117e-01 -6.86649084e-01
-1.19038808e+00 -7.05944300e-01 4.92696434e-01 7.34738931e-02
-5.59268653e-01 5.33010066e-01 7.97471439e-04 -1.85624976e-02
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-2.68534064e-01 3.31511974e-01 3.67729485e-01 -1.04027525... | [7.2021942138671875, 2.1229426860809326] |
3c554dfb-7056-4399-8f8d-5afc4ffcb4f8 | anisotropic-convolutional-networks-for-3d | 2004.02122 | null | https://arxiv.org/abs/2004.02122v1 | https://arxiv.org/pdf/2004.02122v1.pdf | Anisotropic Convolutional Networks for 3D Semantic Scene Completion | As a voxel-wise labeling task, semantic scene completion (SSC) tries to simultaneously infer the occupancy and semantic labels for a scene from a single depth and/or RGB image. The key challenge for SSC is how to effectively take advantage of the 3D context to model various objects or stuffs with severe variations in s... | ['Xia Yuan', 'Yu Liu', 'Kai Han', 'Peng Wang', 'Jie Li'] | 2020-04-05 | anisotropic-convolutional-networks-for-3d-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Li_Anisotropic_Convolutional_Networks_for_3D_Semantic_Scene_Completion_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Anisotropic_Convolutional_Networks_for_3D_Semantic_Scene_Completion_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-semantic-scene-completion', '3d-semantic-scene-completion-from-a-single'] | ['computer-vision', 'computer-vision'] | [ 2.61496305e-01 -6.64990321e-02 7.01493323e-02 -5.06345332e-01
-2.18043625e-01 -6.28047705e-01 5.51523507e-01 -1.32861033e-01
-3.96097273e-01 2.80604482e-01 1.34281203e-01 -3.80407929e-01
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1.92586288e-01 2.54227042e-01 4.97991174e-01 -1.68197080... | [8.574254989624023, -2.7388932704925537] |
7f86660e-af5f-4a28-bf02-79ad8bae0cdc | data-efficient-learning-for-3d-mirror | 2112.12579 | null | https://arxiv.org/abs/2112.12579v2 | https://arxiv.org/pdf/2112.12579v2.pdf | NeRD++: Improved 3D-mirror symmetry learning from a single image | Many objects are naturally symmetric, and this symmetry can be exploited to infer unseen 3D properties from a single 2D image. Recently, NeRD is proposed for accurate 3D mirror plane estimation from a single image. Despite the unprecedented accuracy, it relies on large annotated datasets for training and suffers from s... | ['Jan van Gemert', 'Silvia-Laura Pintea', 'Yancong Lin'] | 2021-12-23 | null | null | null | null | ['symmetry-detection'] | ['computer-vision'] | [ 4.92831767e-02 1.90457955e-01 6.74026534e-02 -5.32012463e-01
-4.97920841e-01 -6.52997434e-01 7.05794334e-01 -3.04465204e-01
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1.08576953e-01 6.76469326e-01 2.38433510e-01 2.81497359... | [8.454551696777344, -2.885263204574585] |
9bd05c17-01f9-4cac-b05f-02faf91fd829 | recommender-systems-a-primer | 2302.02579 | null | https://arxiv.org/abs/2302.02579v1 | https://arxiv.org/pdf/2302.02579v1.pdf | Recommender Systems: A Primer | Personalized recommendations have become a common feature of modern online services, including most major e-commerce sites, media platforms and social networks. Today, due to their high practical relevance, research in the area of recommender systems is flourishing more than ever. However, with the new application scen... | ['Dietmar Jannach', 'Pablo Castells'] | 2023-02-06 | null | null | null | null | ['session-based-recommendations'] | ['miscellaneous'] | [ 2.46873610e-02 -2.09781960e-01 -2.44614258e-01 -5.21144211e-01
-2.52363294e-01 -8.12121630e-01 5.77996492e-01 -3.75491381e-02
-3.56030375e-01 5.31673312e-01 4.33285296e-01 -3.87832493e-01
-8.83416593e-01 -8.19034219e-01 7.16777239e-03 -5.00138164e-01
-3.72492000e-02 5.49517334e-01 2.29667798e-01 -8.16412449... | [9.982830047607422, 5.763070583343506] |
a32780e1-967c-43af-9a71-502c911be720 | contextual-explainable-video-representation | 2212.06206 | null | https://arxiv.org/abs/2212.06206v2 | https://arxiv.org/pdf/2212.06206v2.pdf | Contextual Explainable Video Representation: Human Perception-based Understanding | Video understanding is a growing field and a subject of intense research, which includes many interesting tasks to understanding both spatial and temporal information, e.g., action detection, action recognition, video captioning, video retrieval. One of the most challenging problems in video understanding is dealing wi... | ['Ngan Le', 'Khoa Luu', 'Phat Nguyen', 'Phong X. Nguyen', 'Kashu Yamazaki', 'Khoa Vo'] | 2022-12-12 | null | null | null | null | ['video-understanding'] | ['computer-vision'] | [ 3.71379524e-01 -1.41451225e-01 -4.31145191e-01 -3.30162376e-01
-2.32654244e-01 -4.61364150e-01 4.96296316e-01 -1.64684113e-02
-8.54696985e-03 5.30506849e-01 7.53070652e-01 4.80332552e-03
-2.87876800e-02 -3.39386016e-01 -9.44411159e-01 -4.68074620e-01
9.53220055e-02 -1.18269376e-01 2.24834204e-01 -7.83449411... | [8.728419303894043, 0.6818476319313049] |
3f5b2ce3-b467-48df-a1a7-9143822dd7c4 | natural-language-detectors-emerge-in | null | null | https://openreview.net/forum?id=BJec2Mfosm | https://openreview.net/pdf?id=BJec2Mfosm | Natural Language Detectors Emerge in Individual Neurons | Although deep convolutional networks have achieved improved performance in many natural language tasks, they have been treated as black boxes because they are difficult to interpret. Especially, little is known about how they represent language in their intermediate layers. In an attempt to understand the representatio... | ['Anonymous'] | 2018-10-22 | null | null | null | null | ['concept-alignment'] | ['computer-vision'] | [ 4.08363491e-01 8.34364444e-03 -2.03272805e-01 -5.75897813e-01
-7.61564672e-02 -8.26724172e-01 7.77146578e-01 4.30495471e-01
-4.58953530e-01 4.57025021e-01 6.10970259e-01 -4.82775956e-01
3.30839932e-01 -8.49429846e-01 -7.14329958e-01 -1.69462189e-01
1.58925891e-01 5.92360497e-01 -7.27895945e-02 -4.75091934... | [10.521188735961914, 8.782100677490234] |
3f6ca3ed-0534-4971-b907-b15e0c1f5f0f | meta-repository-of-screening-mammography | 2108.04800 | null | https://arxiv.org/abs/2108.04800v3 | https://arxiv.org/pdf/2108.04800v3.pdf | Meta-repository of screening mammography classifiers | Artificial intelligence (AI) is showing promise in improving clinical diagnosis. In breast cancer screening, recent studies show that AI has the potential to improve early cancer diagnosis and reduce unnecessary workup. As the number of proposed models and their complexity grows, it is becoming increasingly difficult t... | ['Krzysztof J. Geras', 'Kyunghyun Cho', 'Farah E. Shamout', 'Jakub Chłędowski', 'Vishwaesh Rajiv', 'Jan Witowski', 'Benjamin Stadnick'] | 2021-08-10 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection', 'breast-tumour-classification'] | ['knowledge-base', 'medical', 'medical'] | [ 3.10249835e-01 3.24834496e-01 -7.95208573e-01 -5.83286166e-01
-9.77950454e-01 -1.87630087e-01 2.87182301e-01 5.11228859e-01
-3.27984095e-01 2.35603854e-01 2.40709588e-01 -7.76341438e-01
-2.86981285e-01 -8.13179731e-01 -5.90349734e-01 -3.40602070e-01
-1.96955100e-01 7.97957003e-01 2.24668831e-01 7.57428929... | [15.206266403198242, -2.482083559036255] |
9e7790af-8e8a-4ffa-88e5-6f2134005997 | an-examination-of-the-robustness-of-reference | 2305.14998 | null | https://arxiv.org/abs/2305.14998v1 | https://arxiv.org/pdf/2305.14998v1.pdf | An Examination of the Robustness of Reference-Free Image Captioning Evaluation Metrics | Recently, reference-free metrics such as CLIPScore (Hessel et al., 2021) and UMIC (Lee et al., 2021) have been proposed for automatic evaluation of image captions, demonstrating a high correlation with human judgment. In this work, our focus lies in evaluating the robustness of these metrics in scenarios that require d... | ['Aishwarya Agrawal', 'Saba Ahmadi'] | 2023-05-24 | null | null | null | null | ['visual-grounding', 'image-captioning'] | ['computer-vision', 'computer-vision'] | [ 3.40361714e-01 1.07358724e-01 -6.67474419e-02 -3.85943562e-01
-1.01728642e+00 -9.61240470e-01 6.44621253e-01 6.83299243e-01
-6.14182949e-01 5.96661150e-01 4.90334064e-01 -3.14206213e-01
1.61670372e-01 -3.91154051e-01 -8.13550532e-01 -9.78080183e-02
3.97100031e-01 3.39127928e-02 1.85485005e-01 -1.49587825... | [11.120599746704102, 1.2845243215560913] |
55229a9e-4432-49f5-b197-8e0ed09e7f1a | lvos-a-benchmark-for-long-term-video-object | 2211.10181 | null | https://arxiv.org/abs/2211.10181v1 | https://arxiv.org/pdf/2211.10181v1.pdf | LVOS: A Benchmark for Long-term Video Object Segmentation | Existing video object segmentation (VOS) benchmarks focus on short-term videos which just last about 3-5 seconds and where objects are visible most of the time. These videos are poorly representative of practical applications, and the absence of long-term datasets restricts further investigation of VOS on the applicati... | ['Wenqiang Zhang', 'Zhaoyu Chen', 'Pinxue Guo', 'Wei zhang', 'Zhongying Liu', 'Wenchao Chen', 'Lingyi Hong'] | 2022-11-18 | null | null | null | null | ['video-object-segmentation'] | ['computer-vision'] | [-1.45757481e-01 -4.31883156e-01 -6.75785184e-01 -2.44226500e-01
-5.51668227e-01 -5.62743485e-01 4.02782559e-01 -3.24940383e-01
-4.83214051e-01 4.92051154e-01 -4.01898474e-02 -1.02484785e-01
1.82874486e-01 -3.26548904e-01 -9.29721534e-01 -5.62152684e-01
-2.07685858e-01 2.15413705e-01 7.00177133e-01 7.97573030... | [9.198051452636719, 0.09791316837072372] |
72e1a24a-0a29-4d1b-9e78-47a5f54b01d1 | ranking-loss-and-sequestering-learning-for | 2304.08498 | null | https://arxiv.org/abs/2304.08498v1 | https://arxiv.org/pdf/2304.08498v1.pdf | Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathology | Recently, deep learning has started to play an essential role in healthcare applications, including image search in digital pathology. Despite the recent progress in computer vision, significant issues remain for image searching in histopathology archives. A well-known problem is AI bias and lack of generalization. A m... | ['H. R. Tizhoosh', 'Shahryar Rahnamayan', 'Azam Asilian Bidgoli', 'Pooria Mazaheri'] | 2023-04-15 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 3.43179435e-01 1.21321887e-01 -4.19434160e-01 -3.47368151e-01
-7.79040515e-01 -3.63081843e-01 3.32339734e-01 3.51041257e-01
-8.36842537e-01 4.97371316e-01 -2.11558584e-03 -2.56379813e-01
-4.42721188e-01 -8.67108047e-01 -5.36159575e-01 -1.08932042e+00
2.07671151e-01 3.09088379e-01 1.19627930e-01 3.31111159... | [15.004979133605957, -2.5955491065979004] |
906ad8d1-e949-475e-b4c9-585d1d5dd282 | causal-inference-for-the-expected-number-of | 2306.16571 | null | https://arxiv.org/abs/2306.16571v1 | https://arxiv.org/pdf/2306.16571v1.pdf | Causal inference for the expected number of recurrent events in the presence of a terminal event | We study causal inference and efficient estimation for the expected number of recurrent events in the presence of a terminal event. We define our estimand as the vector comprising both the expected number of recurrent events and the failure survival function evaluated along a sequence of landmark times. We identify the... | ['Ashkan Ertefaie', 'Robert L. Strawderman', 'Benjamin R. Baer'] | 2023-06-28 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 3.38404357e-01 1.66474938e-01 -8.08231294e-01 -5.25845587e-01
-6.61121905e-01 -4.67679977e-01 6.44410849e-01 2.42982134e-01
-3.24155658e-01 1.29273057e+00 5.27782261e-01 -4.19642329e-01
-8.28724444e-01 -8.51693988e-01 -8.93392146e-01 -6.85675204e-01
-6.16448164e-01 4.06732172e-01 -3.34333420e-01 4.68185693... | [7.948225975036621, 5.219399929046631] |
d3d9cd06-8834-4197-86d5-2be42b63cd82 | forgetting-exceptions-is-harmful-in-language | cs/9812021 | null | https://arxiv.org/abs/cs/9812021v1 | https://arxiv.org/pdf/cs/9812021v1.pdf | Forgetting Exceptions is Harmful in Language Learning | We show that in language learning, contrary to received wisdom, keeping exceptional training instances in memory can be beneficial for generalization accuracy. We investigate this phenomenon empirically on a selection of benchmark natural language processing tasks: grapheme-to-phoneme conversion, part-of-speech tagging... | ['Antal Van den Bosch', 'Walter Daelemans', 'Jakub Zavrel'] | 1998-12-22 | null | null | null | null | ['prepositional-phrase-attachment'] | ['natural-language-processing'] | [ 4.25061047e-01 3.23184758e-01 -4.39318299e-01 -3.37607563e-01
-5.07093549e-01 -4.69447523e-01 7.41123557e-01 9.61330473e-01
-9.63124633e-01 9.66145277e-01 3.11788648e-01 -5.03055930e-01
-1.84263155e-01 -9.91070151e-01 -5.61104000e-01 -5.28323531e-01
-1.98787585e-01 5.00260711e-01 4.70349401e-01 -1.19013153... | [10.679460525512695, 9.13099479675293] |
a31b06ec-11ca-45cd-b170-7e5f15b4b0f3 | masked-event-modeling-self-supervised | 2212.10368 | null | https://arxiv.org/abs/2212.10368v1 | https://arxiv.org/pdf/2212.10368v1.pdf | Masked Event Modeling: Self-Supervised Pretraining for Event Cameras | Event cameras offer the capacity to asynchronously capture brightness changes with low latency, high temporal resolution, and high dynamic range. Deploying deep learning methods for classification or other tasks to these sensors typically requires large labeled datasets. Since the amount of labeled event data is tiny c... | ['Daniel Cremers', 'Lukas Koestler', 'David Bonello', 'Simon Klenk'] | 2022-12-20 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 3.8702530e-01 -6.4895540e-02 -3.0559201e-02 -7.1129161e-01
-8.1826830e-01 -4.3290925e-01 6.6261750e-01 2.8769458e-02
-8.5087252e-01 5.1661098e-01 -1.7421469e-01 -7.4105501e-02
4.4939041e-01 -8.7069559e-01 -1.2522528e+00 -6.6761547e-01
-3.4814443e-02 1.8509316e-01 6.7644632e-01 2.6744184e-01
-3.6343959e-01... | [8.508133888244629, -0.9786214232444763] |
d836aa39-4913-4030-aa59-b3f56f25e3e8 | multi-modal-face-stylization-with-a | 2305.18009 | null | https://arxiv.org/abs/2305.18009v1 | https://arxiv.org/pdf/2305.18009v1.pdf | Multi-Modal Face Stylization with a Generative Prior | In this work, we introduce a new approach for artistic face stylization. Despite existing methods achieving impressive results in this task, there is still room for improvement in generating high-quality stylized faces with diverse styles and accurate facial reconstruction. Our proposed framework, MMFS, supports multi-... | ['Chongyang Ma', 'Pengfei Wan', 'Haibin Huang', 'Minxuan Lin', 'Yi Dong', 'Mengtian Li'] | 2023-05-29 | null | null | null | null | ['face-generation'] | ['computer-vision'] | [ 3.18484128e-01 3.29252839e-01 -4.09577675e-02 -3.57322186e-01
-7.41302967e-01 -3.75328422e-01 6.64620519e-01 -1.06636095e+00
3.43643241e-02 6.48743033e-01 3.55386674e-01 2.23959059e-01
5.05255401e-01 -9.68649626e-01 -9.26305234e-01 -5.41442454e-01
5.73012471e-01 4.12244588e-01 -3.08795899e-01 -2.92943448... | [12.407503128051758, -0.22814162075519562] |
c01580f6-902c-44b8-bff2-fa6397ebc32c | deep-endovo-a-recurrent-convolutional-neural | 1708.06822 | null | http://arxiv.org/abs/1708.06822v2 | http://arxiv.org/pdf/1708.06822v2.pdf | Deep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots | Ingestible wireless capsule endoscopy is an emerging minimally invasive
diagnostic technology for inspection of the GI tract and diagnosis of a wide
range of diseases and pathologies. Medical device companies and many research
groups have recently made substantial progresses in converting passive capsule
endoscopes to ... | ['Metin Sitti', 'Mehmet Turan', 'Helder Araujo', 'Yasin Almalioglu', 'Ender Konukoglu'] | 2017-08-22 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-5.25058687e-01 2.12365240e-01 -1.33001715e-01 3.29663396e-01
1.95351645e-01 -7.75167704e-01 1.56019688e-01 -2.50584120e-03
-2.47968420e-01 7.98968300e-02 -1.27920946e-02 -2.73402303e-01
-1.21460706e-01 -1.15048751e-01 -6.96326137e-01 -7.08725572e-01
-5.86662471e-01 2.24110410e-01 3.88174504e-02 -1.15137428... | [13.959209442138672, -3.172361135482788] |
64ca3525-864b-4d7a-8dd4-e9a4870bcd93 | multi-content-gan-for-few-shot-font-style | 1712.00516 | null | http://arxiv.org/abs/1712.00516v1 | http://arxiv.org/pdf/1712.00516v1.pdf | Multi-Content GAN for Few-Shot Font Style Transfer | In this work, we focus on the challenge of taking partial observations of
highly-stylized text and generalizing the observations to generate unobserved
glyphs in the ornamented typeface. To generate a set of multi-content images
following a consistent style from very few examples, we propose an end-to-end
stacked condi... | ['Zhaowen Wang', 'Vladimir Kim', 'Samaneh Azadi', 'Matthew Fisher', 'Eli Shechtman', 'Trevor Darrell'] | 2017-12-01 | multi-content-gan-for-few-shot-font-style-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Azadi_Multi-Content_GAN_for_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Azadi_Multi-Content_GAN_for_CVPR_2018_paper.pdf | cvpr-2018-6 | ['font-style-transfer'] | ['computer-vision'] | [ 6.99516654e-01 2.39859093e-02 3.76141906e-01 -3.60507101e-01
-4.09889936e-01 -9.46955562e-01 6.84012830e-01 -6.04734659e-01
2.28750765e-01 8.12447429e-01 3.84405136e-01 5.46724722e-02
3.83468777e-01 -6.31953359e-01 -1.20360994e+00 -5.17846584e-01
3.35370928e-01 3.65655005e-01 -3.38172227e-01 -2.17485219... | [11.606807708740234, -0.4332196116447449] |
0a043675-53db-4029-81ea-8021d3d351e8 | shape-from-water-reflection | 1906.10284 | null | https://arxiv.org/abs/1906.10284v2 | https://arxiv.org/pdf/1906.10284v2.pdf | Appearance and Shape from Water Reflection | This paper introduces single-image geometric and appearance reconstruction from water reflection photography, i.e., images capturing direct and water-reflected real-world scenes. Water reflection offers an additional viewpoint to the direct sight, collectively forming a stereo pair. The water-reflected scene, however, ... | ['Meng-Yu Jennifer Kuo', 'Ryo Kawahara', 'Ko Nishino', 'Shohei Nobuhara'] | 2019-06-25 | null | null | null | null | ['stereo-matching', '3d-scene-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 8.31314862e-01 -6.06696047e-02 8.44950795e-01 -2.50025213e-01
-4.24006194e-01 -8.03427458e-01 4.23783183e-01 -7.62791812e-01
-2.17025399e-01 1.77035034e-01 3.33453000e-01 -2.81347092e-02
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5.94661534e-01 3.27707827e-01 1.36712134e-01 -1.28967538... | [9.877985954284668, -2.8962061405181885] |
14660b96-8091-4cf4-a5de-535aa57517a9 | cross-image-attention-for-conditional | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Kotovenko_Cross-Image-Attention_for_Conditional_Embeddings_in_Deep_Metric_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kotovenko_Cross-Image-Attention_for_Conditional_Embeddings_in_Deep_Metric_Learning_CVPR_2023_paper.pdf | Cross-Image-Attention for Conditional Embeddings in Deep Metric Learning | Learning compact image embeddings that yield semantic similarities between images and that generalize to unseen test classes, is at the core of deep metric learning (DML). Finding a mapping from a rich, localized image feature map onto a compact embedding vector is challenging: Although similarity emerges between t... | ['Björn Ommer', 'Timo Milbich', 'Pingchuan Ma', 'Dmytro Kotovenko'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 2.47762203e-01 2.68929335e-03 -2.01560497e-01 -6.55683219e-01
-8.36380064e-01 -5.89140713e-01 6.57637060e-01 6.54155374e-01
-7.10662007e-01 3.33602846e-01 3.01262408e-01 -9.97682214e-02
-1.98820070e-03 -7.89382458e-01 -9.77172911e-01 -6.25679970e-01
3.63723282e-03 2.30277508e-01 2.50118345e-01 2.80787587... | [9.838667869567871, 2.2453231811523438] |
d64b94bc-ebcc-4a6d-ac52-73b550b14ddc | toward-realistic-single-view-3d-object | 2109.02288 | null | https://arxiv.org/abs/2109.02288v2 | https://arxiv.org/pdf/2109.02288v2.pdf | Toward Realistic Single-View 3D Object Reconstruction with Unsupervised Learning from Multiple Images | Recovering the 3D structure of an object from a single image is a challenging task due to its ill-posed nature. One approach is to utilize the plentiful photos of the same object category to learn a strong 3D shape prior for the object. This approach has successfully been demonstrated by a recent work of Wu et al. (202... | ['Minh Hoai', 'Quynh Phung', 'Anh Tuan Tran', 'Long-Nhat Ho'] | 2021-09-06 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Ho_Toward_Realistic_Single-View_3D_Object_Reconstruction_With_Unsupervised_Learning_From_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Ho_Toward_Realistic_Single-View_3D_Object_Reconstruction_With_Unsupervised_Learning_From_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-object-reconstruction', 'object-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 2.70402879e-01 -7.38776848e-02 1.37552455e-01 -3.06558639e-01
-5.49590111e-01 -6.49312556e-01 4.20931786e-01 -5.06268799e-01
-1.35600716e-01 5.14086723e-01 1.48396760e-01 8.95036906e-02
-3.41613233e-01 -6.03169739e-01 -8.96447539e-01 -8.53042424e-01
3.13079655e-01 4.07441556e-01 3.60839039e-01 -7.44131673... | [9.308294296264648, -2.7401814460754395] |
ea065fa7-f984-4cb8-970a-5d21a1c14d46 | towards-fast-and-accurate-neural-chinese-word | null | null | https://aclanthology.org/2020.coling-main.186 | https://aclanthology.org/2020.coling-main.186.pdf | Towards Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning | The ambiguous annotation criteria lead to divergence of Chinese Word Segmentation (CWS) datasets in various granularities. Multi-criteria Chinese word segmentation aims to capture various annotation criteria among datasets and leverage their common underlying knowledge. In this paper, we propose a domain adaptive segme... | ['Wei Chu', 'Taifeng Wang', 'Kunlong Chen', 'Xingyi Cheng', 'Weipeng Huang'] | 2020-12-01 | null | null | null | coling-2020-8 | ['compiler-optimization', 'chinese-word-segmentation'] | ['computer-code', 'natural-language-processing'] | [ 2.57413507e-01 -6.97161034e-02 -6.43831849e-01 -5.30908287e-01
-9.90815818e-01 -9.63522255e-01 9.04852003e-02 -1.95203274e-01
-6.53718650e-01 6.15403652e-01 5.52755892e-01 -6.67111456e-01
1.27998710e-01 -6.12872720e-01 -5.43922663e-01 -2.48072982e-01
5.80304742e-01 5.12384892e-01 4.93372709e-01 -1.19055100... | [9.966768264770508, 10.095037460327148] |
e5426b98-b96f-4a4c-9e33-f3ee6ca77b0e | rethinking-context-aggregation-in-natural | 2304.01171 | null | https://arxiv.org/abs/2304.01171v1 | https://arxiv.org/pdf/2304.01171v1.pdf | Rethinking Context Aggregation in Natural Image Matting | For natural image matting, context information plays a crucial role in estimating alpha mattes especially when it is challenging to distinguish foreground from its background. Exiting deep learning-based methods exploit specifically designed context aggregation modules to refine encoder features. However, the effective... | ['Liqiang Nie', 'Bineng Zhong', 'Ru Li', 'Quanling Meng', 'Shengping Zhang', 'Qinglin Liu'] | 2023-04-03 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 3.58051062e-01 -5.53896688e-02 9.10585076e-02 -4.24158126e-01
-6.57672524e-01 -1.99914515e-01 4.43141490e-01 -2.70451635e-01
-2.75756896e-01 5.70365846e-01 4.76568900e-02 -3.02269191e-01
2.82971591e-01 -6.61286116e-01 -1.14752686e+00 -8.89034986e-01
2.21007153e-01 -4.56059873e-02 2.31606036e-01 -6.01607338... | [10.650829315185547, -0.9031862020492554] |
6156ec6e-8608-4dae-890b-1eef00702c23 | distributed-energy-management-and-demand | 2211.15858 | null | https://arxiv.org/abs/2211.15858v1 | https://arxiv.org/pdf/2211.15858v1.pdf | Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework | This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users... | ['Morteza Haashemi', 'Reza Ahmadi', 'Alexandru G. Bardas', 'Yousif Dafalla', 'Kailani Jones', 'Arman Ghasemi', 'Amin Shojaeighadikolaei'] | 2022-11-29 | null | null | null | null | ['energy-management'] | ['time-series'] | [-9.13556814e-01 -2.20179021e-01 -1.75211787e-01 1.15315698e-01
-4.94201213e-01 -7.96457231e-01 2.33912751e-01 1.78116098e-01
2.54155517e-01 1.26016223e+00 -9.97262541e-03 -2.10354701e-02
-4.14188534e-01 -1.33702254e+00 6.83714673e-02 -1.40643501e+00
-3.03822488e-01 5.32823145e-01 -5.73803425e-01 -2.12929830... | [5.636225700378418, 2.5449414253234863] |
bb16d651-c1b3-4e32-ae99-3cda5c0cc4f6 | semantic-communication-enabling-robust-edge | 2211.13787 | null | https://arxiv.org/abs/2211.13787v2 | https://arxiv.org/pdf/2211.13787v2.pdf | Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications | This paper aims to design robust Edge Intelligence using semantic communication for time-critical IoT applications. We systematically analyze the effect of image DCT coefficients on inference accuracy and propose the channel-agnostic effectiveness encoding for offloading by transmitting the most meaningful task data fi... | ['Andrea Cavagna', 'Qi Zhang', 'Alexandros Iosifidis', 'Nan Li'] | 2022-11-24 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 5.05055845e-01 -3.69712450e-02 -2.97331542e-01 -2.91855782e-01
-2.56465942e-01 -2.04851702e-01 2.87749559e-01 -4.51854855e-01
-6.77918196e-01 7.05388427e-01 1.09274730e-01 -3.76731575e-01
-3.38666648e-01 -8.88820767e-01 -9.03477550e-01 -5.60935557e-01
-1.52376235e-01 -2.70394087e-02 8.30906779e-02 -3.03498507... | [8.457202911376953, 2.793079137802124] |
04f859f7-9235-4d18-b052-7799e79efdfd | unsupervised-video-object-segmentation-with-1 | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Siyang_Li_Unsupervised_Video_Object_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Siyang_Li_Unsupervised_Video_Object_ECCV_2018_paper.pdf | Unsupervised Video Object Segmentation with Motion-based Bilateral Networks | In this work, we study the unsupervised video object segmentation problem where moving objects are segmented without prior knowledge of these objects. First, we propose a motion-based bilateral network to estimate the background based on the motion pattern of non-object regions. The bilateral network reduces false posi... | ['C. -C. Jay Kuo', 'Xuejing Lei', 'Siyang Li', 'Bryan Seybold', 'Alexey Vorobyov'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['video-salient-object-detection', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.10130945e-01 5.27849123e-02 -4.28112239e-01 -2.22157553e-01
-1.82445571e-01 -5.88551223e-01 1.59020409e-01 -1.55794367e-01
-6.14444196e-01 4.02827233e-01 -1.99035898e-01 -4.57001366e-02
2.06529185e-01 -7.65513182e-01 -7.98820376e-01 -6.15968525e-01
-1.24520712e-01 4.01231855e-01 9.89530206e-01 4.34149563... | [9.160882949829102, -0.2278296798467636] |
8cbfea05-34b5-476a-90a8-e7947bf16b7f | curi-a-benchmark-for-productive-concept-1 | 2010.02855 | null | https://arxiv.org/abs/2010.02855v1 | https://arxiv.org/pdf/2010.02855v1.pdf | CURI: A Benchmark for Productive Concept Learning Under Uncertainty | Humans can learn and reason under substantial uncertainty in a space of infinitely many concepts, including structured relational concepts ("a scene with objects that have the same color") and ad-hoc categories defined through goals ("objects that could fall on one's head"). In contrast, standard classification benchma... | ['Brenden Lake', 'Ari Morcos', 'Maximilian Nickel', 'Arthur Szlam', 'Ramakrishna Vedantam'] | 2020-10-06 | curi-a-benchmark-for-productive-concept | https://openreview.net/forum?id=LuyryrCs6Ez | https://openreview.net/pdf?id=LuyryrCs6Ez | null | ['systematic-generalization'] | ['reasoning'] | [ 3.16263735e-01 2.67664224e-01 -1.79006323e-01 -6.50750339e-01
-6.39829040e-01 -8.74492884e-01 1.00166786e+00 6.04919076e-01
-2.47788042e-01 7.50981390e-01 2.89923936e-01 -2.11371392e-01
-7.04093993e-01 -8.83551061e-01 -6.94239795e-01 -5.72027147e-01
-1.43052548e-01 7.14581788e-01 1.25747219e-01 -2.30235562... | [10.452051162719727, 2.283189058303833] |
1ae94b86-3d03-4024-a7c5-981e4fce0d7b | invariance-aware-randomized-smoothing | 2211.14207 | null | https://arxiv.org/abs/2211.14207v2 | https://arxiv.org/pdf/2211.14207v2.pdf | Invariance-Aware Randomized Smoothing Certificates | Building models that comply with the invariances inherent to different domains, such as invariance under translation or rotation, is a key aspect of applying machine learning to real world problems like molecular property prediction, medical imaging, protein folding or LiDAR classification. For the first time, we study... | ['Stephan Günnemann', 'Jan Schuchardt'] | 2022-11-25 | null | null | null | null | ['molecular-property-prediction', 'protein-folding'] | ['miscellaneous', 'natural-language-processing'] | [ 4.74139959e-01 1.36644304e-01 -4.57896829e-01 -2.50599265e-01
-5.84911823e-01 -7.91813850e-01 4.67873782e-01 3.81623536e-01
-4.28635143e-02 6.78663135e-01 -1.00231193e-01 -6.76679254e-01
-4.24942017e-01 -8.14400136e-01 -1.09923971e+00 -7.40416527e-01
-5.63874781e-01 4.09118384e-01 5.30657411e-01 -3.38805944... | [5.893470287322998, 7.414801120758057] |
243fedc0-36b4-46a5-b764-f09da8b77e8c | supervised-dimensionality-reduction-via | 1601.00236 | null | http://arxiv.org/abs/1601.00236v1 | http://arxiv.org/pdf/1601.00236v1.pdf | Supervised Dimensionality Reduction via Distance Correlation Maximization | In our work, we propose a novel formulation for supervised dimensionality
reduction based on a nonlinear dependency criterion called Statistical Distance
Correlation, Szekely et. al. (2007). We propose an objective which is free of
distributional assumptions on regression variables and regression model
assumptions. Our... | ['Ahmed Elgammal', 'Chetan Tonde', 'Praneeth Vepakomma'] | 2016-01-03 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 7.81096965e-02 1.15276143e-01 -1.30868167e-01 -7.14191258e-01
-8.54945183e-01 -3.53763342e-01 3.96664202e-01 2.22282529e-01
-5.18214166e-01 7.83769488e-01 -1.66517437e-01 8.32455140e-03
-1.19342971e+00 -7.36155033e-01 -5.28221011e-01 -6.74970448e-01
-4.32576567e-01 3.72511059e-01 -4.41003382e-01 -8.63527358... | [7.73327112197876, 4.259019374847412] |
cd3618bf-86bd-4bd9-9431-1b857c72e3c1 | decomposition-based-domain-adaptation-for | 1412.5758 | null | http://arxiv.org/abs/1412.5758v4 | http://arxiv.org/pdf/1412.5758v4.pdf | Decomposition-Based Domain Adaptation for Real-World Font Recognition | We present a domain adaption framework to address a domain mismatch between
synthetic training and real-world testing data. We demonstrate our method on a
challenging fine-grain classification problem: recognizing a font style from an
image of text. In this task, it is very easy to generate lots of rendered font
exampl... | ['Aseem Agarwala', 'Zhangyang Wang', 'Jianchao Yang', 'Thomas S. Huang', 'Hailin Jin', 'Eli Shechtman', 'Jonathan Brandt'] | 2014-12-18 | null | null | null | null | ['font-recognition'] | ['computer-vision'] | [ 7.43026674e-01 -2.97215968e-01 2.57912785e-01 -5.50551116e-01
-8.57443571e-01 -9.27871883e-01 7.70988405e-01 -2.29471594e-01
-2.28020355e-01 7.61568546e-01 -1.03963107e-01 -1.34496376e-01
3.94866705e-01 -7.51443624e-01 -1.08506703e+00 -3.63525093e-01
6.20689332e-01 5.68512678e-01 5.55806458e-02 -2.98842788... | [11.901922225952148, 2.0135674476623535] |
c3a71189-5cf4-426b-a045-dc2d7474b1a2 | structure-aggregation-for-cross-spectral | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sheng_Structure_Aggregation_for_Cross-Spectral_Stereo_Image_Guided_Denoising_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sheng_Structure_Aggregation_for_Cross-Spectral_Stereo_Image_Guided_Denoising_CVPR_2023_paper.pdf | Structure Aggregation for Cross-Spectral Stereo Image Guided Denoising | To obtain clean images with salient structures from noisy observations, a growing trend in current denoising studies is to seek the help of additional guidance images with high signal-to-noise ratios, which are often acquired in different spectral bands such as near infrared. Although previous guided denoising meth... | ['Huaqi Zhang', 'Hui-Liang Shen', 'Yuqi Liu', 'Si-Yuan Cao', 'Xiongwei Liu', 'Zhu Yu', 'Zehua Sheng'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['deblurring', 'stereo-matching-1'] | ['computer-vision', 'computer-vision'] | [ 6.87685966e-01 -4.57609057e-01 4.08710152e-01 -3.91155750e-01
-8.29514861e-01 -3.69092584e-01 4.63809520e-01 -5.05559027e-01
-2.92288423e-01 5.14117718e-01 4.40466046e-01 1.62661448e-01
-3.28472555e-01 -8.01667511e-01 -5.24944663e-01 -1.21103215e+00
6.04107440e-01 -1.55131817e-01 -2.34880764e-02 -3.82055521... | [11.005327224731445, -2.34315824508667] |
2cccb6e5-6567-452e-907b-198d0e7ffe42 | toward-building-general-foundation-models-for | 2301.05065 | null | https://arxiv.org/abs/2301.05065v1 | https://arxiv.org/pdf/2301.05065v1.pdf | Toward Building General Foundation Models for Language, Vision, and Vision-Language Understanding Tasks | Foundation models or pre-trained models have substantially improved the performance of various language, vision, and vision-language understanding tasks. However, existing foundation models can only perform the best in one type of tasks, namely language, vision, or vision-language. It is still an open question whether ... | ['Hang Li', 'Jipeng Zhang', 'Yan Zeng', 'Xinsong Zhang'] | 2023-01-12 | null | null | null | null | ['visual-grounding', 'visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'computer-vision', 'reasoning'] | [ 1.49705887e-01 -1.32596478e-01 -1.47760838e-01 -4.30937052e-01
-6.96114004e-01 2.03448487e-03 7.50291228e-01 -2.56354779e-01
-3.55512083e-01 2.47169465e-01 1.63399801e-01 -4.86543387e-01
5.08679867e-01 -3.81275773e-01 -8.63988101e-01 -4.04784948e-01
8.11791658e-01 2.09314704e-01 1.37657896e-01 -2.00120416... | [10.787663459777832, 1.6617159843444824] |
5a71e896-8780-4f78-bfe2-fe345cdd3647 | quality-assurance-of-generative-dialog-models | 2203.15414 | null | https://arxiv.org/abs/2203.15414v1 | https://arxiv.org/pdf/2203.15414v1.pdf | Quality Assurance of Generative Dialog Models in an Evolving Conversational Agent Used for Swedish Language Practice | Due to the migration megatrend, efficient and effective second-language acquisition is vital. One proposed solution involves AI-enabled conversational agents for person-centered interactive language practice. We present results from ongoing action research targeting quality assurance of proprietary generative dialog mo... | ['Piotr Tomaszewski', 'Isabella Gagner', 'Alexander Hagelborn', 'Harald Österling', 'Johan Bengtsson', 'Markus Borg'] | 2022-03-29 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 3.24879616e-01 9.89046335e-01 -3.32607590e-02 -6.27911687e-01
-1.04755306e+00 -6.96778655e-01 9.19382691e-01 -1.76298454e-01
-2.77089439e-02 7.95022845e-01 5.40677845e-01 -6.98586166e-01
-2.00177729e-01 -1.54683828e-01 1.06338523e-01 4.94820997e-02
2.79954106e-01 1.35558486e+00 -8.02995637e-02 -4.60197806... | [12.820714950561523, 7.957266807556152] |
c1a61075-8e16-40bd-834c-c0ec2e633701 | degree-decomposition-based-explanation-for-1 | 2305.12895 | null | https://arxiv.org/abs/2305.12895v1 | https://arxiv.org/pdf/2305.12895v1.pdf | DEGREE: Decomposition Based Explanation For Graph Neural Networks | Graph Neural Networks (GNNs) are gaining extensive attention for their application in graph data. However, the black-box nature of GNNs prevents users from understanding and trusting the models, thus hampering their applicability. Whereas explaining GNNs remains a challenge, most existing methods fall into approximatio... | ['Xia Hu', 'Mengnan Du', 'Ruixiang Tang', 'Fan Yang', 'Ninghao Liu', 'Qizhang Feng'] | 2023-05-22 | degree-decomposition-based-explanation-for | https://openreview.net/forum?id=Ve0Wth3ptT_ | https://openreview.net/pdf?id=Ve0Wth3ptT_ | iclr-2022-4 | ['graph-classification'] | ['graphs'] | [ 2.20771223e-01 6.48249865e-01 -4.55248445e-01 -2.37391055e-01
3.94771993e-01 -4.32634652e-01 4.24402207e-01 4.77031201e-01
4.06529963e-01 5.95608354e-01 8.44598785e-02 -5.97335041e-01
-3.92449200e-01 -1.12210453e+00 -6.99261487e-01 -3.65748644e-01
-2.14805245e-01 2.58969605e-01 1.06234647e-01 -2.31038317... | [7.459240436553955, 6.300755977630615] |
8f9091e3-be04-4665-a855-d99d8f72c74b | markov-random-field-model-based-salt-and | 1609.06341 | null | http://arxiv.org/abs/1609.06341v1 | http://arxiv.org/pdf/1609.06341v1.pdf | Markov Random Field Model-Based Salt and Pepper Noise Removal | Problem of impulse noise reduction is a very well studied problem in image
processing community and many different approaches have been proposed to tackle
this problem. In the current work, the problem of fixed value impulse noise
(salt and pepper) removal from images is investigated by use of a Markov Random
Field (MR... | ['Ahmadreza Baghaie'] | 2016-09-20 | null | null | null | null | ['salt-and-pepper-noise-removal'] | ['computer-vision'] | [ 5.92660189e-01 -2.93905348e-01 1.67830616e-01 -2.39966765e-01
-5.55457234e-01 -8.31640791e-03 2.71460563e-01 2.09291086e-01
-6.96985185e-01 8.09189260e-01 5.99501431e-02 5.09186052e-02
-5.31015754e-01 -6.44063175e-01 -3.91737789e-01 -8.68527710e-01
-1.46051079e-01 -2.66284317e-01 3.00281703e-01 -1.21625759... | [11.517849922180176, -2.344144105911255] |
f951ac93-6767-4e70-b2bf-7a539dc50db3 | privacy-preserving-remote-heart-rate | 2306.01141 | null | https://arxiv.org/abs/2306.01141v1 | https://arxiv.org/pdf/2306.01141v1.pdf | Privacy-Preserving Remote Heart Rate Estimation from Facial Videos | Remote Photoplethysmography (rPPG) is the process of estimating PPG from facial videos. While this approach benefits from contactless interaction, it is reliant on videos of faces, which often constitutes an important privacy concern. Recent research has revealed that deep learning techniques are vulnerable to attacks,... | ['Ali Etemad', 'Divij Gupta'] | 2023-06-01 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 3.50915819e-01 3.21141303e-01 6.61295429e-02 -3.79782438e-01
-7.58335352e-01 -6.90635502e-01 1.97213486e-01 -4.81583327e-01
-2.95431167e-01 7.55132914e-01 2.04332620e-01 -1.33493161e-02
4.35979992e-01 -5.43508828e-01 -6.43039644e-01 -1.00298858e+00
-2.78395209e-02 -4.55942482e-01 -1.42824188e-01 3.72841716... | [12.838202476501465, 0.8669722080230713] |
1ff9c6f8-d729-4c0a-942a-cca57eeb7dde | socs-semantically-aware-object-coordinate | 2303.10346 | null | https://arxiv.org/abs/2303.10346v1 | https://arxiv.org/pdf/2303.10346v1.pdf | SOCS: Semantically-aware Object Coordinate Space for Category-Level 6D Object Pose Estimation under Large Shape Variations | Most learning-based approaches to category-level 6D pose estimation are design around normalized object coordinate space (NOCS). While being successful, NOCS-based methods become inaccurate and less robust when handling objects of a category containing significant intra-category shape variations. This is because the ob... | ['Kai Xu', 'Yifei Shi', 'Boyan Wan'] | 2023-03-18 | null | null | null | null | ['6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-4.21475992e-02 -1.58535987e-01 -1.69764996e-01 -6.20729506e-01
-1.05982685e+00 -7.03129590e-01 2.97339946e-01 1.97659418e-01
7.83340707e-02 8.68879780e-02 2.49015450e-01 4.56227183e-01
-3.04592341e-01 -4.80995536e-01 -1.14547276e+00 -4.79479253e-01
1.16746321e-01 9.37076807e-01 3.76748949e-01 -9.48638096... | [7.678849220275879, -2.78287410736084] |
9183720b-acd5-461d-825b-1f70815bb7cc | resource-efficient-neural-networks-using | 2306.07030 | null | https://arxiv.org/abs/2306.07030v1 | https://arxiv.org/pdf/2306.07030v1.pdf | Resource Efficient Neural Networks Using Hessian Based Pruning | Neural network pruning is a practical way for reducing the size of trained models and the number of floating-point operations. One way of pruning is to use the relative Hessian trace to calculate sensitivity of each channel, as compared to the more common magnitude pruning approach. However, the stochastic approach use... | ['Lihui Chen', 'Manas Gupta', 'Jack Chong'] | 2023-06-12 | null | null | null | null | ['network-pruning', 'quantization'] | ['methodology', 'methodology'] | [-6.63205981e-02 -1.83129773e-01 3.98759842e-01 -4.44890350e-01
-5.89520454e-01 -2.11778238e-01 8.06251541e-02 2.95665175e-01
-1.04844105e+00 3.34748000e-01 -3.50411624e-01 -6.97771132e-01
1.61078095e-01 -9.82206762e-01 -7.80713797e-01 -5.13505042e-01
1.17488913e-01 1.36381790e-01 5.72490990e-01 -1.17899656... | [8.528153419494629, 3.0976181030273438] |
7c1889cc-9f34-45cc-91b5-51f1721b2363 | neural-message-passing-for-visual | 2208.04165 | null | https://arxiv.org/abs/2208.04165v1 | https://arxiv.org/pdf/2208.04165v1.pdf | Neural Message Passing for Visual Relationship Detection | Visual relationship detection aims to detect the interactions between objects in an image; however, this task suffers from combinatorial explosion due to the variety of objects and interactions. Since the interactions associated with the same object are dependent, we explore the dependency of interactions to reduce the... | ['Xiao Gu', 'Ya zhang', 'Xu Chen', 'Siheng Chen', 'Yue Hu'] | 2022-08-08 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 1.21698305e-01 -1.51907220e-01 -2.71267481e-02 -4.24965709e-01
-2.61925727e-01 -4.83743250e-01 8.07385623e-01 3.65294635e-01
-4.51840550e-01 5.71939111e-01 1.15282699e-01 -1.21201903e-01
-2.80517310e-01 -5.89552999e-01 -8.91030133e-01 -6.02993846e-01
-4.03121054e-01 3.30921143e-01 5.16829610e-01 1.61996976... | [10.118562698364258, 1.559953212738037] |
a5d96153-c1a1-4d27-811d-ae614ef92180 | distilling-focal-knowledge-from-imperfect | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zeng_Distilling_Focal_Knowledge_From_Imperfect_Expert_for_3D_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zeng_Distilling_Focal_Knowledge_From_Imperfect_Expert_for_3D_Object_Detection_CVPR_2023_paper.pdf | Distilling Focal Knowledge From Imperfect Expert for 3D Object Detection | Multi-camera 3D object detection blossoms in recent years and most of state-of-the-art methods are built up on the bird's-eye-view (BEV) representations. Albeit remarkable performance, these works suffer from low efficiency. Typically, knowledge distillation can be used for model compression. However, due to unclea... | ['Hongyang Li', 'Yu Qiao', 'Junchi Yan', 'Lewei Lu', 'Hanming Deng', 'Li Chen', 'Jia Zeng'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['model-compression'] | ['methodology'] | [-1.29819110e-01 -3.45178246e-01 -6.01358153e-02 -2.49145925e-01
-8.99032533e-01 -8.08803201e-01 5.50958633e-01 -9.25567448e-02
-9.29622129e-02 2.46333480e-01 -4.53475788e-02 -7.40663409e-02
-1.66725680e-01 -8.37653041e-01 -8.33721459e-01 -7.69950390e-01
2.80632496e-01 2.39673331e-01 6.77348435e-01 -1.02758408... | [7.903266906738281, -2.5246167182922363] |
b7e9f59d-0980-4843-9b4a-d83d3fccfc11 | wildqa-in-the-wild-video-question-answering | 2209.06650 | null | https://arxiv.org/abs/2209.06650v1 | https://arxiv.org/pdf/2209.06650v1.pdf | WildQA: In-the-Wild Video Question Answering | Existing video understanding datasets mostly focus on human interactions, with little attention being paid to the "in the wild" settings, where the videos are recorded outdoors. We propose WILDQA, a video understanding dataset of videos recorded in outside settings. In addition to video question answering (Video QA), w... | ['Rada Mihalcea', 'Mihai Burzo', 'Pingxuan Huang', 'Naihao Deng', 'Santiago Castro'] | 2022-09-14 | null | null | null | null | ['video-question-answering'] | ['computer-vision'] | [ 1.84246480e-01 -1.51297256e-01 -3.19908053e-01 -4.15953487e-01
-7.53045678e-01 -7.26701736e-01 4.58693832e-01 -9.14416611e-02
-4.49505538e-01 5.03049433e-01 4.94549602e-01 -2.79205441e-01
3.37007642e-01 -2.31546938e-01 -1.12632656e+00 -1.53984964e-01
-1.18460648e-01 5.25559299e-02 3.41022879e-01 9.71338898... | [10.334083557128906, 0.8963070511817932] |
7dd6d69d-6652-4aa8-b6c2-c136d8bd9969 | semeval-2017-task-8-rumoureval-determining | 1704.05972 | null | http://arxiv.org/abs/1704.05972v1 | http://arxiv.org/pdf/1704.05972v1.pdf | SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours | Media is full of false claims. Even Oxford Dictionaries named "post-truth" as
the word of 2016. This makes it more important than ever to build systems that
can identify the veracity of a story, and the kind of discourse there is around
it. RumourEval is a SemEval shared task that aims to identify and handle
rumours an... | ['Rob Procter', 'Leon Derczynski', 'Kalina Bontcheva', 'Maria Liakata', 'Geraldine Wong Sak Hoi', 'Arkaitz Zubiaga'] | 2017-04-20 | semeval-2017-task-8-rumoureval-determining-1 | https://aclanthology.org/S17-2006 | https://aclanthology.org/S17-2006.pdf | semeval-2017-8 | ['rumour-detection'] | ['natural-language-processing'] | [-3.03322017e-01 4.26292717e-01 -2.84586519e-01 -6.93528578e-02
-9.49094594e-01 -5.45338035e-01 1.07612121e+00 6.70783043e-01
-9.42818299e-02 8.89622986e-01 1.16857255e+00 -1.95670202e-01
3.11009139e-01 -4.04829890e-01 -6.87474251e-01 -4.95959865e-03
3.15385342e-01 6.24981523e-01 3.34959328e-01 -8.44787598... | [8.353212356567383, 10.048809051513672] |
3367a571-f809-4b3a-b508-2d6d212b84ab | panogen-text-conditioned-panoramic | 2305.19195 | null | https://arxiv.org/abs/2305.19195v1 | https://arxiv.org/pdf/2305.19195v1.pdf | PanoGen: Text-Conditioned Panoramic Environment Generation for Vision-and-Language Navigation | Vision-and-Language Navigation (VLN) requires the agent to follow language instructions to navigate through 3D environments. One main challenge in VLN is the limited availability of photorealistic training environments, which makes it hard to generalize to new and unseen environments. To address this problem, we propos... | ['Mohit Bansal', 'Jialu Li'] | 2023-05-30 | null | null | null | null | ['image-outpainting', 'navigate', 'vision-and-language-navigation'] | ['computer-vision', 'reasoning', 'robots'] | [ 2.40321219e-01 1.38715938e-01 4.86351550e-01 -3.41986597e-01
-2.77367383e-01 -8.50704491e-01 8.39224756e-01 -6.18618608e-01
-2.32342243e-01 4.80880618e-01 5.03700376e-01 -4.71302539e-01
2.79402971e-01 -1.02434981e+00 -1.13139224e+00 -5.39242566e-01
2.44347364e-01 5.24878860e-01 -1.90242916e-01 -6.46441996... | [4.478994369506836, 0.5620132088661194] |
b2a91721-e307-461b-b5bf-5e74589a724a | dental-claires-contrastive-language-image | 2306.15651 | null | https://arxiv.org/abs/2306.15651v1 | https://arxiv.org/pdf/2306.15651v1.pdf | Dental CLAIRES: Contrastive LAnguage Image REtrieval Search for Dental Research | Learning about diagnostic features and related clinical information from dental radiographs is important for dental research. However, the lack of expert-annotated data and convenient search tools poses challenges. Our primary objective is to design a search tool that uses a user's query for oral-related research. The ... | ['Shayan Shams', 'Xiaoqian Jiang', 'Luca Giancardo', 'Muhammad F Walji', 'Luyao Chen', 'Tanjida Kabir'] | 2023-06-27 | null | null | null | null | ['retrieval'] | ['methodology'] | [ 1.45819977e-01 -5.26447110e-02 -5.98649800e-01 -5.22832096e-01
-1.67667055e+00 -1.36115462e-01 2.99820751e-01 5.46792567e-01
-5.48333228e-01 4.22310323e-01 3.29431653e-01 -1.75656304e-01
-5.32972634e-01 -5.77367425e-01 -2.64100224e-01 -5.69953024e-01
8.94425288e-02 9.19047356e-01 4.29982126e-01 1.10153593... | [14.382733345031738, -1.5526940822601318] |
ad495a93-e6af-47e3-b036-dd8d2015673b | how-will-the-internet-of-things-enable | 1801.00356 | null | http://arxiv.org/abs/1801.00356v1 | http://arxiv.org/pdf/1801.00356v1.pdf | How will the Internet of Things enable Augmented Personalized Health? | Internet-of-Things (IoT) is profoundly redefining the way we create, consume,
and share information. Health aficionados and citizens are increasingly using
IoT technologies to track their sleep, food intake, activity, vital body
signals, and other physiological observations. This is complemented by IoT
systems that con... | ['Amit Sheth', 'Utkarshani Jaimini', 'Hong Yung Yip'] | 2017-12-31 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 4.28973794e-01 3.43495876e-01 -7.12342799e-01 -3.82700145e-01
-3.35591696e-02 -1.95684344e-01 1.73914805e-01 8.58963132e-01
-6.50065616e-02 6.34835899e-01 9.46334004e-01 -1.53685529e-02
-3.96558702e-01 -9.11287844e-01 6.49017692e-02 -4.47961122e-01
3.28245223e-01 3.21574688e-01 -4.49428916e-01 -2.36687422... | [13.634458541870117, 3.2874107360839844] |
ae485b61-f8f0-4728-ac5a-b5cf53c7c4b0 | dreamfusion-text-to-3d-using-2d-diffusion | 2209.14988 | null | https://arxiv.org/abs/2209.14988v1 | https://arxiv.org/pdf/2209.14988v1.pdf | DreamFusion: Text-to-3D using 2D Diffusion | Recent breakthroughs in text-to-image synthesis have been driven by diffusion models trained on billions of image-text pairs. Adapting this approach to 3D synthesis would require large-scale datasets of labeled 3D data and efficient architectures for denoising 3D data, neither of which currently exist. In this work, we... | ['Ben Mildenhall', 'Jonathan T. Barron', 'Ajay Jain', 'Ben Poole'] | 2022-09-29 | null | null | null | null | ['text-to-3d'] | ['computer-vision'] | [ 4.69482929e-01 3.25047970e-01 3.74657273e-01 -3.63107294e-01
-7.96993315e-01 -5.85669160e-01 1.02655554e+00 -4.62793887e-01
-1.82339147e-01 3.87908250e-01 2.71430671e-01 -3.19518328e-01
4.47933882e-01 -8.74847651e-01 -9.13947582e-01 -8.45688760e-01
3.79568607e-01 6.10238433e-01 8.71913359e-02 -2.20550701... | [9.363347053527832, -3.164677143096924] |
cbd642af-3794-4768-98eb-063afcf1e70c | topomask-instance-mask-based-formulation-for | 2306.05419 | null | https://arxiv.org/abs/2306.05419v1 | https://arxiv.org/pdf/2306.05419v1.pdf | TopoMask: Instance-Mask-Based Formulation for the Road Topology Problem via Transformer-Based Architecture | Driving scene understanding task involves detecting static elements such as lanes, traffic signs, and traffic lights, and their relationships with each other. To facilitate the development of comprehensive scene understanding solutions using multiple camera views, a new dataset called Road Genome (OpenLane-V2) has been... | ['Alptekin Temizel', 'Ozsel Kilinc', 'Halil Ibrahim Ozturk', 'M. Esat Kalfaoglu'] | 2023-06-08 | null | null | null | null | ['lane-detection', 'scene-understanding'] | ['computer-vision', 'computer-vision'] | [-2.16787145e-01 5.94620518e-02 -2.18308970e-01 -5.27758420e-01
-2.44305030e-01 -5.71932614e-01 7.23240793e-01 9.64914188e-02
-3.39572541e-02 4.66961563e-01 9.46857333e-02 -5.28547049e-01
-2.08154202e-01 -9.27498102e-01 -7.03328252e-01 -2.11179748e-01
-5.21781631e-02 4.01533335e-01 7.54366875e-01 -5.37191033... | [8.035327911376953, -1.5632903575897217] |
3241b16c-dde0-47c6-bf5e-a3639ef9f06c | lying-aversion-and-vague-communication-an | 2301.00372 | null | https://arxiv.org/abs/2301.00372v1 | https://arxiv.org/pdf/2301.00372v1.pdf | Lying Aversion and Vague Communication: An Experimental Study | An agent may strategically employ a vague message to mislead an audience's belief about the state of the world, but this may cause the agent to feel guilt or negatively impact how the audience perceives the agent. Using a novel experimental design that allows participants to be vague while at the same time isolating th... | ['Stella Papadokonstantaki', 'Keh-Kuan Sun'] | 2023-01-01 | null | null | null | null | ['experimental-design'] | ['methodology'] | [-7.36010373e-02 8.07833314e-01 -1.72942102e-01 -4.68924105e-01
-4.98815238e-01 -7.28268862e-01 4.36415136e-01 6.08850479e-01
-7.64854550e-01 6.80055559e-01 6.62422776e-01 -3.10631305e-01
1.85536206e-01 -6.36925697e-01 -2.34289601e-01 -3.27423930e-01
4.49622393e-01 7.00712875e-02 -4.56656039e-01 -9.84458476... | [9.16436767578125, 6.220601558685303] |
9ec33698-79ac-457f-bc6d-cbf07d96588b | adversarial-learning-for-image-forensics-deep | 1809.02791 | null | http://arxiv.org/abs/1809.02791v1 | http://arxiv.org/pdf/1809.02791v1.pdf | Adversarial Learning for Image Forensics Deep Matching with Atrous Convolution | Constrained image splicing detection and localization (CISDL) is a newly
proposed challenging task for image forensics, which investigates two input
suspected images and identifies whether one image has suspected regions pasted
from the other. In this paper, we propose a novel adversarial learning
framework to train th... | ['Xiaobin Zhu', 'Yun Cao', 'Yaqi Liu', 'Xianfeng Zhao'] | 2018-09-08 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 5.65702796e-01 -1.54216737e-01 2.90545315e-01 -1.42404228e-01
-8.56548250e-01 -2.92780548e-01 4.30323154e-01 -4.03925091e-01
-2.83955783e-01 3.48617375e-01 -1.70782864e-01 -2.39992931e-01
2.00197771e-01 -8.70638192e-01 -9.34187174e-01 -1.00561452e+00
5.50044328e-02 -7.96222314e-02 6.49022400e-01 3.98509391... | [12.345507621765137, 0.8510577082633972] |
06adbc8a-e2bd-41dc-98c1-8244d363fe3e | localised-generative-flows-1 | 1909.13833 | null | https://arxiv.org/abs/1909.13833v5 | https://arxiv.org/pdf/1909.13833v5.pdf | Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows | We show that normalising flows become pathological when used to model targets whose supports have complicated topologies. In this scenario, we prove that a flow must become arbitrarily numerically noninvertible in order to approximate the target closely. This result has implications for all flow-based models, and espec... | ['Rob Cornish', 'Anthony L. Caterini', 'George Deligiannidis', 'Arnaud Doucet'] | 2019-09-30 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/4509-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/4509-Paper.pdf | icml-2020-1 | ['normalising-flows'] | ['methodology'] | [ 8.70529786e-02 2.90705681e-01 -4.05738264e-01 1.37217149e-01
1.23370960e-01 -9.49946105e-01 7.98179269e-01 -1.20909333e-01
-2.30765436e-02 9.88643706e-01 3.98551941e-01 -6.43479824e-01
-4.40857023e-01 -1.19470692e+00 -8.68367851e-01 -4.05071497e-01
-7.37476051e-01 5.80819488e-01 5.31926990e-01 -3.33800107... | [7.213723659515381, 4.014452934265137] |
6fafc2d7-c3c6-4d4f-97d7-b5f8d862fe2e | audio-tagging-on-an-embedded-hardware | 2306.09106 | null | https://arxiv.org/abs/2306.09106v1 | https://arxiv.org/pdf/2306.09106v1.pdf | Audio Tagging on an Embedded Hardware Platform | Convolutional neural networks (CNNs) have exhibited state-of-the-art performance in various audio classification tasks. However, their real-time deployment remains a challenge on resource-constrained devices like embedded systems. In this paper, we analyze how the performance of large-scale pretrained audio neural netw... | ['Mark D. Plumbley', 'Arshdeep Singh', 'Gabriel Bibbo'] | 2023-06-15 | null | null | null | null | ['audio-tagging', 'audio-classification'] | ['audio', 'audio'] | [ 3.82471420e-02 -5.76650083e-01 1.13422312e-01 -8.71155336e-02
-2.96880484e-01 -4.75509405e-01 -1.01054475e-01 -1.31020769e-01
-5.70377350e-01 1.34465098e-01 -2.96832711e-01 -6.78035200e-01
6.46698624e-02 -6.96896374e-01 -7.64635205e-01 -7.32898593e-01
-2.22796410e-01 -6.76446110e-02 2.20055699e-01 1.17539808... | [14.524563789367676, 5.470295429229736] |
5b914aa0-a16c-4427-a039-c376687b65b5 | neural-3d-scene-reconstruction-with-the | 2205.02836 | null | https://arxiv.org/abs/2205.02836v2 | https://arxiv.org/pdf/2205.02836v2.pdf | Neural 3D Scene Reconstruction with the Manhattan-world Assumption | This paper addresses the challenge of reconstructing 3D indoor scenes from multi-view images. Many previous works have shown impressive reconstruction results on textured objects, but they still have difficulty in handling low-textured planar regions, which are common in indoor scenes. An approach to solving this issue... | ['Xiaowei Zhou', 'Hujun Bao', 'Guofeng Zhang', 'Qianqian Wang', 'Haotong Lin', 'Sida Peng', 'Haoyu Guo'] | 2022-05-05 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Guo_Neural_3D_Scene_Reconstruction_With_the_Manhattan-World_Assumption_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Guo_Neural_3D_Scene_Reconstruction_With_the_Manhattan-World_Assumption_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-scene-reconstruction', '2d-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.76470011e-01 -7.74067938e-02 2.76749935e-02 -6.70269132e-01
-5.50280273e-01 -3.65407050e-01 1.91459984e-01 -2.96988696e-01
-2.00561285e-01 6.09730065e-01 1.68185696e-01 -5.81957512e-02
-9.95729789e-02 -1.12362373e+00 -1.05679965e+00 -6.30864918e-01
3.64222497e-01 5.83544731e-01 3.11908543e-01 -1.19710274... | [8.868049621582031, -2.9624416828155518] |
efc7e6eb-d301-44d3-9ff2-0b8d7c6195e2 | soft-gazetteers-for-low-resource-named-entity | 2005.01866 | null | https://arxiv.org/abs/2005.01866v1 | https://arxiv.org/pdf/2005.01866v1.pdf | Soft Gazetteers for Low-Resource Named Entity Recognition | Traditional named entity recognition models use gazetteers (lists of entities) as features to improve performance. Although modern neural network models do not require such hand-crafted features for strong performance, recent work has demonstrated their utility for named entity recognition on English data. However, des... | ['Jaime Carbonell', 'Shuyan Zhou', 'Shruti Rijhwani', 'Graham Neubig'] | 2020-05-04 | soft-gazetteers-for-low-resource-named-entity-1 | https://aclanthology.org/2020.acl-main.722 | https://aclanthology.org/2020.acl-main.722.pdf | acl-2020-6 | ['cross-lingual-entity-linking', 'low-resource-named-entity-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [-5.00214696e-01 -5.35326963e-03 -4.28238928e-01 -6.95438802e-01
-6.59388125e-01 -7.06261098e-01 5.38726032e-01 5.97833171e-02
-9.99508798e-01 7.91842639e-01 3.19249064e-01 -3.28704804e-01
2.97161072e-01 -7.53419280e-01 -7.96048641e-01 1.01748165e-02
-1.05345724e-02 1.62911206e-01 1.11309722e-01 -2.13939041... | [9.705249786376953, 9.398627281188965] |
6d2bba51-5091-4d3d-a2c3-d8230cc480d9 | scene-retrieval-for-contextual-visual-mapping | 2102.12728 | null | https://arxiv.org/abs/2102.12728v1 | https://arxiv.org/pdf/2102.12728v1.pdf | Scene Retrieval for Contextual Visual Mapping | Visual navigation localizes a query place image against a reference database of place images, also known as a `visual map'. Localization accuracy requirements for specific areas of the visual map, `scene classes', vary according to the context of the environment and task. State-of-the-art visual mapping is unable to re... | ['Shoaib Ehsan', 'Klaus D. McDonald-Maier', 'Michael Milford', 'William H. B. Smith'] | 2021-02-25 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 2.39557251e-02 -3.18938553e-01 4.87722382e-02 -5.48703551e-01
-7.20053434e-01 -9.92490351e-01 9.86405969e-01 6.97316349e-01
-9.36305046e-01 5.58415651e-01 3.25857732e-03 -2.65184850e-01
-1.28740519e-01 -1.10340524e+00 -9.46357012e-01 -3.04880351e-01
-2.03106537e-01 5.46098650e-01 7.62230575e-01 -3.62700313... | [7.665507793426514, -1.813576579093933] |
bc0d2f11-6326-45b2-8c0c-6a5418e97999 | how-useful-is-photo-realistic-rendering-for | 1603.08152 | null | http://arxiv.org/abs/1603.08152v2 | http://arxiv.org/pdf/1603.08152v2.pdf | How useful is photo-realistic rendering for visual learning? | Data seems cheap to get, and in many ways it is, but the process of creating
a high quality labeled dataset from a mass of data is time-consuming and
expensive.
With the advent of rich 3D repositories, photo-realistic rendering systems
offer the opportunity to provide nearly limitless data. Yet, their primary
value f... | ['Yaser Sheikh', 'Yair Movshovitz-Attias', 'Takeo Kanade'] | 2016-03-26 | null | null | null | null | ['viewpoint-estimation'] | ['computer-vision'] | [ 2.12750584e-02 -6.41848892e-02 1.86059773e-01 -7.32742071e-01
-1.02797806e+00 -8.32388282e-01 7.15692222e-01 1.96471196e-02
-2.89969087e-01 6.39748156e-01 -1.85622707e-01 -1.54427931e-01
3.48105550e-01 -7.65695572e-01 -9.14999008e-01 -4.61490363e-01
2.15082705e-01 1.14337027e+00 4.43978459e-01 -2.01159701... | [9.082473754882812, -2.4954047203063965] |
6786db97-2b71-45f3-848c-40fd9f22adc8 | multilingual-named-entity-recognition-on | null | null | https://aclanthology.org/W18-3218 | https://aclanthology.org/W18-3218.pdf | Multilingual Named Entity Recognition on Spanish-English Code-switched Tweets using Support Vector Machines | This paper describes our system submission for the ACL 2018 shared task on named entity recognition (NER) in code-switched Twitter data. Our best result (F1 = 53.65) was obtained using a Support Vector Machine (SVM) with 14 features combined with rule-based post processing. | ['Dennis Felske', 'Daniel Claeser', 'Samantha Kent'] | 2018-07-01 | null | null | null | ws-2018-7 | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-2.37673894e-01 5.01727201e-02 -2.12566882e-01 -6.07888460e-01
-5.05609274e-01 -5.59201896e-01 8.29137146e-01 4.84516531e-01
-1.14302313e+00 9.66895521e-01 4.47709024e-01 -4.01345521e-01
2.88378775e-01 -3.35331053e-01 -4.69479591e-01 1.67810142e-01
-3.54811192e-01 1.48899525e-01 1.65000454e-01 -3.79705340... | [9.64309024810791, 9.664505958557129] |
c49f65bf-9021-45f1-8cc9-1ce069259626 | a-framework-for-fast-image-deconvolution-with | 1602.01410 | null | http://arxiv.org/abs/1602.01410v2 | http://arxiv.org/pdf/1602.01410v2.pdf | A Framework for Fast Image Deconvolution with Incomplete Observations | In image deconvolution problems, the diagonalization of the underlying
operators by means of the FFT usually yields very large speedups. When there
are incomplete observations (e.g., in the case of unknown boundaries), standard
deconvolution techniques normally involve non-diagonalizable operators,
resulting in rather ... | ['Jocelyn Chanussot', 'José Bioucas-Dias', 'Miguel Simões', 'Luis B. Almeida'] | 2016-02-03 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 2.56110698e-01 5.45961149e-02 6.25688732e-01 5.85711263e-02
-3.51046383e-01 -2.29825720e-01 4.46001023e-01 -6.19014859e-01
-5.00421643e-01 1.01361394e+00 2.14953944e-01 -3.14111024e-01
-1.56165347e-01 -5.82087100e-01 -7.34905481e-01 -1.13670969e+00
2.08371922e-01 5.24578810e-01 -5.61380051e-02 -2.70126075... | [11.67626953125, -2.5679736137390137] |
7962a4aa-104c-4a32-bae9-18f98fa4cd7b | crpn-sfnet-a-high-performance-object-detector | null | null | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9241817 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9241817 | CRPN-SFNet: A High-Performance Object Detector on Large-Scale Remote Sensing Images | Limited by the GPU memory, the current
mainstream detectors fail to directly apply to large-scale remote
sensing images for object detection. Moreover, the scale range
of objects in remote sensing images is much wider than that
of general images, which also greatly hinders the existing
methods to effectively detec... | ['IEEE', 'Member', 'and Zhiyong Yuan', 'Gang Fu', 'Jianhui Zhao', 'QiFeng Lin'] | 2020-10-28 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 2.72281051e-01 -5.83848357e-01 -7.94236660e-02 9.58793685e-02
-2.90391564e-01 -5.46585560e-01 1.78939179e-01 4.38059755e-02
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-2.00922847e-01 -1.52372408e+00 -3.33701998e-01 -8.13718379e-01
-2.45859459e-01 -1.82904303e-01 1.15216458e+00 -3.44362020... | [8.982901573181152, -0.9969953298568726] |
a9ad8abd-d0d2-424d-be89-d19dfb665add | tpsnet-thin-plate-spline-representation-for | 2110.12826 | null | https://arxiv.org/abs/2110.12826v2 | https://arxiv.org/pdf/2110.12826v2.pdf | TPSNet: Reverse Thinking of Thin Plate Splines for Arbitrary Shape Scene Text Representation | The research focus of scene text detection and recognition has shifted to arbitrary shape text in recent years, where the text shape representation is a fundamental problem. An ideal representation should be compact, complete, efficient, and reusable for subsequent recognition in our opinion. However, previous represen... | ['Weiping Wang', 'Ning Jiang', 'Guoqing Zhao', 'Dayan Wu', 'Jiahao Lv', 'Yu Zhou', 'Wei Wang'] | 2021-10-25 | null | null | null | null | ['text-spotting', 'scene-text-recognition', 'scene-text-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.97197956e-01 -2.53011048e-01 9.59417969e-02 -1.76080436e-01
-5.51075399e-01 -3.64140540e-01 7.76912391e-01 -2.11139455e-01
-1.84023425e-01 1.91781789e-01 6.42552599e-02 -1.07342392e-01
1.59722626e-01 -5.80062747e-01 -3.12490642e-01 -8.24719071e-01
5.77279508e-01 2.08125994e-01 3.84977251e-01 -1.39571324... | [12.016395568847656, 2.241321086883545] |
6bcb9e20-f731-4023-85b4-ceeba1a8fd44 | cd-tta-compound-domain-test-time-adaptation | 2212.08356 | null | https://arxiv.org/abs/2212.08356v3 | https://arxiv.org/pdf/2212.08356v3.pdf | Test-time Adaptation in the Dynamic World with Compound Domain Knowledge Management | Prior to the deployment of robotic systems, pre-training the deep-recognition models on all potential visual cases is infeasible in practice. Hence, test-time adaptation (TTA) allows the model to adapt itself to novel environments and improve its performance during test time (i.e., lifelong adaptation). Several works f... | ['Chaoning Zhang', 'In So Kweon', 'Sanghyun Woo', 'Inkyu Shin', 'KwanYong Park', 'Junha Song'] | 2022-12-16 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [ 7.72956908e-02 -2.92817682e-01 -2.14820001e-02 -5.80653369e-01
-3.54355991e-01 -6.94972456e-01 4.63763654e-01 -4.96579319e-01
-4.72500831e-01 6.46847665e-01 -3.19107950e-01 -2.35692039e-01
-1.61185861e-01 -5.69198668e-01 -8.57285380e-01 -7.76433170e-01
1.73207402e-01 4.91980076e-01 5.68758011e-01 -1.31826669... | [9.862151145935059, 2.034529685974121] |
37eb65e4-4c8f-486d-8159-91b2d01c1471 | revisiting-non-english-text-simplification-a | 2305.15678 | null | https://arxiv.org/abs/2305.15678v1 | https://arxiv.org/pdf/2305.15678v1.pdf | Revisiting non-English Text Simplification: A Unified Multilingual Benchmark | Recent advancements in high-quality, large-scale English resources have pushed the frontier of English Automatic Text Simplification (ATS) research. However, less work has been done on multilingual text simplification due to the lack of a diverse evaluation benchmark that covers complex-simple sentence pairs in many la... | ['Wei Xu', 'Tarek Naous', 'Michael J. Ryan'] | 2023-05-25 | null | null | null | null | ['zero-shot-cross-lingual-transfer', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [-1.78992644e-01 -1.73807219e-01 -1.72303349e-01 -4.63329762e-01
-1.50359225e+00 -6.04439616e-01 5.03359795e-01 2.69093245e-01
-9.61707652e-01 1.05437970e+00 8.48380208e-01 -3.94935727e-01
2.29143500e-01 -3.17446589e-01 -5.76251328e-01 -1.82577334e-02
5.09881020e-01 9.60746884e-01 -2.15436578e-01 -9.06000674... | [11.025705337524414, 10.375375747680664] |
75856817-5a11-4b4b-af02-fc291d7259b8 | occlusion-robust-online-multi-object-visual | 1912.05949 | null | https://arxiv.org/abs/1912.05949v6 | https://arxiv.org/pdf/1912.05949v6.pdf | Occlusion-Robust Online Multi-Object Visual Tracking using a GM-PHD Filter with CNN-Based Re-Identification | We propose a novel online multi-object visual tracker using a Gaussian mixture Probability Hypothesis Density (GM-PHD) filter and deep appearance learning. The GM-PHD filter has a linear complexity with the number of objects and observations while estimating the states and cardinality of time-varying number of objects,... | ['Nathanael L. Baisa'] | 2019-12-10 | null | null | null | null | ['large-scale-person-re-identification'] | ['computer-vision'] | [-2.88915515e-01 -3.18673968e-01 -2.09488526e-01 -1.80645123e-01
-4.90733773e-01 -5.91585159e-01 6.56975508e-01 1.01947211e-01
-5.75875223e-01 6.14871979e-01 -3.55419934e-01 4.62764092e-02
1.27607152e-01 -2.13855073e-01 -1.08143926e+00 -7.02967942e-01
-4.06098723e-01 8.12578619e-01 8.87062550e-01 5.01357436... | [6.442440032958984, -2.0135490894317627] |
c9197547-dbfd-4c82-aad6-a30a0e6cb500 | collective-mind-cleaning-up-the-research-and | 1308.2410 | null | http://arxiv.org/abs/1308.2410v1 | http://arxiv.org/pdf/1308.2410v1.pdf | Collective Mind: cleaning up the research and experimentation mess in computer engineering using crowdsourcing, big data and machine learning | Software and hardware co-design and optimization of HPC systems has become
intolerably complex, ad-hoc, time consuming and error prone due to enormous
number of available design and optimization choices, complex interactions
between all software and hardware components, and multiple strict requirements
placed on perfor... | ['Grigori Fursin'] | 2013-08-11 | null | null | null | null | ['problem-decomposition'] | ['miscellaneous'] | [-5.38938165e-01 -6.03310645e-01 9.61501822e-02 -2.93862611e-01
-4.25195098e-01 -6.67180479e-01 -5.72119141e-03 6.35907590e-01
1.29126161e-01 4.46510077e-01 -8.88015702e-02 -5.73347807e-01
-5.18891573e-01 -6.09992623e-01 -2.91956306e-01 -5.36078215e-01
-5.42237639e-01 8.55595589e-01 1.83939293e-01 -3.03234607... | [6.116702079772949, 3.667987108230591] |
2c2f0d03-7159-48bc-ac8a-f948b2328f25 | pansharpening-via-detail-injection-based | 1806.08898 | null | http://arxiv.org/abs/1806.08898v1 | http://arxiv.org/pdf/1806.08898v1.pdf | Pansharpening via Detail Injection Based Convolutional Neural Networks | Pansharpening aims to fuse a multispectral (MS) image with an associated
panchromatic (PAN) image, producing a composite image with the spectral
resolution of the former and the spatial resolution of the latter. Traditional
pansharpening methods can be ascribed to a unified detail injection context,
which views the inj... | [] | 2018-06-23 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 6.14946663e-01 -2.18416199e-01 -1.38550460e-01 4.67949770e-02
-7.77510464e-01 -4.31219310e-01 6.07911646e-01 -1.67000309e-01
-2.82853752e-01 6.28901124e-01 1.37273267e-01 -2.31182456e-01
-3.35020125e-01 -1.20544398e+00 -5.35407066e-01 -1.17298412e+00
4.93399978e-01 -3.13007593e-01 9.89063382e-02 -4.74594712... | [10.168329238891602, -1.9367244243621826] |
eb622446-1931-4af6-b22d-a3c067d37f76 | scs-co-self-consistent-style-contrastive | 2204.13962 | null | https://arxiv.org/abs/2204.13962v1 | https://arxiv.org/pdf/2204.13962v1.pdf | SCS-Co: Self-Consistent Style Contrastive Learning for Image Harmonization | Image harmonization aims to achieve visual consistency in composite images by adapting a foreground to make it compatible with a background. However, existing methods always only use the real image as the positive sample to guide the training, and at most introduce the corresponding composite image as a single negative... | ['Qingmin Liao', 'Wenming Yang', 'Bin Xia', 'Yucheng Hang'] | 2022-04-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Hang_SCS-Co_Self-Consistent_Style_Contrastive_Learning_for_Image_Harmonization_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Hang_SCS-Co_Self-Consistent_Style_Contrastive_Learning_for_Image_Harmonization_CVPR_2022_paper.pdf | cvpr-2022-1 | ['image-harmonization'] | ['computer-vision'] | [ 4.40658450e-01 -3.05028468e-01 -2.73598105e-01 -2.79098302e-01
-3.47521126e-01 -2.91094661e-01 5.06605327e-01 -2.89267123e-01
-2.40893081e-01 4.52965707e-01 1.06888078e-02 1.36516318e-01
9.58635658e-02 -9.31782484e-01 -5.70159793e-01 -1.04463911e+00
7.83331811e-01 4.03825045e-02 4.77866262e-01 -3.53910059... | [11.222901344299316, -1.1717230081558228] |
6a3c4ac5-2e16-4061-8f45-4a966f9c77fb | supervised-exponential-family-principal | null | null | http://papers.nips.cc/paper/3442-supervised-exponential-family-principal-component-analysis-via-convex-optimization | http://papers.nips.cc/paper/3442-supervised-exponential-family-principal-component-analysis-via-convex-optimization.pdf | Supervised Exponential Family Principal Component Analysis via Convex Optimization | Recently, supervised dimensionality reduction has been gaining attention, owing to the realization that data labels are often available and strongly suggest important underlying structures in the data. In this paper, we present a novel convex supervised dimensionality reduction approach based on exponential family PCA ... | ['Yuhong Guo'] | 2008-12-01 | null | null | null | neurips-2008-12 | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 1.44639969e-01 -9.84939747e-03 1.10500101e-02 -4.11065578e-01
-7.52127588e-01 -3.60937268e-01 6.91574693e-01 -6.62657693e-02
-3.90528679e-01 6.76872730e-01 4.14777324e-02 -3.91273614e-04
-6.42050564e-01 -5.02390862e-01 -3.60524535e-01 -1.09540951e+00
7.67271295e-02 4.79480565e-01 -3.32337171e-01 1.85390934... | [7.748720169067383, 4.203341484069824] |
95fdc57d-0ff5-4dff-aa6d-d3aed1338a32 | application-of-deep-learning-for-predictive | 2306.11040 | null | https://arxiv.org/abs/2306.11040v1 | https://arxiv.org/pdf/2306.11040v1.pdf | Application of Deep Learning for Predictive Maintenance of Oilfield Equipment | This thesis explored applications of the new emerging techniques of artificial intelligence and deep learning (neural networks in particular) for predictive maintenance, diagnostics and prognostics. Many neural architectures such as fully-connected, convolutional and recurrent neural networks were developed and tested ... | ['Abdeldjalil Latrach'] | 2023-06-19 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-4.97197993e-02 -9.27783698e-02 4.20088798e-01 -1.60490900e-01
-3.30732428e-02 1.05794169e-01 1.74455032e-01 3.38950396e-01
1.84627101e-01 5.87850153e-01 2.86451310e-01 -6.47493064e-01
-9.23985064e-01 -8.63003850e-01 -3.37675102e-02 -9.62259650e-01
-7.11123049e-01 3.59652936e-01 -2.42693633e-01 -5.98953903... | [6.804407119750977, 2.4272148609161377] |
6dcbb003-08bc-469d-a457-3a5064a0652b | language-models-use-monotonicity-to-assess | 2105.13818 | null | https://arxiv.org/abs/2105.13818v1 | https://arxiv.org/pdf/2105.13818v1.pdf | Language Models Use Monotonicity to Assess NPI Licensing | We investigate the semantic knowledge of language models (LMs), focusing on (1) whether these LMs create categories of linguistic environments based on their semantic monotonicity properties, and (2) whether these categories play a similar role in LMs as in human language understanding, using negative polarity item lic... | ['Shane Steinert-Threlkeld', 'Dieuwke Hupkes', 'Jakub Szymanik', 'Milica Denić', 'Jaap Jumelet'] | 2021-05-28 | null | https://aclanthology.org/2021.findings-acl.439 | https://aclanthology.org/2021.findings-acl.439.pdf | findings-acl-2021-8 | ['linguistic-acceptability'] | ['natural-language-processing'] | [ 6.53710961e-02 7.55661666e-01 -3.84454489e-01 -6.17146373e-01
-6.05312943e-01 -1.04981279e+00 9.98529792e-01 5.29837251e-01
-2.82388955e-01 3.07054937e-01 9.02235210e-01 -6.90214276e-01
-3.28825682e-01 -7.59472728e-01 -7.08764672e-01 -5.98665513e-02
4.03597802e-02 7.05273449e-01 5.68400979e-01 -3.38891625... | [10.431114196777344, 9.098037719726562] |
8e9eebe1-db72-491d-9de2-045dcb4eef74 | graph-sampling-based-deep-metric-learning-for | 2104.01546 | null | https://arxiv.org/abs/2104.01546v4 | https://arxiv.org/pdf/2104.01546v4.pdf | Graph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification | Recent studies show that, both explicit deep feature matching as well as large-scale and diverse training data can significantly improve the generalization of person re-identification. However, the efficiency of learning deep matchers on large-scale data has not yet been adequately studied. Though learning with classif... | ['Ling Shao', 'Shengcai Liao'] | 2021-04-04 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liao_Graph_Sampling_Based_Deep_Metric_Learning_for_Generalizable_Person_Re-Identification_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liao_Graph_Sampling_Based_Deep_Metric_Learning_for_Generalizable_Person_Re-Identification_CVPR_2022_paper.pdf | cvpr-2022-1 | ['generalizable-person-re-identification', 'graph-sampling'] | ['computer-vision', 'graphs'] | [-0.28658387 -0.29275465 -0.18615885 -0.6455792 -0.6464612 -0.2860453
0.3201131 0.11391839 -0.6454864 0.95579076 -0.04584241 0.15439461
-0.2835031 -1.1397811 -0.6112605 -0.53149277 0.05541422 1.0233785
-0.07985279 -0.08003525 -0.144856 0.24239217 -1.3992581 -0.01101797
1.117155 0.933165 -0.02... | [14.701663970947266, 0.9445982575416565] |
f974ebf7-a006-428f-9056-11c0ea75cfce | deep-attentional-guided-image-filtering | 2112.06401 | null | https://arxiv.org/abs/2112.06401v2 | https://arxiv.org/pdf/2112.06401v2.pdf | Deep Attentional Guided Image Filtering | Guided filter is a fundamental tool in computer vision and computer graphics which aims to transfer structure information from guidance image to target image. Most existing methods construct filter kernels from the guidance itself without considering the mutual dependency between the guidance and the target. However, s... | ['Xiangyang Ji', 'Debin Zhao', 'Junjun Jiang', 'Xianming Liu', 'Zhiwei Zhong'] | 2021-12-13 | null | null | null | null | ['depth-image-upsampling', 'depth-map-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 6.77760541e-01 -4.13079262e-01 2.78681427e-01 -3.36405516e-01
-5.85625947e-01 -1.68777823e-01 2.81281918e-01 -3.49713117e-02
-3.63179654e-01 4.60255086e-01 9.63994116e-02 1.48154020e-01
-4.65100467e-01 -1.09243011e+00 -4.64088023e-01 -8.81040752e-01
4.72754121e-01 -1.39057308e-01 7.75305271e-01 -2.24878639... | [10.68394660949707, -1.7529311180114746] |
56fd5d54-22cc-4f7e-8083-dc1934aa323b | dent-ddsp-data-efficient-noisy-speech | 2208.00987 | null | https://arxiv.org/abs/2208.00987v1 | https://arxiv.org/pdf/2208.00987v1.pdf | DENT-DDSP: Data-efficient noisy speech generator using differentiable digital signal processors for explicit distortion modelling and noise-robust speech recognition | The performances of automatic speech recognition (ASR) systems degrade drastically under noisy conditions. Explicit distortion modelling (EDM), as a feature compensation step, is able to enhance ASR systems under such conditions by simulating the in-domain noisy speeches from the clean counterparts. Yet, existing disto... | ['E. S. Chng', 'C. Chen', 'Z. Guo'] | 2022-08-01 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 1.31711707e-01 1.65177852e-01 4.79320019e-01 -2.38105237e-01
-1.06630754e+00 -2.79911637e-01 6.34420872e-01 -4.73499596e-01
-3.66131485e-01 4.16728944e-01 3.31430078e-01 -2.79617816e-01
-7.40643293e-02 -3.06876779e-01 -7.04118013e-01 -8.01846802e-01
1.82520464e-01 -1.19906822e-02 5.84212542e-02 -5.38748920... | [14.889742851257324, 6.107551574707031] |
9670cb70-4540-47b0-b223-193efcc10007 | a-dataset-for-hyper-relational-extraction-and | 2211.10018 | null | https://arxiv.org/abs/2211.10018v1 | https://arxiv.org/pdf/2211.10018v1.pdf | A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach | Relation extraction has the potential for large-scale knowledge graph construction, but current methods do not consider the qualifier attributes for each relation triplet, such as time, quantity or location. The qualifiers form hyper-relational facts which better capture the rich and complex knowledge graph structure. ... | ['Soujanya Poria', 'Luo Si', 'Sharifah Mahani Aljunied', 'Lidong Bing', 'Yew Ken Chia'] | 2022-11-18 | null | null | null | null | ['hyper-relational-extraction'] | ['natural-language-processing'] | [-2.28599489e-01 4.53610003e-01 -8.74268830e-01 -3.40250671e-01
-5.24218678e-01 -6.30752265e-01 4.50726807e-01 6.78563416e-01
-1.53432697e-01 1.11863160e+00 3.31315011e-01 -5.56686461e-01
-3.71534139e-01 -1.46829844e+00 -6.66917682e-01 -1.59646496e-02
-2.21347362e-01 7.43793607e-01 3.36321115e-01 -1.33622020... | [9.181361198425293, 8.358460426330566] |
eb7e3363-d55c-47f1-9103-7d82a93d8f72 | elsr-extreme-low-power-super-resolution | 2208.14600 | null | https://arxiv.org/abs/2208.14600v1 | https://arxiv.org/pdf/2208.14600v1.pdf | ELSR: Extreme Low-Power Super Resolution Network For Mobile Devices | With the popularity of mobile devices, e.g., smartphone and wearable devices, lighter and faster model is crucial for the application of video super resolution. However, most previous lightweight models tend to concentrate on reducing lantency of model inference on desktop GPU, which may be not energy efficient in curr... | ['Heng Sun', 'Long Bao', 'Yijian Zhang', 'Zhuang Jia', 'Tianyu Xu'] | 2022-08-31 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 0.40907922 -0.29057923 -0.329253 -0.06703544 -0.5934206 -0.15315437
0.12874186 -0.55928963 -0.32951915 0.577637 0.16925748 -0.15311168
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0.43041793 -0.22549888 0.03486754 0.148996 -1.5822369 0.50441325
0.9114362 0.949575 0.6... | [11.026277542114258, -1.8587794303894043] |
bb12ca69-11b1-48a0-b7b8-ad41eb66bd70 | select-extract-and-generate-neural-keyphrase | 2008.01739 | null | https://arxiv.org/abs/2008.01739v2 | https://arxiv.org/pdf/2008.01739v2.pdf | Select, Extract and Generate: Neural Keyphrase Generation with Layer-wise Coverage Attention | Natural language processing techniques have demonstrated promising results in keyphrase generation. However, one of the major challenges in \emph{neural} keyphrase generation is processing long documents using deep neural networks. Generally, documents are truncated before given as inputs to neural networks. Consequent... | ['Kai-Wei Chang', 'Wasi Uddin Ahmad', 'Soomin Lee', 'Xiao Bai'] | 2020-08-04 | null | https://aclanthology.org/2021.acl-long.111 | https://aclanthology.org/2021.acl-long.111.pdf | acl-2021-5 | ['keyphrase-generation'] | ['natural-language-processing'] | [ 3.58761638e-01 3.12334567e-01 1.21711874e-02 -4.97217886e-02
-1.02843416e+00 -5.33710241e-01 9.83138978e-01 2.50367105e-01
-2.97827721e-01 9.24925625e-01 8.35564792e-01 -1.67770728e-01
1.03023276e-01 -1.09384322e+00 -9.20694172e-01 -7.33589768e-01
4.23938572e-01 2.95104355e-01 1.57802299e-01 -4.84655231... | [12.355022430419922, 8.999512672424316] |
bb83df84-096c-4f6d-b2d8-ba8cce6a5a2c | disentangled-motif-aware-graph-learning-for | 2104.06008 | null | https://arxiv.org/abs/2104.06008v1 | https://arxiv.org/pdf/2104.06008v1.pdf | Disentangled Motif-aware Graph Learning for Phrase Grounding | In this paper, we propose a novel graph learning framework for phrase grounding in the image. Developing from the sequential to the dense graph model, existing works capture coarse-grained context but fail to distinguish the diversity of context among phrases and image regions. In contrast, we pay special attention to ... | ['Yueting Zhuang', 'Qiang Yu', 'Jie Tan', 'Siliang Tang', 'Zongshen Mu'] | 2021-04-13 | null | null | null | null | ['phrase-grounding'] | ['natural-language-processing'] | [ 1.21708252e-01 -9.91524160e-02 -3.09016854e-01 -1.90048561e-01
-5.25140464e-01 -8.28143418e-01 7.96250641e-01 4.39242087e-02
-3.11367422e-01 3.66194159e-01 6.44524038e-01 -1.35764673e-01
-3.87609005e-01 -7.39132404e-01 -6.34158611e-01 -6.54857695e-01
1.51566193e-02 -4.56020646e-02 7.48609379e-02 -3.40437174... | [10.445384979248047, 1.4466298818588257] |
102cc6c4-21a1-4a22-9d4a-cc5f31d7d7f5 | length-controllable-image-captioning | 2007.09580 | null | https://arxiv.org/abs/2007.09580v1 | https://arxiv.org/pdf/2007.09580v1.pdf | Length-Controllable Image Captioning | The last decade has witnessed remarkable progress in the image captioning task; however, most existing methods cannot control their captions, \emph{e.g.}, choosing to describe the image either roughly or in detail. In this paper, we propose to use a simple length level embedding to endow them with this ability. Moreove... | ['Qi Wu', 'Mingkui Tan', 'Ning Ding', 'Chaorui Deng'] | 2020-07-19 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2035_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580698.pdf | eccv-2020-8 | ['controllable-image-captioning'] | ['computer-vision'] | [ 2.75793344e-01 1.13808371e-01 -1.28907993e-01 -3.90915751e-01
-1.06907964e+00 -6.98851466e-01 6.10766470e-01 -5.60285151e-01
-1.19619109e-01 5.91940284e-01 4.90299255e-01 -4.09678042e-01
3.54771674e-01 -5.04965663e-01 -1.17728770e+00 -6.81501329e-01
3.05216968e-01 4.63251173e-01 -1.76554769e-01 -2.17922539... | [11.018098831176758, 0.9036690592765808] |
126cb41c-8e7a-4d88-af61-e8e2be09f2ba | depth-aware-image-compositing-model-for | 2303.09334 | null | https://arxiv.org/abs/2303.09334v2 | https://arxiv.org/pdf/2303.09334v2.pdf | Depth-Aware Image Compositing Model for Parallax Camera Motion Blur | Camera motion introduces spatially varying blur due to the depth changes in the 3D world. This work investigates scene configurations where such blur is produced under parallax camera motion. We present a simple, yet accurate, Image Compositing Blur (ICB) model for depth-dependent spatially varying blur. The (forward) ... | ['Joni-Kristian Kämäräinen', 'German F. Torres'] | 2023-03-16 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 2.02624187e-01 -5.59467554e-01 3.04002374e-01 -1.81393266e-01
-3.54946941e-01 -5.89643061e-01 6.75419450e-01 -7.17488825e-01
-1.00981608e-01 9.61298823e-01 6.52988315e-01 -6.17550686e-03
-2.20532760e-01 -1.48847878e-01 -8.64382327e-01 -8.51127982e-01
2.03803971e-01 -2.54285306e-01 1.99226677e-01 2.40786001... | [11.531628608703613, -2.7086801528930664] |
a884f08e-d80b-4c29-b5ea-f06e1baa7c43 | shuttleset22-benchmarking-stroke-forecasting | 2306.15664 | null | https://arxiv.org/abs/2306.15664v2 | https://arxiv.org/pdf/2306.15664v2.pdf | ShuttleSet22: Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset | In recent years, badminton analytics has drawn attention due to the advancement of artificial intelligence and the efficiency of data collection. While there is a line of effective applications to improve and investigate player performance, there are only a few public badminton datasets that can be used for researchers... | ['Wen-Chih Peng', 'Wei-Wei Du', 'Wei-Yao Wang'] | 2023-06-27 | null | null | null | null | ['benchmarking', 'benchmarking'] | ['miscellaneous', 'robots'] | [-5.45933783e-01 -4.79507178e-01 -5.14043212e-01 2.31676386e-04
-7.16373622e-01 -7.39219427e-01 7.86410630e-01 -3.84201795e-01
-4.62411672e-01 4.43192303e-01 6.83526397e-01 -5.35168089e-02
-2.30873108e-01 -1.10280037e+00 -5.80056250e-01 -6.50498867e-02
2.33532906e-01 6.20150506e-01 5.37817121e-01 -5.50940931... | [6.717569828033447, 0.3271353840827942] |
dba3d4a1-dd31-4606-86eb-96b4bf6c8359 | simulated-chats-for-task-oriented-dialog | 2010.10216 | null | https://arxiv.org/abs/2010.10216v4 | https://arxiv.org/pdf/2010.10216v4.pdf | Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions | Popular dialog datasets such as MultiWOZ are created by providing crowd workers an instruction, expressed in natural language, that describes the task to be accomplished. Crowd workers play the role of a user and an agent to generate dialogs to accomplish tasks involving booking restaurant tables, calling a taxi etc. I... | ['Sachindra Joshi', 'Danish Contractor', 'Gaurav Pandey', 'Biswesh Mohapatra'] | 2020-10-20 | null | https://aclanthology.org/2021.findings-emnlp.103 | https://aclanthology.org/2021.findings-emnlp.103.pdf | findings-emnlp-2021-11 | ['dialog-learning'] | ['natural-language-processing'] | [-3.11406672e-01 5.22247195e-01 5.32013535e-01 -4.24489558e-01
-3.26608807e-01 -7.38240898e-01 1.07969105e+00 -1.36149824e-01
-5.55345356e-01 1.10344028e+00 4.70873475e-01 -2.27000847e-01
4.89095181e-01 -8.18250835e-01 -3.87784213e-01 -4.92189735e-01
2.40951970e-01 1.34066343e+00 3.69392693e-01 -7.07545102... | [12.828156471252441, 8.040772438049316] |
ac944085-d9a5-4473-975e-53ce2538cdca | measuring-interlanguage-native-language | null | null | https://aclanthology.org/L12-1016 | https://aclanthology.org/L12-1016.pdf | Measuring Interlanguage: Native Language Identification with L1-influence Metrics | The task of native language (L1) identification suffers from a relative paucity of useful training corpora, and standard within-corpus evaluation is often problematic due to topic bias. In this paper, we introduce a method for L1 identification in second language (L2) texts that relies only on much more plentiful L1 da... | ['Graeme Hirst', 'Julian Brooke'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['native-language-identification'] | ['natural-language-processing'] | [ 3.07943702e-01 -2.51436532e-02 -6.24230683e-01 -3.44751745e-01
-1.50261426e+00 -9.41837728e-01 8.93198669e-01 5.34540057e-01
-7.01038480e-01 9.76486623e-01 4.96313095e-01 -8.24868441e-01
-3.24770552e-03 -4.11975771e-01 -4.35904056e-01 -3.82035226e-01
2.24649489e-01 7.65121818e-01 2.38563493e-01 -2.50036269... | [10.658153533935547, 10.176671028137207] |
fc1e0e29-e79e-464d-8306-a03263126a6a | a-mathematical-abstraction-for-balancing-the | 2306.02295 | null | https://arxiv.org/abs/2306.02295v1 | https://arxiv.org/pdf/2306.02295v1.pdf | A Mathematical Abstraction for Balancing the Trade-off Between Creativity and Reality in Large Language Models | Large Language Models have become popular for their remarkable capabilities in human-oriented tasks and traditional natural language processing tasks. Its efficient functioning is attributed to the attention mechanism in the Transformer architecture, enabling it to concentrate on particular aspects of the input. LLMs a... | ['Tianyi Zhou', 'Zhao Song', 'Ritwik Sinha'] | 2023-06-04 | null | null | null | null | ['chatbot', 'chatbot'] | ['methodology', 'natural-language-processing'] | [-7.44760782e-02 4.08317775e-01 1.55072451e-01 -1.33661404e-01
-1.74039438e-01 -7.66544580e-01 7.99973249e-01 -8.43339413e-03
-2.82955289e-01 4.84235972e-01 2.18391195e-01 -1.23203292e-01
6.22300915e-02 -9.00455654e-01 -1.95503682e-01 -2.99354225e-01
3.29227775e-01 5.10875762e-01 -3.23688895e-01 -5.73952198... | [11.673686027526855, 8.84626293182373] |
c94a1144-d8eb-4bbd-945d-edc2394d57ac | pretraining-approaches-for-spoken-language | 2205.07083 | null | https://arxiv.org/abs/2205.07083v1 | https://arxiv.org/pdf/2205.07083v1.pdf | Pretraining Approaches for Spoken Language Recognition: TalTech Submission to the OLR 2021 Challenge | This paper investigates different pretraining approaches to spoken language identification. The paper is based on our submission to the Oriental Language Recognition 2021 Challenge. We participated in two tracks of the challenge: constrained and unconstrained language recognition. For the constrained track, we first tr... | ['Kunnar Kukk', 'Tanel Alumäe'] | 2022-05-14 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [ 5.74894175e-02 1.88068002e-01 -5.51193394e-02 -7.19267130e-01
-1.31500590e+00 -7.98288763e-01 6.85929298e-01 -2.34457836e-01
-1.10435307e+00 6.44717455e-01 4.96103883e-01 -6.66542828e-01
3.54530483e-01 2.77746115e-02 -5.57332993e-01 -3.20252180e-01
2.40819827e-01 7.20479727e-01 -2.48084992e-01 -2.99125999... | [14.197185516357422, 6.866093158721924] |
40f00477-9c26-405e-8570-cd7927ce4e71 | optical-character-recognition-and | 2303.13549 | null | https://arxiv.org/abs/2303.13549v1 | https://arxiv.org/pdf/2303.13549v1.pdf | Optical Character Recognition and Transcription of Berber Signs from Images in a Low-Resource Language Amazigh | The Berber, or Amazigh language family is a low-resource North African vernacular language spoken by the indigenous Berber ethnic group. It has its own unique alphabet called Tifinagh used across Berber communities in Morocco, Algeria, and others. The Afroasiatic language Berber is spoken by 14 million people, yet lack... | ['Aparna S. Varde', 'Levi Corallo'] | 2023-03-21 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [-2.38446355e-01 -5.74567355e-02 -1.37699068e-01 -1.26866087e-01
-5.67861974e-01 -8.08588266e-01 6.66388214e-01 -2.46283442e-01
-5.82118511e-01 6.10967755e-01 8.90568569e-02 -7.19246984e-01
-8.25925618e-02 -6.98973417e-01 -7.69172907e-01 -6.02396727e-01
2.73733050e-01 6.66068852e-01 -2.32581031e-02 -6.46292627... | [11.840804100036621, 2.5851845741271973] |
1cfef972-5267-4a91-abf3-f5a175b1cd9e | portfolio-optimization-using-predictive | 2304.11856 | null | https://arxiv.org/abs/2304.11856v1 | https://arxiv.org/pdf/2304.11856v1.pdf | Portfolio Optimization using Predictive Auxiliary Classifier Generative Adversarial Networks with Measuring Uncertainty | In financial engineering, portfolio optimization has been of consistent interest. Portfolio optimization is a process of modulating asset distributions to maximize expected returns and minimize risks. To obtain the expected returns, deep learning models have been explored in recent years. However, due to the determinis... | ['Minhyeok Lee', 'Jiwook Kim'] | 2023-04-24 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-2.37800539e-01 1.67995110e-01 1.58248141e-01 -9.79818255e-02
-2.53373325e-01 -4.43339676e-01 6.84156060e-01 -3.38670343e-01
9.26572550e-03 9.67240989e-01 -2.51364317e-02 -3.69697601e-01
-4.05132025e-01 -1.48261249e+00 -6.45627797e-01 -9.37055647e-01
1.86666295e-01 3.05201948e-01 -2.01961175e-01 -2.45330688... | [4.640303134918213, 4.125860691070557] |
52fb5e6d-3857-4b07-b3b4-ad258bdf34f6 | a-fine-tuned-wav2vec-2-0-hubert-benchmark-for | 2111.02735 | null | https://arxiv.org/abs/2111.02735v3 | https://arxiv.org/pdf/2111.02735v3.pdf | A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding | Speech self-supervised models such as wav2vec 2.0 and HuBERT are making revolutionary progress in Automatic Speech Recognition (ASR). However, they have not been totally proven to produce better performance on tasks other than ASR. In this work, we explored partial fine-tuning and entire fine-tuning on wav2vec 2.0 and ... | ['Abdelwahab Heba', 'Abdelmoumene Boumadane', 'Yingzhi Wang'] | 2021-11-04 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [-2.36955613e-01 5.79294622e-01 -9.61985365e-02 -7.75242567e-01
-9.95589793e-01 -4.12933350e-01 3.50942641e-01 -1.14859663e-01
-4.70158607e-01 4.94144261e-01 7.02896714e-01 -5.40697873e-01
4.01153058e-01 -7.09690154e-02 -1.54447541e-01 -3.08315814e-01
2.02491760e-01 5.36569655e-01 -1.18670374e-01 -4.91248399... | [14.187484741210938, 6.681003093719482] |
2fa21923-1f6f-4840-9d54-dd875a1e9708 | adaptive-mutual-supervision-for-weakly | 2104.02357 | null | https://arxiv.org/abs/2104.02357v1 | https://arxiv.org/pdf/2104.02357v1.pdf | Adaptive Mutual Supervision for Weakly-Supervised Temporal Action Localization | Weakly-supervised temporal action localization aims to localize actions in untrimmed videos with only video-level action category labels. Most of previous methods ignore the incompleteness issue of Class Activation Sequences (CAS), suffering from trivial localization results. To solve this issue, we introduce an adapti... | ['Qi Tian', 'Xiaoyun Zhang', 'Ya zhang', 'Siheng Chen', 'Peisen Zhao', 'Chen Ju'] | 2021-04-06 | null | null | null | null | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action'] | ['computer-vision', 'computer-vision'] | [ 4.51820642e-01 5.00766225e-02 -7.23331511e-01 -1.03530526e-01
-7.11749613e-01 -3.70234489e-01 4.10639524e-01 -1.69917345e-01
-4.27332759e-01 6.96898818e-01 4.60050672e-01 1.01974696e-01
1.62133768e-01 -3.01565766e-01 -6.80046558e-01 -9.99061704e-01
-1.58689320e-01 1.47052303e-01 8.45280766e-01 2.45815724... | [8.55659294128418, 0.688491702079773] |
a86654d2-a2de-4382-b2ca-396fa58dbeea | a-self-training-approach-for-short-text | null | null | https://aclanthology.org/W19-4322 | https://aclanthology.org/W19-4322.pdf | A Self-Training Approach for Short Text Clustering | Short text clustering is a challenging problem when adopting traditional bag-of-words or TF-IDF representations, since these lead to sparse vector representations of the short texts. Low-dimensional continuous representations or embeddings can counter that sparseness problem: their high representational power is exploi... | ['Chris Develder', 'Thomas Demeester', 'Lucas Sterckx', 'Amir Hadifar'] | 2019-08-01 | null | null | null | ws-2019-8 | ['text-clustering', 'short-text-clustering'] | ['natural-language-processing', 'natural-language-processing'] | [-1.40761897e-01 -1.34192064e-01 -3.90930951e-01 -4.53962475e-01
-2.56673783e-01 -2.26870775e-01 8.05294991e-01 2.61719346e-01
-4.90003228e-01 2.39460245e-01 7.81618178e-01 5.22623919e-02
-2.69942939e-01 -6.15846872e-01 -2.97824562e-01 -9.86152411e-01
7.72204697e-02 5.68262219e-01 -2.98290372e-01 5.11892848... | [10.446760177612305, 6.77803897857666] |
468d2f31-e538-42c0-a4c9-c23c9f295251 | how-an-electrical-engineer-became-an | 1803.11261 | null | http://arxiv.org/abs/1803.11261v1 | http://arxiv.org/pdf/1803.11261v1.pdf | How an Electrical Engineer Became an Artificial Intelligence Researcher, a Multiphase Active Contours Analysis | This essay examines how what is considered to be artificial intelligence (AI)
has changed over time and come to intersect with the expertise of the author.
Initially, AI developed on a separate trajectory, both topically and
institutionally, from pattern recognition, neural information processing,
decision and control ... | ['Kush R. Varshney'] | 2018-03-29 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [ 2.32754871e-01 5.80100894e-01 -3.35059345e-01 -3.50599766e-01
-3.53880256e-01 -7.47358739e-01 6.89178586e-01 2.69217461e-01
4.46761120e-03 6.74716234e-01 1.42294288e-01 -7.79080391e-01
-4.48003978e-01 -7.99169302e-01 -2.25216672e-01 -3.87186795e-01
-6.95686340e-02 4.95321810e-01 -2.56313622e-01 -2.24153623... | [8.990983009338379, 6.4089789390563965] |
9f3f31e9-85bc-4fd8-897f-84898d2c4ced | self-supervised-surgical-instrument-3d | 2211.14467 | null | https://arxiv.org/abs/2211.14467v1 | https://arxiv.org/pdf/2211.14467v1.pdf | Self-Supervised Surgical Instrument 3D Reconstruction from a Single Camera Image | Surgical instrument tracking is an active research area that can provide surgeons feedback about the location of their tools relative to anatomy. Recent tracking methods are mainly divided into two parts: segmentation and object detection. However, both can only predict 2D information, which is limiting for application... | ['Jack Noble', 'Jintong Han', 'Ziteng Liu', 'Xing Yao', 'Ange Lou'] | 2022-11-26 | null | null | null | null | ['single-view-3d-reconstruction', 'object-reconstruction'] | ['computer-vision', 'computer-vision'] | [-4.00047691e-04 1.26143754e-01 -8.08497548e-01 1.28605384e-02
-6.56133831e-01 -6.67975307e-01 9.75379441e-03 1.17162079e-01
-2.62014121e-01 2.49188125e-01 5.41462861e-02 -4.47463781e-01
-6.86609223e-02 -1.98665634e-01 -4.13228184e-01 -6.57642126e-01
3.75385821e-01 6.40420675e-01 6.34517550e-01 -6.94328770... | [13.853714942932129, -3.127453327178955] |
8853625e-f3c0-4ac1-8d73-0d6157f1a2f0 | label-enhanced-prototypical-network-with | 2206.13980 | null | https://arxiv.org/abs/2206.13980v1 | https://arxiv.org/pdf/2206.13980v1.pdf | Label-enhanced Prototypical Network with Contrastive Learning for Multi-label Few-shot Aspect Category Detection | Multi-label aspect category detection allows a given review sentence to contain multiple aspect categories, which is shown to be more practical in sentiment analysis and attracting increasing attention. As annotating large amounts of data is time-consuming and labor-intensive, data scarcity occurs frequently in real-wo... | ['Xianchao Zhang', 'Hong Yu', 'Junjie Sun', 'Siyang Zhao', 'Xiaotong Zhang', 'Feng Zhang', 'Han Liu'] | 2022-06-14 | null | null | null | null | ['aspect-category-detection'] | ['natural-language-processing'] | [ 1.90051690e-01 2.24016868e-02 -5.95083237e-01 -5.49366057e-01
-5.85018039e-01 -3.50998670e-01 4.65279967e-01 3.16565841e-01
-3.08714837e-01 3.54883790e-01 2.17859671e-01 1.11502573e-01
4.66609746e-02 -6.35842144e-01 -1.37942418e-01 -9.31714535e-01
4.26577032e-01 2.27730528e-01 1.40490467e-02 -1.85050502... | [11.307270050048828, 6.539340019226074] |
7ec0f71f-3d08-4cca-a7a0-09dfa0a7e6d1 | globally-optimal-contrast-maximisation-for | 2002.10686 | null | https://arxiv.org/abs/2002.10686v3 | https://arxiv.org/pdf/2002.10686v3.pdf | Globally Optimal Contrast Maximisation for Event-based Motion Estimation | Contrast maximisation estimates the motion captured in an event stream by maximising the sharpness of the motion compensated event image. To carry out contrast maximisation, many previous works employ iterative optimisation algorithms, such as conjugate gradient, which require good initialisation to avoid converging to... | ['Tat-Jun Chin', 'Álvaro Parra', 'Daqi Liu'] | 2020-02-25 | globally-optimal-contrast-maximisation-for-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Globally_Optimal_Contrast_Maximisation_for_Event-Based_Motion_Estimation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_Globally_Optimal_Contrast_Maximisation_for_Event-Based_Motion_Estimation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['event-based-motion-estimation'] | ['computer-vision'] | [ 4.68591243e-01 7.05826581e-02 1.65771961e-01 -1.22488409e-01
-6.62773073e-01 -5.48068047e-01 7.17634499e-01 9.34049040e-02
-6.76697791e-01 4.74966943e-01 2.52089441e-01 -3.89902174e-01
-1.46715552e-01 -5.84954262e-01 -6.66349709e-01 -5.99743724e-01
-4.98558015e-01 2.20899329e-01 3.90333116e-01 -1.31190524... | [8.71058464050293, -1.449467420578003] |
e924b052-0d76-47ce-ad9e-f4764c3572dd | small-total-cost-constraints-in-contextual | 2305.15807 | null | https://arxiv.org/abs/2305.15807v1 | https://arxiv.org/pdf/2305.15807v1.pdf | Small Total-Cost Constraints in Contextual Bandits with Knapsacks, with Application to Fairness | We consider contextual bandit problems with knapsacks [CBwK], a problem where at each round, a scalar reward is obtained and vector-valued costs are suffered. The learner aims to maximize the cumulative rewards while ensuring that the cumulative costs are lower than some predetermined cost constraints. We assume that c... | ['Gilles Stoltz', 'Zhen Li', 'Christophe Giraud', 'Evgenii Chzhen'] | 2023-05-25 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 4.61859517e-02 3.53993356e-01 -5.30160427e-01 -2.70980269e-01
-9.17941809e-01 -7.71248698e-01 1.45090625e-01 5.99343717e-01
-9.29684579e-01 1.25739682e+00 -3.06063294e-01 -2.38293812e-01
-7.00089931e-01 -8.90011191e-01 -1.12716544e+00 -8.48175824e-01
-4.88022059e-01 6.95551395e-01 -1.12017319e-02 -3.54382545... | [4.599306106567383, 3.384106159210205] |
fa065fcf-acad-4fb5-9d0a-d2022ba991a7 | graph-reasoning-for-question-answering-with | 2305.18742 | null | https://arxiv.org/abs/2305.18742v1 | https://arxiv.org/pdf/2305.18742v1.pdf | Graph Reasoning for Question Answering with Triplet Retrieval | Answering complex questions often requires reasoning over knowledge graphs (KGs). State-of-the-art methods often utilize entities in questions to retrieve local subgraphs, which are then fed into KG encoder, e.g. graph neural networks (GNNs), to model their local structures and integrated into language models for quest... | ['Bing Yin', 'Chao Zhang', 'Xifeng Yan', 'Zheng Li', 'Qingyu Yin', 'Haoming Jiang', 'Yifan Gao', 'Shiyang Li'] | 2023-05-30 | null | null | null | null | ['knowledge-graphs'] | ['knowledge-base'] | [-2.25452796e-01 5.24345756e-01 -1.65030926e-01 -2.59011984e-01
-7.91426182e-01 -7.92218089e-01 2.35902548e-01 6.61276877e-01
-1.71871334e-01 8.93045425e-01 3.37145537e-01 -5.15858710e-01
-3.39242786e-01 -1.37923753e+00 -1.05539846e+00 -1.05190717e-01
6.81681335e-02 6.61872268e-01 7.94238746e-01 -5.84522188... | [10.582100868225098, 7.873810291290283] |
0b71a7d6-f6cd-47d5-bd51-d716b120fd04 | revisiting-the-spatial-and-temporal-modeling | 2301.07944 | null | https://arxiv.org/abs/2301.07944v2 | https://arxiv.org/pdf/2301.07944v2.pdf | Revisiting the Spatial and Temporal Modeling for Few-shot Action Recognition | Spatial and temporal modeling is one of the most core aspects of few-shot action recognition. Most previous works mainly focus on long-term temporal relation modeling based on high-level spatial representations, without considering the crucial low-level spatial features and short-term temporal relations. Actually, the ... | ['Boyu Mu', 'Yong liu', 'Mengmeng Wang', 'Jiazheng Xing'] | 2023-01-19 | null | null | null | null | ['few-shot-action-recognition'] | ['computer-vision'] | [ 2.18181070e-02 -6.29534185e-01 -5.07002354e-01 -3.37321758e-01
-5.10425568e-01 1.97696567e-01 6.53208435e-01 9.99720246e-02
-4.34290469e-01 3.85630310e-01 5.69516242e-01 4.12356794e-01
-3.41603458e-01 -7.98617542e-01 -2.25021690e-01 -7.51553833e-01
1.20504564e-02 -1.65848523e-01 9.60857749e-01 -2.51825452... | [8.497681617736816, 0.6633945107460022] |
3f9e0486-ecda-4902-bb22-fdab8263746a | vector-space-is-not-the-final-frontier | 2304.11473 | null | https://arxiv.org/abs/2304.11473v2 | https://arxiv.org/pdf/2304.11473v2.pdf | (Vector) Space is Not the Final Frontier: Product Search as Program Synthesis | As ecommerce continues growing, huge investments in ML and NLP for Information Retrieval are following. While the vector space model dominated retrieval modelling in product search - even as vectorization itself greatly changed with the advent of deep learning -, our position paper argues in a contrarian fashion that p... | ['Ciro Greco', 'Jacopo Tagliabue'] | 2023-04-22 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [-4.20866907e-01 -8.90445039e-02 -5.98074734e-01 -1.05926052e-01
-7.82120466e-01 -8.65023434e-01 5.84791958e-01 3.09129179e-01
-4.59963918e-01 1.30975813e-01 1.48128778e-01 -1.01783419e+00
-4.06294882e-01 -6.37995303e-01 -3.92059207e-01 -1.67496860e-01
-1.92093607e-02 5.89505494e-01 -3.99307638e-01 -7.65661299... | [11.349995613098145, 7.543038368225098] |
9e074988-b655-41f1-92b4-6745adfc9cef | effects-of-image-compression-on-face-image | 2103.03654 | null | https://arxiv.org/abs/2103.03654v1 | https://arxiv.org/pdf/2103.03654v1.pdf | Effects of Image Compression on Face Image Manipulation Detection: A Case Study on Facial Retouching | In the past years, numerous methods have been introduced to reliably detect digital face image manipulations. Lately, the generalizability of these schemes has been questioned in particular with respect to image post-processing. Image compression represents a post-processing which is frequently applied in diverse biome... | ['Christoph Busch', 'Nathania E. Haryanto', 'Kevin Bernardo', 'Christian Rathgeb'] | 2021-03-05 | null | null | null | null | ['image-manipulation-detection'] | ['computer-vision'] | [ 8.89082789e-01 -2.31235519e-01 1.51716873e-01 -2.62896657e-01
-5.10618269e-01 -4.81610090e-01 6.84593558e-01 -6.97923265e-03
-2.52111584e-01 2.89105415e-01 -2.74877340e-01 2.40325406e-02
-2.72999644e-01 -5.71532190e-01 -6.02527320e-01 -8.75653148e-01
-1.87233359e-01 3.35187539e-02 -1.64015278e-01 -1.75453439... | [13.08424186706543, 1.0160737037658691] |
58d7971e-27bf-4047-bd4d-8a7dcb5c7a9e | with-a-little-help-from-my-friends-nearest | 2104.14548 | null | https://arxiv.org/abs/2104.14548v2 | https://arxiv.org/pdf/2104.14548v2.pdf | With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations | Self-supervised learning algorithms based on instance discrimination train encoders to be invariant to pre-defined transformations of the same instance. While most methods treat different views of the same image as positives for a contrastive loss, we are interested in using positives from other instances in the datase... | ['Andrew Zisserman', 'Pierre Sermanet', 'Jonathan Tompson', 'Yusuf Aytar', 'Debidatta Dwibedi'] | 2021-04-29 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Dwibedi_With_a_Little_Help_From_My_Friends_Nearest-Neighbor_Contrastive_Learning_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Dwibedi_With_a_Little_Help_From_My_Friends_Nearest-Neighbor_Contrastive_Learning_ICCV_2021_paper.pdf | iccv-2021-1 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 5.68812490e-01 1.03731528e-01 -6.92327142e-01 -6.04529858e-01
-9.77391362e-01 -7.79036701e-01 8.03217053e-01 6.11438649e-03
-7.49700963e-01 6.30387127e-01 -8.50560982e-03 -1.20214023e-01
1.22110046e-01 -8.81123960e-01 -1.24632454e+00 -4.99097407e-01
6.98323101e-02 4.43186939e-01 2.32136503e-01 -1.69786826... | [9.535120010375977, 2.6001181602478027] |
2ea30d61-b78e-48f7-b4eb-43dd28f80c32 | a-dual-benchmarking-study-of-facial-forgery | 2111.12912 | null | https://arxiv.org/abs/2111.12912v2 | https://arxiv.org/pdf/2111.12912v2.pdf | A War Beyond Deepfake: Benchmarking Facial Counterfeits and Countermeasures | In recent years, visual forgery has reached a level of sophistication that humans cannot identify fraud, which poses a significant threat to information security. A wide range of malicious applications have emerged, such as fake news, defamation or blackmailing of celebrities, impersonation of politicians in political ... | ['Quoc Viet Hung Nguyen', 'Hongzhi Yin', 'Thanh Thi Nguyen', 'Thanh Tam Nguyen', 'Van Vinh Tong', 'Thanh Trung Huynh', 'Minh Tam Pham'] | 2021-11-25 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [ 3.48251499e-02 -2.49781385e-01 -1.86360657e-01 8.85983184e-02
-4.52334702e-01 -9.32450771e-01 1.16576636e+00 2.78754950e-01
-4.34340268e-01 5.79829276e-01 -3.92072909e-02 -5.79357684e-01
3.24629754e-01 -7.63780892e-01 -2.15011746e-01 -5.77702940e-01
-1.52824223e-02 7.53672495e-02 4.20108974e-01 -3.46590549... | [12.481054306030273, 1.0891897678375244] |
a756e182-be97-45ee-847b-3c89f0908e42 | saliency-detection-in-360a-videos | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Ziheng_Zhang_Saliency_Detection_in_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Ziheng_Zhang_Saliency_Detection_in_ECCV_2018_paper.pdf | Saliency Detection in 360° Videos | This paper presents a novel spherical convolutional neural network based scheme for saliency detection for 360° videos. Specifically, in our spherical convolution neural network definition, kernel is defined on a spherical crown, and the convolution involves the rotation of the kernel along the sphere. Considering tha... | ['Shenghua Gao ', 'Ziheng Zhang', 'Jingyi Yu', 'Yanyu Xu'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['video-saliency-detection'] | ['computer-vision'] | [ 1.34805784e-01 -1.37486234e-01 -1.67845353e-01 -3.33103895e-01
-4.41382527e-02 -2.78483063e-01 2.69988686e-01 -2.95909494e-01
-2.49500185e-01 1.18107453e-01 5.06823480e-01 -3.10389698e-01
3.45197842e-02 -5.69604218e-01 -1.24275887e+00 -5.54089487e-01
2.89116278e-02 -5.89727163e-01 6.16060376e-01 -2.30656594... | [9.72634220123291, -0.3207107484340668] |
bb80beb6-3c68-4c25-9877-70e12eac6b1e | hooknet-multi-resolution-convolutional-neural | 2006.12230 | null | https://arxiv.org/abs/2006.12230v1 | https://arxiv.org/pdf/2006.12230v1.pdf | HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images | We propose HookNet, a semantic segmentation model for histopathology whole-slide images, which combines context and details via multiple branches of encoder-decoder convolutional neural networks. Concentricpatches at multiple resolutions with different fields of view are used to feed different branches of HookNet, and ... | ['Karina Siliņa', 'Maschenka Balkenhol', 'Mart van Rijthoven', 'Jeroen van der Laak', 'Francesco Ciompi'] | 2020-06-22 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [ 4.58924860e-01 5.10232687e-01 -3.82457525e-01 -3.62709701e-01
-1.27897537e+00 -4.70322222e-01 3.10094655e-01 4.58153576e-01
-4.57172424e-01 4.10584956e-01 5.34370318e-02 -4.08548892e-01
-4.49258201e-02 -8.38717759e-01 -6.66250467e-01 -8.28029931e-01
1.29874468e-01 4.63735789e-01 6.64031208e-01 -6.45186156... | [14.883973121643066, -2.82285737991333] |
61dbfbef-2de3-4bc3-af17-f553a3cca941 | semi-supervised-medical-image-segmentation-3 | 2202.06104 | null | https://arxiv.org/abs/2202.06104v1 | https://arxiv.org/pdf/2202.06104v1.pdf | Semi-supervised Medical Image Segmentation via Geometry-aware Consistency Training | The performance of supervised deep learning methods for medical image segmentation is often limited by the scarcity of labeled data. As a promising research direction, semi-supervised learning addresses this dilemma by leveraging unlabeled data information to assist the learning process. In this paper, a novel geometry... | ['Chunhui Zhao', 'Zihang Liu'] | 2022-02-12 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 2.18030423e-01 5.87965786e-01 -5.12665570e-01 -7.12440491e-01
-9.59139407e-01 -4.18206722e-01 5.55210747e-02 5.99205121e-02
-5.31411350e-01 5.94154060e-01 6.32783175e-02 -1.99771985e-01
5.53249754e-02 -4.84221727e-01 -5.51225543e-01 -8.72311831e-01
4.54514444e-01 8.04656088e-01 2.96491385e-01 1.67736650... | [14.655975341796875, -2.0751683712005615] |
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