paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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d30b5a19-0f95-4c57-9733-dcd8d241d153 | adversarial-background-aware-loss-for-weakly | 2007.06643 | null | https://arxiv.org/abs/2007.06643v1 | https://arxiv.org/pdf/2007.06643v1.pdf | Adversarial Background-Aware Loss for Weakly-supervised Temporal Activity Localization | Temporally localizing activities within untrimmed videos has been extensively studied in recent years. Despite recent advances, existing methods for weakly-supervised temporal activity localization struggle to recognize when an activity is not occurring. To address this issue, we propose a novel method named A2CL-PT. T... | ['Kyle Min', 'Jason J. Corso'] | 2020-07-13 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2121_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123590273.pdf | eccv-2020-8 | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action'] | ['computer-vision', 'computer-vision'] | [ 4.04389679e-01 -2.78379798e-01 -4.92030412e-01 1.38230259e-02
-5.53314209e-01 -6.29174531e-01 5.20057619e-01 -2.65922807e-02
-4.13463056e-01 6.89744055e-01 3.23770344e-01 6.30357936e-02
1.98935136e-01 -4.81775761e-01 -7.62333930e-01 -1.08057916e+00
-3.63205105e-01 -3.66156697e-01 7.62846589e-01 2.29793772... | [8.415910720825195, 0.6566603183746338] |
b12bba26-7d89-470c-b5ae-5ff6363d7aee | molecular-graph-convolutions-moving-beyond | 1603.00856 | null | http://arxiv.org/abs/1603.00856v3 | http://arxiv.org/pdf/1603.00856v3.pdf | Molecular Graph Convolutions: Moving Beyond Fingerprints | Molecular "fingerprints" encoding structural information are the workhorse of
cheminformatics and machine learning in drug discovery applications. However,
fingerprint representations necessarily emphasize particular aspects of the
molecular structure while ignoring others, rather than allowing the model to
make data-d... | ['Vijay Pande', 'Kevin McCloskey', 'Steven Kearnes', 'Marc Berndl', 'Patrick Riley'] | 2016-03-02 | null | null | null | null | ['graph-regression'] | ['graphs'] | [ 4.82508749e-01 3.11219972e-02 -6.93815291e-01 -4.88606453e-01
-5.05816936e-02 -6.80829942e-01 3.16764891e-01 7.84880519e-01
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-6.16749108e-01 4.05058801e-01 -1.73757270e-01 -2.54445612... | [5.105561256408691, 5.782642364501953] |
ea1fec54-cfc2-44a0-9e27-17943c2f64ea | no-reference-image-quality-assessment-based | null | null | https://www.mdpi.com/2313-433X/6/8/75 | https://www.mdpi.com/2313-433X/6/8/75/htm | No-Reference Image Quality Assessment Based on the Fusion of Statistical and Perceptual Features | The goal of no-reference image quality assessment (NR-IQA) is to predict the quality of an image as perceived by human observers without using any pristine, reference images. In this study, an NR-IQA algorithm is proposed which is driven by a novel feature vector containing statistical and perceptual features. Differen... | ['Domonkos Varga'] | 2020-07-30 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 9.97546315e-02 -6.08953357e-01 -4.52758884e-03 -1.67011335e-01
-5.72920084e-01 -2.18535006e-01 5.50716996e-01 4.36801091e-02
-3.37597907e-01 5.98331630e-01 2.73861706e-01 2.10047975e-01
-2.04152554e-01 -6.56603694e-01 -7.14692995e-02 -7.87535667e-01
-2.20504448e-01 -5.99644244e-01 3.80466491e-01 -3.37301642... | [11.72169303894043, -1.9646824598312378] |
aea21f14-5c89-45bd-b4d2-91cb7343796c | end-to-end-face-swapping-via-adaptive-latent | 2303.04186 | null | https://arxiv.org/abs/2303.04186v1 | https://arxiv.org/pdf/2303.04186v1.pdf | End-to-end Face-swapping via Adaptive Latent Representation Learning | Taking full advantage of the excellent performance of StyleGAN, style transfer-based face swapping methods have been extensively investigated recently. However, these studies require separate face segmentation and blending modules for successful face swapping, and the fixed selection of the manipulated latent code in t... | ['Qian Li', 'Chao Shen', 'Pengbin Hu', 'Chenhao Lin'] | 2023-03-07 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 5.02206028e-01 -8.48869141e-03 -1.39281359e-02 -5.47992349e-01
-5.40769279e-01 -5.94284177e-01 2.08099797e-01 -1.02068913e+00
-3.86203304e-02 4.78558451e-01 -4.78136130e-02 9.27841440e-02
1.17510721e-01 -6.69995904e-01 -7.15477288e-01 -9.72360253e-01
2.70248502e-01 1.69577569e-01 -1.08192548e-01 -2.81941444... | [12.687067031860352, -0.018864108249545097] |
58dfbd3d-a926-4fb4-9666-3e5d8af72a42 | reactive-and-safe-road-user-simulations-using | 2109.06689 | null | https://arxiv.org/abs/2109.06689v1 | https://arxiv.org/pdf/2109.06689v1.pdf | Reactive and Safe Road User Simulations using Neural Barrier Certificates | Reactive and safe agent modelings are important for nowadays traffic simulator designs and safe planning applications. In this work, we proposed a reactive agent model which can ensure safety without comprising the original purposes, by learning only high-level decisions from expert data and a low-level decentralized c... | ['Chuchu Fan', 'Zengyi Qin', 'Yue Meng'] | 2021-09-14 | null | null | null | null | ['user-simulation'] | ['natural-language-processing'] | [-4.12243545e-01 9.23994839e-01 -3.26367468e-01 5.21614328e-02
-9.20969367e-01 -3.15230459e-01 7.37582505e-01 -1.08688250e-01
-5.65681100e-01 1.40145552e+00 -4.54583913e-02 -5.12851238e-01
-2.50608474e-01 -1.00845635e+00 -7.12075531e-01 -8.04457664e-01
-4.58531052e-01 1.04695725e+00 9.29295242e-01 -8.58745337... | [5.099752426147461, 1.4272388219833374] |
3012205c-f329-481d-aaef-e7b604f4000c | revisiting-event-argument-extraction-can-eae | 2306.00502 | null | https://arxiv.org/abs/2306.00502v1 | https://arxiv.org/pdf/2306.00502v1.pdf | Revisiting Event Argument Extraction: Can EAE Models Learn Better When Being Aware of Event Co-occurrences? | Event co-occurrences have been proved effective for event extraction (EE) in previous studies, but have not been considered for event argument extraction (EAE) recently. In this paper, we try to fill this gap between EE research and EAE research, by highlighting the question that ``Can EAE models learn better when bein... | ['Buzhou Tang', 'Jingyue Hu', 'Yuxin He'] | 2023-06-01 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 1.22469805e-01 4.22627449e-01 -7.81578645e-02 -2.40272641e-01
-1.10703766e+00 -6.01718783e-01 7.32595503e-01 3.25040728e-01
-4.85408276e-01 8.72444212e-01 3.26338887e-01 -6.43022656e-01
-1.45908743e-01 -1.12744951e+00 -9.35918748e-01 -3.00562471e-01
-1.62846938e-01 5.95732391e-01 4.25998688e-01 -1.27110347... | [9.125330924987793, 9.144476890563965] |
869879dc-287b-4a47-9c83-af1138351c1d | sequential-diagnosis-prediction-with | 2109.03069 | null | https://arxiv.org/abs/2109.03069v1 | https://arxiv.org/pdf/2109.03069v1.pdf | Sequential Diagnosis Prediction with Transformer and Ontological Representation | Sequential diagnosis prediction on the Electronic Health Record (EHR) has been proven crucial for predictive analytics in the medical domain. EHR data, sequential records of a patient's interactions with healthcare systems, has numerous inherent characteristics of temporality, irregularity and data insufficiency. Some ... | ['Jing Jiang', 'Sen Wang', 'Tao Shen', 'Guodong Long', 'Xueping Peng'] | 2021-09-07 | null | null | null | null | ['sequential-diagnosis'] | ['medical'] | [ 2.27821153e-03 -2.91016921e-02 -3.21347147e-01 -5.03302991e-01
-5.01669526e-01 1.17810667e-02 4.72508669e-02 7.82001436e-01
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-6.93384230e-01 -7.47030973e-01 -4.25315380e-01 -5.49533725e-01
-5.74281991e-01 7.61825919e-01 -2.05096647e-01 -1.65237486... | [7.914104461669922, 6.2550368309021] |
c792e1ce-7bf3-4435-b799-8a3d50d18cb9 | hierarchical-and-contrastive-representation | 2304.07506 | null | https://arxiv.org/abs/2304.07506v1 | https://arxiv.org/pdf/2304.07506v1.pdf | Hierarchical and Contrastive Representation Learning for Knowledge-aware Recommendation | Incorporating knowledge graph into recommendation is an effective way to alleviate data sparsity. Most existing knowledge-aware methods usually perform recursive embedding propagation by enumerating graph neighbors. However, the number of nodes' neighbors grows exponentially as the hop number increases, forcing the nod... | ['Yongji Wang', 'Bei guan', 'Daoguang Zan', 'Yangyuxuan Kang', 'Bingchao Wu'] | 2023-04-15 | null | null | null | null | ['knowledge-aware-recommendation'] | ['miscellaneous'] | [-2.96933413e-01 2.98047781e-01 -6.17279410e-01 -2.64928848e-01
-1.71223879e-01 -2.68159747e-01 4.58195359e-01 3.30330312e-01
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-4.40743595e-01 -1.27218676e+00 -3.97846937e-01 -7.46769011e-01
-2.26713255e-01 2.20024005e-01 4.00224864e-01 -2.20916927... | [10.243395805358887, 5.664527416229248] |
c2b75b59-6b9c-4483-b2ef-efe6c24940ae | generalized-product-of-experts-for-learning | 2211.03587 | null | https://arxiv.org/abs/2211.03587v1 | https://arxiv.org/pdf/2211.03587v1.pdf | Generalized Product-of-Experts for Learning Multimodal Representations in Noisy Environments | A real-world application or setting involves interaction between different modalities (e.g., video, speech, text). In order to process the multimodal information automatically and use it for an end application, Multimodal Representation Learning (MRL) has emerged as an active area of research in recent times. MRL invol... | ['Danail Stoyanov', 'Ashutosh Modi', 'Binod Bhattarai', 'Jinang Shah', 'Naman Gupta', 'Abhinav Joshi'] | 2022-11-07 | null | null | null | null | ['3d-hand-pose-estimation', '3d-hand-pose-estimation'] | ['computer-vision', 'graphs'] | [ 3.88684571e-01 4.65926155e-02 -2.45162144e-01 -2.42477670e-01
-1.55606556e+00 -4.52825487e-01 4.30530399e-01 5.05534649e-01
-4.43624377e-01 6.06251597e-01 4.53847855e-01 6.16120808e-02
-2.00074464e-01 -2.19490647e-01 -6.70877993e-01 -9.24017727e-01
3.37417364e-01 5.26181102e-01 1.03199013e-01 -4.59879488... | [12.949098587036133, 4.856298446655273] |
e9232959-f946-4541-a746-bc36eac7ba48 | contrastive-deep-supervision | 2207.05306 | null | https://arxiv.org/abs/2207.05306v1 | https://arxiv.org/pdf/2207.05306v1.pdf | Contrastive Deep Supervision | The success of deep learning is usually accompanied by the growth in neural network depth. However, the traditional training method only supervises the neural network at its last layer and propagates the supervision layer-by-layer, which leads to hardship in optimizing the intermediate layers. Recently, deep supervisio... | ['Kaisheng Ma', 'Runpei Dong', 'Junbo Zhang', 'Xin Chen', 'Linfeng Zhang'] | 2022-07-12 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 3.62429082e-01 3.98091644e-01 -2.94062048e-01 -6.79735422e-01
-5.52202389e-02 -3.02281708e-01 5.98115504e-01 7.69587457e-02
-6.67937696e-01 6.97771430e-01 -5.82280234e-02 -1.52824402e-01
3.66351679e-02 -7.68553972e-01 -7.82172740e-01 -8.81935954e-01
4.37242277e-02 2.53462374e-01 3.26636583e-01 -5.37517965... | [9.487485885620117, 2.4082868099212646] |
117a3240-9eae-400d-9c91-98ab97c52b66 | asr-and-emotional-speech-a-word-level | 2305.16065 | null | https://arxiv.org/abs/2305.16065v2 | https://arxiv.org/pdf/2305.16065v2.pdf | ASR and Emotional Speech: A Word-Level Investigation of the Mutual Impact of Speech and Emotion Recognition | In Speech Emotion Recognition (SER), textual data is often used alongside audio signals to address their inherent variability. However, the reliance on human annotated text in most research hinders the development of practical SER systems. To overcome this challenge, we investigate how Automatic Speech Recognition (ASR... | ['Catherine Lai', 'Peter Bell', 'Ondrej Klejch', 'Zeyu Zhao', 'Yuanchao Li'] | 2023-05-25 | null | null | null | null | ['automatic-speech-recognition', 'speech-emotion-recognition'] | ['speech', 'speech'] | [-1.05128892e-01 -8.61031413e-02 4.18689787e-01 -3.79621774e-01
-7.56124079e-01 -5.03273368e-01 5.81919700e-02 2.99781617e-02
-4.67333227e-01 4.17300045e-01 5.27469993e-01 -2.85301328e-01
3.72759849e-01 -4.22474779e-02 -1.65204436e-01 -2.48927563e-01
8.04487765e-02 -2.44929656e-01 -3.28091234e-01 -4.46496964... | [13.64093017578125, 5.836283206939697] |
27131225-591c-4860-8e01-c7dfd24e00da | atrial-fibrillation-detection-using-deep | 1903.11775 | null | http://arxiv.org/abs/1903.11775v1 | http://arxiv.org/pdf/1903.11775v1.pdf | Atrial Fibrillation Detection Using Deep Features and Convolutional Networks | Atrial fibrillation is a cardiac arrhythmia that affects an estimated 33.5
million people globally and is the potential cause of 1 in 3 strokes in people
over the age of 60. Detection and diagnosis of atrial fibrillation (AFIB) is
done noninvasively in the clinical environment through the evaluation of
electrocardiogra... | ['Sara Ross-Howe', 'H. R. Tizhoosh'] | 2019-03-28 | null | null | null | null | ['arrhythmia-detection', 'atrial-fibrillation-detection', 'electrocardiography-ecg'] | ['medical', 'medical', 'methodology'] | [ 3.09445173e-01 -2.23426685e-01 2.07771719e-01 -2.39713177e-01
-5.58122873e-01 -6.88165009e-01 1.88576251e-01 4.09828156e-01
-4.72317576e-01 8.47243845e-01 8.25140551e-02 -7.49016225e-01
-3.92184973e-01 -6.37419581e-01 7.98183605e-02 -5.89201391e-01
-6.30419493e-01 -4.04215092e-03 -4.85104918e-01 5.65677062... | [14.294002532958984, 3.280867099761963] |
ab751077-cf0e-43f5-a18f-2eb8992c44fe | detecting-the-open-world-objects-with-the | 2303.11623 | null | https://arxiv.org/abs/2303.11623v1 | https://arxiv.org/pdf/2303.11623v1.pdf | Detecting the open-world objects with the help of the Brain | Open World Object Detection (OWOD) is a novel computer vision task with a considerable challenge, bridging the gap between classic object detection (OD) benchmarks and real-world object detection. In addition to detecting and classifying seen/known objects, OWOD algorithms are expected to detect unseen/unknown objects ... | ['Thomas H. Li', 'Enming Zhang', 'Jiaqi Fan', 'Zhixiang Ye', 'Peihao Chen', 'Ying WEI', 'Yuefeng Wang', 'Shuailei Ma'] | 2023-03-21 | null | null | null | null | ['open-world-object-detection'] | ['computer-vision'] | [ 9.91999209e-02 5.19141078e-01 8.51010978e-02 -2.28303522e-01
-4.59688425e-01 -7.00537682e-01 6.05919659e-01 8.06121156e-02
-6.67116880e-01 3.53252262e-01 -3.85049544e-02 -1.17263339e-01
3.15542996e-01 -7.90248334e-01 -9.27264392e-01 -3.64171058e-01
-9.75348428e-02 6.69239819e-01 6.81433797e-01 -9.44724903... | [9.491948127746582, 1.5720456838607788] |
7cea3d9c-af50-4887-9361-d83f7db42c3b | profiling-entity-matching-benchmark-tasks | null | null | https://dl.acm.org/doi/10.1145/3340531.3412781 | https://dl.acm.org/doi/pdf/10.1145/3340531.3412781 | Profiling Entity Matching Benchmark Tasks | Entity matching is a central task in data integration which has been researched for decades. Over this time, a wide range of benchmark tasks for evaluating entity matching methods has been developed. This resource paper systematically complements, profiles, and compares 21 entity matching benchmark tasks. In order to b... | ['Christian Bizer', 'Anna Primpeli'] | 2020-10-19 | null | null | null | international-conference-on-information-2 | ['data-integration', 'entity-resolution'] | ['knowledge-base', 'natural-language-processing'] | [ 8.26596916e-02 -2.06485927e-01 -2.61233598e-01 -4.75015759e-01
-6.23546362e-01 -8.42278063e-01 7.72544384e-01 5.98160446e-01
-5.88698387e-01 5.69676399e-01 4.37625535e-02 -5.16024306e-02
-4.17174637e-01 -5.86777687e-01 -3.85844707e-01 -1.87118143e-01
-1.02957360e-01 6.24585629e-01 3.84851187e-01 -2.63648659... | [9.488061904907227, 8.438007354736328] |
bbee712a-b697-4cfc-80b6-558ee0ab783c | hierarchical-video-moment-retrieval-and-step | 2303.16406 | null | https://arxiv.org/abs/2303.16406v1 | https://arxiv.org/pdf/2303.16406v1.pdf | Hierarchical Video-Moment Retrieval and Step-Captioning | There is growing interest in searching for information from large video corpora. Prior works have studied relevant tasks, such as text-based video retrieval, moment retrieval, video summarization, and video captioning in isolation, without an end-to-end setup that can jointly search from video corpora and generate summ... | ['Mohit Bansal', 'Yasher Mehdad', 'Barlas Oğuz', 'Xilun Chen', 'Satwik Kottur', 'Jaemin Cho', 'Abhay Zala'] | 2023-03-29 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zala_Hierarchical_Video-Moment_Retrieval_and_Step-Captioning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zala_Hierarchical_Video-Moment_Retrieval_and_Step-Captioning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-captioning', 'moment-retrieval', 'video-retrieval'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.75870341e-01 -2.14968354e-01 -6.90852344e-01 -2.23255947e-01
-1.72397852e+00 -7.81749845e-01 5.11314571e-01 3.16044450e-01
-4.27123934e-01 4.04769510e-01 8.17485869e-01 -1.08649559e-01
1.37799687e-03 2.58481950e-02 -1.07504535e+00 -4.31717485e-01
4.86151461e-04 3.32996339e-01 4.91073728e-01 7.28730261... | [10.323317527770996, 0.6943582892417908] |
eef01024-29e5-49b7-a615-4e607e649a83 | dynamic-knowledge-graphs-as-semantic-memory | 2101.01099 | null | https://arxiv.org/abs/2101.01099v3 | https://arxiv.org/pdf/2101.01099v3.pdf | Dynamic Knowledge Graphs as Semantic Memory Model for Industrial Robots | In this paper, we present a model for semantic memory that allows machines to collect information and experiences to become more proficient with time. Post semantic analysis of the sensory and other related data, the processed information is stored in the knowledge graph which is then used to comprehend the work instru... | ['Said Zahrai', 'Vishakh Duggal', 'Mohak Sukhwani'] | 2021-01-04 | null | null | null | null | ['industrial-robots'] | ['robots'] | [ 3.24971259e-01 3.81692231e-01 -5.76947331e-02 -5.67464888e-01
7.20921457e-01 -5.19324362e-01 5.38146794e-01 5.47385693e-01
-3.24956506e-01 8.25654387e-01 -4.52814102e-02 -9.04947072e-02
-4.79811370e-01 -1.20645511e+00 -5.12953520e-01 -1.02542110e-01
2.42496535e-01 5.52462339e-01 5.88929713e-01 -4.37693477... | [4.450706958770752, 1.186515212059021] |
a4f7ae79-5073-4063-a88b-8f39a7b7cfba | attacking-cooperative-multi-agent | 2302.03322 | null | https://arxiv.org/abs/2302.03322v2 | https://arxiv.org/pdf/2302.03322v2.pdf | Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence | This study probes the vulnerabilities of cooperative multi-agent reinforcement learning (c-MARL) under adversarial attacks, a critical determinant of c-MARL's worst-case performance prior to real-world implementation. Current observation-based attacks, constrained by white-box assumptions, overlook c-MARL's complex mul... | ['Xianglong Liu', 'Wenjun Wu', 'Aishan Liu', 'Xin Yu', 'Pu Feng', 'Jingqiao Xiu', 'Jun Guo', 'Simin Li'] | 2023-02-07 | null | null | null | null | ['starcraft-ii', 'continuous-control', 'smac-1', 'starcraft', 'smac'] | ['playing-games', 'playing-games', 'playing-games', 'playing-games', 'playing-games'] | [-2.07031503e-01 3.84017110e-01 2.77908623e-01 3.38412136e-01
-2.75647134e-01 -8.49183321e-01 5.74939609e-01 -4.81604040e-02
-6.10500216e-01 9.08463180e-01 -3.18605244e-01 -2.89945066e-01
-5.26229441e-01 -7.50806928e-01 -5.69673359e-01 -9.77166057e-01
-8.80817831e-01 4.71143126e-01 2.36977860e-02 -6.28312528... | [3.8726890087127686, 2.3897526264190674] |
f13126b5-1145-4775-b1b0-e763b27dc643 | towards-real-time-and-light-weight-line | 2106.00186 | null | https://arxiv.org/abs/2106.00186v3 | https://arxiv.org/pdf/2106.00186v3.pdf | Towards Light-weight and Real-time Line Segment Detection | Previous deep learning-based line segment detection (LSD) suffers from the immense model size and high computational cost for line prediction. This constrains them from real-time inference on computationally restricted environments. In this paper, we propose a real-time and light-weight line segment detector for resour... | ['Minchul Shin', 'Jingeun Lee', 'Sung-Hyun Lee', 'SeoungHyun Go', 'Byungsoo Ko', 'Geonmo Gu'] | 2021-06-01 | null | null | null | null | ['line-segment-detection'] | ['computer-vision'] | [ 5.56640513e-03 -3.09367292e-02 -3.72102290e-01 -2.18249202e-01
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-5.73137879e-01 4.49473321e-01 -5.12537301e-01 -6.84153676e-01
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2ad1532f-69d9-45ec-a818-6ff13bcb0168 | semattack-natural-textual-attacks-via | null | null | https://openreview.net/forum?id=03-jwvIDYf | https://openreview.net/pdf?id=03-jwvIDYf | SemAttack: Natural Textual Attacks via Different Semantic Spaces | Recent studies show that pre-trained language models (LMs) are vulnerable to textual adversarial attacks. However, existing attack methods either suffer from low attack success rates or fail to search efficiently in the exponentially large perturbation space. We propose an efficient and effective framework SemAttack to g... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['adversarial-text'] | ['adversarial'] | [-4.97835055e-02 -7.99620152e-02 1.54192045e-01 7.46994764e-02
-8.53207588e-01 -1.24728727e+00 7.80714869e-01 -2.13669553e-01
-4.73277777e-01 7.60276258e-01 2.06187069e-01 -4.68559295e-01
2.96149194e-01 -1.08280873e+00 -8.35518897e-01 -6.16944432e-01
3.82468194e-01 5.72625399e-01 2.53390402e-01 -7.11438775... | [6.034061908721924, 8.11994743347168] |
830fd922-6cf4-4087-9e0e-e296f2153951 | coreference-strategies-in-english-german | null | null | https://aclanthology.org/2020.crac-1.15 | https://aclanthology.org/2020.crac-1.15.pdf | Coreference Strategies in English-German Translation | We present a study focusing on variation of coreferential devices in English original TED talks and news texts and their German translations. Using exploratory techniques we contemplate a diverse set of coreference devices as features which we assume indicate language-specific and register-based variation as well as po... | ['Christian Hardmeier', 'Marie-Pauline Krielke', 'Ekaterina Lapshinova-Koltunski'] | null | null | null | null | coling-crac-2020-12 | ['multilingual-nlp'] | ['natural-language-processing'] | [-2.26455182e-01 8.50868076e-02 -7.59388924e-01 -4.88267392e-01
-1.11971879e+00 -1.34044981e+00 1.07545888e+00 5.33149466e-02
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-3.60839486e-01 -2.85011321e-01 -2.66342789e-01 -1.35598674e-01
2.64034510e-01 9.43151951e-01 2.16222499e-02 -8.08956504... | [11.004838943481445, 10.04818344116211] |
a8370011-6466-47c7-bb35-f0bb125b7a03 | offline-experience-replay-for-continual | 2305.13804 | null | https://arxiv.org/abs/2305.13804v1 | https://arxiv.org/pdf/2305.13804v1.pdf | Offline Experience Replay for Continual Offline Reinforcement Learning | The capability of continuously learning new skills via a sequence of pre-collected offline datasets is desired for an agent. However, consecutively learning a sequence of offline tasks likely leads to the catastrophic forgetting issue under resource-limited scenarios. In this paper, we formulate a new setting, continua... | ['Li He', 'Donglin Wang', 'Sibo Gai'] | 2023-05-23 | null | null | null | null | ['q-learning'] | ['methodology'] | [-3.27697933e-01 -1.63840801e-01 -2.08112404e-01 3.44461799e-02
-5.31136453e-01 -6.68857932e-01 5.29335976e-01 1.36133194e-01
-8.13187003e-01 1.06988776e+00 -1.27032921e-01 -3.07840109e-01
-2.16687784e-01 -7.28994250e-01 -1.05213225e+00 -8.37023258e-01
-2.23643661e-01 4.83221859e-01 4.35464859e-01 -8.07555243... | [4.101278305053711, 2.1355297565460205] |
4227fb78-9650-4fc0-bff5-d78933b5053e | planematch-patch-coplanarity-prediction-for | 1803.08407 | null | http://arxiv.org/abs/1803.08407v3 | http://arxiv.org/pdf/1803.08407v3.pdf | PlaneMatch: Patch Coplanarity Prediction for Robust RGB-D Reconstruction | We introduce a novel RGB-D patch descriptor designed for detecting coplanar
surfaces in SLAM reconstruction. The core of our method is a deep convolutional
neural net that takes in RGB, depth, and normal information of a planar patch
in an image and outputs a descriptor that can be used to find coplanar patches
from ot... | ['Thomas Funkhouser', 'Szymon Rusinkiewicz', 'Matthias Niessner', 'Kai Xu', 'Yifei Shi'] | 2018-03-22 | planematch-patch-coplanarity-prediction-for-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Yifei_Shi_PlaneMatch_Patch_Coplanarity_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Yifei_Shi_PlaneMatch_Patch_Coplanarity_ECCV_2018_paper.pdf | eccv-2018-9 | ['rgb-d-reconstruction'] | ['computer-vision'] | [ 3.49803925e-01 -2.33732402e-01 1.17222993e-02 -3.25250238e-01
-9.31340158e-01 -6.36834204e-01 8.35670292e-01 8.55981279e-03
-4.55560565e-01 1.03224866e-01 7.37796957e-03 2.05048881e-02
-1.35688841e-01 -8.28844070e-01 -1.29654133e+00 -3.58510643e-01
-1.29218176e-01 8.24876606e-01 3.08144242e-01 -2.99945951... | [7.8477630615234375, -2.7618274688720703] |
22e93af1-cd5c-40dc-9984-d6ff05a174b2 | adversarial-text-normalization | 2206.04137 | null | https://arxiv.org/abs/2206.04137v1 | https://arxiv.org/pdf/2206.04137v1.pdf | Adversarial Text Normalization | Text-based adversarial attacks are becoming more commonplace and accessible to general internet users. As these attacks proliferate, the need to address the gap in model robustness becomes imminent. While retraining on adversarial data may increase performance, there remains an additional class of character-level attac... | ['Ivan Evtimov', 'Maya Pavlova', 'Joanna Bitton'] | 2022-06-08 | null | https://aclanthology.org/2022.naacl-industry.30 | https://aclanthology.org/2022.naacl-industry.30.pdf | naacl-acl-2022-7 | ['adversarial-text'] | ['adversarial'] | [ 5.15051305e-01 -5.23862094e-02 9.23471004e-02 -1.69198498e-01
-7.95525789e-01 -1.22801399e+00 7.69031823e-01 3.04634243e-01
-5.42734146e-01 4.71338630e-01 3.15409064e-01 -6.32030368e-01
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2.59808898e-01 7.56803751e-02 2.19808966e-01 -6.73090279... | [6.036111354827881, 8.091459274291992] |
a964d5b9-d4ce-4794-93cb-03c02ed249ec | annobert-effectively-representing-multiple | 2212.10405 | null | https://arxiv.org/abs/2212.10405v2 | https://arxiv.org/pdf/2212.10405v2.pdf | AnnoBERT: Effectively Representing Multiple Annotators' Label Choices to Improve Hate Speech Detection | Supervised approaches generally rely on majority-based labels. However, it is hard to achieve high agreement among annotators in subjective tasks such as hate speech detection. Existing neural network models principally regard labels as categorical variables, while ignoring the semantic information in diverse label tex... | ['Nishanth Sastry', 'Arkaitz Zubiaga', 'Aiqi Jiang', 'Vibhor Agarwal', 'Wenjie Yin'] | 2022-12-20 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [-1.19650103e-01 2.86944181e-01 -6.11554384e-02 -2.52204090e-01
-5.19752502e-01 -7.43363142e-01 4.55984682e-01 5.35894632e-01
-5.31219959e-01 4.42354500e-01 3.55831206e-01 1.01325206e-01
8.50710943e-02 -2.76245594e-01 -4.66077439e-02 -8.57074678e-01
1.79748803e-01 6.23806119e-01 -1.29929110e-02 -8.95108655... | [8.746495246887207, 10.562102317810059] |
e9549742-703b-48ac-9557-cff06253aff3 | deep-superpixel-based-network-for-blind-image | 2110.06564 | null | https://arxiv.org/abs/2110.06564v1 | https://arxiv.org/pdf/2110.06564v1.pdf | Deep Superpixel-based Network for Blind Image Quality Assessment | The goal in a blind image quality assessment (BIQA) model is to simulate the process of evaluating images by human eyes and accurately assess the quality of the image. Although many approaches effectively identify degradation, they do not fully consider the semantic content in images resulting in distortion. In order t... | ['Yuxuan Wang', 'Yang Zhan.', 'Guangyi Yang'] | 2021-10-13 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [ 2.47854337e-01 -4.25086141e-01 1.86443105e-01 -5.25288343e-01
-6.03168845e-01 -5.74590862e-01 7.49904960e-02 -3.00256222e-01
-2.37008289e-01 3.54109466e-01 2.48210192e-01 -1.52308285e-01
-4.59657842e-03 -7.88036227e-01 -4.69683230e-01 -4.24388349e-01
3.93584907e-01 1.06037757e-03 4.74860638e-01 -2.43355736... | [11.88250732421875, -1.8439362049102783] |
5bc43591-6cb9-40d4-9181-84bcc8b20d60 | biobert-a-pre-trained-biomedical-language | 1901.08746 | null | https://arxiv.org/abs/1901.08746v4 | https://arxiv.org/pdf/1901.08746v4.pdf | BioBERT: a pre-trained biomedical language representation model for biomedical text mining | Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effe... | ['Wonjin Yoon', 'Jinhyuk Lee', 'Sunkyu Kim', 'Sungdong Kim', 'Jaewoo Kang', 'Chan Ho So', 'Donghyeon Kim'] | 2019-01-25 | null | null | null | null | ['medical-relation-extraction', 'drug-drug-interaction-extraction', 'medical-named-entity-recognition'] | ['medical', 'natural-language-processing', 'natural-language-processing'] | [ 1.83626726e-01 2.89666921e-01 -3.63866001e-01 -3.83014381e-01
-1.04952061e+00 -7.28878230e-02 1.76765978e-01 5.29146254e-01
-8.22214186e-01 9.71138537e-01 3.21230620e-01 -4.83589083e-01
5.45527413e-03 -6.86218798e-01 -6.45424843e-01 -5.59514582e-01
-1.63384303e-01 6.94480419e-01 -1.99071243e-01 -1.28737047... | [8.529837608337402, 8.70948600769043] |
1ea8342d-3fa1-4b25-b06d-89582ae30d98 | interrupt-me-politely-recommending-products | null | null | https://aclanthology.org/2020.ecomnlp-1.4 | https://aclanthology.org/2020.ecomnlp-1.4.pdf | Interrupt me Politely: Recommending Products and Services by Joining Human Conversation | We propose a novel way of conversational recommendation, where instead of asking questions to the user to acquire their preferences; the recommender tracks their conversation with other people, including customer support agents (CSA), and joins the conversation only when it is time to introduce a recommendation. Buildi... | ['Dmitry Ilvovsky', 'Boris Galitsky'] | null | null | null | null | ecomnlp-coling-2020-12 | ['dialogue-management'] | ['natural-language-processing'] | [ 3.71306449e-01 1.07431579e+00 6.63973158e-03 -4.98539776e-01
-3.79078597e-01 -8.51622403e-01 9.55826044e-01 3.39345485e-01
-1.16296366e-01 7.48616636e-01 7.47490227e-01 -4.64280158e-01
-1.11981817e-01 -6.69919252e-01 2.76316166e-01 -4.62179869e-01
3.06307852e-01 9.93592262e-01 3.08937103e-01 -8.07415247... | [12.714344024658203, 8.381360054016113] |
cfffe0f0-b0e1-4015-a407-298bdf075c83 | on-the-effectiveness-of-system-api-related | 1805.09563 | null | https://arxiv.org/abs/1805.09563v4 | https://arxiv.org/pdf/1805.09563v4.pdf | On the Effectiveness of System API-Related Information for Android Ransomware Detection | Ransomware constitutes a significant threat to the Android operating system. It can either lock or encrypt the target devices, and victims are forced to pay ransoms to restore their data. Hence, the prompt detection of such attacks has a priority in comparison to other malicious threats. Previous works on Android malwa... | ['Fabio Martinelli', 'Corrado Aaron Visaggio', 'Giorgio Giacinto', 'Michele Scalas', 'Francesco Mercaldo', 'Davide Maiorca'] | 2018-05-24 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 1.79320276e-01 -1.84917137e-01 -6.25396252e-01 1.74678236e-01
-2.32009754e-01 -1.02981699e+00 9.24241364e-01 4.10542190e-02
-1.67389899e-01 4.47025359e-01 -3.96919698e-01 -9.16042328e-01
-2.21765861e-01 -6.89163029e-01 -3.75905782e-01 -5.70425689e-01
-2.55817086e-01 1.17415629e-01 4.05865073e-01 -1.61765501... | [14.414223670959473, 9.676346778869629] |
142544c4-8063-448c-8705-0ca1ee0d7222 | expressive-talking-head-generation-with | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liang_Expressive_Talking_Head_Generation_With_Granular_Audio-Visual_Control_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liang_Expressive_Talking_Head_Generation_With_Granular_Audio-Visual_Control_CVPR_2022_paper.pdf | Expressive Talking Head Generation With Granular Audio-Visual Control | Generating expressive talking heads is essential for creating virtual humans. However, existing one- or few-shot methods focus on lip-sync and head motion, ignoring the emotional expressions that make talking faces realistic. In this paper, we propose the Granularly Controlled Audio-Visual Talking Heads (GC-AVT), w... | ['Jingdong Wang', 'Errui Ding', 'Jingtuo Liu', 'Junyu Han', 'Xiaoguang Han', 'Zhibin Hong', 'Hang Zhou', 'Zhizhi Guo', 'Yan Pan', 'Borong Liang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['talking-head-generation'] | ['computer-vision'] | [ 3.72941457e-02 4.79522854e-01 5.10472581e-02 -5.47174454e-01
-5.45518160e-01 -4.45421875e-01 6.12229168e-01 -8.09269369e-01
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4.37989324e-01 -3.50311339e-01 -6.91875517e-01 -8.89571846e-01
2.57509708e-01 -5.60574271e-02 -1.05328873e-01 -4.62701768... | [13.225805282592773, -0.46099555492401123] |
36ea9a51-913e-4b91-b1ab-efaac42e6c85 | twitter-geolocation-using-knowledge-based | null | null | https://aclanthology.org/W18-6102 | https://aclanthology.org/W18-6102.pdf | Twitter Geolocation using Knowledge-Based Methods | Automatic geolocation of microblog posts from their text content is particularly difficult because many location-indicative terms are rare terms, notably entity names such as locations, people or local organisations. Their low frequency means that key terms observed in testing are often unseen in training, such that st... | ['Timothy Baldwin', 'Taro Miyazaki', 'Afshin Rahimi', 'Trevor Cohn'] | 2018-11-01 | null | null | null | ws-2018-11 | ['stock-market-prediction'] | ['time-series'] | [-0.3552089 0.4883299 -0.41551867 -0.3866793 -0.5735259 -0.8787422
1.0879765 0.88318735 -0.81284493 0.79484403 0.7498671 -0.34456882
-0.29033965 -1.209563 -0.64525557 -0.3948548 -0.55624056 0.63947046
0.37306297 -0.38211495 0.1327938 0.3790643 -1.2451452 0.02328859
0.4269011 0.68526995 -0.3... | [9.284517288208008, 8.392631530761719] |
311596bf-115b-4bff-b6da-1156e7c46086 | from-synsets-to-videos-enriching-italwordnet | null | null | https://aclanthology.org/L14-1559 | https://aclanthology.org/L14-1559.pdf | From Synsets to Videos: Enriching ItalWordNet Multimodally | The paper describes the multimodal enrichment of ItalWordNet action verbsÂ’ entries by means of an automatic mapping with an ontology of action types instantiated by video scenes (ImagAct). The two resources present important differences as well as interesting complementary features, such that a mapping of these two re... | ['Valeria Quochi', 'Monica Monachini', 'Irene Russo', 'Irene De Felice', 'Roberto Bartolini'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['ontology-matching'] | ['knowledge-base'] | [ 1.92861110e-01 4.46466118e-01 -1.63417697e-01 -1.60940602e-01
-1.80128217e-01 -5.56828797e-01 1.25656271e+00 2.42621765e-01
-8.67375553e-01 8.47398818e-01 5.35113096e-01 2.67095536e-01
-5.28750002e-01 -8.17529380e-01 -4.28456485e-01 -5.22416711e-01
-2.02831533e-02 4.37855124e-01 5.66063762e-01 -7.48595476... | [10.44688892364502, 0.5905734896659851] |
1fdafd2c-306d-4771-b7be-bf340ec9d582 | exploring-structure-wise-uncertainty-for-3d | 2211.00303 | null | https://arxiv.org/abs/2211.00303v1 | https://arxiv.org/pdf/2211.00303v1.pdf | Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentation | When applying a Deep Learning model to medical images, it is crucial to estimate the model uncertainty. Voxel-wise uncertainty is a useful visual marker for human experts and could be used to improve the model's voxel-wise output, such as segmentation. Moreover, uncertainty provides a solid foundation for out-of-distri... | ['Boris Shirokikh', 'Mikhail Belyaev', 'Daria Frolova', 'Anton Vasiliuk'] | 2022-11-01 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [-1.59877576e-02 5.01781225e-01 -1.91707700e-01 -5.44501603e-01
-1.09515202e+00 -2.24990293e-01 4.83047903e-01 4.63481903e-01
-4.59239990e-01 7.54646778e-01 6.63014948e-02 -2.68037766e-01
-2.89717108e-01 -6.99367225e-01 -7.10332155e-01 -9.23425555e-01
-5.95680363e-02 7.48414040e-01 3.02989542e-01 4.36199695... | [14.415485382080078, -2.0882089138031006] |
1351cc8e-92fe-42ff-818a-b7ca5bbcada7 | text-based-rl-agents-with-commonsense-1 | null | null | https://openreview.net/forum?id=Aws7Sgnej4G | https://openreview.net/pdf?id=Aws7Sgnej4G | Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Approaches | Text-based games have emerged as an important test-bed for Reinforcement Learning (RL) research, requiring RL agents to combine grounded language understanding with sequential decision making. In this paper, we examine the problem of infusing RL agents with commonsense knowledge. This allows agents to efficiently act i... | ['Murray Campbell', 'Mrinmaya Sachan', 'Kartik Talamadupula', 'Gerald Tesauro', 'Sadhana Kumaravel', 'Pushkar Shukla', 'Pavan Kapanipathi', 'Mattia Atzeni', 'Keerthiram Murugesan'] | 2020-07-12 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [ 1.59125865e-01 4.02925432e-01 1.64817438e-01 -8.12684670e-02
-7.25507736e-02 -8.23664248e-01 7.95193493e-01 2.51842868e-02
-8.45353246e-01 1.06908453e+00 3.61960828e-01 -4.61642593e-01
-1.01000227e-01 -1.40930271e+00 -6.02572501e-01 -3.21464092e-01
-3.59955907e-01 1.10145926e+00 5.62975407e-01 -8.52859020... | [3.886899471282959, 1.221832275390625] |
57624eb1-5721-47b3-a6b2-5e9dc654059c | preservation-of-anomalous-subgroups-on | 1911.03674 | null | https://arxiv.org/abs/1911.03674v1 | https://arxiv.org/pdf/1911.03674v1.pdf | Preservation of Anomalous Subgroups On Machine Learning Transformed Data | In this paper, we investigate the effect of machine learning based anonymization on anomalous subgroup preservation. In particular, we train a binary classifier to discover the most anomalous subgroup in a dataset by maximizing the bias between the group's predicted odds ratio from the model and observed odds ratio fro... | ['Robert-Florian Samoilescu', 'Reginald E. Bryant', 'Kush R. Varshney', 'William O. Goal', 'Samuel C. Maina', 'Komminist Weldemariam'] | 2019-11-09 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 3.28687578e-01 7.56212115e-01 1.17270444e-02 -6.19064867e-01
-8.85471642e-01 -7.62830138e-01 7.70788193e-01 2.65361965e-01
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4.75939922e-02 -9.73243535e-01 -1.04473984e+00 -7.92095363e-01
1.34150729e-01 7.83061922e-01 -2.98964381e-01 1.28571570... | [6.190451145172119, 6.8487420082092285] |
c9f9f2af-9070-4699-9d44-d8c2a52a3595 | optimal-multi-agent-path-finding-for | 2202.10449 | null | https://arxiv.org/abs/2202.10449v1 | https://arxiv.org/pdf/2202.10449v1.pdf | Optimal Multi-Agent Path Finding for Precedence Constrained Planning Tasks | Multi-Agent Path Finding (MAPF) is the problem of finding collision-free paths for multiple agents from their start locations to end locations. We consider an extension to this problem, Precedence Constrained Multi-Agent Path Finding (PC-MAPF), wherein agents are assigned a sequence of planning tasks that contain prece... | ['Partha Pratim Chakrabarti', 'Aritra Hazra', 'Rajat Kumar Jenamani', 'Kushal Kedia'] | 2022-02-08 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 3.20571840e-01 1.95357725e-02 -9.46803913e-02 -1.30782038e-01
-4.35693979e-01 -1.02483320e+00 2.29420245e-01 7.27344334e-01
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-1.14934146e+00 -8.75247896e-01 -4.73013401e-01 -5.57835162e-01
-9.97139394e-01 1.43025637e+00 6.54179096e-01 -4.41449612... | [4.965420246124268, 1.7447339296340942] |
8e84cb96-709f-4b8d-ab7f-13748728eb7b | tragedy-plus-time-capturing-unintended-human | 2204.13548 | null | https://arxiv.org/abs/2204.13548v1 | https://arxiv.org/pdf/2204.13548v1.pdf | Tragedy Plus Time: Capturing Unintended Human Activities from Weakly-labeled Videos | In videos that contain actions performed unintentionally, agents do not achieve their desired goals. In such videos, it is challenging for computer vision systems to understand high-level concepts such as goal-directed behavior, an ability present in humans from a very early age. Inculcating this ability in artificiall... | ['Yezhou Yang', 'Zhiyuan Fang', 'Arnav Chakravarthy'] | 2022-04-28 | null | null | null | null | ['action-understanding'] | ['computer-vision'] | [ 3.6603925e-01 3.7118456e-01 -4.2241758e-01 -1.6654672e-01
-1.7546481e-01 -4.3019921e-01 7.4336511e-01 -2.7860129e-01
-5.8051306e-01 5.6347382e-01 3.3826315e-01 4.7440663e-02
-9.0069570e-02 -4.5930958e-01 -9.4637150e-01 -6.8811882e-01
-4.8923361e-01 1.5481021e-01 1.1718652e-01 7.6685958e-02
1.8925788e-01... | [8.512505531311035, 0.7234265208244324] |
6b1e8da7-cf1c-4a2c-92e9-770b51ca12a7 | robust-image-protection-countering-cropping | 2206.02405 | null | https://arxiv.org/abs/2206.02405v5 | https://arxiv.org/pdf/2206.02405v5.pdf | Image Protection for Robust Cropping Localization and Recovery | Existing image cropping detection schemes ignore that recovering the cropped-out contents can unveil the purpose of the behaved cropping attack. This paper presents \textbf{CLR}-Net, a novel image protection scheme addressing the combined challenge of image \textbf{C}ropping \textbf{L}ocalization and \textbf{R}ecovery.... | ['Xinpeng Zhang', 'Zhenxing Qian', 'Xiaoxiao Hu', 'Sheng Li', 'Hang Zhou', 'Qichao Ying'] | 2022-06-06 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 9.43764865e-01 -1.35362431e-01 8.93667862e-02 7.16185644e-02
-6.67550981e-01 -1.25639224e+00 1.94793269e-01 4.80473787e-02
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8.16390067e-02 -7.88219929e-01 1.56989217e-01 -2.75868922... | [4.451807022094727, 8.006694793701172] |
385998e2-adc1-4161-ad74-39b554845f34 | unconstrained-face-sketch-synthesis-via | 2112.01019 | null | https://arxiv.org/abs/2112.01019v1 | https://arxiv.org/pdf/2112.01019v1.pdf | Unconstrained Face Sketch Synthesis via Perception-Adaptive Network and A New Benchmark | Face sketch generation has attracted much attention in the field of visual computing. However, existing methods either are limited to constrained conditions or heavily rely on various preprocessing steps to deal with in-the-wild cases. In this paper, we argue that accurately perceiving facial region and facial componen... | ['Wenxiong Kang', 'Zhengtao Wu', 'Lingbo Liu', 'Lin Nie'] | 2021-12-02 | null | null | null | null | ['face-sketch-synthesis'] | ['computer-vision'] | [ 2.10849747e-01 -2.09446818e-01 -9.33019519e-02 -6.75143003e-01
-4.85968083e-01 -5.37330925e-01 6.50216222e-01 -9.36205089e-01
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3.60481620e-01 1.00641266e-01 -3.14618677e-01 -1.68902591... | [12.567315101623535, -0.08340821415185928] |
afa0c052-48b0-4253-be28-631b3415453d | an-approach-for-adaptive-automatic-threat | 1903.10604 | null | https://arxiv.org/abs/1903.10604v2 | https://arxiv.org/pdf/1903.10604v2.pdf | An Approach for Adaptive Automatic Threat Recognition Within 3D Computed Tomography Images for Baggage Security Screening | The screening of baggage using X-ray scanners is now routine in aviation security with automatic threat detection approaches, based on 3D X-ray computed tomography (CT) images, known as Automatic Threat Recognition (ATR) within the aviation security industry. These current strategies use pre-defined threat material sig... | ['Toby P. Breckon', 'Qian Wang', 'Khalid N. Ismail'] | 2019-03-25 | null | null | null | null | ['material-recognition'] | ['computer-vision'] | [ 7.11811543e-01 -3.82724345e-01 4.73327011e-01 -7.82013591e-03
-5.85988939e-01 -7.80339420e-01 5.37511349e-01 3.99773687e-01
-6.15632772e-01 3.06751817e-01 -4.60195929e-01 -5.77513397e-01
-8.17515671e-01 -9.02155340e-01 -3.46402496e-01 -6.43174946e-01
-3.91874313e-01 9.31933403e-01 7.23387599e-01 -5.66476405... | [7.421346664428711, 2.0246164798736572] |
cc8d7059-cf18-472d-8dec-6e691744f5cd | all-snow-removed-single-image-desnowing | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Chen_ALL_Snow_Removed_Single_Image_Desnowing_Algorithm_Using_Hierarchical_Dual-Tree_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Chen_ALL_Snow_Removed_Single_Image_Desnowing_Algorithm_Using_Hierarchical_Dual-Tree_ICCV_2021_paper.pdf | ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-Tree Complex Wavelet Representation and Contradict Channel Loss | Snow is a highly complicated atmospheric phenomenon that usually contains snowflake, snow streak, and veiling effect (similar to the haze or the mist). In this literature, we propose a single image desnowing algorithm to address the diversity of snow particles in shape and size. First, to better represent the compl... | ['Sy-Yen Kuo', 'Jian-Jiun Ding', 'I-Hsiang Chen', 'Cheng-Che Tsai', 'Cheng-Lin Hsieh', 'Hao-Yu Fang', 'Wei-Ting Chen'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['single-image-desnowing'] | ['computer-vision'] | [ 7.08343238e-02 -5.80700636e-01 2.90128142e-01 -2.45070398e-01
-1.47036761e-01 -2.31545389e-01 7.71729946e-02 -2.44775563e-01
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-6.48573861e-02 -3.26555163e-01 4.40653861e-01 -4.71057475... | [10.9191312789917, -3.225064277648926] |
a0bf8862-9d6f-41ee-93da-0abc9debca9a | visual-deep-learning-based-explanation-for | 2302.08511 | null | https://arxiv.org/abs/2302.08511v1 | https://arxiv.org/pdf/2302.08511v1.pdf | Visual deep learning-based explanation for neuritic plaques segmentation in Alzheimer's Disease using weakly annotated whole slide histopathological images | Quantifying the distribution and morphology of tau protein structures in brain tissues is key to diagnosing Alzheimer's Disease (AD) and its subtypes. Recently, deep learning (DL) models such as UNet have been successfully used for automatic segmentation of histopathological whole slide images (WSI) of biological tissu... | ['Daniel Racoceanu', 'Lev Stimmer', 'Benoît Delatour', 'Susana Boluda', 'Léa Ingrassia', 'Mehdi Ounissi', 'Anuradha Kar', 'Gabriel Jimenez'] | 2023-01-13 | null | null | null | null | ['whole-slide-images', 'morphological-analysis'] | ['computer-vision', 'natural-language-processing'] | [ 1.54907942e-01 -1.77856125e-02 1.79961130e-01 -4.47772890e-01
-8.68337989e-01 -6.92089140e-01 1.44772559e-01 3.89211684e-01
-4.29656833e-01 7.43671477e-01 -1.14921451e-01 -2.97053695e-01
-3.86103764e-02 -5.29668868e-01 -4.48619932e-01 -8.60347867e-01
-4.23771679e-01 9.32845652e-01 6.24983370e-01 -5.83341792... | [14.11558723449707, -2.1132256984710693] |
b504926b-46af-4f71-9bc4-29542d8bbad1 | residual-contrastive-learning-for-joint | 2106.1007 | null | https://arxiv.org/abs/2106.10070v2 | https://arxiv.org/pdf/2106.10070v2.pdf | Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images | This paper is concerned with contrastive learning (CL) for low-level image restoration and enhancement tasks. We propose a new label-efficient learning paradigm based on residuals, residual contrastive learning (RCL), and derive an unsupervised visual representation learning framework, suitable for low-level vision tas... | ['Steven McDonagh', 'Ales Leonardis', 'Eduardo Pérez-Pellitero', 'Yongxin Yang', 'Matteo Maggioni', 'Nanqing Dong'] | 2021-06-18 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 9.88249242e-01 2.18643114e-01 -1.64058432e-02 -4.77793425e-01
-1.26637852e+00 -2.52975166e-01 7.10194051e-01 -2.04415303e-02
-4.61549312e-01 6.33179009e-01 4.54608887e-01 -7.87912980e-02
-1.74435496e-01 -4.28142697e-01 -6.65149689e-01 -7.88103759e-01
2.57960975e-01 -5.90744708e-03 1.15202166e-01 -1.02397315... | [11.209341049194336, -2.171700954437256] |
119553dd-1dc6-4458-9203-80ba06cbbbb3 | make-an-audio-2-temporal-enhanced-text-to | 2305.18474 | null | https://arxiv.org/abs/2305.18474v1 | https://arxiv.org/pdf/2305.18474v1.pdf | Make-An-Audio 2: Temporal-Enhanced Text-to-Audio Generation | Large diffusion models have been successful in text-to-audio (T2A) synthesis tasks, but they often suffer from common issues such as semantic misalignment and poor temporal consistency due to limited natural language understanding and data scarcity. Additionally, 2D spatial structures widely used in T2A works lead to u... | ['Zhou Zhao', 'Zejun Ma', 'Xiang Yin', 'Jinglin Liu', 'Chen Zhang', 'Zhenhui Ye', 'Dongchao Yang', 'Rongjie Huang', 'Yi Ren', 'Jiawei Huang'] | 2023-05-29 | null | null | null | null | ['audio-generation', 'temporal-information-extraction'] | ['audio', 'natural-language-processing'] | [ 1.78190693e-01 -7.40544945e-02 -1.93683341e-01 -3.17175537e-01
-1.39651108e+00 -4.60023612e-01 4.43945557e-01 -5.62945344e-02
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-1.30148843e-01 -6.16832197e-01 -4.87770647e-01 -5.69056749e-01
4.06504460e-02 2.25106537e-01 2.52291739e-01 -1.16156153... | [15.414769172668457, 5.650062084197998] |
1cccd72a-4ab4-4cab-ba5e-d3b1212f5d41 | using-a-novel-fractional-order-gradient | 2205.00581 | null | https://arxiv.org/abs/2205.00581v1 | https://arxiv.org/pdf/2205.00581v1.pdf | Using a novel fractional-order gradient method for CNN back-propagation | Computer-aided diagnosis tools have experienced rapid growth and development in recent years. Among all, deep learning is the most sophisticated and popular tool. In this paper, researchers propose a novel deep learning model and apply it to COVID-19 diagnosis. Our model uses the tool of fractional calculus, which has ... | ['Weihua Guo', 'Mohammed Alghaili', 'Talal Ahmed Ali Ali', 'Ningbo Zhu', 'Mundher Mohammed Taresh'] | 2022-05-01 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [-2.19013363e-01 -5.63401021e-02 4.30388525e-02 -1.87789530e-01
3.54253163e-04 2.19696045e-01 -2.60627288e-02 -9.47021246e-02
-4.77150321e-01 8.59265387e-01 -2.92691886e-01 -4.11537468e-01
-1.60560504e-01 -8.02193940e-01 -5.64841449e-01 -7.57261634e-01
-1.42266661e-01 9.72137749e-02 2.28023976e-01 -1.08330883... | [7.151516914367676, 3.564427137374878] |
46e66172-6786-4f2e-9db5-e8837c65d2dd | learn-spelling-from-teachers-transferring | 1907.06017 | null | https://arxiv.org/abs/1907.06017v1 | https://arxiv.org/pdf/1907.06017v1.pdf | Learn Spelling from Teachers: Transferring Knowledge from Language Models to Sequence-to-Sequence Speech Recognition | Integrating an external language model into a sequence-to-sequence speech recognition system is non-trivial. Previous works utilize linear interpolation or a fusion network to integrate external language models. However, these approaches introduce external components, and increase decoding computation. In this paper, w... | ['Jian-Hua Tao', 'Zhengkun Tian', 'Zhengqi Wen', 'Jiangyan Yi', 'Ye Bai'] | 2019-07-13 | null | null | null | null | ['sequence-to-sequence-speech-recognition'] | ['speech'] | [ 5.20347059e-01 4.66247126e-02 -8.58453065e-02 -3.71557176e-01
-8.86704028e-01 -5.09965837e-01 1.79053262e-01 -2.23460704e-01
-6.16750598e-01 9.19965446e-01 4.63993140e-02 -9.09064829e-01
7.95128226e-01 -5.46034873e-01 -7.16031611e-01 -6.05893135e-01
5.77665031e-01 4.07968998e-01 4.36870903e-01 -2.47436300... | [14.447226524353027, 7.015989780426025] |
14d90ddc-dff7-4c12-82e9-9e510beda458 | efficient-machine-translation-corpus-1 | 2306.11838 | null | https://arxiv.org/abs/2306.11838v1 | https://arxiv.org/pdf/2306.11838v1.pdf | Efficient Machine Translation Corpus Generation | This paper proposes an efficient and semi-automated method for human-in-the-loop post-editing for machine translation (MT) corpus generation. The method is based on online training of a custom MT quality estimation metric on-the-fly as linguists perform post-edits. The online estimator is used to prioritize worse hypot... | ['Hassan Sawaf', 'Shreyas Sharma', 'Ahmet Gunduz', 'Kamer Ali Yuksel'] | 2023-06-20 | efficient-machine-translation-corpus | https://aclanthology.org/2022.amta-coco4mt.2 | https://aclanthology.org/2022.amta-coco4mt.2.pdf | amta-2022-9 | ['machine-translation'] | ['natural-language-processing'] | [ 3.82391989e-01 7.29318619e-01 -4.99338843e-02 -4.12496120e-01
-9.84058261e-01 -7.07667112e-01 5.55403709e-01 4.71953928e-01
-5.41927874e-01 9.47255373e-01 -1.01588055e-01 -4.45303321e-01
2.14412019e-01 -4.88211513e-01 -7.56955564e-01 -4.11623389e-01
3.24355155e-01 9.62776184e-01 1.23526484e-01 -2.43066847... | [11.685892105102539, 10.258333206176758] |
09ba7aef-d999-482a-95d1-9e53a3b9ee7d | coded-illumination-and-imaging-for | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Yuta_Asano_Coded_Illumination_and_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Yuta_Asano_Coded_Illumination_and_ECCV_2018_paper.pdf | Coded Illumination and Imaging for Fluorescence Based Classification | The quick detection of specific substances in objects such as produce items via non-destructive visual cues is vital to ensuring the quality and safety of consumer products. At the same time, it is well-known that the fluorescence excitation-emission characteristics of many organic objects can serve as a kind of ``fing... | ['Misaki Meguro', 'Yinqiang Zheng', 'Chao Wang', 'Yuta Asano', 'Takahiro Okabe', 'Imari Sato', 'Antony Lam'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['material-classification'] | ['computer-vision'] | [ 9.03610647e-01 -4.85732704e-01 -2.63270706e-01 -3.39659274e-01
-4.71579552e-01 -1.14107358e+00 2.86557704e-01 5.05339026e-01
-1.58633307e-01 5.65066218e-01 -5.23305357e-01 -9.47133377e-02
8.68127272e-02 -8.43530953e-01 -6.59095168e-01 -1.15532339e+00
2.66652316e-01 -2.31960509e-02 6.50691846e-03 1.53056681... | [10.184423446655273, -2.6007742881774902] |
07c17ee8-6d4e-4e88-a985-24d1f4a95922 | graph-structure-learning-for-robust-graph | 2005.10203 | null | https://arxiv.org/abs/2005.10203v3 | https://arxiv.org/pdf/2005.10203v3.pdf | Graph Structure Learning for Robust Graph Neural Networks | Graph Neural Networks (GNNs) are powerful tools in representation learning for graphs. However, recent studies show that GNNs are vulnerable to carefully-crafted perturbations, called adversarial attacks. Adversarial attacks can easily fool GNNs in making predictions for downstream tasks. The vulnerability to adversari... | ['Xianfeng Tang', 'Wei Jin', 'Suhang Wang', 'Xiaorui Liu', 'Jiliang Tang', 'Yao Ma'] | 2020-05-20 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 1.48662686e-01 2.87276089e-01 -1.14467621e-01 6.07453138e-02
-4.83152151e-01 -1.05146301e+00 4.71468538e-01 9.95239541e-02
2.33172312e-01 6.08762026e-01 1.03691205e-01 -7.63054013e-01
-1.75696492e-01 -1.09810245e+00 -9.88911152e-01 -6.77787125e-01
-5.18082917e-01 1.02392182e-01 2.64414907e-01 -6.03876710... | [6.153866291046143, 7.30024528503418] |
730773d2-5bea-41fd-a56a-ff69f55b927b | improving-document-level-relation-extraction | null | null | https://ieeexplore.ieee.org/abstract/document/9194547 | https://conferences.computer.org/ickg/pdfs/ICKG2020-66r9RP2mQIZywMjHhQVtDI/815600a305/815600a305.pdf | Improving Document-level Relation Extraction via Contextualizing Mention Representations and Weighting Mention Pairs | Document-level relation extraction (RE) has attracted considerable attention, because a large number of relational facts are expressed in multiple sentences. Recently, encoder-aggregator based models have become promising for document-level RE. However, these models have two shortcomings: (i) they cannot obtain context... | ['Ping Jiang;Xian-Ling Mao;Binbin Bian;Heyan Huang'] | 2020-08-09 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [-1.08556084e-01 2.41821095e-01 -5.18661559e-01 -3.72460753e-01
-1.15781283e+00 -3.19055855e-01 6.97158337e-01 4.99005854e-01
-3.92662466e-01 9.30345535e-01 6.76494062e-01 -8.28686655e-02
-4.05699424e-02 -8.14332783e-01 -6.04427099e-01 -5.60437500e-01
1.82234511e-01 3.70658785e-01 2.64801413e-01 -1.28439978... | [9.267608642578125, 8.669413566589355] |
539c882e-ef01-42ec-b9a3-237306a0c70a | automatic-sleep-stage-classification-with-1 | 2302.03227 | null | https://arxiv.org/abs/2302.03227v1 | https://arxiv.org/pdf/2302.03227v1.pdf | Automatic Sleep Stage Classification with Cross-modal Self-supervised Features from Deep Brain Signals | The detection of human sleep stages is widely used in the diagnosis and intervention of neurological and psychiatric diseases. Some patients with deep brain stimulator implanted could have their neural activities recorded from the deep brain. Sleep stage classification based on deep brain recording has great potential ... | ['Luming Li', 'Yanan Sui', 'Yue Chen', 'Chen Gong'] | 2023-02-07 | null | null | null | null | ['automatic-sleep-stage-classification'] | ['medical'] | [ 6.71096593e-02 -1.56498060e-01 -2.39769757e-01 -4.35406387e-01
-6.37798190e-01 -1.15010723e-01 -3.16262767e-02 -5.78644156e-01
-6.23006761e-01 7.52179444e-01 4.65789258e-01 -4.36942540e-02
-1.63477752e-02 -3.77190232e-01 9.44260955e-02 -8.78122449e-01
-1.87185317e-01 5.21354914e-01 2.15160996e-01 -2.26108804... | [13.536161422729492, 3.466477394104004] |
02da2dce-41d5-40a0-af68-5defa8d64f7a | black-box-dissector-towards-erasing-based | 2105.00623 | null | https://arxiv.org/abs/2105.00623v3 | https://arxiv.org/pdf/2105.00623v3.pdf | Black-Box Dissector: Towards Erasing-based Hard-Label Model Stealing Attack | Previous studies have verified that the functionality of black-box models can be stolen with full probability outputs. However, under the more practical hard-label setting, we observe that existing methods suffer from catastrophic performance degradation. We argue this is due to the lack of rich information in the prob... | ['Feiyue Huang', 'Yan Wang', 'Rongrong Ji', 'Yongjian Wu', 'Hong Liu', 'Jie Li', 'Yixu Wang'] | 2021-05-03 | null | https://openreview.net/forum?id=5jaqt-Hsqir | https://openreview.net/pdf?id=5jaqt-Hsqir | neurips-2021-12 | ['self-knowledge-distillation'] | ['computer-vision'] | [ 5.19766808e-01 2.02631578e-01 -4.12903309e-01 -1.47112787e-01
-1.05299926e+00 -9.05000031e-01 2.91510612e-01 -4.08465296e-01
-1.94724470e-01 7.37788916e-01 -3.83970201e-01 -7.33641148e-01
2.49229684e-01 -5.67370772e-01 -1.07272041e+00 -7.24360704e-01
6.88963756e-02 2.94947714e-01 3.66763920e-01 6.73386529... | [5.852485179901123, 7.664951801300049] |
8d66d154-5e63-431d-b0e2-667e059c41a4 | rescaling-egocentric-vision | 2006.13256 | null | https://arxiv.org/abs/2006.13256v4 | https://arxiv.org/pdf/2006.13256v4.pdf | Rescaling Egocentric Vision | This paper introduces the pipeline to extend the largest dataset in egocentric vision, EPIC-KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M frames, 90K actions in 700 variable-length videos, capturing long-term unscripted activities in 45 environments, using head-mounted cameras. Co... | ['Davide Moltisanti', 'Giovanni Maria Farinella', 'Michael Wray', 'Jian Ma', 'Will Price', 'Jonathan Munro', 'Toby Perrett', 'Hazel Doughty', 'Evangelos Kazakos', 'Antonino Furnari', 'Dima Damen'] | 2020-06-23 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 4.67153877e-01 3.44629884e-02 -3.25481147e-01 -4.04306531e-01
-7.71163642e-01 -5.09148777e-01 7.64744282e-01 -7.69591749e-01
-6.30827367e-01 5.69922268e-01 1.12029672e+00 5.60488820e-01
2.99194336e-01 -1.63919106e-02 -8.26879740e-01 -5.86847067e-01
-3.04233730e-01 4.22978014e-01 2.34093428e-01 1.44682840... | [8.242668151855469, 0.5423344969749451] |
a36e5427-4082-4296-9a9f-8a4101c2b294 | automatic-speech-disentanglement-for-voice | 2306.12259 | null | https://arxiv.org/abs/2306.12259v1 | https://arxiv.org/pdf/2306.12259v1.pdf | Automatic Speech Disentanglement for Voice Conversion using Rank Module and Speech Augmentation | Voice Conversion (VC) converts the voice of a source speech to that of a target while maintaining the source's content. Speech can be mainly decomposed into four components: content, timbre, rhythm and pitch. Unfortunately, most related works only take into account content and timbre, which results in less natural spee... | ['Ning Chen', 'Shijun Wang', 'Zhonghua Liu'] | 2023-06-21 | null | null | null | null | ['voice-conversion', 'disentanglement', 'voice-conversion'] | ['audio', 'methodology', 'speech'] | [-3.73495137e-03 -1.56616181e-01 -1.77664384e-01 -9.29833278e-02
-9.18925881e-01 -8.64309669e-01 6.55732334e-01 -3.02175730e-01
-2.31596738e-01 5.19649327e-01 4.97705370e-01 -5.36500931e-01
3.23270261e-01 -5.07151783e-01 -2.19300315e-01 -6.89597011e-01
2.93249547e-01 1.07630156e-02 5.36828721e-03 -1.96011126... | [14.934476852416992, 6.437119483947754] |
11b100fb-77a0-4ec3-b21e-1235523e0b8b | predicting-emotion-from-music-videos | 2202.10453 | null | https://arxiv.org/abs/2202.10453v1 | https://arxiv.org/pdf/2202.10453v1.pdf | Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses | Although media content is increasingly produced, distributed, and consumed in multiple combinations of modalities, how individual modalities contribute to the perceived emotion of a media item remains poorly understood. In this paper we present MusicVideos (MuVi), a novel dataset for affective multimedia content analys... | ['Kat Agres', 'Gemma Roig', 'Dorien Herremans', 'Dimos Makris', 'Phoebe Chua'] | 2022-02-19 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 4.45709080e-02 -4.38584179e-01 -2.16536641e-01 -2.97480643e-01
-6.82140648e-01 -7.04967976e-01 5.06774366e-01 5.63456655e-01
-4.51792508e-01 1.98734283e-01 7.97641695e-01 5.36551952e-01
9.97234657e-02 -4.57811266e-01 -4.92876679e-01 -4.95687902e-01
-5.10768332e-02 -4.57293838e-01 -4.82735574e-01 -1.63126558... | [13.305747985839844, 5.134044170379639] |
322fb979-30a5-4860-bc83-786f49aa11b6 | darf-boosting-radiance-fields-from-sparse | 2305.19201 | null | https://arxiv.org/abs/2305.19201v1 | https://arxiv.org/pdf/2305.19201v1.pdf | DäRF: Boosting Radiance Fields from Sparse Inputs with Monocular Depth Adaptation | Neural radiance fields (NeRF) shows powerful performance in novel view synthesis and 3D geometry reconstruction, but it suffers from critical performance degradation when the number of known viewpoints is drastically reduced. Existing works attempt to overcome this problem by employing external priors, but their succes... | ['Seungryong Kim', 'SungJin Cho', 'Min-Seop Kwak', 'Seokju Cho', 'Honggyu An', 'Seonghoon Park', 'Jiuhn Song'] | 2023-05-30 | null | null | null | null | ['monocular-depth-estimation', 'novel-view-synthesis'] | ['computer-vision', 'computer-vision'] | [ 9.32848528e-02 -2.22805291e-01 1.02316223e-01 -4.80895102e-01
-6.73443854e-01 -5.93101084e-01 4.85672534e-01 -6.50373340e-01
-2.04360455e-01 6.35686219e-01 2.73670167e-01 -2.69116331e-02
6.88088778e-03 -9.20374393e-01 -8.33186269e-01 -6.67821288e-01
2.86980659e-01 -2.62510870e-02 8.68780166e-02 -4.17699009... | [9.06135082244873, -2.6754562854766846] |
8742fe6b-75ed-44a4-ae1b-ff858b350c7a | disarm-detecting-the-victims-targeted-by | null | null | https://openreview.net/forum?id=Woi12EDR_fj | https://openreview.net/pdf?id=Woi12EDR_fj | DISARM: Detecting the Victims Targeted by Harmful Memes | Internet memes have emerged as an increasingly popular means of communication on the web. Although memes are typically intended to elicit humour, they have been increasingly used to spread hatred, trolling, and cyberbullying, as well as to target specific individuals, communities, or society on political, socio-cultura... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['person-identification'] | ['computer-vision'] | [-1.10074796e-01 -1.16459969e-02 5.03797270e-02 7.86130875e-02
-3.54402751e-01 -8.53702545e-01 9.35269237e-01 4.86239731e-01
-4.88265276e-01 7.24176109e-01 3.30897778e-01 -3.82654704e-02
4.78516579e-01 -8.51950467e-01 -4.15455997e-01 -6.01195097e-01
1.60647571e-01 3.65739852e-01 9.65554640e-02 -3.38459074... | [8.499442100524902, 10.680249214172363] |
ee6b4110-69e5-4c19-be45-8e205f868e42 | valuing-distributed-energy-resources-for-non | 2301.06636 | null | https://arxiv.org/abs/2301.06636v2 | https://arxiv.org/pdf/2301.06636v2.pdf | Valuing Distributed Energy Resources for Non-Wires Alternatives | Distributed energy resources (DER) as non-wires alternatives, regardless of owner, have the potential to reduce system operating costs and delay system upgrades. However, it is difficult to determine the appropriate economic signal to incentivize DER investors to install capacity that will benefit both the DER investor... | ['Michael E. Webber', 'Nicholas D. Laws'] | 2023-01-16 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-5.30480206e-01 4.30484325e-01 -3.54175657e-01 1.32100329e-01
-5.33772588e-01 -9.06518638e-01 1.60461560e-01 4.63258326e-02
-6.63200691e-02 1.04519403e+00 -9.42383260e-02 -5.24215937e-01
-5.00982225e-01 -9.53862965e-01 -4.26412135e-01 -8.20196867e-01
9.35965851e-02 3.08100909e-01 -4.27181512e-01 -2.68523723... | [5.647950649261475, 2.450148344039917] |
e3cde8b3-691c-4fe9-a007-7095398f68bc | infrared-image-super-resolution-via | 2109.0096 | null | https://arxiv.org/abs/2109.00960v1 | https://arxiv.org/pdf/2109.00960v1.pdf | Infrared Image Super-Resolution via Heterogeneous Convolutional WGAN | Image super-resolution is important in many fields, such as surveillance and remote sensing. However, infrared (IR) images normally have low resolution since the optical equipment is relatively expensive. Recently, deep learning methods have dominated image super-resolution and achieved remarkable performance on visibl... | ['Guoming Pang', 'Qi Jiang', 'Qingzhong Wang', 'Zetao Jiang', 'Yongsong Huang'] | 2021-09-02 | null | null | null | null | ['infrared-image-super-resolution'] | ['computer-vision'] | [ 5.38469434e-01 -2.03013957e-01 -5.24595529e-02 -2.02201590e-01
-9.91057098e-01 -1.16580211e-01 4.19562578e-01 -7.50171661e-01
-2.02642083e-01 8.69825602e-01 4.35739346e-02 -4.14655693e-02
-1.08046785e-01 -1.05097568e+00 -5.24353266e-01 -1.19629049e+00
6.08327925e-01 -1.72639534e-01 6.20965064e-02 -2.85888642... | [10.868151664733887, -2.0071334838867188] |
fad909e0-88bc-46a0-be33-898bf9064bbe | open-domain-response-generation-guided-by | null | null | https://openreview.net/forum?id=6sXWzu3pWQE | https://openreview.net/pdf?id=6sXWzu3pWQE | Open Domain Response Generation Guided by Retrieved Conversations | Open domain response generation is the task of creating a response givena user query in any topics/domain. Limited by context and referenceinformation, responses generated by current systems are often "bland"or generic. In this paper, we combine a response generation model witha retrieval system that searches for rele... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['keyword-extraction'] | ['natural-language-processing'] | [ 1.43756690e-02 4.07963306e-01 -2.35122576e-01 -4.59523946e-01
-1.58850443e+00 -8.96186352e-01 8.69042158e-01 4.62870933e-02
-7.42815793e-01 9.51361835e-01 6.09291613e-01 -1.13193132e-01
2.29246654e-02 -5.84520698e-01 -3.46728116e-01 -4.48226094e-01
4.95270342e-01 7.78289557e-01 6.84521616e-01 -6.84604287... | [12.376802444458008, 8.005331039428711] |
e804cf45-0bd8-495f-b7f6-08d7ce34cf81 | counterfactual-inference-of-the-mean-outcome | 2102.08975 | null | https://arxiv.org/abs/2102.08975v2 | https://arxiv.org/pdf/2102.08975v2.pdf | Adaptive Doubly Robust Estimator from Non-stationary Logging Policy under a Convergence of Average Probability | Adaptive experiments, including efficient average treatment effect estimation and multi-armed bandit algorithms, have garnered attention in various applications, such as social experiments, clinical trials, and online advertisement optimization. This paper considers estimating the mean outcome of an action from samples... | ['Masahiro Kato'] | 2021-02-17 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 1.61336109e-01 -9.65002701e-02 -9.12440002e-01 -3.04950386e-01
-5.46438575e-01 -4.55380499e-01 4.15971220e-01 1.27535731e-01
-6.68829322e-01 1.18457103e+00 2.71588087e-01 -5.94602406e-01
-3.67225051e-01 -6.62172318e-01 -9.05165553e-01 -1.06384027e+00
-1.29435018e-01 5.35723567e-01 -9.44788009e-03 4.06496495... | [7.903348922729492, 5.171093940734863] |
51974065-c261-450e-a78f-f1abfe9d1bcc | predictive-coding-based-multiscale-network | 2212.11642 | null | https://arxiv.org/abs/2212.11642v2 | https://arxiv.org/pdf/2212.11642v2.pdf | Predictive Coding Based Multiscale Network with Encoder-Decoder LSTM for Video Prediction | We are introducing a multi-scale predictive model for video prediction here, whose design is inspired by the "Predictive Coding" theories and "Coarse to Fine" approach. As a predictive coding model, it is updated by a combination of bottom-up and top-down information flows, which is different from traditional bottom-up... | ['Weihua Li', 'Junpei Zhong', 'Chaofan Ling'] | 2022-12-22 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 2.41229877e-01 3.40584606e-01 -2.65353858e-01 -5.88281304e-02
-3.66528064e-01 -1.85603276e-01 4.94748354e-01 -3.79038960e-01
6.26321882e-02 7.96705246e-01 4.29691881e-01 -9.39050317e-02
2.84582913e-01 -1.21313024e+00 -1.03109252e+00 -6.40313327e-01
2.48670280e-01 -6.28827959e-02 5.30126870e-01 -1.83894038... | [11.080900192260742, -1.1839765310287476] |
c39055b9-b216-4326-b43c-156c779a72f5 | a-fast-evidential-approach-for-stock | 2104.05204 | null | https://arxiv.org/abs/2104.05204v2 | https://arxiv.org/pdf/2104.05204v2.pdf | A Fast Evidential Approach for Stock Forecasting | Within the framework of evidence theory, the confidence functions of different information can be combined into a combined confidence function to solve uncertain problems. The Dempster combination rule is a classic method of fusing different information. This paper proposes a similar confidence function for the time po... | ['Fuyuan Xiao', 'Tianxiang Zhan'] | 2021-04-12 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-7.28053272e-01 -6.75284088e-01 -2.20165119e-01 -3.30740005e-01
-1.66945428e-01 -4.71074313e-01 4.08176720e-01 1.18294068e-01
-1.32717595e-01 9.13554907e-01 -1.39091372e-01 -4.35878575e-01
-4.35187936e-01 -1.22365916e+00 -1.20163605e-01 -7.96444535e-01
5.72320670e-02 2.15769395e-01 3.14816177e-01 -2.78380930... | [4.741006374359131, 4.103579521179199] |
a2c29424-08a1-4779-b414-6ba36769662d | seeing-by-haptic-glance-reinforcement | 2102.07599 | null | https://arxiv.org/abs/2102.07599v1 | https://arxiv.org/pdf/2102.07599v1.pdf | Seeing by haptic glance: reinforcement learning-based 3D object Recognition | Human is able to conduct 3D recognition by a limited number of haptic contacts between the target object and his/her fingers without seeing the object. This capability is defined as `haptic glance' in cognitive neuroscience. Most of the existing 3D recognition models were developed based on dense 3D data. Nonetheless, ... | ['Patrick Le Callet', 'Guillaume Gallot', 'Suiyi Ling', 'Kevin Riou'] | 2021-02-15 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [ 1.20023496e-01 2.00394347e-01 -9.84294116e-02 -4.04308038e-03
-3.41216445e-01 -1.51805490e-01 1.27488360e-01 3.23901236e-01
-5.42448521e-01 3.16964239e-01 -2.39541337e-01 1.00031488e-01
-4.01037037e-01 -4.55010504e-01 -7.51918852e-01 -5.94514787e-01
-3.78300309e-01 9.18040276e-01 -4.27680835e-02 -6.32872581... | [5.8506951332092285, -0.8655223250389099] |
9259acf0-9fcb-42b9-9262-5fc9ac7f8503 | detflowtrack-3d-multi-object-tracking-based | 2203.02157 | null | https://arxiv.org/abs/2203.02157v1 | https://arxiv.org/pdf/2203.02157v1.pdf | DetFlowTrack: 3D Multi-object Tracking based on Simultaneous Optimization of Object Detection and Scene Flow Estimation | 3D Multi-Object Tracking (MOT) is an important part of the unmanned vehicle perception module. Most methods optimize object detection and data association independently. These methods make the network structure complicated and limit the improvement of MOT accuracy. we proposed a 3D MOT framework based on simultaneous o... | ['Hesheng Wang', 'Guangming Wang', 'Yueling Shen'] | 2022-03-04 | null | null | null | null | ['scene-flow-estimation', '3d-multi-object-tracking'] | ['computer-vision', 'computer-vision'] | [-6.75330535e-02 -4.16058153e-01 2.13115606e-02 -5.86870611e-02
6.65412843e-02 -3.59168410e-01 5.24395525e-01 -2.04342008e-01
-6.38703763e-01 4.55722243e-01 -3.41848701e-01 -2.72128165e-01
-3.90640646e-02 -8.11765254e-01 -5.34113526e-01 -6.90648139e-01
4.64280620e-02 5.10935545e-01 9.33770597e-01 -1.45943388... | [6.748386383056641, -2.1469478607177734] |
5bbaede4-191c-4c93-a882-a370ebdf9801 | exploring-depth-information-for-face | 2212.1423 | null | https://arxiv.org/abs/2212.14230v1 | https://arxiv.org/pdf/2212.14230v1.pdf | Exploring Depth Information for Face Manipulation Detection | Face manipulation detection has been receiving a lot of attention for the reliability and security of the face images. Recent studies focus on using auxiliary information or prior knowledge to capture robust manipulation traces, which are shown to be promising. As one of the important face features, the face depth map,... | ['Xinpeng Zhang', 'Zhenxing Qian', 'Sheng Li', 'Meiling Li', 'Haoyue Wang'] | 2022-12-29 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [ 4.44729507e-01 9.31317434e-02 2.67944094e-02 -3.82484049e-01
-3.53359729e-01 -1.76519692e-01 5.65594614e-01 -4.48295802e-01
-7.21292291e-03 2.56651849e-01 -1.29518127e-02 3.83035928e-01
-7.86655992e-02 -7.22215712e-01 -5.91210246e-01 -1.00415862e+00
1.66648582e-01 5.53347804e-02 1.84537411e-01 -1.52388588... | [13.330334663391113, 0.46657058596611023] |
6413a346-8428-4f91-9a44-cc842d15ca16 | event-coreference-resolution-using-neural | 1810.04216 | null | http://arxiv.org/abs/1810.04216v1 | http://arxiv.org/pdf/1810.04216v1.pdf | Event Coreference Resolution Using Neural Network Classifiers | This paper presents a neural network classifier approach to detecting both
within- and cross- document event coreference effectively using only event
mention based features. Our approach does not (yet) rely on any event argument
features such as semantic roles or spatiotemporal arguments. Experimental
results on the EC... | ['Kemal Oflazer', 'Lamana Mulaffer', 'Amna AlZeyara', 'Arun Pandian'] | 2018-10-09 | null | null | null | null | ['cross-document-coreference-resolution'] | ['natural-language-processing'] | [ 1.04413107e-01 1.98531389e-01 -5.20627737e-01 -3.71398687e-01
-1.33541572e+00 -9.33177710e-01 1.25070930e+00 6.77174330e-01
-1.01921988e+00 1.00723267e+00 7.56277919e-01 -1.84247538e-01
-6.67365670e-01 -5.91780841e-01 -4.63985801e-01 -4.58159208e-01
-1.69954598e-01 6.82570159e-01 5.66486418e-01 -2.87151396... | [9.245667457580566, 9.52568244934082] |
56d34372-e61b-4702-ba8c-68dfab100062 | very-long-natural-scenery-image-prediction-by-1 | 1912.12688 | null | https://arxiv.org/abs/1912.12688v1 | https://arxiv.org/pdf/1912.12688v1.pdf | Very Long Natural Scenery Image Prediction by Outpainting | Comparing to image inpainting, image outpainting receives less attention due to two challenges in it. The first challenge is how to keep the spatial and content consistency between generated images and original input. The second challenge is how to maintain high quality in generated results, especially for multi-step g... | ['Jian Dong', 'Zongxin Yang', 'Yi Yang', 'Ping Liu', 'Shuicheng Yan'] | 2019-12-29 | very-long-natural-scenery-image-prediction-by | http://openaccess.thecvf.com/content_ICCV_2019/html/Yang_Very_Long_Natural_Scenery_Image_Prediction_by_Outpainting_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Yang_Very_Long_Natural_Scenery_Image_Prediction_by_Outpainting_ICCV_2019_paper.pdf | iccv-2019-10 | ['image-outpainting'] | ['computer-vision'] | [ 3.34370375e-01 -1.09486863e-01 -3.98025848e-02 -2.51006842e-01
-4.65795010e-01 -2.26554245e-01 1.57237679e-01 -4.20999736e-01
-1.27245709e-01 9.08468008e-01 2.66293347e-01 1.26471922e-01
3.55829477e-01 -8.27174962e-01 -8.60637903e-01 -5.27393103e-01
3.85605127e-01 -3.51715200e-02 3.69104534e-01 -2.56096482... | [11.396196365356445, -1.0075087547302246] |
79946c35-eba2-4ab4-bd7c-948524ec5ae3 | global-deep-learning-methods-for | 1812.04103 | null | https://arxiv.org/abs/1812.04103v2 | https://arxiv.org/pdf/1812.04103v2.pdf | Non-local U-Net for Biomedical Image Segmentation | Deep learning has shown its great promise in various biomedical image segmentation tasks. Existing models are typically based on U-Net and rely on an encoder-decoder architecture with stacked local operators to aggregate long-range information gradually. However, only using the local operators limits the efficiency and... | ['Shuiwang Ji', 'Zhengyang Wang', 'Dinggang Shen', 'Na Zou'] | 2018-12-10 | null | null | null | null | ['brain-image-segmentation'] | ['medical'] | [ 2.46946588e-01 9.47231054e-03 -1.91673115e-01 -5.76459587e-01
-7.10538208e-01 4.66356091e-02 1.28206670e-01 7.39009753e-02
-5.97637892e-01 6.17463768e-01 1.39310345e-01 -3.06776315e-01
7.37948529e-03 -6.60484910e-01 -9.28545296e-01 -7.81319439e-01
-3.33163083e-01 4.04057354e-01 5.46848059e-01 1.37060806... | [14.562223434448242, -2.614645481109619] |
107fa7e6-013a-46a8-ad68-2b83e9d28202 | rgb-multispectral-matching-dataset-learning-1 | 2206.07047 | null | https://arxiv.org/abs/2206.07047v1 | https://arxiv.org/pdf/2206.07047v1.pdf | RGB-Multispectral Matching: Dataset, Learning Methodology, Evaluation | We address the problem of registering synchronized color (RGB) and multi-spectral (MS) images featuring very different resolution by solving stereo matching correspondences. Purposely, we introduce a novel RGB-MS dataset framing 13 different scenes in indoor environments and providing a total of 34 image pairs annotate... | ['Luigi Di Stefano', 'Stefano Mattoccia', 'Samuele Salti', 'Matteo Poggi', 'Pierluigi Zama Ramirez', 'Fabio Tosi'] | 2022-06-14 | rgb-multispectral-matching-dataset-learning | http://openaccess.thecvf.com//content/CVPR2022/html/Tosi_RGB-Multispectral_Matching_Dataset_Learning_Methodology_Evaluation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Tosi_RGB-Multispectral_Matching_Dataset_Learning_Methodology_Evaluation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['stereo-matching-1'] | ['computer-vision'] | [ 5.19081116e-01 1.46302119e-01 1.92929178e-01 -6.05042100e-01
-1.34818912e+00 -5.61572492e-01 4.49753553e-01 -2.96950024e-02
-7.61642098e-01 5.16712010e-01 -2.14450747e-01 -5.29403165e-02
1.26454324e-01 -6.17936909e-01 -9.99778688e-01 -5.78594625e-01
2.95288861e-01 6.81750715e-01 2.49387309e-01 -2.34538600... | [8.193516731262207, -2.2617876529693604] |
8c6e8821-26ac-4c36-a1c6-98426d7ae4f3 | optimal-activation-of-halting-multi-armed | 2304.10302 | null | https://arxiv.org/abs/2304.10302v1 | https://arxiv.org/pdf/2304.10302v1.pdf | Optimal Activation of Halting Multi-Armed Bandit Models | We study new types of dynamic allocation problems the {\sl Halting Bandit} models. As an application, we obtain new proofs for the classic Gittins index decomposition result and recent results of the authors in `Multi-armed bandits under general depreciation and commitment.' | ['Sheldon M. Ross', 'Michael N. Katehakis', 'Wesley Cowan'] | 2023-04-20 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [-3.25010747e-01 8.36568996e-02 -1.27462602e+00 -1.96092859e-01
-9.43957329e-01 -1.17313516e+00 -2.53058374e-01 -3.81993473e-01
-6.42238021e-01 1.51359129e+00 1.80068500e-02 -1.02272522e+00
-9.10820186e-01 -5.31335175e-01 -6.18924499e-01 -1.09834242e+00
7.43509233e-02 1.06103790e+00 -4.75893840e-02 -1.06113337... | [4.5008440017700195, 3.2752442359924316] |
fe94ed9c-2de1-4e6f-a328-5dc2c223a9cd | high-performance-neural-networks-for-visual | 1102.0183 | null | https://arxiv.org/abs/1102.0183v1 | https://arxiv.org/pdf/1102.0183v1.pdf | High-Performance Neural Networks for Visual Object Classification | We present a fast, fully parameterizable GPU implementation of Convolutional Neural Network variants. Our feature extractors are neither carefully designed nor pre-wired, but rather learned in a supervised way. Our deep hierarchical architectures achieve the best published results on benchmarks for object classificatio... | ['Jürgen Schmidhuber', 'Luca M. Gambardella', 'Jonathan Masci', 'Ueli Meier', 'Dan C. Cireşan'] | 2011-02-01 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-1.70872375e-01 -8.83546695e-02 1.27216682e-01 -4.34785724e-01
-2.42387652e-01 -6.44339979e-01 6.31908119e-01 2.77137220e-01
-8.24553668e-01 8.46189141e-01 -2.36962020e-01 -3.89793158e-01
6.14765584e-02 -9.23205793e-01 -8.31558824e-01 -6.84034646e-01
-2.99695283e-01 4.09940481e-01 4.25667614e-01 -1.50155216... | [8.998150825500488, 2.434209108352661] |
43967bf1-0084-402d-8a6d-2fc26d7df0c8 | real-time-localized-photorealistic-video | 2010.10056 | null | https://arxiv.org/abs/2010.10056v1 | https://arxiv.org/pdf/2010.10056v1.pdf | Real-time Localized Photorealistic Video Style Transfer | We present a novel algorithm for transferring artistic styles of semantically meaningful local regions of an image onto local regions of a target video while preserving its photorealism. Local regions may be selected either fully automatically from an image, through using video segmentation algorithms, or from casual u... | ['Jiawen Chen', 'Brian Kulis', 'Abby Chang', 'Zheng Sun', 'Wei-Sheng Lai', 'Tianfan Xue', 'Xide Xia'] | 2020-10-20 | null | null | null | null | ['video-style-transfer'] | ['computer-vision'] | [ 6.83475673e-01 -2.33368412e-01 1.69665039e-01 -5.02405763e-01
-5.45949340e-01 -1.20875108e+00 4.45530862e-01 -4.32567894e-01
-2.73214340e-01 5.35032153e-01 -2.79678460e-02 9.37823951e-02
2.51377910e-01 -6.52437747e-01 -9.82359350e-01 -3.41447145e-01
3.47321033e-01 4.50546265e-01 2.63874412e-01 -2.20260113... | [11.377276420593262, -0.6455422043800354] |
7b61b0a7-e616-4302-a409-cdf704d6b117 | breaking-the-cycle-colleagues-are-all-you-1 | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Nizan_Breaking_the_Cycle_-_Colleagues_Are_All_You_Need_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Nizan_Breaking_the_Cycle_-_Colleagues_Are_All_You_Need_CVPR_2020_paper.pdf | Breaking the Cycle - Colleagues Are All You Need | This paper proposes a novel approach to performing image-to-image translation between unpaired domains. Rather than relying on a cycle constraint, our method takes advantage of collaboration between various GANs. This results in a multi modal method, in which multiple optional and diverse images are produced for a give... | [' Ayellet Tal', 'Ori Nizan'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['multimodal-unsupervised-image-to-image'] | ['computer-vision'] | [ 6.56163931e-01 2.91944265e-01 2.08642140e-01 2.06513461e-02
-7.52328336e-01 -8.07652056e-01 7.31267750e-01 -3.69022757e-01
-1.23147257e-01 9.56934869e-01 -1.06359702e-02 1.10324085e-01
1.08370677e-01 -8.34016323e-01 -7.58111715e-01 -7.99092710e-01
5.77121794e-01 6.87631190e-01 5.97098947e-01 -3.10791612... | [11.650792121887207, -0.5441509485244751] |
afe0ebd8-ac22-4d22-941f-b48d7df0ea89 | time-sequence-channel-inference-for-beam | 1812.0122 | null | http://arxiv.org/abs/1812.01220v1 | http://arxiv.org/pdf/1812.01220v1.pdf | Time-Sequence Channel Inference for Beam Alignment in Vehicular Networks | In this paper, we propose a learning-based low-overhead beam alignment method
for vehicle-to-infrastructure communication in vehicular networks. The main
idea is to remotely infer the optimal beam directions at a target base station
in future time slots, based on the CSI of a source base station in previous
time slots.... | ['Zhiyuan Jiang', 'Sheng Zhou', 'Zhisheng Niu', 'Sheng Chen'] | 2018-12-04 | null | null | null | null | ['neural-network-simulation'] | ['computer-code'] | [-9.03553050e-03 4.16615695e-01 -2.80573905e-01 -4.10133421e-01
-1.05332422e+00 -1.83030546e-01 -1.52791478e-02 -3.67352694e-01
-4.83683556e-01 1.05460656e+00 -3.13700825e-01 -1.22573733e+00
-1.05272710e-01 -9.56623793e-01 -8.03490520e-01 -1.10222197e+00
-7.36543298e-01 1.42817110e-01 -4.69951555e-02 -3.91562432... | [6.27890682220459, 1.219303011894226] |
d8d4bab0-69f6-458c-8a8e-22071b54aebc | robin-a-benchmark-for-robustness-to | 2111.14341 | null | https://arxiv.org/abs/2111.14341v4 | https://arxiv.org/pdf/2111.14341v4.pdf | OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images | Enhancing the robustness of vision algorithms in real-world scenarios is challenging. One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or ignore the effects of individual nuisance factors. We introduce OOD-CV, a benchmark dataset that includes out-of-distribution exam... | ['Adam Kortylewski', 'Alan Yuille', 'Ju He', 'Angtian Wang', 'Shenxiao Mei', 'Mingxin Yu', 'Wufei Ma', 'Shaozuo Yu', 'Bingchen Zhao'] | 2021-11-29 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [-6.37272820e-02 -5.67282498e-01 2.33353227e-01 -2.41540253e-01
-4.86111909e-01 -1.05601430e+00 9.34268951e-01 -4.11538966e-02
-5.07373333e-01 3.80081147e-01 2.56655991e-01 -6.66782781e-02
1.38023019e-01 -4.67437297e-01 -8.98723364e-01 -6.95471585e-01
-2.51487434e-01 4.76053841e-02 5.94821692e-01 -3.31344366... | [8.075532913208008, -1.406450629234314] |
8cee172f-40ae-4ecc-9758-e6bf645935b8 | on-training-sketch-recognizers-for-new | 2104.0885 | null | https://arxiv.org/abs/2104.08850v1 | https://arxiv.org/pdf/2104.08850v1.pdf | On Training Sketch Recognizers for New Domains | Sketch recognition algorithms are engineered and evaluated using publicly available datasets contributed by the sketch recognition community over the years. While existing datasets contain sketches of a limited set of generic objects, each new domain inevitably requires collecting new data for training domain specific ... | ['T. Metin Sezgin', 'Kemal Tugrul Yesilbek'] | 2021-04-18 | null | null | null | null | ['sketch-recognition'] | ['computer-vision'] | [ 3.46469611e-01 -2.68105209e-01 -2.18606949e-01 -3.49349678e-01
-6.62843287e-01 -9.90145922e-01 7.15454996e-01 -1.24524593e-01
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-1.74732700e-01 -8.56327176e-01 -8.26206625e-01 -2.99686998e-01
1.15400322e-01 5.77507675e-01 -8.56495053e-02 -2.20709071... | [9.97230052947998, 2.5305545330047607] |
6fcc621d-70b3-4834-9dce-2e1b8f217af0 | material-recognition-from-local-appearance-in | 1611.09394 | null | http://arxiv.org/abs/1611.09394v3 | http://arxiv.org/pdf/1611.09394v3.pdf | Material Recognition from Local Appearance in Global Context | Recognition of materials has proven to be a challenging problem due to the
wide variation in appearance within and between categories. Global image
context, such as where the material is or what object it makes up, can be
crucial to recognizing the material. Existing methods, however, operate on an
implicit fusion of m... | ['Ko Nishino', 'Gabriel Schwartz'] | 2016-11-28 | null | null | null | null | ['material-recognition'] | ['computer-vision'] | [ 7.18696356e-01 -4.01594192e-01 -7.39215389e-02 -3.95176142e-01
-6.85457289e-01 -7.63741016e-01 8.14711750e-01 3.44803572e-01
-3.43601674e-01 3.64971101e-01 6.33258894e-02 1.02178685e-01
-8.09683427e-02 -8.68821263e-01 -1.22600126e+00 -8.51889670e-01
2.77578562e-01 2.90280640e-01 4.38501894e-01 1.00818947... | [10.170536994934082, -0.10981159657239914] |
69a3aee3-6052-4f0f-b58b-1e7adca26d99 | language-embeddings-for-typology-and-cross | 2106.02082 | null | https://arxiv.org/abs/2106.02082v1 | https://arxiv.org/pdf/2106.02082v1.pdf | Language Embeddings for Typology and Cross-lingual Transfer Learning | Cross-lingual language tasks typically require a substantial amount of annotated data or parallel translation data. We explore whether language representations that capture relationships among languages can be learned and subsequently leveraged in cross-lingual tasks without the use of parallel data. We generate dense ... | ['Kenji Sagae', 'Taiqi He', 'Dian Yu'] | 2021-06-03 | null | https://aclanthology.org/2021.acl-long.560 | https://aclanthology.org/2021.acl-long.560.pdf | acl-2021-5 | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [-4.5320669e-01 1.3132535e-01 -4.2874956e-01 -9.5028275e-01
-1.1836717e+00 -1.0248578e+00 8.7469667e-01 2.8007177e-01
-9.3186337e-01 6.4525127e-01 6.6551495e-01 -4.8186862e-01
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-8.9532167e-02 8.1125897e-01 -3.3619338e-01 -3.0501109e-01
-4.9958456e-01... | [11.005242347717285, 9.904871940612793] |
d6b6a20a-00fd-482b-a2e8-7037d6147a85 | the-rl-perceptron-generalisation-dynamics-of | 2306.10404 | null | https://arxiv.org/abs/2306.10404v3 | https://arxiv.org/pdf/2306.10404v3.pdf | The RL Perceptron: Generalisation Dynamics of Policy Learning in High Dimensions | Reinforcement learning (RL) algorithms have proven transformative in a range of domains. To tackle real-world domains, these systems often use neural networks to learn policies directly from pixels or other high-dimensional sensory input. By contrast, much theory of RL has focused on discrete state spaces or worst-case... | ['Adrew Saxe', 'Sebastian Goldt', 'Stefano Sarao Mannelli', 'Sebastian Lee', 'Nishil Patel'] | 2023-06-17 | null | null | null | null | ['atari-games'] | ['playing-games'] | [ 5.67676611e-02 1.36172637e-01 -1.79455936e-01 -9.94182006e-03
-7.94558525e-01 -8.22441459e-01 7.24862576e-01 -1.21640369e-01
-9.23324764e-01 9.56988335e-01 -8.19018260e-02 -4.42345500e-01
-5.56051731e-01 -4.48376566e-01 -7.52580285e-01 -1.15691257e+00
-4.57899570e-01 5.09460628e-01 1.02408655e-01 -5.43758571... | [4.095468044281006, 1.8568180799484253] |
32d76955-3cb1-4d18-a620-7c01abc2d620 | robust-multi-image-based-blind-face | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Jin_Robust_Multi-Image_Based_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Jin_Robust_Multi-Image_Based_2015_CVPR_paper.pdf | Robust Multi-Image Based Blind Face Hallucination | This paper proposes a robust multi-image based blind face hallucination framework to super-resolve LR faces. The proposed framework first estimates both blurring kernel and transformations of multiple LR faces by robust deblurring and registration in PCA subspace. A patch-wise mixture of probabilistic PCA prior is then... | ['Christos-Savvas Bouganis', 'Yonggang Jin'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['face-hallucination'] | ['computer-vision'] | [ 3.02141011e-01 -4.14000630e-01 3.25990617e-01 -3.89824122e-01
-8.66925001e-01 -4.35035765e-01 6.77155852e-01 -1.26011205e+00
7.87111968e-02 7.46724427e-01 6.87727094e-01 3.72664779e-01
-2.83823639e-01 -8.43872577e-02 -4.22179371e-01 -8.72632563e-01
2.12087169e-01 1.65499851e-01 -2.98483551e-01 1.71488393... | [12.85014533996582, -0.002096820157021284] |
a81b2951-b40d-4e18-a4d1-029cf650be3f | textbox-2-0-a-text-generation-library-with | 2212.13005 | null | https://arxiv.org/abs/2212.13005v1 | https://arxiv.org/pdf/2212.13005v1.pdf | TextBox 2.0: A Text Generation Library with Pre-trained Language Models | To facilitate research on text generation, this paper presents a comprehensive and unified library, TextBox 2.0, focusing on the use of pre-trained language models (PLMs). To be comprehensive, our library covers $13$ common text generation tasks and their corresponding $83$ datasets and further incorporates $45$ PLMs c... | ['Ji-Rong Wen', 'Jian-Yun Nie', 'Wayne Xin Zhao', 'Yuhao Wang', 'Xiaoxue Cheng', 'Zican Dong', 'Wenxun Dai', 'Zhuohao Yu', 'Yiwen Hu', 'Zhipeng Chen', 'Junyi Li', 'Tianyi Tang'] | 2022-12-26 | null | null | null | null | ['dialogue', 'abstractive-text-summarization', 'data-to-text-generation', 'story-generation', 'question-generation', 'task-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 3.99576351e-02 4.08508301e-01 -3.17614019e-01 -3.86494905e-01
-1.01607490e+00 -7.79305577e-01 7.90735841e-01 -1.98057920e-01
-2.48339415e-01 9.57199156e-01 1.83494002e-01 -7.95371175e-01
3.19339037e-01 -7.43949354e-01 -5.37293613e-01 -1.67049497e-01
2.12385327e-01 4.90446627e-01 -4.37556326e-01 -3.00822139... | [11.478611946105957, 9.12226676940918] |
95715e21-d2ad-4aed-bdaf-d2bc4533384b | deeper-profiles-and-cascaded-recurrent-and | null | null | https://doi.org/10.1038/s41598-019-48786-x | https://www.nature.com/articles/s41598-019-48786-x.pdf | Deeper Profiles and Cascaded Recurrent and Convolutional Neural Networks for state-of-the-art Protein Secondary Structure Prediction | Protein Secondary Structure prediction has been a central topic of research in Bioinformatics for decades. In spite of this, even the most sophisticated ab initio SS predictors are not able to reach the theoretical limit of three-state prediction accuracy (88–90%), while only a few predict more than the 3 traditional H... | ['Mirko Torrisi', 'Gianluca Pollastri', 'Manaz Kaleel'] | 2019-10-12 | null | null | null | scientific-reports-2019-10 | ['protein-secondary-structure-prediction'] | ['medical'] | [ 3.98081630e-01 8.08743536e-02 -1.89748362e-01 -2.64118642e-01
-7.68554449e-01 -4.13594097e-01 3.96074951e-01 4.00494635e-01
-7.04514086e-01 1.23641670e+00 -1.24833230e-02 -9.24246728e-01
1.40945047e-01 -3.80826950e-01 -8.84968579e-01 -9.80903268e-01
-5.35638444e-02 7.73863733e-01 5.40745676e-01 -5.63350379... | [4.717804908752441, 5.562806129455566] |
57bdbc61-518b-436a-a663-2edcfff9db0e | balancing-novelty-and-salience-adaptive | 1701.03947 | null | http://arxiv.org/abs/1701.03947v1 | http://arxiv.org/pdf/1701.03947v1.pdf | Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events | Long-running, high-impact events such as the Boston Marathon bombing often
develop through many stages and involve a large number of entities in their
unfolding. Timeline summarization of an event by key sentences eases story
digestion, but does not distinguish between what a user remembers and what she
might want to r... | ['Claudia Niederée', 'Nattiya Kanhabua', 'Avishek Anand', 'Ujwal Gadiraju', 'Tuan Tran'] | 2017-01-14 | null | null | null | null | ['timeline-summarization'] | ['natural-language-processing'] | [-2.27157772e-02 2.29824841e-01 -2.85112023e-01 -3.77254605e-01
-9.61146951e-01 -5.58417737e-01 8.28876317e-01 1.46658874e+00
-6.58828855e-01 9.21820223e-01 1.28339958e+00 1.90569982e-01
-3.44294608e-01 -9.04303253e-01 -7.23159671e-01 -3.84823412e-01
-3.15660268e-01 5.55886328e-01 4.41904098e-01 -9.42635611... | [12.56828498840332, 9.517938613891602] |
4afdbae4-5a64-49ab-8c7a-0b47a404f499 | speech-modeling-with-a-hierarchical | 2303.09404 | null | https://arxiv.org/abs/2303.09404v2 | https://arxiv.org/pdf/2303.09404v2.pdf | Speech Modeling with a Hierarchical Transformer Dynamical VAE | The dynamical variational autoencoders (DVAEs) are a family of latent-variable deep generative models that extends the VAE to model a sequence of observed data and a corresponding sequence of latent vectors. In almost all the DVAEs of the literature, the temporal dependencies within each sequence and across the two seq... | ['Xavier Alameda-Pineda', 'Laurent Girin', 'Simon Leglaive', 'Xiaoyu Bie', 'Xiaoyu Lin'] | 2023-03-07 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 5.99104576e-02 2.04239637e-01 3.53254601e-02 -1.54689690e-02
-3.43927205e-01 -5.41529357e-01 9.54603910e-01 -6.25466645e-01
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1.21657522e-02 -6.98004246e-01 -5.84468007e-01 -1.09268844e+00
3.24823111e-02 9.38593969e-02 -1.02821738e-01 -1.23660736... | [15.080416679382324, 6.312829971313477] |
f87eb258-d059-40d9-b39e-c72ca38b73d6 | non-rigid-point-cloud-registration-for-middle | 2304.13618 | null | https://arxiv.org/abs/2304.13618v1 | https://arxiv.org/pdf/2304.13618v1.pdf | Non-rigid Point Cloud Registration for Middle Ear Diagnostics with Endoscopic Optical Coherence Tomography | Purpose: Middle ear infection is the most prevalent inflammatory disease, especially among the pediatric population. Current diagnostic methods are subjective and depend on visual cues from an otoscope, which is limited for otologists to identify pathology. To address this shortcoming, endoscopic optical coherence tomo... | ['Stefanie Speidel', 'Marcus Neudert', 'Edmund Koch', 'Zhaoyu Chen', 'Yujia Hu', 'Chenpan Li', 'Sebastian Bodenstedt', 'Joseph Morgenstern', 'Jonas Golde', 'Peng Liu'] | 2023-04-26 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-2.91088581e-01 -2.25779742e-01 3.42744797e-01 -4.67600627e-03
-9.15633678e-01 -5.44052780e-01 -3.21742445e-01 1.36176303e-01
-3.00240695e-01 3.72219592e-01 5.00957556e-02 -1.25194564e-01
-3.11814286e-02 -5.31041265e-01 -4.70351934e-01 -4.94322002e-01
5.38944919e-03 9.79884267e-01 3.74745339e-01 1.30267441... | [13.881196022033691, -2.9506514072418213] |
722a7e8c-14f0-4af0-8ecd-513e1e1c2c51 | information-gain-sampling-for-active-learning | 2208.00974 | null | https://arxiv.org/abs/2208.00974v1 | https://arxiv.org/pdf/2208.00974v1.pdf | Information Gain Sampling for Active Learning in Medical Image Classification | Large, annotated datasets are not widely available in medical image analysis due to the prohibitive time, costs, and challenges associated with labelling large datasets. Unlabelled datasets are easier to obtain, and in many contexts, it would be feasible for an expert to provide labels for a small subset of images. Thi... | ['Tal Arbel', 'Brennan Nichyporuk', 'Changjian Shui', 'Raghav Mehta'] | 2022-08-01 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 8.32314014e-01 6.50751352e-01 -7.44177520e-01 -8.29147756e-01
-1.26222682e+00 -2.31615067e-01 2.27518708e-01 6.77306771e-01
-8.56802940e-01 9.53901410e-01 -2.01136805e-02 -3.12518589e-02
-4.25619841e-01 -5.65226495e-01 -3.54972929e-01 -1.05167210e+00
1.30864695e-01 7.41001606e-01 3.37822014e-03 6.69053912... | [14.921767234802246, -2.3250699043273926] |
3705d603-2871-4657-8e9f-08d3b7d814ab | rfc-net-learning-high-resolution-global | 2302.06134 | null | https://arxiv.org/abs/2302.06134v1 | https://arxiv.org/pdf/2302.06134v1.pdf | RFC-Net: Learning High Resolution Global Features for Medical Image Segmentation on a Computational Budget | Learning High-Resolution representations is essential for semantic segmentation. Convolutional neural network (CNN)architectures with downstream and upstream propagation flow are popular for segmentation in medical diagnosis. However, due to performing spatial downsampling and upsampling in multiple stages, information... | ['David Chapman', 'Tim Oates', 'Md Osman Gani', 'Shaswati Saha', 'Sourajit Saha'] | 2023-02-13 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [ 2.29501322e-01 3.27788085e-01 -3.51778269e-01 -4.32788938e-01
-7.45172381e-01 1.64882187e-02 1.23531818e-01 1.55959144e-01
-4.35631394e-01 6.55438423e-01 2.15021372e-01 -2.17789814e-01
-2.65802592e-01 -1.01868224e+00 -7.23759532e-01 -4.81149673e-01
-1.07509859e-01 1.53052464e-01 4.78842586e-01 -7.78555870... | [14.53510570526123, -2.592127561569214] |
ff912a90-e43d-4f55-9d8c-4749d35ef4e5 | why-words-alone-are-not-enough-error-analysis | null | null | https://aclanthology.org/W13-4101 | https://aclanthology.org/W13-4101.pdf | Why Words Alone Are Not Enough: Error Analysis of Lexicon-based Polarity Classifier for Czech | null | ['Jan Haji{\\v{c}} jr.', "Kate{\\v{r}}ina Veselovsk{\\'a}"] | 2013-10-01 | null | null | null | ws-2013-10 | ['fine-grained-opinion-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.156679153442383, 3.7958171367645264] |
54694759-f82d-4a3a-8750-ba524632f266 | optimized-three-deep-learning-models-based | 2306.07296 | null | https://arxiv.org/abs/2306.07296v1 | https://arxiv.org/pdf/2306.07296v1.pdf | Optimized Three Deep Learning Models Based-PSO Hyperparameters for Beijing PM2.5 Prediction | Deep learning is a machine learning approach that produces excellent performance in various applications, including natural language processing, image identification, and forecasting. Deep learning network performance depends on the hyperparameter settings. This research attempts to optimize the deep learning architect... | ['Felix Andika Dwiyanto', 'Agung Bella Putra Utama', 'Aji Prasetya Wibawa', 'Yingchi Mao', 'Andri Pranolo'] | 2023-06-10 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [-2.65063733e-01 -6.79832280e-01 3.01476475e-02 4.17226665e-02
6.15744814e-02 6.12174068e-03 4.05728847e-01 1.63325161e-01
-5.64553499e-01 9.31747854e-01 -3.91978398e-02 -5.34257770e-01
-6.18528128e-01 -1.05909157e+00 -4.70412105e-01 -1.12334538e+00
-2.92301774e-01 1.12099908e-01 -9.52733979e-02 -2.45928109... | [6.208837032318115, 2.8080077171325684] |
f5c0df20-48a8-4a8e-85e9-17f01be52334 | alfworld-aligning-text-and-embodied | 2010.03768 | null | https://arxiv.org/abs/2010.03768v2 | https://arxiv.org/pdf/2010.03768v2.pdf | ALFWorld: Aligning Text and Embodied Environments for Interactive Learning | Given a simple request like Put a washed apple in the kitchen fridge, humans can reason in purely abstract terms by imagining action sequences and scoring their likelihood of success, prototypicality, and efficiency, all without moving a muscle. Once we see the kitchen in question, we can update our abstract plans to f... | ['Matthew Hausknecht', 'Adam Trischler', 'Yonatan Bisk', 'Marc-Alexandre Côté', 'Xingdi Yuan', 'Mohit Shridhar'] | 2020-10-08 | null | null | null | null | ['natural-language-visual-grounding'] | ['reasoning'] | [-1.66168720e-01 5.28659046e-01 2.32431024e-01 -1.10899761e-01
-2.41449684e-01 -7.76110470e-01 8.25336695e-01 8.49458724e-02
-4.54164654e-01 6.06761754e-01 4.83621418e-01 -6.00640655e-01
3.96933407e-02 -6.60447001e-01 -8.79109561e-01 -3.29742521e-01
-1.60561964e-01 6.94540381e-01 1.02322049e-01 -4.23333913... | [4.2787184715271, 0.8953312039375305] |
04740d3a-6a51-4e98-9888-89caf83e92c0 | fully-sparse-fusion-for-3d-object-detection | 2304.1231 | null | https://arxiv.org/abs/2304.12310v2 | https://arxiv.org/pdf/2304.12310v2.pdf | Fully Sparse Fusion for 3D Object Detection | Currently prevalent multimodal 3D detection methods are built upon LiDAR-based detectors that usually use dense Bird's-Eye-View (BEV) feature maps. However, the cost of such BEV feature maps is quadratic to the detection range, making it not suitable for long-range detection. Fully sparse architecture is gaining attent... | ['Tieniu Tan', 'Zhaoxiang Zhang', 'Naiyan Wang', 'Yuntao Chen', 'Zehao Huang', 'Yang Liu', 'Lue Fan', 'Yingyan Li'] | 2023-04-24 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 1.46448851e-01 -1.47174060e-01 -1.41216338e-01 -5.21597326e-01
-1.20304024e+00 -5.51527739e-01 4.85300660e-01 5.40691987e-02
-6.27857506e-01 1.47828624e-01 -2.64800191e-01 1.97134999e-04
2.37762071e-02 -8.68503511e-01 -7.87945330e-01 -6.41186297e-01
2.78572530e-01 5.66066504e-01 5.68697393e-01 -2.11497083... | [7.786181926727295, -2.603565216064453] |
bab385e5-ba3e-4169-8dbc-c153fde224a5 | dynamic-context-sensitive-filtering-network | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhang_Dynamic_Context-Sensitive_Filtering_Network_for_Video_Salient_Object_Detection_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhang_Dynamic_Context-Sensitive_Filtering_Network_for_Video_Salient_Object_Detection_ICCV_2021_paper.pdf | Dynamic Context-Sensitive Filtering Network for Video Salient Object Detection | The ability to capture inter-frame dynamics has been critical to the development of video salient object detection (VSOD). While many works have achieved great success in this field, a deeper insight into its dynamic nature should be developed. In this work, we aim to answer the following questions: How can a model... | ['Zhongxuan Luo', 'Huchuan Lu', 'Jingjing Li', 'Wei Ji', 'Shunyu Yao', 'Yongri Piao', 'Yifei Wang', 'Jie Liu', 'Miao Zhang'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['video-salient-object-detection', 'video-polyp-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.50541300e-01 -5.37208974e-01 -9.76144299e-02 -3.65324944e-01
-2.61576891e-01 -4.61741865e-01 5.48657358e-01 -4.14566956e-02
-3.93990546e-01 5.10262191e-01 2.95962095e-01 1.45669088e-01
-1.51558191e-01 -6.95299149e-01 -6.20339632e-01 -6.95749938e-01
-1.22899614e-01 -1.87540710e-01 1.11050189e+00 -5.32705903... | [9.418055534362793, -0.4368710219860077] |
f6f84f09-297b-4e37-922f-5d0a47b1262b | video-colorization-with-pre-trained-text-to | 2306.01732 | null | https://arxiv.org/abs/2306.01732v1 | https://arxiv.org/pdf/2306.01732v1.pdf | Video Colorization with Pre-trained Text-to-Image Diffusion Models | Video colorization is a challenging task that involves inferring plausible and temporally consistent colors for grayscale frames. In this paper, we present ColorDiffuser, an adaptation of a pre-trained text-to-image latent diffusion model for video colorization. With the proposed adapter-based approach, we repropose th... | ['Tien-Tsin Wong', 'Chengze Li', 'Jinbo Xing', 'Minshan Xie', 'Hanyuan Liu'] | 2023-06-02 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 2.73702681e-01 -5.10717571e-01 -1.07119143e-01 -1.18035920e-01
-4.20546383e-01 -5.31677306e-01 6.21676385e-01 -4.91601914e-01
-3.23774129e-01 6.56064212e-01 1.98638842e-01 -2.14460567e-01
2.23113894e-01 -5.08901954e-01 -7.35244215e-01 -9.67560828e-01
2.28513017e-01 -2.66209573e-01 3.11425209e-01 2.19560832... | [11.141531944274902, -1.1852284669876099] |
15ea292b-8390-4d1b-b654-7da21b68ea3f | semi-supervised-credit-card-fraud-detection | null | null | https://doi.org/10.1609/aaai.v37i12.26702 | https://ojs.aaai.org/index.php/AAAI/article/view/26702/26474 | Semi-supervised Credit Card Fraud Detection via Attribute-driven Graph Representation | Credit card fraud incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based classifiers to detect fraudulent behavior from labeled transaction records. But labeled data are usually a small proportion of billions of real transactions due to expensive labeling co... | ['Yefeng Zheng', 'Ling Chen', 'Yi Ouyang', 'Ruihui Zhao', 'Enxia Li', 'Dawei Cheng', 'Mingzhi Zhu', 'Sheng Xiang'] | 2023-06-26 | null | null | null | aaai-2023-6 | ['fraud-detection'] | ['miscellaneous'] | [-2.51431763e-01 -2.58726835e-01 -5.09283066e-01 -6.94628298e-01
-1.50399417e-01 -6.08092397e-02 3.39138865e-01 2.31539845e-01
-3.68359178e-01 5.10134399e-01 2.57846899e-02 -4.69671905e-01
3.42461228e-01 -1.11996806e+00 -5.79539120e-01 -1.38905346e-01
-5.12338042e-01 5.32764494e-01 1.08452760e-01 -2.13039041... | [7.326705455780029, 5.809619903564453] |
507932d6-0dd4-4511-8ea6-308f0a87c7dc | multimodal-garment-designer-human-centric | 2304.02051 | null | https://arxiv.org/abs/2304.02051v1 | https://arxiv.org/pdf/2304.02051v1.pdf | Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editing | Fashion illustration is used by designers to communicate their vision and to bring the design idea from conceptualization to realization, showing how clothes interact with the human body. In this context, computer vision can thus be used to improve the fashion design process. Differently from previous works that mainly... | ['Rita Cucchiara', 'Marco Bertini', 'Marcella Cornia', 'Giuseppe Cartella', 'Davide Morelli', 'Alberto Baldrati'] | 2023-04-04 | null | null | null | null | ['multimodal-fashion-image-editing'] | ['computer-vision'] | [ 1.59566671e-01 1.11680338e-02 2.58866362e-02 -3.41968507e-01
-2.42167726e-01 -7.77371943e-01 1.00202751e+00 3.97775471e-02
-8.76179859e-02 3.73959810e-01 4.24408406e-01 -3.33491005e-02
2.91911457e-02 -6.03854656e-01 -5.86536527e-01 -4.41348970e-01
4.53332931e-01 5.04792452e-01 -6.52476624e-02 -5.38584173... | [11.71531867980957, -0.6738238334655762] |
e2b23958-7393-4504-a689-3d925ec75c2e | phd-thesis-exploring-the-role-of-self | 2306.1465 | null | https://arxiv.org/abs/2306.14650v2 | https://arxiv.org/pdf/2306.14650v2.pdf | PhD Thesis: Exploring the role of (self-)attention in cognitive and computer vision architecture | We investigate the role of attention and memory in complex reasoning tasks. We analyze Transformer-based self-attention as a model and extend it with memory. By studying a synthetic visual reasoning test, we refine the taxonomy of reasoning tasks. Incorporating self-attention with ResNet50, we enhance feature maps usin... | ['Mohit Vaishnav'] | 2023-06-26 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [-3.65703143e-02 3.16753954e-01 3.09587270e-01 1.64627045e-01
-6.00793399e-02 -4.60717082e-01 6.75386071e-01 9.53289717e-02
-5.66136241e-01 2.64192909e-01 6.75139189e-01 -2.65290916e-01
-6.41338825e-01 -9.69989717e-01 -4.11700457e-01 -3.57620209e-01
4.91041429e-02 5.81148684e-01 4.52706724e-01 -5.94189942... | [10.628929138183594, 2.237187623977661] |
bd3b8fd5-0630-4f58-a208-0e4727b2dfd9 | superocr-a-conversion-from-optical-character | 2012.02033 | null | https://arxiv.org/abs/2012.02033v1 | https://arxiv.org/pdf/2012.02033v1.pdf | SuperOCR: A Conversion from Optical Character Recognition to Image Captioning | Optical Character Recognition (OCR) has many real world applications. The existing methods normally detect where the characters are, and then recognize the character for each detected location. Thus the accuracy of characters recognition is impacted by the performance of characters detection. In this paper, we propose ... | ['Lin Yang', 'Hao Sha', 'Michael Lin', 'Baohua Sun'] | 2020-11-21 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 4.73863721e-01 -5.70995510e-01 -3.01571824e-02 -2.35970840e-01
1.05776213e-01 -7.19707072e-01 2.25759059e-01 8.54481361e-04
-5.19105136e-01 3.64502043e-01 -5.60664296e-01 -4.79564965e-01
5.30868411e-01 -7.55839229e-01 -4.03019249e-01 -7.09770501e-01
5.73085964e-01 2.66275167e-01 7.29717731e-01 2.69096226... | [9.843330383300781, -4.929326057434082] |
5d5229c5-f6be-4937-b155-2495de52d3ab | decoding-visemes-improving-machine-lipreading | 1710.01288 | null | http://arxiv.org/abs/1710.01288v1 | http://arxiv.org/pdf/1710.01288v1.pdf | Decoding visemes: improving machine lipreading | Machine lipreading (MLR) is speech recognition from visual cues and a niche
research problem in speech processing & computer vision. Current challenges
fall into two groups: the content of the video, such as rate of speech or; the
parameters of the video recording e.g, video resolution. We show that HD video
is not nee... | ['Helen L. Bear'] | 2017-10-03 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 2.94889122e-01 -2.23306403e-01 -3.52062315e-01 -1.23646140e-01
-8.82520795e-01 -4.84093726e-01 6.00154102e-01 -2.55310655e-01
-2.91625082e-01 6.64015472e-01 4.28665370e-01 -5.02110064e-01
1.56036600e-01 -1.98445886e-01 -7.45346963e-01 -8.18697989e-01
1.41255304e-01 1.52156204e-01 3.05484176e-01 -1.92345589... | [14.308297157287598, 5.003406524658203] |
b9f20393-93a8-451e-b96e-7496008a733a | update-3-0-to-puma-the-porous-microstructure | null | null | https://www.sciencedirect.com/science/article/pii/S235271102100090X | https://www.sciencedirect.com/science/article/pii/S235271102100090X/pdfft?isDTMRedir=true&download=true | Update 3.0 to “PuMA: The Porous Microstructure Analysis software” | A major update of the Porous Microstructure Analysis (PuMA) software is presented. PuMA is a framework for computing effective material properties and response based on material microstructures. Version 3.0 of the software extends the PuMA capabilities to include computation of anisotropic conductivity, elasticity, per... | ['Nagi N. Mansour', 'Arnaud Borner', 'Francesco Panerai', 'John M. Thornton', 'Federico Semeraro', 'Joseph C. Ferguson'] | 2021-07-19 | null | null | null | softwarex-2021-7 | ['physical-simulations'] | ['miscellaneous'] | [-2.09254563e-01 -1.01345658e-01 6.15277171e-01 1.43385813e-01
-2.98559159e-01 -1.86590314e-01 3.12641889e-01 1.69534400e-01
-1.59753069e-01 9.53308523e-01 3.14507693e-01 -2.53557891e-01
-4.94733363e-01 -1.44374526e+00 -5.38227975e-01 -1.00640690e+00
-4.57334310e-01 6.05003834e-01 1.08149040e+00 1.30527630... | [6.393115997314453, 3.335890293121338] |
041ca111-b997-4b38-9779-e862e4a9f361 | meaformer-multi-modal-entity-alignment | 2212.14454 | null | https://arxiv.org/abs/2212.14454v3 | https://arxiv.org/pdf/2212.14454v3.pdf | MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid | As an important variant of entity alignment (EA), multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) with relevant images attached. We noticed that current MMEA algorithms all globally adopt the KG-level modality fusion strategies for multi-modal entity repre... | ['Yuxia Geng', 'Yichi Zhang', 'Huajun Chen', 'Wenting Song', 'Jeff Z. Pan', 'Yufeng Huang', 'Yin Fang', 'Lingbing Guo', 'Wen Zhang', 'Jiaoyan Chen', 'Zhuo Chen'] | 2022-12-29 | null | null | null | null | ['multi-modal-entity-alignment', 'entity-alignment', 'entity-alignment'] | ['knowledge-base', 'knowledge-base', 'natural-language-processing'] | [-7.40770474e-02 2.87303776e-01 -2.62606084e-01 -2.43298799e-01
-1.09410059e+00 -6.84009373e-01 4.98242170e-01 3.79034251e-01
-6.56762868e-02 6.88125789e-01 4.60842162e-01 2.89370418e-01
-4.32460874e-01 -6.73384786e-01 -5.25820315e-01 -5.90784490e-01
6.35143230e-03 4.94411200e-01 1.60512462e-01 9.82825682... | [8.656244277954102, 7.677020072937012] |
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