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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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
51574bde-a381-44a8-99b6-4a9bd1112bb7 | learning-from-synthetic-data-generated-with | 2305.04282 | null | https://arxiv.org/abs/2305.04282v2 | https://arxiv.org/pdf/2305.04282v2.pdf | Learning from synthetic data generated with GRADE | Recently, synthetic data generation and realistic rendering has advanced tasks like target tracking and human pose estimation. Simulations for most robotics applications are obtained in (semi)static environments, with specific sensors and low visual fidelity. To solve this, we present a fully customizable framework for... | ['Aamir Ahmad', 'Chenghao Xu', 'Elia Bonetto'] | 2023-05-07 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [-1.43070385e-01 -7.11942017e-02 3.91570240e-01 -2.54456788e-01
-2.39467293e-01 -5.07838488e-01 5.54938614e-01 -2.19583750e-01
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2.96881646e-01 -7.91627824e-01 -9.82785821e-01 -3.41105491e-01
-4.67449099e-01 8.26844633e-01 5.43513298e-01 -4.54169631... | [7.611990451812744, -1.0849730968475342] |
cde7671f-5d31-47c2-9193-37d266aa486b | pevl-position-enhanced-pre-training-and | 2205.11169 | null | https://arxiv.org/abs/2205.11169v2 | https://arxiv.org/pdf/2205.11169v2.pdf | PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models | Vision-language pre-training (VLP) has shown impressive performance on a wide range of cross-modal tasks, where VLP models without reliance on object detectors are becoming the mainstream due to their superior computation efficiency and competitive performance. However, the removal of object detectors also deprives the... | ['Maosong Sun', 'Tat-Seng Chua', 'Zhiyuan Liu', 'Wei Ji', 'Ao Zhang', 'Qianyu Chen', 'Yuan YAO'] | 2022-05-23 | null | null | null | null | ['visual-relationship-detection', 'phrase-grounding', 'visual-commonsense-reasoning'] | ['computer-vision', 'natural-language-processing', 'reasoning'] | [ 1.44605428e-01 -9.99974310e-02 -1.88915253e-01 -4.18253332e-01
-9.20409262e-01 -6.84561133e-01 6.15114272e-01 1.31770015e-01
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4.15875763e-01 4.21044946e-01 3.93370062e-01 -2.90541470... | [10.55911922454834, 1.7133899927139282] |
dcc4a176-d367-43e4-a2db-3a8dddc3da4f | hqp-a-human-annotated-dataset-for-detecting | 2304.14931 | null | https://arxiv.org/abs/2304.14931v2 | https://arxiv.org/pdf/2304.14931v2.pdf | HQP: A Human-Annotated Dataset for Detecting Online Propaganda | Online propaganda poses a severe threat to the integrity of societies. However, existing datasets for detecting online propaganda have a key limitation: they were annotated using weak labels that can be noisy and even incorrect. To address this limitation, our work makes the following contributions: (1) We present HQP:... | ['Stefan Feuerriegel', 'Dominique Geissler', 'Dominik Bär', 'Abdurahman Maarouf'] | 2023-04-28 | null | null | null | null | ['propaganda-detection'] | ['natural-language-processing'] | [ 4.07001153e-02 4.36250716e-02 -4.54097331e-01 8.56959596e-02
-1.05345678e+00 -7.57299721e-01 8.64565611e-01 5.11814177e-01
-5.67621589e-01 7.33731985e-01 4.82204795e-01 -5.23511708e-01
9.17907581e-02 -9.49722946e-01 -3.95235181e-01 -3.80482227e-01
-2.08874509e-01 1.03584476e-01 2.90832072e-01 -2.60769248... | [8.52349853515625, 10.626734733581543] |
1fd7da8d-1328-4aa1-9434-a44632a72d23 | diff-ttsg-denoising-probabilistic-integrated | 2306.09417 | null | https://arxiv.org/abs/2306.09417v2 | https://arxiv.org/pdf/2306.09417v2.pdf | Diff-TTSG: Denoising probabilistic integrated speech and gesture synthesis | With read-aloud speech synthesis achieving high naturalness scores, there is a growing research interest in synthesising spontaneous speech. However, human spontaneous face-to-face conversation has both spoken and non-verbal aspects (here, co-speech gestures). Only recently has research begun to explore the benefits of... | ['Gustav Eje Henter', 'Éva Székely', 'Jonas Beskow', 'Simon Alexanderson', 'Siyang Wang', 'Shivam Mehta'] | 2023-06-15 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [ 7.73621127e-02 3.36689144e-01 1.15284912e-01 -4.74521220e-01
-1.13499391e+00 -5.55473983e-01 1.17079222e+00 -9.00707304e-01
-6.65037930e-02 4.82506424e-01 7.89965332e-01 -1.08030364e-02
2.69177437e-01 -1.55402064e-01 -4.40480858e-01 -8.53355527e-01
2.47732669e-01 4.84069884e-01 3.78241509e-01 1.91565938... | [5.622539520263672, -0.11584766954183578] |
2f184c2f-0571-49b6-be29-8ab11a0ba6a5 | heterogeneous-graph-contrastive-multi-view | 2210.00248 | null | https://arxiv.org/abs/2210.00248v2 | https://arxiv.org/pdf/2210.00248v2.pdf | Heterogeneous Graph Contrastive Multi-view Learning | Inspired by the success of contrastive learning (CL) in computer vision and natural language processing, graph contrastive learning (GCL) has been developed to learn discriminative node representations on graph datasets. However, the development of GCL on Heterogeneous Information Networks (HINs) is still in the infant... | ['Shigen Shen', 'Xiao-Zhi Gao', 'Xiaolong Han', 'Donghua Yu', 'Qi Li', 'Zehong Wang'] | 2022-10-01 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 4.25208807e-01 3.70914519e-01 -5.15882254e-01 -3.18301320e-01
-3.85861099e-01 -4.44936723e-01 6.54646456e-01 4.33385409e-02
1.73780024e-01 4.91729051e-01 3.24458450e-01 -2.41299979e-02
-6.53138906e-02 -1.04695165e+00 -6.23671651e-01 -6.02254272e-01
2.90275291e-02 2.61445373e-01 3.09266150e-01 -1.29823193... | [7.387381553649902, 6.157910346984863] |
b69505cf-254b-4262-81e9-26de64268d71 | informed-down-sampled-lexicase-selection | 2301.01488 | null | https://arxiv.org/abs/2301.01488v1 | https://arxiv.org/pdf/2301.01488v1.pdf | Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving | Genetic Programming (GP) often uses large training sets and requires all individuals to be evaluated on all training cases during selection. Random down-sampled lexicase selection evaluates individuals on only a random subset of the training cases allowing for more individuals to be explored with the same amount of pro... | ['Lee Spector', 'Charles Ofria', 'Franz Rothlauf', 'Thomas Helmuth', 'Alexander Lalejini', 'Dominik Sobania', 'Martin Briesch', 'Ryan Boldi'] | 2023-01-04 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 5.40973306e-01 1.98117688e-01 -2.19177246e-01 -3.95749271e-01
-7.41791964e-01 -6.44372106e-01 2.93817639e-01 4.10354614e-01
-2.14003459e-01 1.00826311e+00 -5.90090975e-02 -2.16119319e-01
-2.56617248e-01 -1.05912924e+00 -6.72114491e-01 -7.26682961e-01
-1.96847022e-01 7.61264086e-01 3.02636415e-01 -2.55870402... | [8.008892059326172, 7.175429821014404] |
7e3df778-c5f3-4c31-9269-a7f6b3767a64 | fake-news-or-truth-using-satirical-cues-to | null | null | https://aclanthology.org/W16-0802 | https://aclanthology.org/W16-0802.pdf | Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News | null | ['Sarah Cornwell', 'Victoria Rubin', 'Niall Conroy', 'Yimin Chen'] | 2016-06-01 | null | null | null | ws-2016-6 | ['deception-detection'] | ['miscellaneous'] | [-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... | [-7.340583801269531, 3.738247871398926] |
b8348b72-399c-40ec-907e-fc573c25d5e2 | towards-human-centered-explainable-ai-user | 2210.11584 | null | https://arxiv.org/abs/2210.11584v2 | https://arxiv.org/pdf/2210.11584v2.pdf | Towards Human-centered Explainable AI: User Studies for Model Explanations | Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A better understanding of the needs of XAI users, as well as human-centered evaluations of explainable models are both a necessity and a challenge. In this paper, we explore how HCI and AI researchers conduct user studies in XAI app... | ['Enkelejda Kasneci', 'Gjergji Kasneci', 'Tina Seidel', 'Vaibhav Unhelkar', 'Peizhu Qian', 'Lisa Fiedler', 'Thai-trang Nguyen', 'Tobias Leemann', 'Yao Rong'] | 2022-10-20 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [-6.16735555e-02 3.78734499e-01 -4.26297843e-01 -5.33783317e-01
5.27572632e-02 -4.29729193e-01 2.61688441e-01 1.62848681e-01
-6.10313341e-02 4.67042625e-01 3.52929413e-01 -5.30924261e-01
-6.30207777e-01 -9.95823517e-02 -3.25837612e-01 -2.63990425e-02
1.98905960e-01 4.40366715e-01 -6.97392225e-01 -2.73371577... | [9.040423393249512, 6.188049793243408] |
7acba4e6-39fa-42f7-ae8b-d13f4da192fd | xsemplr-cross-lingual-semantic-parsing-in | 2306.04085 | null | https://arxiv.org/abs/2306.04085v1 | https://arxiv.org/pdf/2306.04085v1.pdf | XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations | Cross-Lingual Semantic Parsing (CLSP) aims to translate queries in multiple natural languages (NLs) into meaning representations (MRs) such as SQL, lambda calculus, and logic forms. However, existing CLSP models are separately proposed and evaluated on datasets of limited tasks and applications, impeding a comprehensiv... | ['Rui Zhang', 'Zhiguo Wang', 'Jun Wang', 'Yusen Zhang'] | 2023-06-07 | null | null | null | null | ['semantic-parsing', 'cross-lingual-transfer', 'xlm-r'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.33761808e-01 -1.74445771e-02 -5.55241108e-01 -5.49374938e-01
-1.45512247e+00 -8.46914411e-01 4.41188604e-01 -1.01209998e-01
-2.79326051e-01 7.41818964e-01 3.68416846e-01 -7.47997344e-01
2.59191662e-01 -8.16399455e-01 -1.01204455e+00 -8.66964608e-02
4.03923005e-01 3.68824065e-01 2.68193394e-01 -4.64870602... | [10.879834175109863, 9.375723838806152] |
29a09de9-a1a9-4f7f-b6c4-12aa3767b204 | unsupervised-extractive-opinion-summarization | 2203.07921 | null | https://arxiv.org/abs/2203.07921v3 | https://arxiv.org/pdf/2203.07921v3.pdf | Unsupervised Extractive Opinion Summarization Using Sparse Coding | Opinion summarization is the task of automatically generating summaries that encapsulate information from multiple user reviews. We present Semantic Autoencoder (SemAE) to perform extractive opinion summarization in an unsupervised manner. SemAE uses dictionary learning to implicitly capture semantic information from t... | ['Snigdha Chaturvedi', 'Chao Zhao', 'Somnath Basu Roy Chowdhury'] | 2022-03-15 | null | https://aclanthology.org/2022.acl-long.86 | https://aclanthology.org/2022.acl-long.86.pdf | acl-2022-5 | ['extractive-summarization'] | ['natural-language-processing'] | [ 2.66456544e-01 6.47527635e-01 -2.34008268e-01 -4.63007510e-01
-1.06010866e+00 -4.35023844e-01 6.48370326e-01 5.44237792e-01
5.73342061e-03 8.39761436e-01 1.25346577e+00 2.00710580e-01
4.84418839e-01 -8.72217655e-01 -6.95815921e-01 -3.56478602e-01
5.12173593e-01 4.62897748e-01 -4.79626447e-01 -3.39410633... | [12.42184829711914, 9.362201690673828] |
56d9e2cb-8484-4d2c-b3bf-939cf7cffa74 | practical-auto-calibration-for-spatial-scene | 2012.08375 | null | https://arxiv.org/abs/2012.08375v1 | https://arxiv.org/pdf/2012.08375v1.pdf | Practical Auto-Calibration for Spatial Scene-Understanding from Crowdsourced Dashcamera Videos | Spatial scene-understanding, including dense depth and ego-motion estimation, is an important problem in computer vision for autonomous vehicles and advanced driver assistance systems. Thus, it is beneficial to design perception modules that can utilize crowdsourced videos collected from arbitrary vehicular onboard or ... | ['Bahram Zonooz', 'Elahe Arani', 'Shabbir Marzban', 'Matti Jukola', 'Hemang Chawla'] | 2020-12-15 | null | null | null | null | ['camera-auto-calibration'] | ['computer-vision'] | [-3.09016913e-01 -2.33838961e-01 1.78184122e-01 -5.98455608e-01
-6.24674857e-01 -7.14507937e-01 2.33770177e-01 -4.81425464e-01
-6.06075346e-01 7.88348377e-01 -3.59819293e-01 -2.76847720e-01
3.33332777e-01 -4.66376632e-01 -1.09607112e+00 -5.48736691e-01
4.56603229e-01 5.82251370e-01 6.42992556e-01 -2.78805017... | [8.104170799255371, -2.0753567218780518] |
cadfa467-c46d-4038-a31c-73ad3d6df9e9 | a-robust-visual-system-for-small-target | 1904.04363 | null | http://arxiv.org/abs/1904.04363v1 | http://arxiv.org/pdf/1904.04363v1.pdf | A Robust Visual System for Small Target Motion Detection Against Cluttered Moving Backgrounds | Monitoring small objects against cluttered moving backgrounds is a huge
challenge to future robotic vision systems. As a source of inspiration, insects
are quite apt at searching for mates and tracking prey -- which always appear
as small dim speckles in the visual field. The exquisite sensitivity of insects
for small ... | ['Shigang Yue', 'Jigen Peng', 'Hongxin Wang', 'Xuqiang Zheng'] | 2019-04-08 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 1.98123246e-01 -3.75893950e-01 -2.33627968e-02 6.94710910e-02
3.43505055e-01 -7.46670961e-01 6.07653737e-01 -6.50043607e-01
-6.15496576e-01 6.59553707e-01 -3.72944236e-01 1.38817713e-01
5.71248889e-01 -6.18898749e-01 -4.53376472e-01 -1.04347253e+00
2.75205523e-02 -2.35216826e-01 1.21476972e+00 1.57046821... | [8.371395111083984, -0.8705784678459167] |
c12137fe-63d5-4fec-a719-9014fccc5f82 | leveraging-automated-unit-tests-for-1 | 2110.06773 | null | https://arxiv.org/abs/2110.06773v2 | https://arxiv.org/pdf/2110.06773v2.pdf | Leveraging Automated Unit Tests for Unsupervised Code Translation | With little to no parallel data available for programming languages, unsupervised methods are well-suited to source code translation. However, the majority of unsupervised machine translation approaches rely on back-translation, a method developed in the context of natural language translation and one that inherently i... | ['Guillaume Lample', 'Gabriel Synnaeve', 'Mark Harman', 'Francois Charton', 'Jie M. Zhang', 'Baptiste Roziere'] | 2021-10-13 | leveraging-automated-unit-tests-for | https://openreview.net/forum?id=cmt-6KtR4c4 | https://openreview.net/pdf?id=cmt-6KtR4c4 | iclr-2022-4 | ['code-translation', 'unsupervised-machine-translation'] | ['computer-code', 'natural-language-processing'] | [ 0.45856282 0.13274637 -0.03413518 -0.4566904 -1.2525196 -0.9712853
0.425884 0.5536622 -0.39570454 0.85381716 0.05556704 -0.6187658
0.2997037 -0.8370475 -1.0326649 -0.15900418 0.14223619 0.36781517
0.15271646 -0.35426217 0.44076338 -0.18916741 -1.5086565 0.4781607
1.244189 0.11044304 -0.072... | [7.7479047775268555, 7.854860305786133] |
e6f4b2b4-4a65-4299-9a47-f88a18d09e85 | deep-supervised-and-convolutional-generative | 1403.1347 | null | http://arxiv.org/abs/1403.1347v1 | http://arxiv.org/pdf/1403.1347v1.pdf | Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction | Predicting protein secondary structure is a fundamental problem in protein
structure prediction. Here we present a new supervised generative stochastic
network (GSN) based method to predict local secondary structure with deep
hierarchical representations. GSN is a recently proposed deep learning
technique (Bengio & Thi... | ['Olga G. Troyanskaya', 'Jian Zhou'] | 2014-03-06 | null | null | null | null | ['protein-secondary-structure-prediction'] | ['medical'] | [ 5.08171737e-01 4.34198439e-01 1.12574033e-01 -5.21470666e-01
-1.05965865e+00 -5.47715664e-01 3.38185906e-01 1.33204788e-01
-2.26478055e-01 1.16164517e+00 2.05293536e-01 -5.37473500e-01
2.82129079e-01 -6.43757582e-01 -1.17341256e+00 -1.02549088e+00
-1.80717900e-01 9.33429956e-01 1.23450682e-01 -3.95459570... | [4.689384460449219, 5.639500617980957] |
9d5d42c2-9027-4e64-9904-5b00599e1629 | temporal-action-segmentation-from-timestamp | 2103.06669 | null | https://arxiv.org/abs/2103.06669v3 | https://arxiv.org/pdf/2103.06669v3.pdf | Temporal Action Segmentation from Timestamp Supervision | Temporal action segmentation approaches have been very successful recently. However, annotating videos with frame-wise labels to train such models is very expensive and time consuming. While weakly supervised methods trained using only ordered action lists require less annotation effort, the performance is still worse ... | ['Juergen Gall', 'Yazan Abu Farha', 'Zhe Li'] | 2021-03-11 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Li_Temporal_Action_Segmentation_From_Timestamp_Supervision_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Li_Temporal_Action_Segmentation_From_Timestamp_Supervision_CVPR_2021_paper.pdf | cvpr-2021-1 | ['weakly-supervised-action-localization'] | ['computer-vision'] | [ 5.86333334e-01 1.90733105e-01 -5.52100480e-01 -9.01324391e-01
-8.09475183e-01 -6.09057307e-01 7.27521122e-01 1.49218202e-01
-6.60929203e-01 6.80428147e-01 1.48154169e-01 6.55866712e-02
3.15196544e-01 -2.67490536e-01 -8.60375106e-01 -5.77779174e-01
-2.94456512e-01 4.46608841e-01 8.41068983e-01 3.71032119... | [8.379790306091309, 0.516192615032196] |
7ad8bf55-4b91-456c-8369-30a24d304287 | adc-net-an-open-source-deep-learning-network | 2201.12625 | null | https://arxiv.org/abs/2201.12625v1 | https://arxiv.org/pdf/2201.12625v1.pdf | ADC-Net: An Open-Source Deep Learning Network for Automated Dispersion Compensation in Optical Coherence Tomography | Chromatic dispersion is a common problem to degrade the system resolution in optical coherence tomography (OCT). This study is to develop a deep learning network for automated dispersion compensation (ADC-Net) in OCT. The ADC-Net is based on a redesigned UNet architecture which employs an encoder-decoder pipeline. The ... | ['Visual Science', 'Department of Ophthalmology', 'University of Illinois at Chicago', 'Department of Biomedical Engineering', 'Xincheng Yao', 'Tobiloba Adejumo', 'Taeyoon Son', 'David Le', 'Shaiban Ahmed'] | 2022-01-29 | null | null | null | null | ['ms-ssim'] | ['computer-vision'] | [ 2.11783290e-01 -1.98683456e-01 2.98797786e-01 -2.58297175e-01
-3.35192949e-01 -1.88029274e-01 -4.52723540e-02 -1.20131604e-01
-7.22112298e-01 7.98953474e-01 2.07080051e-01 -3.60062510e-01
-2.50494242e-01 -1.28150672e-01 -2.50251561e-01 -6.88203335e-01
-2.73483038e-01 -2.27256924e-01 3.96685869e-01 3.11974645... | [15.820513725280762, -3.998412609100342] |
0fcbe66a-f998-441e-99fa-7e7b3fda79d2 | cursive-caption-text-detection-in-videos | 2301.03164 | null | https://arxiv.org/abs/2301.03164v1 | https://arxiv.org/pdf/2301.03164v1.pdf | Cursive Caption Text Detection in Videos | Textual content appearing in videos represents an interesting index for semantic retrieval of videos (from archives), generation of alerts (live streams) as well as high level applications like opinion mining and content summarization. One of the key components of such systems is the detection of textual content in vid... | ['Imran Siddiqi', 'Ali Mirza'] | 2023-01-09 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [ 4.84251887e-01 -2.68980801e-01 5.15603982e-02 -1.63561702e-01
-8.47639680e-01 -9.34020162e-01 8.95713449e-01 3.42308730e-01
-4.71669853e-01 3.09584916e-01 3.53927255e-01 1.48976266e-01
3.07677239e-01 -3.21532100e-01 -9.17237580e-01 -6.70445681e-01
-1.11281484e-01 3.16080526e-02 6.91466391e-01 1.53839469... | [10.408671379089355, 0.6204200983047485] |
25601c27-4d3b-46ba-b5f4-d3ee214dc206 | addressing-time-bias-in-bipartite-graph | 1911.12558 | null | https://arxiv.org/abs/1911.12558v1 | https://arxiv.org/pdf/1911.12558v1.pdf | Addressing Time Bias in Bipartite Graph Ranking for Important Node Identification | The goal of the ranking problem in networks is to rank nodes from best to worst, according to a chosen criterion. In this work, we focus on ranking the nodes according to their quality. The problem of ranking the nodes in bipartite networks is valuable for many real-world applications. For instance, high-quality produc... | ['Mingyang Zhou', 'Jiao Wu', 'Hao Liao', 'Alexandre Vidmer'] | 2019-11-28 | null | null | null | null | ['graph-ranking'] | ['graphs'] | [-2.64960468e-01 2.42845073e-01 -5.13288140e-01 -2.99659997e-01
-5.74892648e-02 -4.87955421e-01 3.79842609e-01 6.43266499e-01
-3.60013932e-01 9.05315459e-01 6.59357086e-02 9.72610414e-02
-9.34508026e-01 -1.32849419e+00 -1.94704473e-01 -5.17426610e-01
-2.68247843e-01 6.72037065e-01 5.13607740e-01 -6.86082423... | [9.498974800109863, 5.843824863433838] |
06c869ed-2674-4288-9d78-1ee24311f4e3 | spatio-temporal-point-process-for-multiple | 2302.02444 | null | https://arxiv.org/abs/2302.02444v1 | https://arxiv.org/pdf/2302.02444v1.pdf | Spatio-Temporal Point Process for Multiple Object Tracking | Multiple Object Tracking (MOT) focuses on modeling the relationship of detected objects among consecutive frames and merge them into different trajectories. MOT remains a challenging task as noisy and confusing detection results often hinder the final performance. Furthermore, most existing research are focusing on imp... | ['Xia Jia', 'Qian Xu', 'Zenghui Zhang', 'John See', 'Weiyao Lin', 'Kean Chen', 'Tao Wang'] | 2023-02-05 | null | null | null | null | ['multiple-object-tracking'] | ['computer-vision'] | [ 1.65022418e-01 -6.72635972e-01 -8.79178010e-03 -7.49296397e-02
-4.72752303e-01 -3.45789611e-01 6.03549123e-01 7.86499307e-02
-4.61279452e-01 3.26643407e-01 -1.96227189e-02 1.06376171e-01
-1.02412358e-01 -8.02684367e-01 -8.27127516e-01 -9.01193917e-01
-2.79659741e-02 4.97450709e-01 7.00132966e-01 1.04328863... | [6.268341541290283, -2.0895845890045166] |
a9c826c0-646e-4adb-ac83-d16d049e09b9 | beyond-sift-using-binary-features-for-loop | 1709.05833 | null | http://arxiv.org/abs/1709.05833v1 | http://arxiv.org/pdf/1709.05833v1.pdf | Beyond SIFT using Binary features for Loop Closure Detection | In this paper a binary feature based Loop Closure Detection (LCD) method is
proposed, which for the first time achieves higher precision-recall (PR)
performance compared with state-of-the-art SIFT feature based approaches. The
proposed system originates from our previous work Multi-Index hashing for Loop
closure Detect... | ['Lan Xu', 'Lu Fang', 'Guyue Zhou', 'Lei Han'] | 2017-09-18 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [-6.82027952e-04 -6.64249718e-01 -4.63648200e-01 -1.52939364e-01
-1.09781420e+00 -3.71147484e-01 5.62592328e-01 8.55816960e-01
-4.83565658e-01 4.36025470e-01 -2.60235928e-02 -2.42388114e-01
-2.88530558e-01 -8.86127830e-01 -5.86973548e-01 -5.12608349e-01
-4.02175695e-01 1.05347224e-01 6.28819823e-01 -1.08774245... | [7.502941608428955, -1.9274152517318726] |
613735f6-2c38-439a-9ba0-9e0de41647a6 | spatial-reuse-in-dense-wireless-areas-a-cross | 2202.05655 | null | https://arxiv.org/abs/2202.05655v1 | https://arxiv.org/pdf/2202.05655v1.pdf | Spatial Reuse in Dense Wireless Areas: A Cross-layer Optimization Approach via ADMM | This paper introduces an efficient method for communication resource use in dense wireless areas where all nodes must communicate with a common destination node. The proposed method groups nodes based on their \newt{distance from the destination} and creates a structured multi-hop configuration in which each group can ... | ['Ghadah Aldabbagh', 'John M. Cioffi', 'Golnaz Farhadi', 'Borja Peleato', 'Haleh Tabrizi'] | 2022-02-11 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [ 3.35244894e-01 7.25996494e-01 -7.01378047e-01 -1.90795928e-01
-2.48653412e-01 -4.49451715e-01 -2.49511991e-02 -6.67244643e-02
-6.77854538e-01 1.34961820e+00 -1.61740467e-01 -3.58030081e-01
-6.42218471e-01 -9.88609850e-01 5.69199547e-02 -1.05378914e+00
-5.02389073e-01 6.19208068e-02 -1.50528565e-01 -1.01292416... | [6.000926971435547, 1.5596858263015747] |
929b4c2d-f9ab-4d92-9f08-720842f98bc1 | mesaha-net-multi-encoders-based-self-adaptive | 2304.01576 | null | https://arxiv.org/abs/2304.01576v1 | https://arxiv.org/pdf/2304.01576v1.pdf | MESAHA-Net: Multi-Encoders based Self-Adaptive Hard Attention Network with Maximum Intensity Projections for Lung Nodule Segmentation in CT Scan | Accurate lung nodule segmentation is crucial for early-stage lung cancer diagnosis, as it can substantially enhance patient survival rates. Computed tomography (CT) images are widely employed for early diagnosis in lung nodule analysis. However, the heterogeneity of lung nodules, size diversity, and the complexity of t... | ['Yeong Gil Shin', 'Tariq Mahmood Khan', 'Sung Hyun Kim', 'Shi Sub Byon', 'Siddique Latif', 'Abdullah Shahid', 'Azka Rehman', 'Muhammad Usman'] | 2023-04-04 | null | null | null | null | ['lung-cancer-diagnosis', 'lung-nodule-segmentation', 'hard-attention'] | ['medical', 'medical', 'methodology'] | [ 3.42624694e-01 3.77227038e-01 -2.98910499e-01 -1.45115897e-01
-1.03932190e+00 -3.05823117e-01 2.04191625e-01 -2.16707855e-01
-3.17093939e-01 3.40868026e-01 3.28823626e-02 -6.68586195e-01
-1.91408712e-02 -6.33678496e-01 -3.45871478e-01 -7.70228148e-01
1.29619673e-01 9.19697881e-01 6.74549580e-01 3.49460334... | [15.409163475036621, -2.128084897994995] |
043673de-beda-4532-99af-4850fb4819d5 | identifying-nuanced-dialect-for-arabic-tweets | null | null | https://aclanthology.org/2020.wanlp-1.30 | https://aclanthology.org/2020.wanlp-1.30.pdf | Identifying Nuanced Dialect for Arabic Tweets with Deep Learning and Reverse Translation Corpus Extension System | In this paper, we present our work for the NADI Shared Task (Abdul-Mageed and Habash, 2020): Nuanced Arabic Dialect Identification for Subtask-1: country-level dialect identification. We introduce a Reverse Translation Corpus Extension Systems (RTCES) to handle data imbalance along with reported results on several expe... | ['Marwan Torki', 'Youssef Kishk', 'Rawan Tahssin'] | null | null | null | null | coling-wanlp-2020-12 | ['dialect-identification'] | ['natural-language-processing'] | [-2.52027482e-01 3.65867503e-02 1.50296822e-01 -5.59423923e-01
-1.14028966e+00 -6.77527666e-01 9.60765481e-01 4.34113406e-02
-5.76897442e-01 6.90974891e-01 4.81132179e-01 -3.48059386e-01
-6.87160119e-02 -4.28796262e-01 -8.67746174e-02 -3.39181602e-01
-3.41015160e-02 1.29547060e+00 -2.61718571e-01 -1.43836951... | [10.16650104522705, 10.767913818359375] |
e1cd970e-d493-4600-8ed9-33853f018ee0 | representation-learning-for-resource | null | null | https://openreview.net/forum?id=zlR44Dbobb7 | https://openreview.net/pdf?id=zlR44Dbobb7 | Representation Learning for Resource-Constrained Keyphrase Generation | State-of-the-art keyphrase generation methods generally depend on large annotated datasets, limiting their performance in domains with constrained resources. To overcome this challenge, we investigate pre-training strategies to learn an intermediate representation suitable for the keyphrase generation task. We introduc... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['keyphrase-generation'] | ['natural-language-processing'] | [ 9.75590572e-02 -1.73893888e-02 -5.54388463e-01 2.75789976e-01
-1.21179390e+00 -6.55537844e-01 9.56863523e-01 6.59625411e-01
-4.38656956e-01 9.24985349e-01 7.33430624e-01 -1.63318276e-01
1.21050617e-02 -7.88469374e-01 -6.53519571e-01 -2.19610959e-01
1.85252614e-02 2.96016425e-01 3.70485902e-01 -4.73845840... | [12.284310340881348, 8.89174747467041] |
5ffb0272-4bb9-42a9-8e96-e293142cd21e | linguistic-analysis-processing-line-for | null | null | https://aclanthology.org/L12-1494 | https://aclanthology.org/L12-1494.pdf | Linguistic Analysis Processing Line for Bulgarian | This paper presents a linguistic processing pipeline for Bulgarian including morphological analysis, lemmatization and syntactic analysis of Bulgarian texts. The morphological analysis is performed by three modules ― two statistical-based and one rule-based. The combination of these modules achieves the best result f... | ['ar', 'Laska Laskova', 'Aleks Savkov', 'Stanislava Kancheva', 'Petya Osenova', 'Kiril Simov'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['morphological-tagging'] | ['natural-language-processing'] | [-2.22002372e-01 2.79565215e-01 4.11681771e-01 -4.95153606e-01
-8.98875296e-01 -8.16911817e-01 5.41359544e-01 7.55431592e-01
-9.02289093e-01 4.53558892e-01 1.89473480e-01 -6.63867772e-01
1.06011488e-01 -1.02673781e+00 -5.90181649e-02 -7.23639190e-01
3.03379837e-02 9.37721133e-01 5.72345138e-01 -2.10646510... | [10.297926902770996, 10.203086853027344] |
a212e04f-e3e9-4fd1-be12-20114b4309e1 | disentangled-latent-transformer-for | 2201.06357 | null | https://arxiv.org/abs/2201.06357v2 | https://arxiv.org/pdf/2201.06357v2.pdf | Disentangled Latent Transformer for Interpretable Monocular Height Estimation | Monocular height estimation (MHE) from remote sensing imagery has high potential in generating 3D city models efficiently for a quick response to natural disasters. Most existing works pursue higher performance. However, there is little research exploring the interpretability of MHE networks. In this paper, we target a... | ['Sining Chen', 'Zhitong Xiong', 'Xiao Xiang Zhu', 'Yilei Shi'] | 2022-01-17 | null | null | null | null | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 4.44823235e-01 5.26597738e-01 -3.19945157e-01 -5.78575015e-01
-4.64508623e-01 -1.29557803e-01 4.80624557e-01 9.60893929e-02
6.10907786e-02 5.85473657e-01 5.16406953e-01 -2.51463532e-01
-2.51005203e-01 -1.50644708e+00 -8.37798595e-01 -8.07773888e-01
5.84309958e-02 5.62613964e-01 -9.53642651e-02 -8.28483105... | [9.154725074768066, -1.3824307918548584] |
6f945a0a-1cb4-4425-a160-5033bd7b3cdf | evd-surgical-guidance-with-retro-reflective | 2306.15490 | null | https://arxiv.org/abs/2306.15490v2 | https://arxiv.org/pdf/2306.15490v2.pdf | EVD Surgical Guidance with Retro-Reflective Tool Tracking and Spatial Reconstruction using Head-Mounted Augmented Reality Device | Augmented Reality (AR) has been used to facilitate surgical guidance during External Ventricular Drain (EVD) surgery, reducing the risks of misplacement in manual operations. During this procedure, the key challenge is accurately estimating the spatial relationship between pre-operative images and actual patient anatom... | ['Guangzhi Wang', 'Hui Ding', 'Zhe Zhao', 'Yihao Liu', 'Yuxing Yang', 'Long Qian', 'Du Liu', 'Wenqing Yan', 'Haowei Li'] | 2023-06-27 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [-2.92602360e-01 4.46867257e-01 4.57390875e-01 1.80219412e-02
-8.27485204e-01 -2.25027636e-01 -4.66968827e-02 1.61850959e-01
-5.34123600e-01 5.05055606e-01 2.85666399e-02 -4.94271487e-01
8.28452185e-02 -3.65131795e-01 -5.31766295e-01 -5.74556589e-01
-3.29701990e-01 4.47469801e-01 1.33685768e-01 -4.27239621... | [13.75894832611084, -2.984117031097412] |
59d1e902-3f86-4f37-8c34-f742d4200107 | online-segmentation-of-lidar-sequences | 2206.08194 | null | https://arxiv.org/abs/2206.08194v2 | https://arxiv.org/pdf/2206.08194v2.pdf | Online Segmentation of LiDAR Sequences: Dataset and Algorithm | Roof-mounted spinning LiDAR sensors are widely used by autonomous vehicles. However, most semantic datasets and algorithms used for LiDAR sequence segmentation operate on $360^\circ$ frames, causing an acquisition latency incompatible with real-time applications. To address this issue, we first introduce HelixNet, a $1... | ['Loïc Landrieu', 'Mathieu Aubry', 'Romain Loiseau'] | 2022-06-16 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [-5.52405370e-03 -3.77137184e-01 -4.13333327e-02 -5.22800922e-01
-7.19201446e-01 -8.00034225e-01 4.44691062e-01 2.86094189e-01
-7.34686673e-01 2.74027765e-01 -7.11307347e-01 -5.64838767e-01
2.00815976e-01 -1.04711890e+00 -7.55770922e-01 -1.65822104e-01
-1.19818173e-01 8.51896405e-01 9.23505902e-01 1.49860233... | [8.072729110717773, -2.61867094039917] |
8e77eb08-5220-4016-8dcf-06691dce6cac | fitannotator-a-flexible-and-intelligent-text | null | null | https://aclanthology.org/2021.naacl-demos.5 | https://aclanthology.org/2021.naacl-demos.5.pdf | FITAnnotator: A Flexible and Intelligent Text Annotation System | In this paper, we introduce FITAnnotator, a generic web-based tool for efficient text annotation. Benefiting from the fully modular architecture design, FITAnnotator provides a systematic solution for the annotation of a variety of natural language processing tasks, including classification, sequence tagging and semant... | ['Tingwen Liu', 'Li Quangang', 'Bowen Yu', 'Yanzeng Li'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['text-annotation'] | ['natural-language-processing'] | [-5.48880026e-02 6.87629104e-01 -2.50377417e-01 -5.58500111e-01
-4.50970739e-01 -1.10203063e+00 2.69019216e-01 8.68001759e-01
-5.72476268e-01 8.94610226e-01 9.87866223e-02 4.75260578e-02
-3.17749918e-01 -2.79679865e-01 8.54671970e-02 -2.56914228e-01
3.25081915e-01 1.10346210e+00 5.67690253e-01 -1.14510268... | [9.285938262939453, 8.758474349975586] |
aebc2895-d06f-49c7-aaa4-bf928de89113 | deep-reinforcement-learning-for-automatic-run | 2210.15498 | null | https://arxiv.org/abs/2210.15498v1 | https://arxiv.org/pdf/2210.15498v1.pdf | Deep reinforcement learning for automatic run-time adaptation of UWB PHY radio settings | Ultra-wideband technology has become increasingly popular for indoor localization and location-based services. This has led recent advances to be focused on reducing the ranging errors, whilst research focusing on enabling more reliable and energy efficient communication has been largely unexplored. The IEEE 802.15.4 U... | ['Eli de Poorter', 'Adnan Shahid', 'Dieter Coppens'] | 2022-10-13 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 9.63606387e-02 -1.75586477e-01 -4.07437027e-01 -4.34330404e-01
-9.76502001e-01 -2.52271891e-01 -4.41292338e-02 1.21336371e-01
-6.80055976e-01 1.25036144e+00 1.87176801e-02 -4.69678879e-01
-7.74796307e-01 -9.49104905e-01 -6.66928664e-02 -8.09967399e-01
-3.92454952e-01 5.18218726e-02 -5.10186367e-02 4.77755405... | [6.244296550750732, 1.0978450775146484] |
c28c0ed7-5a0f-41e9-907b-319517e64540 | multilingual-word-embeddings-using | 1612.04732 | null | http://arxiv.org/abs/1612.04732v1 | http://arxiv.org/pdf/1612.04732v1.pdf | Multilingual Word Embeddings using Multigraphs | We present a family of neural-network--inspired models for computing
continuous word representations, specifically designed to exploit both
monolingual and multilingual text. This framework allows us to perform
unsupervised training of embeddings that exhibit higher accuracy on syntactic
and semantic compositionality, ... | ['Radu Soricut', 'Nan Ding'] | 2016-12-14 | null | null | null | null | ['multilingual-word-embeddings'] | ['methodology'] | [-2.05710188e-01 -7.98759982e-02 -6.04173601e-01 -4.22083765e-01
-6.79757953e-01 -6.66599810e-01 9.14004147e-01 5.04074574e-01
-7.86365092e-01 6.10748053e-01 6.68293774e-01 -6.54564559e-01
1.35333806e-01 -6.94250286e-01 -5.14259219e-01 -3.12552333e-01
7.79059995e-03 8.26956153e-01 -3.74155164e-01 -5.52836835... | [11.007097244262695, 9.90283203125] |
923d9055-51ad-43cc-ac3a-a67e8bcca57c | fact-based-text-editing-1 | 2007.00916 | null | https://arxiv.org/abs/2007.00916v1 | https://arxiv.org/pdf/2007.00916v1.pdf | Fact-based Text Editing | We propose a novel text editing task, referred to as \textit{fact-based text editing}, in which the goal is to revise a given document to better describe the facts in a knowledge base (e.g., several triples). The task is important in practice because reflecting the truth is a common requirement in text editing. First, ... | ['chao qiao', 'Hayate Iso', 'Hang Li'] | 2020-07-02 | fact-based-text-editing | https://aclanthology.org/2020.acl-main.17 | https://aclanthology.org/2020.acl-main.17.pdf | acl-2020-6 | ['fact-based-text-editing'] | ['natural-language-processing'] | [ 5.69678783e-01 6.62210226e-01 -1.48644656e-01 -6.28888130e-01
-8.35969925e-01 -2.94647813e-01 8.31115603e-01 4.40250605e-01
-2.31880665e-01 1.24937224e+00 4.98864561e-01 -3.39546740e-01
-4.12736495e-04 -1.11967421e+00 -1.38679457e+00 1.73253253e-01
5.79216361e-01 5.96890867e-01 -6.80540800e-02 -3.45968336... | [11.610897064208984, 8.715604782104492] |
eba2f3e0-9250-43d9-8ed0-a0937d058f92 | pulse-shape-aided-multipath-delay-estimation | 2306.15320 | null | https://arxiv.org/abs/2306.15320v1 | https://arxiv.org/pdf/2306.15320v1.pdf | Pulse Shape-Aided Multipath Delay Estimation for Fine-Grained WiFi Sensing | Due to the finite bandwidth of practical wireless systems, one multipath component can manifest itself as a discrete pulse consisting of multiple taps in the digital delay domain. This effect is called channel leakage, which complicates the multipath delay estimation problem. In this paper, we develop a new algorithm t... | ['Chenshu Wu', 'He Chen', 'Ke Xu'] | 2023-06-27 | null | null | null | null | ['benchmarking', 'benchmarking'] | ['miscellaneous', 'robots'] | [ 1.57142967e-01 -5.05220473e-01 -3.36508863e-02 -7.15480074e-02
-9.29287136e-01 -5.53265989e-01 -7.56944492e-02 -3.44507918e-02
-2.95708198e-02 7.22135544e-01 3.61176789e-01 -4.14942026e-01
-8.51717964e-02 -5.74464202e-01 -5.18329620e-01 -1.01926756e+00
-4.98569131e-01 -1.20145984e-01 4.25158665e-02 2.26510391... | [6.410588264465332, 1.2915033102035522] |
c2dccd80-b7c3-4287-a660-505f7e5c6d99 | classification-of-breast-tumours-based-on | 2209.01380 | null | https://arxiv.org/abs/2209.01380v1 | https://arxiv.org/pdf/2209.01380v1.pdf | Classification of Breast Tumours Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods | Breast cancer is the most common cancer among women worldwide. Early-stage diagnosis of breast cancer can significantly improve the efficiency of treatment. Computer-aided diagnosis (CAD) systems are widely adopted in this issue due to their reliability, accuracy and affordability. There are different imaging technique... | ['Samaneh Emami', 'Hamid Nasiri', 'Sayed Ali Sheikholeslamzadeh', 'Mohammad Reza Abbasniya'] | 2022-09-03 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [-1.24524474e-01 1.90315526e-02 -1.56259164e-01 -3.45267236e-01
-5.11656284e-01 -8.63490626e-03 5.66308975e-01 2.89000690e-01
-6.97309375e-01 7.73449540e-01 -1.49385676e-01 -5.40951729e-01
-3.87499988e-01 -8.45740080e-01 -2.01366037e-01 -9.74383712e-01
4.16229852e-02 2.80067563e-01 1.66268826e-01 -3.53874445... | [15.26440715789795, -2.786406993865967] |
4cb33dcb-e00a-491e-9d75-180a7b7e4248 | double-permutation-equivariance-for-knowledge | 2302.01313 | null | https://arxiv.org/abs/2302.01313v5 | https://arxiv.org/pdf/2302.01313v5.pdf | Inductive Link Prediction for Both New Nodes and New Relation Types via Double Equivariance | Despite recent advances in relational learning, the task of inductive link prediction in discrete attributed multigraphs with both new nodes and new relation types in test remains an open problem. In this work we tackle this task by defining the concept of double exchangeability and its associated double-permutation eq... | ['Bruno Ribeiro', 'Jincheng Zhou', 'Yangze Zhou', 'Jianfei Gao'] | 2023-02-02 | null | null | null | null | ['inductive-link-prediction', 'relational-reasoning', 'logical-reasoning'] | ['graphs', 'natural-language-processing', 'reasoning'] | [ 4.60096985e-01 7.71523237e-01 -5.74335694e-01 -4.89661932e-01
-7.41878152e-02 -6.72289491e-01 7.31969774e-01 2.14625373e-01
4.03928086e-02 1.18643034e+00 -1.54564261e-01 -6.73787355e-01
-7.40643859e-01 -1.41696143e+00 -1.03087771e+00 -3.72156084e-01
-8.92385483e-01 1.02218568e+00 3.39111537e-01 -4.25562471... | [7.116056442260742, 6.351359844207764] |
1541ed69-768a-4633-ac6b-252300683426 | tcgan-semantic-aware-and-structure-preserved | 2302.08047 | null | https://arxiv.org/abs/2302.08047v1 | https://arxiv.org/pdf/2302.08047v1.pdf | TcGAN: Semantic-Aware and Structure-Preserved GANs with Individual Vision Transformer for Fast Arbitrary One-Shot Image Generation | One-shot image generation (OSG) with generative adversarial networks that learn from the internal patches of a given image has attracted world wide attention. In recent studies, scholars have primarily focused on extracting features of images from probabilistically distributed inputs with pure convolutional neural netw... | ['Danfeng Sun', 'Yong liu', 'Xiongtao Zhang', 'Lili Yan', 'Yunliang Jiang'] | 2023-02-16 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 4.56260085e-01 1.65681452e-01 1.47393290e-02 -1.53837904e-01
-6.47045434e-01 -2.68382430e-01 4.74842280e-01 -8.87916446e-01
-3.72640118e-02 9.09463346e-01 1.85487956e-01 2.37774730e-01
-2.12967992e-01 -1.15708685e+00 -9.36143458e-01 -1.09428000e+00
6.06579423e-01 -2.78715730e-01 2.37906843e-01 -5.46659768... | [11.53748893737793, -1.1051793098449707] |
c37c6a0d-a1e2-4ff8-945c-7b0c5b8cd340 | surrogate-assisted-multi-objective-neural | 2208.06820 | null | https://arxiv.org/abs/2208.06820v1 | https://arxiv.org/pdf/2208.06820v1.pdf | Surrogate-assisted Multi-objective Neural Architecture Search for Real-time Semantic Segmentation | The architectural advancements in deep neural networks have led to remarkable leap-forwards across a broad array of computer vision tasks. Instead of relying on human expertise, neural architecture search (NAS) has emerged as a promising avenue toward automating the design of architectures. While recent achievements in... | ['Fan Yang', 'Changxiao Qiu', 'Haoming Zhang', 'Shihua Huang', 'Ran Cheng', 'Zhichao Lu'] | 2022-08-14 | null | null | null | null | ['real-time-semantic-segmentation'] | ['computer-vision'] | [ 4.48618531e-01 -7.23624751e-02 8.13477300e-03 -4.88218248e-01
-9.90958214e-01 -3.98982793e-01 4.50878114e-01 -2.28814945e-01
-6.22322321e-01 3.59019995e-01 -3.38952899e-01 -3.90726566e-01
-1.73902884e-01 -5.38074493e-01 -6.64983332e-01 -7.06947327e-01
2.40254313e-01 6.03145361e-01 4.07561123e-01 -1.84008345... | [9.4392728805542, -0.16261568665504456] |
e65fd484-a0e9-4870-83da-3869c8370efc | role-of-data-augmentation-in-unsupervised | 2208.07734 | null | https://arxiv.org/abs/2208.07734v4 | https://arxiv.org/pdf/2208.07734v4.pdf | Self-supervision is not magic: Understanding Data Augmentation in Image Anomaly Detection | Self-supervised learning (SSL) has emerged as a promising alternative to create supervisory signals to real-world tasks, avoiding the extensive cost of labeling. SSL is particularly attractive for unsupervised tasks such as anomaly detection (AD), where labeled anomalies are costly to secure, difficult to simulate, or ... | ['Leman Akoglu', 'Tiancheng Zhao', 'Jaemin Yoo'] | 2022-08-16 | null | null | null | null | ['self-supervised-anomaly-detection'] | ['computer-vision'] | [ 4.69609648e-01 1.56940117e-01 -2.66793966e-01 -5.00026584e-01
-3.61402243e-01 -2.37400770e-01 9.47344184e-01 3.82541299e-01
-2.91372985e-01 3.05563599e-01 1.11312382e-01 -4.43865031e-01
-5.57203963e-02 -4.38554674e-01 -7.24177897e-01 -7.61530578e-01
5.05297258e-02 1.89531639e-01 7.35530853e-02 -1.89358518... | [7.693565368652344, 2.427936315536499] |
6cbb465a-0a3d-453f-8319-89684a1e8abe | geometry-complete-diffusion-for-3d-molecule | 2302.04313 | null | https://arxiv.org/abs/2302.04313v4 | https://arxiv.org/pdf/2302.04313v4.pdf | Geometry-Complete Diffusion for 3D Molecule Generation and Optimization | Denoising diffusion probabilistic models (DDPMs) have recently taken the field of generative modeling by storm, pioneering new state-of-the-art results in disciplines such as computer vision and computational biology for diverse tasks ranging from text-guided image generation to structure-guided protein design. Along t... | ['Jianlin Cheng', 'Alex Morehead'] | 2023-02-08 | null | null | null | null | ['protein-design', '3d-molecule-generation'] | ['medical', 'medical'] | [ 3.14202458e-01 9.90739539e-02 -8.49095955e-02 3.29223312e-02
-7.65489399e-01 -9.53184307e-01 8.51553202e-01 2.72528738e-01
-8.69270042e-02 9.53162074e-01 2.02804536e-01 -7.43633866e-01
-1.69418335e-01 -1.00360167e+00 -9.32206511e-01 -1.04921949e+00
-1.57380745e-01 6.96981192e-01 -1.09286688e-01 -3.39326203... | [5.057145595550537, 5.699237823486328] |
c7b3b676-e417-4db5-b681-0b9c6b7eb73c | semantic-scene-segmentation-for-robotics | 2108.11128 | null | https://arxiv.org/abs/2108.11128v1 | https://arxiv.org/pdf/2108.11128v1.pdf | Semantic Scene Segmentation for Robotics Applications | Semantic scene segmentation plays a critical role in a wide range of robotics applications, e.g., autonomous navigation. These applications are accompanied by specific computational restrictions, e.g., operation on low-power GPUs, at sufficient speed, and also for high-resolution input. Existing state-of-the-art segmen... | ['Anastasios Tefas', 'Maria Tzelepi'] | 2021-08-25 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 3.51185769e-01 -1.32096574e-01 5.23874424e-02 -2.87332922e-01
2.08637454e-02 -4.98963147e-01 5.22053540e-01 2.86680013e-01
-8.73019099e-01 2.85331100e-01 -5.75057566e-01 -5.14710307e-01
-2.56241351e-01 -9.19344962e-01 -5.97404599e-01 -4.15424973e-01
3.87099124e-02 8.93386960e-01 9.45639789e-01 -3.05642486... | [8.504948616027832, -1.996851921081543] |
3ba1364d-e5de-4e97-8475-6234ff177324 | a-content-adaptive-learnable-time-frequency | 2303.10446 | null | https://arxiv.org/abs/2303.10446v2 | https://arxiv.org/pdf/2303.10446v2.pdf | Content Adaptive Front End For Audio Signal Processing | We propose a learnable content adaptive front end for audio signal processing. Before the modern advent of deep learning, we used fixed representation non-learnable front-ends like spectrogram or mel-spectrogram with/without neural architectures. With convolutional architectures supporting various applications such as ... | ['Chris Chafe', 'Prateek Verma'] | 2023-03-18 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 2.72481024e-01 1.03848368e-01 2.13868305e-01 -4.77239996e-01
-7.14545906e-01 -6.79011583e-01 2.93627143e-01 -1.47377580e-01
-2.71520734e-01 2.82497227e-01 6.53953969e-01 -1.03779808e-01
-3.76319826e-01 -7.63129771e-01 -5.79448104e-01 -5.75853050e-01
-3.94702613e-01 9.92157981e-02 -1.75023302e-01 -3.19605321... | [15.362954139709473, 5.54480504989624] |
e9d7417a-8a0c-49ab-8d2d-5b283afab89f | additive-feature-hashing | 2102.03943 | null | https://arxiv.org/abs/2102.03943v1 | https://arxiv.org/pdf/2102.03943v1.pdf | Additive Feature Hashing | The hashing trick is a machine learning technique used to encode categorical features into a numerical vector representation of pre-defined fixed length. It works by using the categorical hash values as vector indices, and updating the vector values at those indices. Here we discuss a different approach based on additi... | ['M. Andrecut'] | 2021-02-07 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-7.33548477e-02 -3.85441095e-01 -3.79422575e-01 -3.03055823e-01
-8.53604376e-01 -9.18317556e-01 7.18397439e-01 5.70014238e-01
-4.74570841e-01 6.95433676e-01 2.67546952e-01 -3.49919528e-01
6.72018752e-02 -9.68841672e-01 -5.28886735e-01 -9.09082413e-01
-6.08192146e-01 5.17579436e-01 1.31171554e-01 -3.55815679... | [8.289899826049805, 4.005582332611084] |
18c25a95-0df9-4dd4-ae71-759dd2ac159a | using-semantic-similarity-and-text-embedding | 2303.16694 | null | https://arxiv.org/abs/2303.16694v1 | https://arxiv.org/pdf/2303.16694v1.pdf | Using Semantic Similarity and Text Embedding to Measure the Social Media Echo of Strategic Communications | Online discourse covers a wide range of topics and many actors tailor their content to impact online discussions through carefully crafted messages and targeted campaigns. Yet the scale and diversity of online media content make it difficult to evaluate the impact of a particular message. In this paper, we present a ne... | ['Hywel T. P. Williams', "Saffron O'Neill", 'Travis Coan', 'Ben Dennes', 'Tristan J. B. Cann'] | 2023-03-29 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.23275477e-01 3.28728408e-01 -2.52488196e-01 -3.07908386e-01
-8.66016150e-01 -1.19593763e+00 1.40222478e+00 9.73068178e-01
-5.16306520e-01 7.77404547e-01 1.36916220e+00 -5.53850353e-01
-8.40572491e-02 -8.69966209e-01 -4.45811689e-01 -4.81341362e-01
6.08040802e-02 2.13500842e-01 6.07198000e-01 -7.28692412... | [8.623408317565918, 9.88591480255127] |
9f17276e-a5a8-4272-a7c9-dc382333fa36 | pac-gan-an-effective-pose-augmentation-scheme | 1906.01792 | null | https://arxiv.org/abs/1906.01792v1 | https://arxiv.org/pdf/1906.01792v1.pdf | PAC-GAN: An Effective Pose Augmentation Scheme for Unsupervised Cross-View Person Re-identification | Person re-identification (person Re-Id) aims to retrieve the pedestrian images of a same person that captured by disjoint and non-overlapping cameras. Lots of researchers recently focuse on this hot issue and propose deep learning based methods to enhance the recognition rate in a supervised or unsupervised manner. How... | ['Chengyuan Zhang', 'Shichao Zhang', 'Lei Zhu'] | 2019-06-05 | null | null | null | null | ['cross-view-person-re-identification'] | ['computer-vision'] | [ 1.24886774e-01 -2.07787648e-01 9.87155437e-02 -3.09799284e-01
-6.36244178e-01 -2.61312485e-01 6.98746622e-01 -5.95573485e-01
-4.71993715e-01 7.55192995e-01 5.26405096e-01 5.00165761e-01
1.97637901e-01 -9.18876827e-01 -6.58229113e-01 -7.30227232e-01
5.74853480e-01 6.03318572e-01 1.20102994e-01 -3.15492749... | [14.589067459106445, 0.8653116822242737] |
52acc169-c60d-4780-bea5-6d5e2d1c6bdf | semi-supervised-junction-tree-variational | 2208.05119 | null | https://arxiv.org/abs/2208.05119v5 | https://arxiv.org/pdf/2208.05119v5.pdf | Semi-Supervised Junction Tree Variational Autoencoder for Molecular Property Prediction | Molecular Representation Learning is essential to solving many drug discovery and computational chemistry problems. It is a challenging problem due to the complex structure of molecules and the vast chemical space. Graph representations of molecules are more expressive than traditional representations, such as molecula... | ['Tony Shen', 'Martin Ester', 'Atia Hamidizadeh'] | 2022-08-10 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 5.05408823e-01 1.18025936e-01 -7.12029576e-01 -4.21946466e-01
-5.89637160e-01 -5.50847173e-01 7.01814115e-01 2.27029935e-01
-1.59340259e-02 1.18596137e+00 2.33370334e-01 -5.51201940e-01
6.19411580e-02 -1.03081262e+00 -1.04752779e+00 -9.79341745e-01
1.32111967e-01 6.36468410e-01 -4.50052693e-02 7.52703324... | [5.134910583496094, 5.9044976234436035] |
3c3019be-2956-417a-88f6-0e45cedd8c37 | few-shot-learning-of-new-sound-classes-for | 2106.07144 | null | https://arxiv.org/abs/2106.07144v1 | https://arxiv.org/pdf/2106.07144v1.pdf | Few-shot learning of new sound classes for target sound extraction | Target sound extraction consists of extracting the sound of a target acoustic event (AE) class from a mixture of AE sounds. It can be realized using a neural network that extracts the target sound conditioned on a 1-hot vector that represents the desired AE class. With this approach, embedding vectors associated with t... | ['Shoko Araki', 'Keisuke Kinoshita', 'Tsubasa Ochiai', 'Jorge Bennasar Vázquez', 'Marc Delcroix'] | 2021-06-14 | null | null | null | null | ['target-sound-extraction'] | ['audio'] | [ 4.22216147e-01 4.01920918e-03 3.13331127e-01 -1.19973995e-01
-1.21368611e+00 -5.87042272e-01 3.52882057e-01 1.58790886e-01
-3.79140556e-01 2.78279990e-01 2.42781758e-01 5.15798032e-02
-2.80902889e-02 -6.95763767e-01 -5.39403856e-01 -7.24997282e-01
-2.45140582e-01 6.55668750e-02 2.13062793e-01 6.86527416... | [15.217419624328613, 5.395630836486816] |
06fb6197-2117-43af-bf36-17b0b034866c | deep-crisp-boundaries-from-boundaries-to | 1801.02439 | null | http://arxiv.org/abs/1801.02439v3 | http://arxiv.org/pdf/1801.02439v3.pdf | Deep Crisp Boundaries: From Boundaries to Higher-level Tasks | Edge detection has made significant progress with the help of deep
Convolutional Networks (ConvNet). These ConvNet based edge detectors have
approached human level performance on standard benchmarks. We provide a
systematical study of these detectors' outputs. We show that the detection
results did not accurately local... | ['Yupei Wang', 'Xin Zhao', 'Kaiqi Huang', 'Yin Li'] | 2018-01-08 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [ 1.42318025e-01 5.07344902e-02 9.35831442e-02 -3.53413634e-02
-3.01027268e-01 -6.16113484e-01 5.22011280e-01 -2.28137061e-01
-6.70029104e-01 6.01529360e-01 -8.52982923e-02 -4.32687372e-01
3.81251812e-01 -7.19011605e-01 -7.36283720e-01 -2.49424219e-01
-2.15332825e-02 3.60643528e-02 5.72206020e-01 -2.32236564... | [9.450489044189453, 0.12095154821872711] |
3461e50d-adff-4dd9-951a-e87f7ffa1325 | promil-probabilistic-multiple-instance | 2306.10535 | null | https://arxiv.org/abs/2306.10535v1 | https://arxiv.org/pdf/2306.10535v1.pdf | ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging | Multiple Instance Learning (MIL) is a weakly-supervised problem in which one label is assigned to the whole bag of instances. An important class of MIL models is instance-based, where we first classify instances and then aggregate those predictions to obtain a bag label. The most common MIL model is when we consider a ... | ['Bartosz Zieliński', 'Jacek Tabor', 'Robert Sabiniewicz', 'Arkadiusz Lewicki', 'Dawid Rymarczyk', 'Łukasz Struski'] | 2023-06-18 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 4.19398546e-01 4.74955708e-01 -7.99939990e-01 -4.79194045e-01
-9.64046597e-01 -4.06241566e-01 1.68663695e-01 8.42277050e-01
-6.13974072e-02 1.22414744e+00 -1.68594301e-01 -2.68780798e-01
-4.39594775e-01 -1.24487543e+00 -9.72536683e-01 -9.24706995e-01
-1.69749022e-01 9.61958826e-01 -1.72717407e-01 2.55714595... | [9.230507850646973, 3.9543490409851074] |
026f8a25-f27f-4ad7-ab0c-6941e31735fc | multi-gat-a-graphical-attention-based | null | null | https://ieeexplore.ieee.org/abstract/document/9354900/ | https://ieeexplore.ieee.org/abstract/document/9354900/ | Multi-GAT: A Graphical Attention-based Hierarchical Multimodal Representation Learning Approach for Human Activity Recognition | Recognizing human activities is one of the crucial capabilities that a robot needs to have to be useful around people. Although modern robots are equipped with various types of sensors, human activity recognition (HAR) still remains a challenging problem, particularly in the presence of noisy sensor data. In this work,... | ['Tariq Iqbal', 'Md Mofijul Islam'] | 2021-04-01 | null | null | null | ieee-robotics-and-automation-letters-2021-4 | ['multimodal-activity-recognition'] | ['computer-vision'] | [ 5.58098368e-02 -7.73677751e-02 1.03275761e-01 -3.84984195e-01
-1.01360655e+00 -2.05585778e-01 7.76081920e-01 1.35417596e-01
-4.96679246e-01 5.72418928e-01 6.70583010e-01 4.17242557e-01
-2.77687401e-01 -1.85077652e-01 -5.48986077e-01 -7.15770721e-01
-2.71543294e-01 5.02050400e-01 -9.51458514e-02 -3.49706948... | [13.087021827697754, 4.878861427307129] |
84567dab-3de1-4c46-a4cc-078d17ac0595 | a-comprehensive-multi-scale-approach-for | 2307.03270 | null | https://arxiv.org/abs/2307.03270v1 | https://arxiv.org/pdf/2307.03270v1.pdf | A Comprehensive Multi-scale Approach for Speech and Dynamics Synchrony in Talking Head Generation | Animating still face images with deep generative models using a speech input signal is an active research topic and has seen important recent progress. However, much of the effort has been put into lip syncing and rendering quality while the generation of natural head motion, let alone the audio-visual correlation betw... | ['Xavier Alameda-Pineda', 'Dominique Vaufreydaz', 'Louis Airale'] | 2023-07-04 | null | null | null | null | ['talking-head-generation'] | ['computer-vision'] | [-1.71928734e-01 1.34619534e-01 2.49953210e-01 -2.52916634e-01
-9.25361693e-01 -2.24397525e-01 7.35753536e-01 -6.83935761e-01
1.15813702e-01 3.80450517e-01 6.71921492e-01 4.34950978e-01
3.24800402e-01 -3.40552926e-01 -6.99029088e-01 -9.61507797e-01
6.59594759e-02 2.48846650e-01 1.12863831e-01 -3.09283704... | [13.217887878417969, -0.4503404498100281] |
aeb525e5-7270-48d9-b754-8d929a1898ef | semi-supervised-clustering-with-inaccurate | 2104.02146 | null | https://arxiv.org/abs/2104.02146v1 | https://arxiv.org/pdf/2104.02146v1.pdf | Semi-Supervised Clustering with Inaccurate Pairwise Annotations | Pairwise relational information is a useful way of providing partial supervision in domains where class labels are difficult to acquire. This work presents a clustering model that incorporates pairwise annotations in the form of must-link and cannot-link relations and considers possible annotation inaccuracies (i.e., a... | ['Thibaut Vidal', 'Michel Gendreau', 'Daniel Gribel'] | 2021-04-05 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 5.04655614e-02 5.82700193e-01 -4.38995987e-01 -8.26010585e-01
-7.90167451e-01 -5.66276610e-01 5.06425202e-01 4.09942269e-01
-1.85516536e-01 9.54653084e-01 1.41957283e-01 -3.40186894e-01
-7.09228098e-01 -6.35670960e-01 -8.36024582e-01 -6.88785255e-01
3.11856121e-01 9.71449554e-01 2.28869230e-01 1.18286900... | [9.533527374267578, 4.321042060852051] |
8d74f0b0-e66e-4060-9dfe-67f1a0fdd406 | optimistic-bounds-for-multi-output-prediction | 2002.09769 | null | https://arxiv.org/abs/2002.09769v1 | https://arxiv.org/pdf/2002.09769v1.pdf | Optimistic bounds for multi-output prediction | We investigate the challenge of multi-output learning, where the goal is to learn a vector-valued function based on a supervised data set. This includes a range of important problems in Machine Learning including multi-target regression, multi-class classification and multi-label classification. We begin our analysis b... | ['Ata Kaban', 'Henry WJ Reeve'] | 2020-02-22 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 2.10648358e-01 -2.66562682e-02 -5.17844260e-01 -7.00091362e-01
-1.45243394e+00 -8.11015368e-01 1.38340980e-01 6.24776661e-01
-5.48511326e-01 8.49714518e-01 -2.01669350e-01 -3.75134379e-01
-2.42577448e-01 -5.06524742e-01 -8.68975699e-01 -1.01167774e+00
-8.35537240e-02 2.47426882e-01 1.17053322e-01 -2.03059435... | [8.272382736206055, 4.145859718322754] |
71cd0c9d-bb7a-487f-8839-116ed49550c4 | a-one-class-classifier-for-the-detection-of | 2305.11795 | null | https://arxiv.org/abs/2305.11795v1 | https://arxiv.org/pdf/2305.11795v1.pdf | A One-Class Classifier for the Detection of GAN Manipulated Multi-Spectral Satellite Images | The highly realistic image quality achieved by current image generative models has many academic and industrial applications. To limit the use of such models to benign applications, though, it is necessary that tools to conclusively detect whether an image has been generated synthetically or not are developed. For this... | ['Mauro Barni', 'Giovanna Maria Dimitri', 'Lydia Abady'] | 2023-05-19 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [ 3.98029149e-01 -8.26670378e-02 1.64638400e-01 -1.58833325e-01
-5.17076373e-01 -4.91511017e-01 8.82305562e-01 -2.37756193e-01
-2.97949076e-01 8.69187176e-01 -5.97000599e-01 -2.16104895e-01
-1.55343011e-01 -1.13480675e+00 -6.26031697e-01 -1.04426908e+00
1.11125901e-01 4.78519648e-01 4.86916304e-01 -3.54649484... | [10.105962753295898, -1.5087966918945312] |
a5979ddf-da90-4b6c-953c-94d2e904dc3e | generalized-gloves-of-neural-additive-models | 2209.10082 | null | https://arxiv.org/abs/2209.10082v1 | https://arxiv.org/pdf/2209.10082v1.pdf | Generalized Gloves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance | For many years, machine learning methods have been used in a wide range of fields, including computer vision and natural language processing. While machine learning methods have significantly improved model performance over traditional methods, their black-box structure makes it difficult for researchers to interpret r... | ['Weicheng Ye', 'Dangxing Chen'] | 2022-09-21 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 8.36529136e-02 2.92275012e-01 -2.45999545e-01 -7.79408336e-01
-2.48949438e-01 -5.75317085e-01 5.10549247e-01 -3.62442993e-02
-1.57429636e-01 7.28769660e-01 -1.62056103e-01 -5.68766534e-01
-4.16539520e-01 -5.51100373e-01 -5.15004873e-01 -4.41185743e-01
-5.93442330e-03 1.76318526e-01 -2.93199211e-01 1.17767394... | [8.786056518554688, 5.507346153259277] |
3efbf1e1-974d-4c82-bd54-709864455d0c | graph-similarity-drives-zeolite-diffusionless | 1812.02685 | null | https://arxiv.org/abs/1812.02685v2 | https://arxiv.org/pdf/1812.02685v2.pdf | Graph similarity drives zeolite diffusionless transformations and intergrowth | Predicting and directing polymorphic transformations is a critical challenge in zeolite synthesis. Although interzeolite transformations enable selective crystallization, their design lacks predictions to connect framework similarity and experimental observations. Here, computational and theoretical tools are combined ... | ['Rafael Gomez-Bombarelli', 'Elsa Olivetti', 'Zach Jensen', 'Daniel Schwalbe-Koda'] | 2018-12-06 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 2.72564471e-01 2.44269907e-01 -5.06069660e-01 -1.21566072e-01
3.77127938e-02 -7.10266173e-01 8.26073408e-01 1.69991538e-01
3.50582123e-01 9.66127157e-01 2.68672943e-01 -4.12943572e-01
-3.98359120e-01 -1.37180126e+00 -7.58436203e-01 -8.07063162e-01
-7.27459714e-02 1.20806110e+00 2.90190160e-01 -6.24729276... | [5.11847448348999, 5.533304691314697] |
3eaf39ca-ea84-4d09-8e24-4f76b57010b4 | dpll-mapf-an-integration-of-multi-agent-path | 2111.06494 | null | https://arxiv.org/abs/2111.06494v1 | https://arxiv.org/pdf/2111.06494v1.pdf | DPLL(MAPF): an Integration of Multi-Agent Path Finding and SAT Solving Technologies | In multi-agent path finding (MAPF), the task is to find non-conflicting paths for multiple agents from their initial positions to given individual goal positions. MAPF represents a classical artificial intelligence problem often addressed by heuristic-search. An important alternative to search-based techniques is compi... | ['Pavel Surynek', 'Martin Čapek'] | 2021-11-11 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 3.08478743e-01 6.14295483e-01 -2.69289494e-01 -3.33238959e-01
-4.95889872e-01 -8.87531102e-01 5.32583952e-01 4.23644453e-01
1.50847197e-01 1.42275381e+00 -3.63767743e-01 -5.41172385e-01
-5.31082034e-01 -1.36710346e+00 -3.85987788e-01 -4.90668565e-01
-2.66612113e-01 1.29270232e+00 6.72498226e-01 -4.05768216... | [4.986104965209961, 1.9085493087768555] |
00fe571d-210b-4755-865c-8ed8e066ff07 | omni-aggregation-networks-for-lightweight | 2304.10244 | null | https://arxiv.org/abs/2304.10244v2 | https://arxiv.org/pdf/2304.10244v2.pdf | Omni Aggregation Networks for Lightweight Image Super-Resolution | While lightweight ViT framework has made tremendous progress in image super-resolution, its uni-dimensional self-attention modeling, as well as homogeneous aggregation scheme, limit its effective receptive field (ERF) to include more comprehensive interactions from both spatial and channel dimensions. To tackle these d... | ['Jinfan Liu', 'Yutian Liu', 'Bingbing Ni', 'Xuanhong Chen', 'Hang Wang'] | 2023-04-20 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Omni_Aggregation_Networks_for_Lightweight_Image_Super-Resolution_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Omni_Aggregation_Networks_for_Lightweight_Image_Super-Resolution_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-super-resolution'] | ['computer-vision'] | [ 1.07793301e-01 2.89745796e-02 4.59841080e-02 -1.67705685e-01
-8.42188120e-01 4.67991866e-02 2.52119511e-01 -4.14406896e-01
-2.27330193e-01 6.61906004e-01 4.58392262e-01 -1.85203984e-01
-1.15145385e-01 -7.96391964e-01 -6.15691006e-01 -8.02738369e-01
-4.66801226e-01 -3.63582939e-01 6.81676567e-01 -2.77319878... | [10.869632720947266, -1.9115865230560303] |
9008af95-4efc-4a8a-aeec-eadea8289404 | robust-model-predictive-techno-economic | 2305.03272 | null | https://arxiv.org/abs/2305.03272v1 | https://arxiv.org/pdf/2305.03272v1.pdf | Robust Model Predictive Techno-Economic Control of Active Distribution Networks | Stochastic controllers are perceived as a promising solution for techno-economic operation of distribution networks having higher generation uncertainties at large penetration of renewables. These controllers are supported by forecasters capable of predicting generation uncertainty by means of lower/upper bounds rather... | ['Zhaoyu Wang', 'Rui Cheng', 'Prashant Tiwari', 'Salish Maharjan'] | 2023-05-05 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-3.93475533e-01 1.04108982e-01 -1.73606411e-01 1.28036201e-01
-1.75346240e-01 -9.72610056e-01 4.98745680e-01 2.19500467e-01
2.60942847e-01 1.37845802e+00 -1.87886998e-01 -4.91418451e-01
-7.10191429e-01 -1.08531141e+00 -1.96599424e-01 -7.40186214e-01
6.81035295e-02 4.65143710e-01 -2.70931218e-02 -2.12046757... | [5.665369510650635, 2.5491013526916504] |
39a7b7e0-513d-4053-a9b7-cdb910ba1284 | qpp-real-time-quantization-parameter | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Kryzhanovskiy_QPP_Real-Time_Quantization_Parameter_Prediction_for_Deep_Neural_Networks_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Kryzhanovskiy_QPP_Real-Time_Quantization_Parameter_Prediction_for_Deep_Neural_Networks_CVPR_2021_paper.pdf | QPP: Real-Time Quantization Parameter Prediction for Deep Neural Networks | Modern deep neural networks (DNNs) cannot be effectively used in mobile and embedded devices due to strict requirements for computational complexity, memory, and power consumption. The quantization of weights and feature maps (activations) is a popular approach to solve this problem. Training-aware quantization oft... | ['Aleksandr Zuruev', 'Nikolay Kozyrskiy', 'Gleb Balitskiy', 'Vladimir Kryzhanovskiy'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['parameter-prediction'] | ['miscellaneous'] | [ 2.64700532e-01 -9.53946635e-02 -3.91630143e-01 -5.56113780e-01
-7.51961708e-01 -2.41912484e-01 2.64970094e-01 7.72779360e-02
-6.79954112e-01 6.81728542e-01 -2.97080368e-01 -1.88270286e-01
9.35883820e-02 -1.00899506e+00 -6.57482028e-01 -7.67093897e-01
3.47220838e-01 3.66149247e-01 5.78667402e-01 6.06625602... | [8.601255416870117, 3.112410545349121] |
5655ba17-5669-49d4-8844-01c90926f3a6 | modern-hopfield-networks-for-few-and-zero | 2104.03279 | null | https://arxiv.org/abs/2104.03279v3 | https://arxiv.org/pdf/2104.03279v3.pdf | Modern Hopfield Networks for Few- and Zero-Shot Reaction Template Prediction | Finding synthesis routes for molecules of interest is an essential step in the discovery of new drugs and materials. To find such routes, computer-assisted synthesis planning (CASP) methods are employed which rely on a model of chemical reactivity. In this study, we model single-step retrosynthesis in a template-based ... | ['Günter Klambauer', 'Sepp Hochreiter', 'Jörg K. Wegner', 'Marwin Segler', 'Jonas Verhoeven', 'Paulo Neves', 'Natalia Dyubankova', 'Philipp Renz', 'Philipp Seidl'] | 2021-04-07 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 4.04129803e-01 2.18807664e-02 -4.94469881e-01 -5.65856993e-02
-5.68336606e-01 -9.76685226e-01 8.30304027e-01 4.33811098e-01
-4.57238376e-01 1.06380558e+00 2.14194730e-01 -5.26606321e-01
-2.18940288e-01 -8.73460293e-01 -7.77300894e-01 -7.78460562e-01
9.48041081e-02 3.84280622e-01 6.67620972e-02 -3.36781710... | [4.50202751159668, 6.0998029708862305] |
9c2b46dd-b5b6-4818-8fee-38f52a2594e9 | opa-3d-occlusion-aware-pixel-wise-aggregation | 2211.01142 | null | https://arxiv.org/abs/2211.01142v1 | https://arxiv.org/pdf/2211.01142v1.pdf | OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection | Despite monocular 3D object detection having recently made a significant leap forward thanks to the use of pre-trained depth estimators for pseudo-LiDAR recovery, such two-stage methods typically suffer from overfitting and are incapable of explicitly encapsulating the geometric relation between depth and object boundi... | ['Federico Tombari', 'Didier Stricker', 'Benjamin Busam', 'Jason Rambach', 'Guangyao Zhai', 'Fabian Manhardt', 'Yan Di', 'Yongzhi Su'] | 2022-11-02 | null | null | null | null | ['monocular-3d-object-detection'] | ['computer-vision'] | [ 1.63715601e-01 -1.55411422e-01 -8.52706358e-02 -4.53132749e-01
-9.46856856e-01 -4.27753896e-01 7.05796361e-01 2.41082534e-01
-5.18935442e-01 2.38100260e-01 -1.66556507e-01 -3.33502918e-01
2.51081854e-01 -8.85160863e-01 -9.81616080e-01 -6.71079934e-01
5.41152805e-02 5.73185802e-01 6.78837657e-01 2.00303048... | [7.8098320960998535, -2.637403726577759] |
986c6807-3705-4c74-83ee-b30e094ec091 | user-centric-conversational-recommendation | 2204.09263 | null | https://arxiv.org/abs/2204.09263v2 | https://arxiv.org/pdf/2204.09263v2.pdf | User-Centric Conversational Recommendation with Multi-Aspect User Modeling | Conversational recommender systems (CRS) aim to provide highquality recommendations in conversations. However, most conventional CRS models mainly focus on the dialogue understanding of the current session, ignoring other rich multi-aspect information of the central subjects (i.e., users) in recommendation. In this wor... | ['Qing He', 'Fuzhen Zhuang', 'Xiang Ao', 'Yongchun Zhu', 'Ruobing Xie', 'Shuokai Li'] | 2022-04-20 | null | null | null | null | ['dialogue-understanding'] | ['natural-language-processing'] | [-9.17679667e-02 -1.23506702e-01 -5.92556715e-01 -7.53480554e-01
-8.57111812e-01 -5.64437330e-01 6.98957980e-01 -3.05305034e-01
5.03327288e-02 3.88515890e-01 1.29552865e+00 -3.51830497e-02
-3.38245034e-01 -5.69798589e-01 -1.41958714e-01 -4.68403071e-01
4.17254716e-01 4.71893013e-01 -1.84982806e-01 -8.16068649... | [12.346664428710938, 7.499161243438721] |
c4010e06-1a0b-45eb-a6af-d1c8dbefa6a5 | learning-to-explore-intrinsic-saliency-for | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_Learning_to_Explore_Intrinsic_Saliency_for_Stereoscopic_Video_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Learning_to_Explore_Intrinsic_Saliency_for_Stereoscopic_Video_CVPR_2019_paper.pdf | Learning to Explore Intrinsic Saliency for Stereoscopic Video | The human visual system excels at biasing the stereoscopic visual signals by the attention mechanisms. Traditional methods relying on the low-level features and depth relevant information for stereoscopic video saliency prediction have fundamental limitations. For example, it is cumbersome to model the interactions bet... | [' Jianmin Jiang', ' Sam Kwong', ' Shikai Li', ' Shiqi Wang', ' Xu Wang', 'Qiudan Zhang'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['video-saliency-detection'] | ['computer-vision'] | [ 3.08114439e-01 -3.73362660e-01 -2.43969128e-01 -2.14054182e-01
-4.06729251e-01 3.56173702e-02 3.68977368e-01 -2.16611564e-01
-2.12270781e-01 7.28804111e-01 5.34604609e-01 2.12151483e-01
3.70513871e-02 -4.13207710e-01 -8.60845506e-01 -8.21162164e-01
1.27889961e-02 -3.96813899e-01 8.59323680e-01 -3.71409625... | [9.771821022033691, -0.29674339294433594] |
58c8e2c6-4961-44f5-b885-9de160cb0285 | identity-preserving-pose-robust-face | 2111.10634 | null | https://arxiv.org/abs/2111.10634v1 | https://arxiv.org/pdf/2111.10634v1.pdf | Identity-Preserving Pose-Robust Face Hallucination Through Face Subspace Prior | Over the past few decades, numerous attempts have been made to address the problem of recovering a high-resolution (HR) facial image from its corresponding low-resolution (LR) counterpart, a task commonly referred to as face hallucination. Despite the impressive performance achieved by position-patch and deep learning-... | ['Mohammad Rahmati', 'Ali Abbasi'] | 2021-11-20 | null | null | null | null | ['3d-face-reconstruction', 'face-hallucination', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.62680525e-01 2.54150301e-01 1.73787236e-01 -1.60445839e-01
-5.85788071e-01 -1.24156527e-01 7.76655436e-01 -4.33151096e-01
-9.66827050e-02 8.36334407e-01 3.35278481e-01 5.02849579e-01
-1.10953465e-01 -5.84765136e-01 -5.41718006e-01 -8.49592745e-01
1.14967883e-01 3.06132138e-01 -5.12184501e-01 -1.87698081... | [12.845039367675781, -0.025765951722860336] |
1a13e854-fd43-4deb-9df2-b2b9e516cfc2 | discrete-attacks-and-submodular-optimization | 1812.00151 | null | http://arxiv.org/abs/1812.00151v2 | http://arxiv.org/pdf/1812.00151v2.pdf | Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification | Adversarial examples are carefully constructed modifications to an input that
completely change the output of a classifier but are imperceptible to humans.
Despite these successful attacks for continuous data (such as image and audio
samples), generating adversarial examples for discrete structures such as text
has pro... | ['Pin-Yu Chen', 'Qi Lei', 'Michael Witbrock', 'Lingfei Wu', 'Inderjit S. Dhillon', 'Alexandros G. Dimakis'] | 2018-12-01 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 6.95151269e-01 3.51528049e-01 2.40949303e-01 -5.01629472e-01
-1.03997791e+00 -1.17613459e+00 6.28424585e-01 1.11476935e-01
-4.80397493e-01 7.50606716e-01 1.05820917e-01 -2.90893763e-01
2.67512649e-01 -6.88003480e-01 -1.11642516e+00 -6.98625803e-01
8.95310715e-02 7.54814520e-02 -1.31452590e-01 -4.11904752... | [5.958797454833984, 8.00013542175293] |
58a8b229-d3a2-4b5a-8ea3-977ff875dfa7 | personalized-multi-faceted-trust-modeling-to | 2111.06440 | null | https://arxiv.org/abs/2111.06440v1 | https://arxiv.org/pdf/2111.06440v1.pdf | Personalized multi-faceted trust modeling to determine trust links in social media and its potential for misinformation management | In this paper, we present an approach for predicting trust links between peers in social media, one that is grounded in the artificial intelligence area of multiagent trust modeling. In particular, we propose a data-driven multi-faceted trust modeling which incorporates many distinct features for a comprehensive analys... | ['Queenie Chen', 'Gaurav Sahu', 'Xueguang Ma', 'Robin Cohen', 'Alexandre Parmentier'] | 2021-11-11 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [-4.62631464e-01 6.60129249e-01 -3.14132690e-01 -7.88904607e-01
4.22970019e-02 6.46011829e-02 3.18089008e-01 9.70067978e-01
-3.91481698e-01 8.02712083e-01 6.52705431e-01 7.48972148e-02
-6.18662059e-01 -6.82127416e-01 -2.53302246e-01 -1.21534623e-01
-5.88859022e-01 6.46912932e-01 1.14008702e-01 -6.35083079... | [9.937392234802246, 6.021758079528809] |
22b95013-d7b7-4e71-b3df-35a0e627303f | extracting-event-temporal-relations-via | 2109.05527 | null | https://arxiv.org/abs/2109.05527v1 | https://arxiv.org/pdf/2109.05527v1.pdf | Extracting Event Temporal Relations via Hyperbolic Geometry | Detecting events and their evolution through time is a crucial task in natural language understanding. Recent neural approaches to event temporal relation extraction typically map events to embeddings in the Euclidean space and train a classifier to detect temporal relations between event pairs. However, embeddings in ... | ['Yulan He', 'Gabriele Pergola', 'Xingwei Tan'] | 2021-09-12 | null | https://aclanthology.org/2021.emnlp-main.636 | https://aclanthology.org/2021.emnlp-main.636.pdf | emnlp-2021-11 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [-1.01665370e-01 4.48433518e-01 -5.30584753e-02 -4.85780269e-01
-3.74751657e-01 -5.84884882e-01 1.09335399e+00 1.03511369e+00
-7.46396124e-01 2.87004769e-01 8.30496967e-01 -2.15026870e-01
-4.42814589e-01 -1.14430273e+00 -4.16918486e-01 -3.57091039e-01
-7.60608077e-01 3.76166642e-01 2.86411703e-01 -2.34037831... | [9.18298053741455, 9.140591621398926] |
f6298fed-3dc7-4dbb-beda-47b0126fb072 | deep-learning-methods-allow-fully-automated | null | null | https://www.nature.com/articles/s41598-021-89111-9 | https://www.nature.com/articles/s41598-021-89111-9.pdf | Deep learning methods allow fully automated segmentation of metacarpal bones to quantify volumetric bone mineral density | Arthritis patients develop hand bone loss, which leads to destruction and functional impairment of the affected joints. High resolution peripheral quantitative computed tomography (HR-pQCT) allows the quantification of volumetric bone mineral density (vBMD) and bone microstructure in vivo with an isotropic voxel size o... | ['Andreas Maier', 'Arnd Kleyer', 'Georg Schett', 'Gerhard Krönke', 'Anna-Maria Liphardt', 'David Simon', 'Timo Meinderink', 'Lukas Folle'] | 2021-05-06 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [-6.33865222e-02 2.83429861e-01 5.29916398e-02 -3.38490486e-01
-8.34659576e-01 -9.46295541e-03 -3.20788193e-03 4.75082755e-01
-5.96658409e-01 7.62950480e-01 4.80057932e-02 -3.48962061e-02
-3.06510776e-01 -1.06930137e+00 -4.08575416e-01 -7.03592360e-01
-5.02642572e-01 1.46533108e+00 6.02245748e-01 2.44385868... | [14.174847602844238, -2.1623783111572266] |
37540365-46c6-4457-820d-571907fb5bc5 | epicardial-adipose-tissue-segmentation-from | 2204.12904 | null | https://arxiv.org/abs/2204.12904v1 | https://arxiv.org/pdf/2204.12904v1.pdf | Epicardial Adipose Tissue Segmentation from CT Images with A Semi-3D Neural Network | Epicardial adipose tissue is a type of adipose tissue located between the heart wall and a protective layer around the heart called the pericardium. The volume and thickness of epicardial adipose tissue are linked to various cardiovascular diseases. It is shown to be an independent cardiovascular disease risk factor. F... | ['Irena Galić', 'Marija Habijan', 'Marin Benčević'] | 2022-04-27 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 2.68846005e-01 3.10727954e-01 -2.53062159e-01 -3.34423125e-01
-4.12741452e-01 -5.05743861e-01 -3.25707011e-02 3.29261273e-01
-4.71244454e-01 3.75901371e-01 1.04837850e-01 -3.50985080e-01
2.71011561e-01 -8.56145859e-01 -8.34563002e-02 -6.20322108e-01
-5.72060049e-01 7.22969651e-01 2.23938972e-01 5.88390648... | [14.278718948364258, -2.5439538955688477] |
5d9ee25d-adc6-4a57-affc-ec880f9a7d5a | babyslm-language-acquisition-friendly | 2306.01506 | null | https://arxiv.org/abs/2306.01506v2 | https://arxiv.org/pdf/2306.01506v2.pdf | BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models | Self-supervised techniques for learning speech representations have been shown to develop linguistic competence from exposure to speech without the need for human labels. In order to fully realize the potential of these approaches and further our understanding of how infants learn language, simulations must closely emu... | ['Alejandrina Cristia', 'Emmanuel Dupoux', 'Hervé Bredin', 'Okko Räsänen', 'María Andrea Cruz Blandón', 'Hadrien Titeux', 'Yaya Sy', 'Marvin Lavechin'] | 2023-06-02 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 2.35418916e-01 5.19035637e-01 2.51539331e-02 -8.59019995e-01
-6.51826143e-01 -6.30308807e-01 6.02278411e-01 3.14137131e-01
-4.94358271e-01 3.64494532e-01 4.02709037e-01 -3.54307055e-01
1.92791268e-01 -4.96788621e-01 -6.24381602e-01 -2.41169766e-01
-2.05507800e-01 3.91054541e-01 2.74273425e-01 -3.14053178... | [10.773660659790039, 9.091550827026367] |
eeacc466-44d8-4efd-9e64-1fde43b0897f | small-footprint-keyword-spotting-on-raw-audio | 1911.02086 | null | https://arxiv.org/abs/1911.02086v2 | https://arxiv.org/pdf/1911.02086v2.pdf | Small-Footprint Keyword Spotting on Raw Audio Data with Sinc-Convolutions | Keyword Spotting (KWS) enables speech-based user interaction on smart devices. Always-on and battery-powered application scenarios for smart devices put constraints on hardware resources and power consumption, while also demanding high accuracy as well as real-time capability. Previous architectures first extracted aco... | ['Ludwig Kürzinger', 'Simon Mittermaier', 'Gerhard Rigoll', 'Bernd Waschneck'] | 2019-11-05 | null | null | null | null | ['small-footprint-keyword-spotting'] | ['speech'] | [ 4.10982668e-01 -1.30353510e-01 -7.90188462e-03 -4.99076635e-01
-9.62845027e-01 -5.49873888e-01 1.49526805e-01 -7.36845424e-04
-8.36661398e-01 1.96642190e-01 -2.49774922e-02 -8.80524874e-01
7.57282302e-02 -6.35106921e-01 -3.71220142e-01 -4.78424430e-01
1.09725595e-02 6.42383993e-02 3.16968352e-01 3.03778827... | [14.435965538024902, 5.864406108856201] |
cc37530d-ea37-46ea-9c77-23c433efbf9d | rule-based-classification-of-hyperspectral | 2107.10638 | null | https://arxiv.org/abs/2107.10638v1 | https://arxiv.org/pdf/2107.10638v1.pdf | Rule-Based Classification of Hyperspectral Imaging Data | Due to its high spatial and spectral information content, hyperspectral imaging opens up new possibilities for a better understanding of data and scenes in a wide variety of applications. An essential part of this process of understanding is the classification part. In this article we present a general classification a... | ['Frank Boochs', 'Alain Tremeau', 'Songuel Polat'] | 2021-07-21 | null | null | null | null | ['classification'] | ['methodology'] | [ 5.69539189e-01 -5.13828158e-01 -9.88488793e-02 -4.64161843e-01
-7.05427006e-02 -9.30347502e-01 6.09598339e-01 3.89359355e-01
-1.95154727e-01 7.70303369e-01 -4.58128154e-01 -4.17094857e-01
-8.33786964e-01 -9.00347829e-01 -8.98668319e-02 -9.67424035e-01
-1.08706839e-01 2.33997315e-01 2.35285789e-01 -3.88560802... | [9.763328552246094, -1.7420289516448975] |
789d5160-e214-4d53-996c-16382cb04c3d | daer-to-reject-seeds-with-dual-loss | 2009.07414 | null | https://arxiv.org/abs/2009.07414v3 | https://arxiv.org/pdf/2009.07414v3.pdf | Ground-truth or DAER: Selective Re-query of Secondary Information | Many vision tasks use secondary information at inference time -- a seed -- to assist a computer vision model in solving a problem. For example, an initial bounding box is needed to initialize visual object tracking. To date, all such work makes the assumption that the seed is a good one. However, in practice, from crow... | ['Stephan J. Lemmer', 'Jason J. Corso'] | 2020-09-16 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Lemmer_Ground-Truth_or_DAER_Selective_Re-Query_of_Secondary_Information_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Lemmer_Ground-Truth_or_DAER_Selective_Re-Query_of_Secondary_Information_ICCV_2021_paper.pdf | iccv-2021-1 | ['viewpoint-estimation'] | ['computer-vision'] | [ 4.58169937e-01 1.21845156e-01 1.14561133e-01 -4.30641294e-01
-8.81191552e-01 -7.74530649e-01 6.04977548e-01 8.26456696e-02
-6.85775161e-01 6.64507747e-01 -1.12104610e-01 -1.72300220e-01
5.25047898e-01 -3.17084789e-01 -9.26008880e-01 -5.78802645e-01
3.66352946e-01 5.53548455e-01 9.89047408e-01 6.59234598... | [9.140669822692871, 1.0341920852661133] |
846090fd-832b-4ea6-83bf-bc32e413601b | face2exp-combating-data-biases-for-facial | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zeng_Face2Exp_Combating_Data_Biases_for_Facial_Expression_Recognition_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zeng_Face2Exp_Combating_Data_Biases_for_Facial_Expression_Recognition_CVPR_2022_paper.pdf | Face2Exp: Combating Data Biases for Facial Expression Recognition | Facial expression recognition (FER) is challenging due to the class imbalance caused by data collection. Existing studies tackle the data bias problem using only labeled facial expression dataset. Orthogonal to existing FER methods, we propose to utilize large unlabeled face recognition (FR) datasets to enhance FER... | ['Bo Tang', 'Fei Wang', 'YuTing Liu', 'Xiao Yan', 'Zhiyuan Lin', 'Dan Zeng'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['facial-expression-recognition'] | ['computer-vision'] | [ 2.24584490e-01 2.41426006e-01 -3.60187918e-01 -1.15265143e+00
-7.00449765e-01 6.81704059e-02 -7.31981397e-02 -1.00834644e+00
-2.07029969e-01 8.17039728e-01 -9.08903033e-02 2.66281337e-01
4.01876748e-01 -5.14547765e-01 -6.92278385e-01 -6.80250823e-01
2.45020494e-01 8.50752145e-02 -4.58740234e-01 -4.52504396... | [13.59283447265625, 1.6457409858703613] |
f2eb9cb2-c241-410d-a91f-be1a82fba5dd | transformer-based-multi-grained-features-for | 2211.12280 | null | https://arxiv.org/abs/2211.12280v1 | https://arxiv.org/pdf/2211.12280v1.pdf | Transformer Based Multi-Grained Features for Unsupervised Person Re-Identification | Multi-grained features extracted from convolutional neural networks (CNNs) have demonstrated their strong discrimination ability in supervised person re-identification (Re-ID) tasks. Inspired by them, this work investigates the way of extracting multi-grained features from a pure transformer network to address the unsu... | ['Xiaojin Gong', 'Menglin Wang', 'Jiachen Li'] | 2022-11-22 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 5.84234782e-02 -5.54763973e-02 -3.26598100e-02 -4.13698971e-01
-5.33967435e-01 -3.74109358e-01 7.52911508e-01 -2.22228184e-01
-6.42538249e-01 6.27639592e-01 2.78439462e-01 2.71935254e-01
-1.23493150e-01 -7.11999238e-01 -4.92801130e-01 -6.90854490e-01
1.64330915e-01 4.12219763e-01 2.68891305e-02 1.32770285... | [14.751924514770508, 1.03270423412323] |
bd124f03-f2c2-4a24-893d-e53d11ff0e64 | annotation-inspired-implicit-discourse | 2306.06480 | null | https://arxiv.org/abs/2306.06480v1 | https://arxiv.org/pdf/2306.06480v1.pdf | Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation | Implicit discourse relation classification is a challenging task due to the absence of discourse connectives. To overcome this issue, we design an end-to-end neural model to explicitly generate discourse connectives for the task, inspired by the annotation process of PDTB. Specifically, our model jointly learns to gene... | ['Michael Strube', 'Wei Liu'] | 2023-06-10 | null | null | null | null | ['relation-classification', 'implicit-discourse-relation-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.79392332e-01 1.00142395e+00 -3.91713411e-01 -5.48638761e-01
-7.45901883e-01 -5.03088415e-01 9.58700597e-01 1.48272365e-01
-1.07628122e-01 1.07240105e+00 7.66351819e-01 -5.44275880e-01
2.73770481e-01 -8.14488292e-01 -6.43135607e-01 -2.85650820e-01
1.96725056e-01 5.10981023e-01 2.53560424e-01 -3.20275426... | [10.808899879455566, 9.259331703186035] |
99f3d754-573b-4654-a7be-3d18b9118a4a | computational-ergonomics-for-task-delegation | 2203.11007 | null | https://arxiv.org/abs/2203.11007v2 | https://arxiv.org/pdf/2203.11007v2.pdf | Computational ergonomics for task delegation in Human-Robot Collaboration: spatiotemporal adaptation of the robot to the human through contactless gesture recognition | The high prevalence of work-related musculoskeletal disorders (WMSDs) could be addressed by optimizing Human-Robot Collaboration (HRC) frameworks for manufacturing applications. In this context, this paper proposes two hypotheses for ergonomically effective task delegation and HRC. The first hypothesis states that it i... | ['Alina Glushkova', 'Sotiris Manitsaris', 'Gavriela Senteri', 'Dimitris Papanagiotou', 'Brenda Elizabeth Olivas-Padilla'] | 2022-03-21 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 1.23877026e-01 5.00495017e-01 1.37775391e-01 8.50607753e-02
-3.07949215e-01 -1.55988544e-01 -7.98356682e-02 2.25838367e-02
-8.46937954e-01 1.36494562e-01 8.75099972e-02 -1.89351320e-01
-1.06297469e+00 -3.30029815e-01 -5.60301661e-01 -4.71245944e-01
-3.99221443e-02 5.36756992e-01 -7.00866953e-02 -2.31924340... | [4.991964817047119, 0.9143423438072205] |
6ec97456-9d9e-4312-a6c1-8a2ee29171c7 | an-end-to-end-neural-network-for-image | 1907.01432 | null | https://arxiv.org/abs/1907.01432v3 | https://arxiv.org/pdf/1907.01432v3.pdf | An End-to-End Neural Network for Image Cropping by Learning Composition from Aesthetic Photos | As one of the fundamental techniques for image editing, image cropping discards unrelevant contents and remains the pleasing portions of the image to enhance the overall composition and achieve better visual/aesthetic perception. In this paper, we primarily focus on improving the accuracy of automatic image cropping, a... | ['Xujun Peng', 'Peng Lu', 'Hao Zhang', 'Xiaofu Jin'] | 2019-07-02 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 6.04311824e-01 5.39532974e-02 3.24589685e-02 -1.89707175e-01
-2.64447182e-01 -2.74382502e-01 1.99363187e-01 1.32387534e-01
-2.80892372e-01 4.11520243e-01 -1.90282241e-01 7.20085800e-02
2.02082381e-01 -1.09293318e+00 -9.77702260e-01 -8.29454660e-01
2.53185898e-01 -3.79499912e-01 2.47545943e-01 -7.41290301... | [11.2971830368042, -1.091201663017273] |
7c629e42-6f4c-476e-8a1d-4d0b2755bda6 | stratified-transfer-learning-for-cross-domain | 1801.00820 | null | http://arxiv.org/abs/1801.00820v1 | http://arxiv.org/pdf/1801.00820v1.pdf | Stratified Transfer Learning for Cross-domain Activity Recognition | In activity recognition, it is often expensive and time-consuming to acquire
sufficient activity labels. To solve this problem, transfer learning leverages
the labeled samples from the source domain to annotate the target domain which
has few or none labels. Existing approaches typically consider learning a
global doma... | ['Philip S. Yu', 'Lisha Hu', 'Yiqiang Chen', 'Jindong Wang', 'Xiaohui Peng'] | 2017-12-25 | null | null | null | null | ['cross-domain-activity-recognition'] | ['computer-vision'] | [ 4.63830978e-01 -2.86608428e-01 -6.78706765e-01 -3.92244071e-01
-8.33096921e-01 -7.56194174e-01 4.66494143e-01 -5.55846393e-02
-2.62634873e-01 1.03222167e+00 2.34422326e-01 4.74102609e-02
-1.56657279e-01 -7.16853738e-01 -6.09171152e-01 -8.37205589e-01
6.97880238e-02 1.85364604e-01 3.36835891e-01 3.56890410... | [8.068449020385742, 1.044541835784912] |
7974a011-580b-4ed5-821c-97d3ba3e4e96 | cif-pt-bridging-speech-and-text | 2305.17499 | null | https://arxiv.org/abs/2305.17499v1 | https://arxiv.org/pdf/2305.17499v1.pdf | CIF-PT: Bridging Speech and Text Representations for Spoken Language Understanding via Continuous Integrate-and-Fire Pre-Training | Speech or text representation generated by pre-trained models contains modal-specific information that could be combined for benefiting spoken language understanding (SLU) tasks. In this work, we propose a novel pre-training paradigm termed Continuous Integrate-and-Fire Pre-Training (CIF-PT). It relies on a simple but ... | ['Zejun Ma', 'Lu Lu', 'Jun Zhang', 'Peihao Wu', 'Zhecheng An', 'Linhao Dong'] | 2023-05-27 | null | null | null | null | ['spoken-language-understanding', 'intent-classification', 'slot-filling', 'spoken-language-understanding'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 4.51295376e-01 6.55398905e-01 -3.06014091e-01 -5.59655070e-01
-1.18877137e+00 -2.04933971e-01 7.71466494e-01 5.50709590e-02
-3.87087703e-01 6.03283703e-01 5.60580611e-01 -4.56248552e-01
5.12937725e-01 -4.97960865e-01 -7.98031926e-01 -3.84200126e-01
3.62999231e-01 6.65333152e-01 3.49678695e-02 -4.09265935... | [14.025487899780273, 6.988629341125488] |
82dbe740-6a55-4e98-a919-a94368188e46 | blended-grammar-network-for-human-parsing | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4548_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123690188.pdf | Blended Grammar Network for Human Parsing | Although human parsing has made great progress, it still faces a challenge, i.e., how to extract the whole foreground from similar or cluttered scenes effectively. In this paper, we propose a Blended Grammar Network (BGNet), to deal with the challenge. BGNet exploits the inherent hierarchical structure of a human body ... | ['Ming Tang', 'Yingying Chen', 'Xiaomei Zhang', 'Bingke Zhu', 'Jinqiao Wang'] | null | null | null | null | eccv-2020-8 | ['human-parsing'] | ['computer-vision'] | [ 4.22752082e-01 3.95379514e-01 1.84334502e-01 -5.56043267e-01
-4.19227600e-01 -3.04182500e-01 2.78491676e-01 -3.80948663e-01
-3.11264783e-01 3.90941769e-01 -2.48263660e-03 -1.44829288e-01
4.83338058e-01 -7.45339394e-01 -7.76180863e-01 -6.65527642e-01
3.29958111e-01 2.47596845e-01 8.61320913e-01 -3.88641171... | [8.691500663757324, 0.05304848402738571] |
91c4937e-1812-4ee9-adec-561e6dbb56d9 | dvg-face-dual-variational-generation-for | 2009.09399 | null | https://arxiv.org/abs/2009.09399v2 | https://arxiv.org/pdf/2009.09399v2.pdf | DVG-Face: Dual Variational Generation for Heterogeneous Face Recognition | Heterogeneous Face Recognition (HFR) refers to matching cross-domain faces and plays a crucial role in public security. Nevertheless, HFR is confronted with challenges from large domain discrepancy and insufficient heterogeneous data. In this paper, we formulate HFR as a dual generation problem, and tackle it via a nov... | ['Yibo Hu', 'Xiang Wu', 'Huaibo Huang', 'Ran He', 'Chaoyou Fu'] | 2020-09-20 | null | null | null | null | ['heterogeneous-face-recognition'] | ['computer-vision'] | [ 8.88946131e-02 -2.48760864e-01 -7.71591961e-02 -3.22910905e-01
-1.11573553e+00 -5.43645978e-01 5.45684516e-01 -6.68729842e-01
3.92201217e-03 7.83598006e-01 5.02087362e-02 2.67711818e-01
-3.73493247e-02 -7.82079339e-01 -5.09850502e-01 -1.18497109e+00
6.33943379e-01 2.87970185e-01 -3.62886369e-01 -3.36600751... | [13.104403495788574, 0.314214289188385] |
a6629414-b9fd-4b44-8991-dffbfc44c72a | brain-inspired-spiking-neural-network-for | 2304.04697 | null | https://arxiv.org/abs/2304.04697v2 | https://arxiv.org/pdf/2304.04697v2.pdf | Brain-Inspired Spiking Neural Network for Online Unsupervised Time Series Prediction | Energy and data-efficient online time series prediction for predicting evolving dynamical systems are critical in several fields, especially edge AI applications that need to update continuously based on streaming data. However, current DNN-based supervised online learning models require a large amount of training data... | ['Saibal Mukhopadhyay', 'Biswadeep Chakraborty'] | 2023-04-10 | null | null | null | null | ['topological-data-analysis', 'time-series-prediction'] | ['graphs', 'time-series'] | [ 1.29935279e-01 -2.78776675e-01 3.67350131e-01 -5.97799569e-02
2.08754167e-01 -4.45263416e-01 5.38925350e-01 3.50060046e-01
-4.63475674e-01 9.07740474e-01 -2.18857363e-01 -1.08592689e-01
-6.43311083e-01 -8.14980030e-01 -8.81458461e-01 -1.06051314e+00
-7.29959846e-01 3.83980066e-01 6.76160991e-01 -3.12796921... | [6.750194072723389, 3.4342970848083496] |
ab1973d9-24f3-4843-911d-3a9a89540420 | deepstory-video-story-qa-by-deep-embedded | 1707.00836 | null | http://arxiv.org/abs/1707.00836v1 | http://arxiv.org/pdf/1707.00836v1.pdf | DeepStory: Video Story QA by Deep Embedded Memory Networks | Question-answering (QA) on video contents is a significant challenge for
achieving human-level intelligence as it involves both vision and language in
real-world settings. Here we demonstrate the possibility of an AI agent
performing video story QA by learning from a large amount of cartoon videos. We
develop a video-s... | ['Byoung-Tak Zhang', 'Seong-Ho Choi', 'Min-Oh Heo', 'Kyung-Min Kim'] | 2017-07-04 | null | null | null | null | ['video-story-qa'] | ['computer-vision'] | [ 1.93552047e-01 -6.13120198e-02 1.50008321e-01 -5.28525591e-01
-1.25456262e+00 -2.40492553e-01 5.36580265e-01 -2.63465375e-01
-2.94291645e-01 4.25982744e-01 7.78699458e-01 2.60653079e-01
2.12710053e-01 -7.75745451e-01 -1.44210362e+00 -4.65127319e-01
-6.17662966e-02 5.78025103e-01 3.03253651e-01 -1.87876225... | [10.465916633605957, 0.9155983924865723] |
9351c643-6607-49fc-9dcc-a233eae9f0f5 | towards-adversarial-realism-and-robust | 2301.13122 | null | https://arxiv.org/abs/2301.13122v3 | https://arxiv.org/pdf/2301.13122v3.pdf | Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification | The Internet of Things (IoT) faces tremendous security challenges. Machine learning models can be used to tackle the growing number of cyber-attack variations targeting IoT systems, but the increasing threat posed by adversarial attacks restates the need for reliable defense strategies. This work describes the types of... | ['Eva Maia', 'Isabel Praça', 'João Vitorino'] | 2023-01-30 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 3.80605161e-01 -7.54832104e-02 1.81465298e-01 -2.36470625e-01
-2.00274527e-01 -8.48274469e-01 9.56229329e-01 1.34204645e-02
-1.78312197e-01 9.47534919e-01 -3.45629781e-01 -7.76720822e-01
-3.97247165e-01 -1.06315196e+00 -6.63150489e-01 -9.65894818e-01
-4.06316906e-01 4.02599275e-01 4.92749438e-02 -4.01032239... | [5.541747570037842, 7.571475982666016] |
c8c73c90-8dc0-4fdf-90f4-e4b36a02344f | layout-and-task-aware-instruction-prompt-for | 2306.00526 | null | https://arxiv.org/abs/2306.00526v2 | https://arxiv.org/pdf/2306.00526v2.pdf | Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering | The pre-training-fine-tuning paradigm based on layout-aware multimodal pre-trained models has achieved significant progress on document image question answering. However, domain pre-training and task fine-tuning for additional visual, layout, and task modules prevent them from directly utilizing off-the-shelf instructi... | ['Yin Zhang', 'Yixin Ou', 'Yunhao Li', 'Wenjin Wang'] | 2023-06-01 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 2.92990178e-01 1.17167115e-01 -3.73961300e-01 -3.79907936e-01
-1.17401814e+00 -7.92553067e-01 6.38178110e-01 1.47389635e-01
-4.49291378e-01 1.72740445e-02 4.13503617e-01 -7.44260252e-01
-4.48587835e-02 -5.76572239e-01 -8.86913300e-01 -6.59312829e-02
7.73620605e-01 3.88719440e-01 4.32332039e-01 -4.08101618... | [11.263021469116211, 1.9080158472061157] |
713d9cf9-6a85-493d-a5b8-7c7be4aba458 | it-takes-two-to-tango-navigating | 2305.09022 | null | https://arxiv.org/abs/2305.09022v1 | https://arxiv.org/pdf/2305.09022v1.pdf | It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance | Progress in NLP is increasingly measured through benchmarks; hence, contextualizing progress requires understanding when and why practitioners may disagree about the validity of benchmarks. We develop a taxonomy of disagreement, drawing on tools from measurement modeling, and distinguish between two types of disagreeme... | ['Su Lin Blodgett', 'Hal Daumé III', 'Xingdi Yuan', 'Arjun Subramonian'] | 2023-05-15 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 1.83820739e-01 4.37810451e-01 -7.98813105e-01 -5.11286974e-01
-1.10418952e+00 -9.86559749e-01 6.79903328e-01 4.76316094e-01
-5.99287450e-01 6.77686512e-01 9.11241710e-01 -5.57402968e-01
-6.19210720e-01 -2.07563117e-01 -4.70469713e-01 5.33292517e-02
9.43922222e-01 4.67223793e-01 -2.35284612e-01 1.06731176... | [9.921162605285645, 8.379427909851074] |
7c2ff1c9-7d60-48ba-adc9-e3ac1f06675d | relative-facial-action-unit-detection | 1405.0085 | null | http://arxiv.org/abs/1405.0085v1 | http://arxiv.org/pdf/1405.0085v1.pdf | Relative Facial Action Unit Detection | This paper presents a subject-independent facial action unit (AU) detection
method by introducing the concept of relative AU detection, for scenarios where
the neutral face is not provided. We propose a new classification objective
function which analyzes the temporal neighborhood of the current frame to
decide if the ... | ['Louis-Philippe Morency', 'Mahmoud Khademi'] | 2014-05-01 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 4.75764543e-01 -1.86333299e-01 -2.06767157e-01 -5.85342884e-01
-3.97706896e-01 -3.98700684e-01 6.54655695e-01 -1.84441328e-01
-4.24023479e-01 7.61443198e-01 2.52672255e-01 3.85012597e-01
3.52377325e-01 -3.73316288e-01 -3.50570112e-01 -1.22232771e+00
-1.18158497e-01 -3.20451230e-01 2.47842610e-01 -2.68256098... | [13.667009353637695, 1.7776947021484375] |
e119f68d-5b5d-4daa-beac-f496d1209bbb | a-spectral-approach-to-unsupervised-object | 1907.02731 | null | https://arxiv.org/abs/1907.02731v5 | https://arxiv.org/pdf/1907.02731v5.pdf | A 3D Convolutional Approach to Spectral Object Segmentation in Space and Time | We formulate object segmentation in video as a graph partitioning problem in space and time, in which nodes are pixels and their relations form local neighborhoods. We claim that the strongest cluster in this pixel-level graph represents the salient object segmentation. We compute the main cluster using a novel and fas... | ['Marius Leordeanu', 'Elena Burceanu'] | 2019-07-05 | null | null | null | null | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 2.16098085e-01 8.64452347e-02 6.89698085e-02 5.02725877e-02
-3.47393334e-01 -6.82276189e-01 1.86268687e-01 2.17157751e-01
-6.15814447e-01 -2.95431688e-02 -2.81237096e-01 -3.00319016e-01
-1.42742276e-01 -8.26504588e-01 -8.91011059e-01 -8.44510138e-01
-2.25688711e-01 4.51406240e-01 8.33407342e-01 6.54411316... | [9.148632049560547, -0.20750819146633148] |
ef069eac-b44f-4712-a430-41a154b754a0 | small-signal-stability-techniques-for-power | 2111.01694 | null | https://arxiv.org/abs/2111.01694v1 | https://arxiv.org/pdf/2111.01694v1.pdf | Small-Signal Stability Techniques for Power System Modal Analysis, Control, and Numerical Integration | This thesis proposes novel Small-Signal Stability Analysis (SSSA)-based techniques that contribute to electric power system modal analysis, automatic control, and numerical integration. Modal analysis is a fundamental tool for power system stability analysis and control. The thesis proposes a SSSA approach to determine... | ['Georgios Tzounas'] | 2021-11-02 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [ 5.40495664e-02 4.06602696e-02 2.98032854e-02 6.68524981e-01
-1.10514656e-01 -1.06236041e+00 1.70961872e-01 1.94120795e-01
3.90161484e-01 9.80057955e-01 -5.58520138e-01 -5.18253207e-01
-8.84851336e-01 -5.34275711e-01 -5.80208004e-02 -1.10858059e+00
-7.63679966e-02 -1.59824956e-02 -1.26606971e-01 -6.88123882... | [5.617125511169434, 2.676239490509033] |
0b3415e0-191d-462b-8c3c-750f3b22af56 | towards-robot-vision-module-development-with | null | null | https://openreview.net/forum?id=H1BHbmWCZ | https://openreview.net/pdf?id=H1BHbmWCZ | TOWARDS ROBOT VISION MODULE DEVELOPMENT WITH EXPERIENTIAL ROBOT LEARNING | n this paper we present a thrust in three directions of visual development us- ing supervised and semi-supervised techniques. The first is an implementation of semi-supervised object detection and recognition using the principles of Soft At- tention and Generative Adversarial Networks (GANs). The second and the third a... | ['Joanne Bechta Dugan', 'Ahmed A Aly'] | 2018-01-01 | null | null | null | iclr-2018-1 | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 2.52833784e-01 7.67540574e-01 2.30189916e-02 -3.58123571e-01
6.39228225e-02 -5.60077071e-01 1.18411255e+00 -5.38268924e-01
-2.67245263e-01 9.28779900e-01 -6.52620643e-02 -2.11267605e-01
9.82214604e-03 -9.35778558e-01 -1.14504898e+00 -6.66731775e-01
-2.52366990e-01 2.11881340e-01 2.27582976e-01 -7.06228912... | [9.768508911132812, 2.0912349224090576] |
5c229098-0836-4db7-bd31-0ac0ac4919f2 | sample-average-approximation-for-black-box-vi | 2304.06803 | null | https://arxiv.org/abs/2304.06803v2 | https://arxiv.org/pdf/2304.06803v2.pdf | Sample Average Approximation for Black-Box VI | We present a novel approach for black-box VI that bypasses the difficulties of stochastic gradient ascent, including the task of selecting step-sizes. Our approach involves using a sequence of sample average approximation (SAA) problems. SAA approximates the solution of stochastic optimization problems by transforming ... | ['Daniel Sheldon', 'Justin Domke', 'Javier Burroni'] | 2023-04-13 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 6.42867237e-02 -2.15431988e-01 -2.26767197e-01 -1.25478357e-01
-1.39268839e+00 -6.53807521e-01 4.87284869e-01 -1.55900836e-01
-7.05366790e-01 1.33951044e+00 -1.74141470e-02 -7.85595834e-01
-1.61132142e-01 -6.16758764e-01 -6.28668785e-01 -1.02639079e+00
1.50467455e-01 7.62875497e-01 3.26570153e-01 -3.69106174... | [6.650849342346191, 4.051882743835449] |
f3ea5920-0abe-427a-9880-b189e3b9a9b8 | zero-shot-single-image-restoration-through | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Kar_Zero-Shot_Single_Image_Restoration_Through_Controlled_Perturbation_of_Koschmieders_Model_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Kar_Zero-Shot_Single_Image_Restoration_Through_Controlled_Perturbation_of_Koschmieders_Model_CVPR_2021_paper.pdf | Zero-Shot Single Image Restoration Through Controlled Perturbation of Koschmieder's Model | Real-world image degradation due to light scattering can be described based on the Koschmieder's model. Training deep models to restore such degraded images is challenging as real-world paired data is scarcely available and synthetic paired data may suffer from domain-shift issues. In this paper, a zero-shot single... | ['Prabir Kumar Biswas', 'Debashis Sen', 'Sobhan Kanti Dhara', 'Aupendu Kar'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['underwater-image-restoration', 'image-dehazing'] | ['computer-vision', 'computer-vision'] | [ 4.86298859e-01 -4.53912131e-02 6.11516416e-01 -1.12149812e-01
-6.81577981e-01 -2.27553807e-02 4.12153482e-01 -3.84928733e-01
-5.64193428e-01 8.13050926e-01 8.01424533e-02 2.12444738e-02
-3.81389081e-01 -5.68358779e-01 -1.01699603e+00 -1.44141042e+00
6.32223114e-02 -1.65317580e-01 8.98652151e-02 -6.55635118... | [11.272497177124023, -2.465681314468384] |
9882ee96-c81b-4661-a5dc-d8ac84b0a3e9 | backpropagating-through-structured-argmax | 1805.04658 | null | http://arxiv.org/abs/1805.04658v1 | http://arxiv.org/pdf/1805.04658v1.pdf | Backpropagating through Structured Argmax using a SPIGOT | We introduce the structured projection of intermediate gradients optimization
technique (SPIGOT), a new method for backpropagating through neural networks
that include hard-decision structured predictions (e.g., parsing) in
intermediate layers. SPIGOT requires no marginal inference, unlike structured
attention networks... | ['Sam Thomson', 'Noah A. Smith', 'Hao Peng'] | 2018-05-12 | backpropagating-through-structured-argmax-1 | https://aclanthology.org/P18-1173 | https://aclanthology.org/P18-1173.pdf | acl-2018-7 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [ 2.22741768e-01 9.19486225e-01 -2.63985664e-01 -8.54210258e-01
-1.05846810e+00 -5.22205770e-01 4.42591906e-01 2.20018327e-01
-5.11637032e-01 6.60775423e-01 4.41383183e-01 -6.18660688e-01
2.27187291e-01 -6.52525425e-01 -1.15090168e+00 -3.83428156e-01
-2.04256475e-01 5.67243814e-01 8.02820548e-02 -2.88558528... | [10.477815628051758, 9.320786476135254] |
0107907d-b0fd-4db6-9fea-5fa266828175 | deep-factorized-metric-learning | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Deep_Factorized_Metric_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Deep_Factorized_Metric_Learning_CVPR_2023_paper.pdf | Deep Factorized Metric Learning | Learning a generalizable and comprehensive similarity metric to depict the semantic discrepancies between images is the foundation of many computer vision tasks. While existing methods approach this goal by learning an ensemble of embeddings with diverse objectives, the backbone network still receives a mix of all ... | ['Jiwen Lu', 'Jie zhou', 'Junlong Li', 'Wenzhao Zheng', 'Chengkun Wang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [-9.82734263e-02 -1.34042069e-01 -1.77830905e-01 -8.34087789e-01
-8.96944225e-01 -3.75314057e-01 3.68289351e-01 -8.46415311e-02
-6.27000749e-01 1.73787013e-01 6.72823712e-02 -3.21325883e-02
-2.11529836e-01 -6.68477476e-01 -7.02813447e-01 -6.01470351e-01
-6.03627227e-02 3.59424770e-01 2.12771043e-01 -1.71743810... | [9.475468635559082, 3.019587993621826] |
5811f138-1784-4b17-b74b-82e93a0bc0e3 | annotating-columns-with-pre-trained-language | 2104.01785 | null | https://arxiv.org/abs/2104.01785v2 | https://arxiv.org/pdf/2104.01785v2.pdf | Annotating Columns with Pre-trained Language Models | Inferring meta information about tables, such as column headers or relationships between columns, is an active research topic in data management as we find many tables are missing some of this information. In this paper, we study the problem of annotating table columns (i.e., predicting column types and the relationshi... | ['Wang-Chiew Tan', 'Chen Chen', 'Çağatay Demiralp', 'Dan Zhang', 'Yuliang Li', 'Jinfeng Li', 'Yoshihiko Suhara'] | 2021-04-05 | null | null | null | null | ['type-prediction', 'table-annotation', 'table-annotation', 'column-type-annotation', 'columns-property-annotation'] | ['computer-code', 'knowledge-base', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.35638759e-01 2.06457108e-01 -5.57000339e-01 -4.01231378e-01
-1.00509477e+00 -6.76598012e-01 2.80376762e-01 9.45153713e-01
-1.01355486e-01 1.01908958e+00 1.67097196e-01 -5.49884975e-01
1.79703012e-01 -1.24360371e+00 -1.20882523e+00 -1.65793419e-01
-1.67987481e-01 8.25619161e-01 1.13591768e-01 -2.71132976... | [9.616878509521484, 7.894745826721191] |
da59c980-ac95-49f3-915e-fafc9e60a35c | assessing-the-unitary-rnn-as-an-end-to-end | 2208.05719 | null | https://arxiv.org/abs/2208.05719v1 | https://arxiv.org/pdf/2208.05719v1.pdf | Assessing the Unitary RNN as an End-to-End Compositional Model of Syntax | We show that both an LSTM and a unitary-evolution recurrent neural network (URN) can achieve encouraging accuracy on two types of syntactic patterns: context-free long distance agreement, and mildly context-sensitive cross serial dependencies. This work extends recent experiments on deeply nested context-free long dist... | ['Shalom Lappin', 'Jean-Philippe Bernardy'] | 2022-08-11 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 2.75826812e-01 2.27737904e-01 -3.32868338e-01 -6.52591646e-01
-7.47285485e-01 -6.96162701e-01 4.32137489e-01 1.55913681e-01
-6.83002234e-01 7.51285195e-01 5.81774473e-01 -1.08878028e+00
7.47340731e-03 -7.11578071e-01 -7.88918734e-01 -6.14167750e-01
-2.17432618e-01 4.71287042e-01 -9.40946192e-02 -5.16361296... | [10.570244789123535, 9.146241188049316] |
5d690c90-37f2-4392-b3cf-2c912100b21e | learning-structured-embeddings-of-knowledge | 2004.07265 | null | https://arxiv.org/abs/2004.07265v1 | https://arxiv.org/pdf/2004.07265v1.pdf | Learning Structured Embeddings of Knowledge Graphs with Adversarial Learning Framework | Many large-scale knowledge graphs are now available and ready to provide semantically structured information that is regarded as an important resource for question answering and decision support tasks. However, they are built on rigid symbolic frameworks which makes them hard to be used in other intelligent systems. We... | ['Xiaoqing Zheng', 'Jiehang Zeng', 'Lu Liu'] | 2020-04-15 | null | null | null | null | ['triple-classification'] | ['graphs'] | [ 2.46641055e-01 8.04743350e-01 -2.79614061e-01 -1.67314276e-01
-5.12859106e-01 -7.03333437e-01 6.65263414e-01 1.30879745e-01
-1.81799904e-02 8.94836485e-01 -2.24359244e-01 -3.72543842e-01
-6.37956560e-02 -1.59777486e+00 -1.06679630e+00 -6.84921145e-01
1.13631912e-01 9.86956537e-01 3.85244548e-01 -6.21423364... | [8.698064804077148, 7.884810924530029] |
2510d729-f9d5-4446-9d47-bd6a054473f3 | federated-semi-supervised-medical-image | 2106.08600 | null | https://arxiv.org/abs/2106.08600v1 | https://arxiv.org/pdf/2106.08600v1.pdf | Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching | Federated learning (FL) has emerged with increasing popularity to collaborate distributed medical institutions for training deep networks. However, despite existing FL algorithms only allow the supervised training setting, most hospitals in realistic usually cannot afford the intricate data labeling due to absence of b... | ['Pheng-Ann Heng', 'Qi Dou', 'Hongzheng Yang', 'Quande Liu'] | 2021-06-16 | null | null | null | null | ['semi-supervised-medical-image-classification'] | ['medical'] | [ 3.17375474e-02 3.83512437e-01 -6.54141486e-01 -7.75188446e-01
-1.22689879e+00 -2.49824584e-01 9.72726643e-02 -2.23173514e-01
-2.82580584e-01 9.33743358e-01 1.56668350e-01 -2.77810931e-01
-4.01642382e-01 -3.14947337e-01 -7.21832752e-01 -9.56325233e-01
4.67539206e-03 5.38755655e-01 -4.98037636e-01 4.26982492... | [6.037525177001953, 6.442866325378418] |
5ff612ca-e6be-49fe-a0a4-e2558c94450f | communicative-subgraph-representation | 2205.05957 | null | https://arxiv.org/abs/2205.05957v1 | https://arxiv.org/pdf/2205.05957v1.pdf | Communicative Subgraph Representation Learning for Multi-Relational Inductive Drug-Gene Interaction Prediction | Illuminating the interconnections between drugs and genes is an important topic in drug development and precision medicine. Currently, computational predictions of drug-gene interactions mainly focus on the binding interactions without considering other relation types like agonist, antagonist, etc. In addition, existin... | ['Yuedong Yang', 'Sijie Mai', 'Shuangjia Zheng', 'Jiahua Rao'] | 2022-05-12 | null | null | null | null | ['gene-interaction-prediction'] | ['graphs'] | [ 2.99008429e-01 2.28285059e-01 -5.90280175e-01 -3.57605994e-01
-4.48175132e-01 -3.02555412e-01 4.75479543e-01 4.49399650e-01
1.30789950e-01 1.21123576e+00 2.34155551e-01 -4.94142145e-01
-5.83765745e-01 -1.06022930e+00 -9.92421865e-01 -8.97344470e-01
-2.54777104e-01 6.44344091e-01 1.19921245e-01 -3.65788609... | [5.310478687286377, 5.8765764236450195] |
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