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5eded2ee-2bfe-4878-a6d4-69e652c583f9 | enforcing-temporal-consistency-in-deep | 1906.07160 | null | https://arxiv.org/abs/1906.07160v1 | https://arxiv.org/pdf/1906.07160v1.pdf | Enforcing temporal consistency in Deep Learning segmentation of brain MR images | Longitudinal analysis has great potential to reveal developmental trajectories and monitor disease progression in medical imaging. This process relies on consistent and robust joint 4D segmentation. Traditional techniques are dependent on the similarity of images over time and the use of subject-specific priors to redu... | ['Malav Bateriwala', 'Pierrick Bourgeat'] | 2019-06-13 | null | null | null | null | ['4d-spatio-temporal-semantic-segmentation', '3d-medical-imaging-segmentation', 'brain-image-segmentation'] | ['computer-vision', 'medical', 'medical'] | [-2.49498814e-01 5.09174205e-02 3.83200459e-02 -6.13910198e-01
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-3.77064824e-01 7.45170176e-01 5.52124560e-01 -7.43989646... | [14.17313003540039, -2.321460247039795] |
a24f6474-f6b1-4d42-ac0e-92297051a3b2 | segmentation-evaluation-metrics-a-comparison | null | null | https://aclanthology.org/L14-1709 | https://aclanthology.org/L14-1709.pdf | Segmentation evaluation metrics, a comparison grounded on prosodic and discourse units | Knowledge on evaluation metrics and best practices of using them have improved fast in the recent years Fort et al. (2012). However, the advances concern mostly evaluation of classification related tasks. Segmentation tasks have received less attention. Nevertheless, there are crucial in a large number of linguistic st... | ["Laurent Pr{\\'e}vot", 'Klim Peshkov'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['discourse-segmentation'] | ['natural-language-processing'] | [ 1.57357633e-01 3.28178704e-01 -1.46731153e-01 -4.40076798e-01
-9.05795753e-01 -9.33480799e-01 7.25321233e-01 7.84471631e-01
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-9.13373530e-02 5.03022611e-01 7.16952860e-01 -1.82866812... | [10.64479923248291, 9.82609748840332] |
ba721f20-e5c4-4563-aae8-c4966fea39cb | alignve-visual-entailment-recognition-based | 2211.08736 | null | https://arxiv.org/abs/2211.08736v1 | https://arxiv.org/pdf/2211.08736v1.pdf | AlignVE: Visual Entailment Recognition Based on Alignment Relations | Visual entailment (VE) is to recognize whether the semantics of a hypothesis text can be inferred from the given premise image, which is one special task among recent emerged vision and language understanding tasks. Currently, most of the existing VE approaches are derived from the methods of visual question answering.... | ['James Tin-Yau Kwok', 'Yuan Yan Tang', 'Lei He', 'Bo Liu', 'Jiayun Shen', 'Jie Gui', 'Jiuxin Cao', 'Biwei Cao'] | 2022-11-16 | null | null | null | null | ['visual-entailment'] | ['reasoning'] | [ 2.56125689e-01 -2.30868921e-01 -2.43216604e-02 -6.26326442e-01
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5.89636922e-01 3.38652683e-03 3.24973315e-01 2.53015086... | [10.727478981018066, 1.6515034437179565] |
b29d0ed7-a0af-4957-98da-82326ebed86c | privacy-preserving-deep-learning-model-for | 2209.04445 | null | https://arxiv.org/abs/2209.04445v2 | https://arxiv.org/pdf/2209.04445v2.pdf | Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection | Recent studies demonstrated that X-ray radiography showed higher accuracy than Polymerase Chain Reaction (PCR) testing for COVID-19 detection. Therefore, applying deep learning models to X-rays and radiography images increases the speed and accuracy of determining COVID-19 cases. However, due to Health Insurance Portab... | ['Xiali Hei', 'Sonya Hsu', 'Vijay Srinivas Tida Sai Venkatesh Chilukoti'] | 2022-09-07 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [-8.30622464e-02 -4.46086526e-02 -1.38195261e-01 -5.84032655e-01
-1.02701259e+00 -6.56351388e-01 -6.81086034e-02 2.92522401e-01
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-1.49176091e-01 3.71991426e-01 -3.68264288e-01 5.62519968... | [6.15775728225708, 6.663617134094238] |
27a51490-35f4-4295-a804-b3dd6086ab43 | using-self-supervised-co-training-to-improve | 2105.06421 | null | https://arxiv.org/abs/2105.06421v3 | https://arxiv.org/pdf/2105.06421v3.pdf | Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial Representation | In this paper, at first, the impact of ImageNet pre-training on fine-grained Facial Emotion Recognition (FER) is investigated which shows that when enough augmentations on images are applied, training from scratch provides better result than fine-tuning on ImageNet pre-training. Next, we propose a method to improve fin... | ['Farzaneh Esmaili', 'Gholam Ali Montazer', 'Mahdi Pourmirzaei'] | 2021-05-13 | null | null | null | null | ['facial-emotion-recognition', 'head-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 3.04195791e-01 1.84831619e-01 3.30081843e-02 -7.87934780e-01
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1.50392547e-01 2.76540756e-01 -2.58774310e-01 -4.61550742... | [13.508712768554688, 1.3637762069702148] |
ef335c69-d6e0-4f9b-815b-93f2190a56fe | large-scale-privacy-preserving-network | 2205.14440 | null | https://arxiv.org/abs/2205.14440v1 | https://arxiv.org/pdf/2205.14440v1.pdf | Large-Scale Privacy-Preserving Network Embedding against Private Link Inference Attacks | Network embedding represents network nodes by a low-dimensional informative vector. While it is generally effective for various downstream tasks, it may leak some private information of networks, such as hidden private links. In this work, we address a novel problem of privacy-preserving network embedding against priva... | ['Yuncong Yang', 'Junjie Wu', 'Leye Wang', 'Xiao Han'] | 2022-05-28 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-1.05767976e-03 5.29354692e-01 -5.17966092e-01 -1.56160459e-01
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-2.87530512e-01 -1.05738926e+00 -7.14941800e-01 -8.96624446e-01
-2.59431303e-01 1.72518000e-01 5.43231796e-03 8.15748945... | [6.018126010894775, 6.891495704650879] |
061c386c-bc52-4fb2-bae4-51829215e87d | i2-sdf-intrinsic-indoor-scene-reconstruction | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhu_I2-SDF_Intrinsic_Indoor_Scene_Reconstruction_and_Editing_via_Raytracing_in_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhu_I2-SDF_Intrinsic_Indoor_Scene_Reconstruction_and_Editing_via_Raytracing_in_CVPR_2023_paper.pdf | I2-SDF: Intrinsic Indoor Scene Reconstruction and Editing via Raytracing in Neural SDFs | In this work, we present I^2-SDF, a new method for intrinsic indoor scene reconstruction and editing using differentiable Monte Carlo raytracing on neural signed distance fields (SDFs). Our holistic neural SDF-based framework jointly recovers the underlying shapes, incident radiance and materials from multi-view im... | ['Rui Wang', 'Hujun Bao', 'Wei Hua', 'Rui Tang', 'Lisha Wang', 'Dianbing Xi', 'Jifan Li', 'Fujun Luan', 'Qi Ye', 'Yuchi Huo', 'Jingsen Zhu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['indoor-scene-reconstruction', 'novel-view-synthesis'] | ['computer-vision', 'computer-vision'] | [ 4.85916704e-01 -3.48615646e-01 5.89692295e-01 -6.00135624e-01
-6.88013852e-01 -6.47065401e-01 5.69165885e-01 -4.06222314e-01
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4.17005658e-01 2.41192281e-01 -8.89257900e-03 -1.15695857... | [9.618024826049805, -3.1217360496520996] |
5fc77b47-b9b9-4573-84f9-7485401ce63d | weighted-combination-of-bert-and-n-gram | null | null | https://aclanthology.org/2020.wanlp-1.27 | https://aclanthology.org/2020.wanlp-1.27.pdf | Weighted combination of BERT and N-GRAM features for Nuanced Arabic Dialect Identification | Around the Arab world, different Arabic dialects are spoken by more than 300M persons, and are increasingly popular in social media texts. However, Arabic dialects are considered to be low-resource languages, limiting the development of machine-learning based systems for these dialects. In this paper, we investigate th... | ['Ismail Berrada', 'Ahmed Khoumsi', 'Hamza Alami', 'Ahmed Alami', 'Abdellah El Mekki'] | null | null | null | null | coling-wanlp-2020-12 | ['dialect-identification'] | ['natural-language-processing'] | [-6.91040218e-01 -2.44660407e-01 -1.31895363e-01 -4.48567420e-01
-9.73190248e-01 -1.02215123e+00 1.28949690e+00 -1.54547524e-02
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-1.33617476e-01 9.99143779e-01 2.50814878e-03 -8.44832659... | [10.183980941772461, 10.770488739013672] |
fadaf665-7a8a-4947-84bf-043107374857 | multimodal-information-bottleneck-learning | 2210.17444 | null | https://arxiv.org/abs/2210.17444v3 | https://arxiv.org/pdf/2210.17444v3.pdf | Multimodal Information Bottleneck: Learning Minimal Sufficient Unimodal and Multimodal Representations | Learning effective joint embedding for cross-modal data has always been a focus in the field of multimodal machine learning. We argue that during multimodal fusion, the generated multimodal embedding may be redundant, and the discriminative unimodal information may be ignored, which often interferes with accurate predi... | ['Haifeng Hu', 'Ying Zeng', 'Sijie Mai'] | 2022-10-31 | null | null | null | null | ['multimodal-sentiment-analysis', 'multimodal-emotion-recognition', 'multimodal-sentiment-analysis', 'multimodal-emotion-recognition'] | ['computer-vision', 'computer-vision', 'natural-language-processing', 'speech'] | [ 6.51834533e-02 -1.37435064e-01 -2.03652591e-01 -2.97428906e-01
-9.39848244e-01 -4.73456830e-01 4.63126779e-01 2.36163497e-01
-2.87036151e-01 4.60065037e-01 1.42172650e-01 6.02741130e-02
-3.86960745e-01 -4.37819332e-01 -4.55075145e-01 -1.13072622e+00
2.25508869e-01 5.29116951e-02 -3.80992651e-01 -4.77984130... | [13.094594955444336, 5.0102949142456055] |
57fdd85c-19a6-4792-b940-b9c3231b2347 | robust-human-matting-via-semantic-guidance | 2210.05210 | null | https://arxiv.org/abs/2210.05210v1 | https://arxiv.org/pdf/2210.05210v1.pdf | Robust Human Matting via Semantic Guidance | Automatic human matting is highly desired for many real applications. We investigate recent human matting methods and show that common bad cases happen when semantic human segmentation fails. This indicates that semantic understanding is crucial for robust human matting. From this, we develop a fast yet accurate human ... | ['Shan Liu', 'Ying Shan', 'Lei Sun', 'Bingtao Fu', 'Yu Li', 'Ye Zhu', 'Xiangguang Chen'] | 2022-10-11 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 1.92621574e-01 2.62580723e-01 -1.46631449e-01 -3.63929302e-01
-8.00056398e-01 -3.67396414e-01 3.17678750e-01 -2.50128210e-01
-2.95618594e-01 5.02841055e-01 -1.28626764e-01 -1.16175719e-01
4.65718120e-01 -8.19098771e-01 -1.06251109e+00 -4.09580141e-01
6.73172534e-01 9.41993594e-01 3.41476500e-01 -1.38783470... | [10.692398071289062, -0.9482375383377075] |
7a598d0e-ef8f-4f4d-ad3a-be2abb812cf8 | motion-magnification-algorithms-for-video | 2211.16046 | null | https://arxiv.org/abs/2211.16046v1 | https://arxiv.org/pdf/2211.16046v1.pdf | Motion Magnification Algorithms for Video-Based Breathing Monitoring | In this paper, we present two video processing techniques for contact-less estimation of the Respiratory Rate (RR) of framed subjects. Due to the modest extent of movements related to respiration in both infants and adults, specific algorithms to efficiently detect breathing are needed. For this reason, motion-related ... | ['Riccardo Raheli', 'Francesco Pisani', 'Gianluigi Ferrari', 'Davide Alinovi', 'Veronica Mattioli'] | 2022-11-29 | null | null | null | null | ['motion-magnification'] | ['computer-vision'] | [ 6.98858976e-01 -8.48891214e-02 6.64671510e-02 -1.19845495e-01
-5.14056921e-01 -3.29774201e-01 1.02289945e-01 9.35571417e-02
-5.61738193e-01 6.20127380e-01 1.26325607e-01 2.15686113e-01
-3.32675219e-01 -2.60168284e-01 -5.55558838e-02 -1.05383313e+00
-3.14247102e-01 -4.45684999e-01 3.19380879e-01 3.53234828... | [13.936112403869629, 2.9482533931732178] |
c484c209-de08-4a6f-95c5-09c25c0e181b | learning-monocular-visual-odometry-through | 1903.10543 | null | https://arxiv.org/abs/1903.10543v2 | https://arxiv.org/pdf/1903.10543v2.pdf | Learning Monocular Visual Odometry through Geometry-Aware Curriculum Learning | Inspired by the cognitive process of humans and animals, Curriculum Learning (CL) trains a model by gradually increasing the difficulty of the training data. In this paper, we study whether CL can be applied to complex geometry problems like estimating monocular Visual Odometry (VO). Unlike existing CL approaches, we p... | ['Andrew Markham', 'Sen Wang', 'Muhamad Risqi U. Saputra', 'Niki Trigoni', 'Pedro P. B. de Gusmao'] | 2019-03-25 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-1.08304285e-01 -4.84516658e-02 3.06111928e-02 -3.85400474e-01
-1.48221910e-01 -2.95525938e-01 5.64909577e-01 -3.51802468e-01
-6.15332484e-01 6.73438251e-01 8.61974657e-02 -9.44797024e-02
-2.03405749e-02 -6.25073433e-01 -9.76908624e-01 -3.90325457e-01
9.22560692e-02 7.42542744e-02 6.99278489e-02 -3.46429385... | [8.350001335144043, -2.138300657272339] |
15121d37-56ef-4b19-a6ed-ee0a0790412c | kradagrad-kronecker-approximation-domination | 2305.19416 | null | https://arxiv.org/abs/2305.19416v1 | https://arxiv.org/pdf/2305.19416v1.pdf | KrADagrad: Kronecker Approximation-Domination Gradient Preconditioned Stochastic Optimization | Second order stochastic optimizers allow parameter update step size and direction to adapt to loss curvature, but have traditionally required too much memory and compute for deep learning. Recently, Shampoo [Gupta et al., 2018] introduced a Kronecker factored preconditioner to reduce these requirements: it is used for ... | ['Luke Walters', 'Alexander Moreno', 'Jonathan Mei'] | 2023-05-30 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-3.63510877e-01 -1.20228104e-01 -6.53456226e-02 -2.54583687e-01
-8.11680257e-01 -5.28272688e-01 2.87126750e-01 1.18177041e-01
-9.26169753e-01 9.22450244e-01 -9.21934471e-02 -5.35580575e-01
-2.36170739e-01 -6.23169720e-01 -1.08893085e+00 -6.69481397e-01
-2.98443466e-01 3.84884924e-01 -1.84915274e-01 -1.55643923... | [8.408596992492676, 3.3758227825164795] |
d7589ca7-4456-44cf-a0c3-fe46fca8fb3a | a-dual-symmetric-gauss-seidel-alternating | 1902.09135 | null | https://arxiv.org/abs/1902.09135v2 | https://arxiv.org/pdf/1902.09135v2.pdf | A Dual Symmetric Gauss-Seidel Alternating Direction Method of Multipliers for Hyperspectral Sparse Unmixing | Since sparse unmixing has emerged as a promising approach to hyperspectral unmixing, some spatial-contextual information in the hyperspectral images has been exploited to improve the performance of the unmixing recently. The total variation (TV) has been widely used to promote the spatial homogeneity as well as the smo... | ['Peipei Tang', 'Zheng Ma', 'Chengjing Wang', 'Longfei Ren'] | 2019-02-25 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 4.39000160e-01 -6.12489045e-01 9.41977799e-02 -9.85833164e-03
-7.76766360e-01 -2.79963791e-01 2.05332920e-01 -4.53637183e-01
2.58510914e-02 6.32683873e-01 1.22159488e-01 -1.01317629e-01
-4.27638501e-01 -5.27990162e-01 -4.23488379e-01 -1.45939314e+00
4.88058746e-01 4.51643839e-02 -5.29899001e-01 -1.09154731... | [10.106460571289062, -2.0343573093414307] |
d9ce17af-226f-49bd-b1b4-21be6cc61ccc | cblue-a-chinese-biomedical-language | 2106.08087 | null | https://arxiv.org/abs/2106.08087v6 | https://arxiv.org/pdf/2106.08087v6.pdf | CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark | Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice. With the development of biomedical language understanding benchmarks, AI applications are widely used in the medical field. However, most benchmarks are limited to English, which ma... | ['Xiaozhuan Liang', 'Xin Shang', 'Ningyu Zhang', 'Kangping Yin', 'Chuanqi Tan', 'Qingcai Chen', 'Buzhou Tang', 'Kunli Zhang', 'Hongying Zan', 'Jun Yan', 'Linfeng Li', 'Zheng Yuan', 'Hui Zong', 'Baobao Chang', 'Zhifang Sui', 'Guotong Xie', 'Yuan Ni', 'Luo Si', 'Fei Huang', 'Mosha Chen', 'Jian Xu', 'Lei LI', 'Zhen Bi'] | 2021-06-15 | null | https://aclanthology.org/2022.acl-long.544 | https://aclanthology.org/2022.acl-long.544.pdf | acl-2022-5 | ['medical-concept-normalization', 'medical-relation-extraction', 'sentence-pair-classification'] | ['medical', 'medical', 'natural-language-processing'] | [ 4.06838059e-01 3.88449281e-02 -3.21471423e-01 -5.40778458e-01
-9.80189025e-01 -5.70413284e-02 2.42719680e-01 4.28403974e-01
-8.75847936e-01 9.06354368e-01 4.70146239e-01 -4.92883623e-01
1.82092890e-01 -4.63846326e-01 -2.87548244e-01 -4.43901747e-01
1.05071932e-01 7.67073095e-01 -3.91749859e-01 -2.47090328... | [8.529882431030273, 8.759053230285645] |
15ea70d2-1e47-4415-8717-5eec4eec632e | multi-modal-bifurcated-network-for-depth | 2105.00690 | null | https://arxiv.org/abs/2105.00690v2 | https://arxiv.org/pdf/2105.00690v2.pdf | Multi-modal Bifurcated Network for Depth Guided Image Relighting | Image relighting aims to recalibrate the illumination setting in an image. In this paper, we propose a deep learning-based method called multi-modal bifurcated network (MBNet) for depth guided image relighting. That is, given an image and the corresponding depth maps, a new image with the given illuminant angle and col... | ['Sy-Yen Kuo', 'Hao-Lun Luo', 'Wei-Ting Chen', 'Hao-Hsiang Yang'] | 2021-05-03 | null | null | null | null | ['image-relighting'] | ['computer-vision'] | [ 3.06129962e-01 4.99058440e-02 -1.35895107e-02 -5.03427804e-01
-5.10249496e-01 -2.60250598e-01 2.86853760e-01 -4.79883403e-01
-2.98358679e-01 5.40135384e-01 2.67005891e-01 -2.35292673e-01
2.52237171e-01 -8.85425508e-01 -8.68652821e-01 -6.78627193e-01
4.43483144e-01 2.96599772e-02 7.62838647e-02 -1.98661596... | [10.777402877807617, -2.1869451999664307] |
7bf9cf02-0e3a-411f-9686-ba3da3dba380 | locate-who-you-are-matching-geo-location-to | 2104.09119 | null | https://arxiv.org/abs/2104.09119v2 | https://arxiv.org/pdf/2104.09119v2.pdf | Locate Who You Are: Matching Geo-location to Text for User Identity Linkage | Nowadays, users are encouraged to activate across multiple online social networks simultaneously. Anchor link prediction, which aims to reveal the correspondence among different accounts of the same user across networks, has been regarded as a fundamental problem for user profiling, marketing, cybersecurity, and recomm... | ['Yangyang Li', 'Xueqi Cheng', 'HuaWei Shen', 'Hao Gao', 'Yongqing Wang', 'Jiangli Shao'] | 2021-04-19 | null | null | null | null | ['anchor-link-prediction'] | ['graphs'] | [-4.98362295e-02 -5.77812940e-02 -7.96833515e-01 -3.47165883e-01
-3.95926476e-01 -7.54551291e-01 6.38675332e-01 7.31275797e-01
-2.33351901e-01 4.18814063e-01 3.15387279e-01 -1.01326749e-01
-5.26261866e-01 -9.06018734e-01 -3.07305474e-02 1.34231905e-02
-3.61920297e-02 5.55009425e-01 5.30883014e-01 -2.13609099... | [7.443170547485352, 6.284997940063477] |
bb0516b5-9673-4e40-8ade-b9b081de1008 | improving-long-context-document-level-machine | 2306.05183 | null | https://arxiv.org/abs/2306.05183v1 | https://arxiv.org/pdf/2306.05183v1.pdf | Improving Long Context Document-Level Machine Translation | Document-level context for neural machine translation (NMT) is crucial to improve the translation consistency and cohesion, the translation of ambiguous inputs, as well as several other linguistic phenomena. Many works have been published on the topic of document-level NMT, but most restrict the system to only local co... | ['Hermann Ney', 'Christian Herold'] | 2023-06-08 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 4.16345417e-01 -1.35252282e-01 -2.68408924e-01 -2.46225432e-01
-9.39349174e-01 -7.13638663e-01 7.24344075e-01 3.47599089e-01
-5.21903396e-01 9.03316438e-01 4.37725514e-01 -6.70799434e-01
1.37682080e-01 -4.65209514e-01 -6.24501586e-01 -6.48775697e-01
4.86396432e-01 5.24735689e-01 2.00402915e-01 -4.75247145... | [11.5418119430542, 10.061893463134766] |
ed133690-186c-4624-9305-e4fe2d9fbe51 | video-compression-with-arbitrary-rescaling | 2306.04202 | null | https://arxiv.org/abs/2306.04202v1 | https://arxiv.org/pdf/2306.04202v1.pdf | Video Compression with Arbitrary Rescaling Network | Most video platforms provide video streaming services with different qualities, and the quality of the services is usually adjusted by the resolution of the videos. So high-resolution videos need to be downsampled for compression. In order to solve the problem of video coding at different resolutions, we propose a rate... | ['Li Zhang', 'Junlin Li', 'Hao Jiang', 'Shijie Zhao', 'Mengxi Guo'] | 2023-06-07 | null | null | null | null | ['video-compression'] | ['computer-vision'] | [ 3.30107868e-01 -3.16444993e-01 -3.99314106e-01 -1.29589081e-01
-3.63316685e-01 -1.79318264e-01 9.96676311e-02 -3.70990157e-01
-1.41942337e-01 3.80152941e-01 4.03503507e-01 -3.73944998e-01
1.48852691e-01 -8.72161508e-01 -5.44444680e-01 -5.76543510e-01
-3.43754917e-01 -2.28846610e-01 5.80228746e-01 -2.70096928... | [11.160856246948242, -1.7746981382369995] |
251570c5-0cda-4156-ae5d-076b264ae3e9 | segment-graph-based-image-filtering-fast | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Zhang_Segment_Graph_Based_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Zhang_Segment_Graph_Based_ICCV_2015_paper.pdf | Segment Graph Based Image Filtering: Fast Structure-Preserving Smoothing | In this paper, we design a new edge-aware structure, named segment graph, to represent the image and we further develop a novel double weighted average image filter (SGF) based on the segment graph. In our SGF, we use the tree distance on the segment graph to define the internal weight function of the filtering kernel,... | ['Xiaopeng Zhang', 'Feihu Zhang', 'Longquan Dai', 'Shiming Xiang'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['stereo-matching'] | ['computer-vision'] | [ 2.73870349e-01 -3.01372945e-01 1.03347120e-03 -1.80178508e-01
-7.92100579e-02 -2.63993889e-01 2.02546254e-01 1.53811991e-01
-5.86659253e-01 4.74289477e-01 -1.01142293e-02 -2.15705425e-01
4.57186922e-02 -1.15794337e+00 -4.41879600e-01 -7.56045699e-01
8.08002800e-02 -4.87605810e-01 9.73970830e-01 -2.85142884... | [11.058063507080078, -2.5060036182403564] |
1838eb76-1927-4f02-891f-9fd99c3eb417 | interpretable-and-accurate-fine-grained | 2005.10411 | null | https://arxiv.org/abs/2005.10411v1 | https://arxiv.org/pdf/2005.10411v1.pdf | Interpretable and Accurate Fine-grained Recognition via Region Grouping | We present an interpretable deep model for fine-grained visual recognition. At the core of our method lies the integration of region-based part discovery and attribution within a deep neural network. Our model is trained using image-level object labels, and provides an interpretation of its results via the segmentation... | ['Zixuan Huang', 'Yin Li'] | 2020-05-21 | interpretable-and-accurate-fine-grained-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Interpretable_and_Accurate_Fine-grained_Recognition_via_Region_Grouping_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Huang_Interpretable_and_Accurate_Fine-grained_Recognition_via_Region_Grouping_CVPR_2020_paper.pdf | cvpr-2020-6 | ['fine-grained-visual-recognition'] | ['computer-vision'] | [ 2.00664610e-01 3.16199988e-01 -3.69687051e-01 -6.61646485e-01
-5.31340420e-01 -8.36078525e-01 8.98493350e-01 -6.48783939e-03
-3.42514552e-02 4.59391326e-01 1.94064513e-01 -3.46373841e-02
5.54439947e-02 -6.29413545e-01 -1.26093340e+00 -4.20506239e-01
1.22198522e-01 9.10199404e-01 3.73788148e-01 2.38790959... | [9.537657737731934, 1.495361328125] |
d6ae7b91-32e8-4a17-8d51-3866940f8df5 | ps8-net-a-deep-convolutional-neural-network | 2009.10380 | null | https://arxiv.org/abs/2009.10380v1 | https://arxiv.org/pdf/2009.10380v1.pdf | PS8-Net: A Deep Convolutional Neural Network to Predict the Eight-State Protein Secondary Structure | Protein secondary structure is crucial to creating an information bridge between the primary and tertiary (3D) structures. Precise prediction of eight-state protein secondary structure (PSS) has significantly utilized in the structural and functional analysis of proteins in bioinformatics. Deep learning techniques have... | ['Won-Sook Lee', 'Md. Aminur Rab Ratul', 'M. Hamed Mozaffari', 'Maryam Tavakol Elahi'] | 2020-09-22 | null | null | null | null | ['protein-secondary-structure-prediction'] | ['medical'] | [ 3.10482711e-01 -2.71927238e-01 5.27839847e-02 -4.83643055e-01
-3.32101464e-01 -3.18842560e-01 1.48461595e-01 4.66643304e-01
-3.25655043e-01 1.02497494e+00 4.45189280e-03 -6.58413708e-01
-4.79235016e-02 -4.41977948e-01 -8.65905166e-01 -1.10357034e+00
-1.44399419e-01 5.84313385e-02 3.36791307e-01 -3.39522958... | [4.699323654174805, 5.60764741897583] |
ed105c48-38c3-4c75-8682-25c94e11ea10 | artificial-perceptual-learning-image | 2106.07559 | null | https://arxiv.org/abs/2106.07559v1 | https://arxiv.org/pdf/2106.07559v1.pdf | Artificial Perceptual Learning: Image Categorization with Weak Supervision | Machine learning has achieved much success on supervised learning tasks with large sets of well-annotated training samples. However, in many practical situations, such strong and high-quality supervision provided by training data is unavailable due to the expensive and labor-intensive labeling process. Automatically id... | ['Tian Zheng', 'Douglas C. Morton', 'Helen Jin', 'María Uriarte', 'Chengliang Tang'] | 2021-06-02 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 6.57922506e-01 2.37308383e-01 -2.96000749e-01 -4.94337231e-01
-3.56075078e-01 -6.88365757e-01 4.02512938e-01 4.42294776e-01
-6.36606276e-01 7.12164581e-01 -3.79543841e-01 -3.71570468e-01
-2.66879618e-01 -7.24455416e-01 -8.29869568e-01 -4.85855103e-01
-1.12478301e-01 3.45816463e-01 2.25985005e-01 5.40203303... | [9.660857200622559, 2.4263370037078857] |
96104e20-e0e9-4767-9f51-6897246a5ea7 | autosamp-autoencoding-mri-sampling-via | 2306.02888 | null | https://arxiv.org/abs/2306.02888v2 | https://arxiv.org/pdf/2306.02888v2.pdf | AutoSamp: Autoencoding MRI Sampling via Variational Information Maximization | Accelerated MRI protocols routinely involve a predefined sampling pattern that undersamples the k-space. Finding an optimal pattern can enhance the reconstruction quality, however this optimization is a challenging task. To address this challenge, we introduce a novel deep learning framework, AutoSamp, based on variati... | ['John M. Pauly', 'Shreyas S. Vasanawala', 'Morteza Mardani', 'Cagan Alkan'] | 2023-06-05 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [ 1.89826444e-01 1.47740180e-02 -1.90338552e-01 -4.08270597e-01
-1.11815965e+00 -3.73727977e-01 1.75251275e-01 -5.55478968e-02
-6.06114209e-01 7.86159575e-01 5.30216634e-01 -1.74392149e-01
-4.13509846e-01 -5.33442438e-01 -1.02421308e+00 -1.12349713e+00
-2.62154460e-01 3.31218779e-01 1.07860856e-01 3.26830357... | [13.54008674621582, -2.3761818408966064] |
9b698372-e145-4f64-864d-5c9966c666d7 | thompson-sampling-efficiently-learns-to | 2206.09977 | null | https://arxiv.org/abs/2206.09977v1 | https://arxiv.org/pdf/2206.09977v1.pdf | Thompson Sampling Efficiently Learns to Control Diffusion Processes | Diffusion processes that evolve according to linear stochastic differential equations are an important family of continuous-time dynamic decision-making models. Optimal policies are well-studied for them, under full certainty about the drift matrices. However, little is known about data-driven control of diffusion proc... | ['Mohsen Bayati', 'Mohamad Sadegh Shirani Faradonbeh', 'Mohamad Kazem Shirani Faradonbeh'] | 2022-06-20 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 2.82564819e-01 2.55028814e-01 -3.49581957e-01 1.28039777e-01
-4.15030152e-01 -7.62934506e-01 2.84080684e-01 3.27509165e-01
-4.39861298e-01 9.81357217e-01 -2.36671150e-01 -5.24990082e-01
-6.58419371e-01 -4.82797384e-01 -7.90278792e-01 -1.20683599e+00
-3.80284369e-01 5.32135010e-01 -1.10042743e-01 -2.83621669... | [4.510873794555664, 2.7094788551330566] |
081df2fe-138b-4ede-91ec-e676922a5602 | explaining-groups-of-points-in-low | 2003.01640 | null | https://arxiv.org/abs/2003.01640v3 | https://arxiv.org/pdf/2003.01640v3.pdf | Explaining Groups of Points in Low-Dimensional Representations | A common workflow in data exploration is to learn a low-dimensional representation of the data, identify groups of points in that representation, and examine the differences between the groups to determine what they represent. We treat this workflow as an interpretable machine learning problem by leveraging the model t... | ['Sriram Sankararaman', 'Jonathan Terhorst', 'Ameet Talwalkar', 'Gregory Plumb'] | 2020-03-03 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/2264-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/2264-Paper.pdf | icml-2020-1 | ['counterfactual-explanation'] | ['miscellaneous'] | [ 4.32578534e-01 6.80505991e-01 -4.92141038e-01 -3.53625238e-01
-5.61366618e-01 -6.43418252e-01 7.51362741e-01 4.04893279e-01
4.09028888e-01 4.14049119e-01 9.09426928e-01 -5.65211117e-01
-6.88982069e-01 -5.96963704e-01 -7.38004744e-01 -5.06234705e-01
-6.13653898e-01 7.54165590e-01 -4.81026143e-01 2.53634810... | [8.781569480895996, 5.678574562072754] |
1f105f2f-8b4c-4fe5-9a3a-bf8c81dec0c8 | unsupervised-acoustic-unit-discovery-by | 2104.00994 | null | https://arxiv.org/abs/2104.00994v2 | https://arxiv.org/pdf/2104.00994v2.pdf | Unsupervised Acoustic Unit Discovery by Leveraging a Language-Independent Subword Discriminative Feature Representation | This paper tackles automatically discovering phone-like acoustic units (AUD) from unlabeled speech data. Past studies usually proposed single-step approaches. We propose a two-stage approach: the first stage learns a subword-discriminative feature representation and the second stage applies clustering to the learned re... | ['Odette Scharenborg', 'Laureano Moro-Velázquez', 'Piotr Żelasko', 'Siyuan Feng'] | 2021-04-02 | null | null | null | null | ['acoustic-unit-discovery'] | ['speech'] | [ 2.37666324e-01 5.53770177e-02 -1.91576123e-01 -5.13007402e-01
-1.68308771e+00 -5.69776237e-01 3.88228774e-01 -1.02067478e-01
-5.59543252e-01 5.81780851e-01 3.77776623e-01 -5.08799255e-01
2.44777009e-01 -2.65813202e-01 -5.60111880e-01 -7.97239184e-01
1.51130527e-01 7.52832055e-01 2.21411124e-01 2.12238468... | [14.435632705688477, 6.678610324859619] |
e4a50e39-ab0d-4ae0-8734-64fa8483a9a9 | graph-representation-for-order-aware-visual | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Qiu_Graph_Representation_for_Order-Aware_Visual_Transformation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Qiu_Graph_Representation_for_Order-Aware_Visual_Transformation_CVPR_2023_paper.pdf | Graph Representation for Order-Aware Visual Transformation | This paper proposes a new visual reasoning formulation that aims at discovering changes between image pairs and their temporal orders. Recognizing scene dynamics and their chronological orders is a fundamental aspect of human cognition. The aforementioned abilities make it possible to follow step-by-step instructio... | ['Hirokatsu Kataoka', 'Kenji Iwata', 'Fumiya Matsuzawa', 'Yanjun Sun', 'Yue Qiu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 4.19926494e-01 -3.18779826e-01 -5.72610721e-02 -3.17087471e-01
2.76241213e-01 -8.91713560e-01 1.05973828e+00 6.56729758e-01
-6.09879605e-02 3.90674263e-01 3.39774400e-01 -3.46299678e-01
-2.72690415e-01 -7.22750664e-01 -5.19091964e-01 -1.96989849e-01
-2.97148913e-01 2.73627281e-01 5.61142564e-01 -3.62094641... | [8.777017593383789, 0.8789583444595337] |
865a6267-f091-4c5c-acf3-ef5fa92101f7 | temporal-knowledge-graph-completion-using-box | 2109.08970 | null | https://arxiv.org/abs/2109.08970v1 | https://arxiv.org/pdf/2109.08970v1.pdf | Temporal Knowledge Graph Completion using Box Embeddings | Knowledge graph completion is the task of inferring missing facts based on existing data in a knowledge graph. Temporal knowledge graph completion (TKGC) is an extension of this task to temporal knowledge graphs, where each fact is additionally associated with a time stamp. Current approaches for TKGC primarily build o... | ['İsmail İlkan Ceylan', 'Ralph Abboud', 'Johannes Messner'] | 2021-09-18 | null | null | null | null | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-3.53868723e-01 5.89005470e-01 -8.62183213e-01 -9.09702256e-02
-3.49029243e-01 -6.09151781e-01 6.86516881e-01 5.25240421e-01
-2.29833320e-01 7.87328780e-01 6.44099832e-01 -2.83853620e-01
-4.86711264e-01 -1.20010722e+00 -8.10955465e-01 -2.47692838e-01
-7.77896047e-01 7.39591181e-01 3.64971906e-01 4.04339358... | [8.568046569824219, 7.9064860343933105] |
aa0429fe-0b08-4261-918f-e7854e4c89ad | a-report-on-the-2020-vua-and-toefl-metaphor | null | null | https://aclanthology.org/2020.figlang-1.3 | https://aclanthology.org/2020.figlang-1.3.pdf | A Report on the 2020 VUA and TOEFL Metaphor Detection Shared Task | In this paper, we report on the shared task on metaphor identification on VU Amsterdam Metaphor Corpus and on a subset of the TOEFL Native Language Identification Corpus. The shared task was conducted as apart of the ACL 2020 Workshop on Processing Figurative Language. | ['Egon Stemle', 'Chee Wee (Ben) Leong', 'Xianyang Chen', 'Beata Beigman Klebanov', 'Rutuja Ubale', 'Chris Hamill'] | 2020-07-01 | null | null | null | ws-2020-7 | ['native-language-identification'] | ['natural-language-processing'] | [-6.75350521e-03 1.88359588e-01 -2.14983061e-01 -2.42632017e-01
-3.14176172e-01 -9.55731034e-01 1.19430208e+00 2.49408409e-01
-8.25018227e-01 6.79066658e-01 8.52562249e-01 -7.13273227e-01
-1.83184016e-02 -2.09965557e-01 7.94460922e-02 2.03109980e-01
-4.88915294e-03 6.44516468e-01 -2.39418432e-01 -7.00592399... | [10.749269485473633, 9.20854377746582] |
5a7b25b9-a4ff-4d77-9405-9f2df61fd6a2 | transfer-learning-with-semi-supervised | 2306.16760 | null | https://arxiv.org/abs/2306.16760v1 | https://arxiv.org/pdf/2306.16760v1.pdf | Transfer Learning with Semi-Supervised Dataset Annotation for Birdcall Classification | We present working notes on transfer learning with semi-supervised dataset annotation for the BirdCLEF 2023 competition, focused on identifying African bird species in recorded soundscapes. Our approach utilizes existing off-the-shelf models, BirdNET and MixIT, to address representation and labeling challenges in the c... | ['Chris Hayduk', 'Murilo Gustineli', 'Nathan Zhong', 'Anthony Miyaguchi'] | 2023-06-29 | null | null | null | null | ['feature-engineering', 'transfer-learning'] | ['methodology', 'miscellaneous'] | [-7.44344369e-02 -2.16968626e-01 -2.40624949e-01 -7.01584876e-01
-6.98830605e-01 -1.05023122e+00 5.18056214e-01 8.27273075e-03
-9.50373352e-01 7.54691839e-01 5.46234488e-01 2.13392209e-02
-6.75152941e-03 -4.03802454e-01 -6.59855962e-01 2.90286075e-02
-5.13764024e-01 4.22006220e-01 4.92299460e-02 -2.74600118... | [15.096685409545898, 5.18523645401001] |
ab531bb3-9c9c-4b08-8769-ebe777690b37 | mask-dngan-multi-stage-raw-video-denoising | 2103.02861 | null | https://arxiv.org/abs/2103.02861v3 | https://arxiv.org/pdf/2103.02861v3.pdf | Multi-Stage Raw Video Denoising with Adversarial Loss and Gradient Mask | In this paper, we propose a learning-based approach for denoising raw videos captured under low lighting conditions. We propose to do this by first explicitly aligning the neighboring frames to the current frame using a convolutional neural network (CNN). We then fuse the registered frames using another CNN to obtain t... | ['Nima Khademi Kalantari', 'Libing Zeng', 'Avinash Paliwal'] | 2021-03-04 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 4.39854980e-01 -2.05371559e-01 3.97053003e-01 -2.06539661e-01
-7.77629733e-01 -4.88323241e-01 4.13140297e-01 -1.91421211e-01
-6.26805067e-01 6.67149842e-01 1.08755231e-01 6.29541576e-02
2.58311599e-01 -6.65487647e-01 -9.36683178e-01 -8.90568316e-01
-1.77539643e-02 -3.49074304e-01 2.98878849e-01 -5.29957004... | [11.296283721923828, -2.0782501697540283] |
f8373282-6728-4cc1-93ec-eca56ad23046 | mucs-lt-edi-eacl2021-cohope-hope-speech | null | null | https://aclanthology.org/2021.ltedi-1.27 | https://aclanthology.org/2021.ltedi-1.27.pdf | MUCS@LT-EDI-EACL2021:CoHope-Hope Speech Detection for Equality, Diversity, and Inclusion in Code-Mixed Texts | This paper describes the models submitted by the team MUCS for “Hope Speech Detection for Equality, Diversity, and Inclusion-EACL 2021” shared task that aims at classifying a comment / post in English and code-mixed texts in two language pairs, namely, Tamil-English (Ta-En) and Malayalam-English (Ma-En) into one of the... | ['H L Shashirekha', 'Aparna B K', 'Fazlourrahman Balouchzahi'] | null | null | null | null | eacl-ltedi-2021-4 | ['hope-speech-detection'] | ['natural-language-processing'] | [-3.77611041e-01 2.88643707e-02 -1.44494876e-01 -1.07618198e-01
-1.12193060e+00 -3.09214413e-01 6.96445704e-01 1.87351435e-01
-7.33344972e-01 8.68250668e-01 7.08339512e-01 -8.08840156e-01
3.52696627e-01 -2.76701540e-01 -3.43722254e-01 -3.35579693e-01
3.96808416e-01 2.81624585e-01 8.70038643e-02 -3.21265876... | [9.393171310424805, 10.678457260131836] |
bf447dbb-53b6-40c6-85e6-b8bc78456363 | cnn-based-methods-for-object-recognition-with | 2305.12417 | null | https://arxiv.org/abs/2305.12417v1 | https://arxiv.org/pdf/2305.12417v1.pdf | CNN-based Methods for Object Recognition with High-Resolution Tactile Sensors | Novel high-resolution pressure-sensor arrays allow treating pressure readings as standard images. Computer vision algorithms and methods such as Convolutional Neural Networks (CNN) can be used to identify contact objects. In this paper, a high-resolution tactile sensor has been attached to a robotic end-effector to ide... | ['Jesús M. Gómez-de-Gabriel', 'Alfonso J. García-Cerezo', 'Juan M. Gandarias'] | 2023-05-21 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 6.19804919e-01 1.44249082e-01 1.60242915e-01 -3.09290439e-01
-2.82251984e-01 -2.65313517e-02 4.14290965e-01 -7.41087645e-02
-8.46592963e-01 6.22091770e-01 -4.42015111e-01 5.62656410e-02
-1.58915758e-01 -9.33171034e-01 -1.01972485e+00 -6.02280736e-01
1.46262810e-01 5.87820172e-01 7.27640867e-01 -6.10202737... | [5.85770845413208, -0.7982520461082458] |
222b2a15-8646-4d56-a956-67c50201c5f4 | sketchxai-a-first-look-at-explainability-for | 2304.11744 | null | https://arxiv.org/abs/2304.11744v1 | https://arxiv.org/pdf/2304.11744v1.pdf | SketchXAI: A First Look at Explainability for Human Sketches | This paper, for the very first time, introduces human sketches to the landscape of XAI (Explainable Artificial Intelligence). We argue that sketch as a ``human-centred'' data form, represents a natural interface to study explainability. We focus on cultivating sketch-specific explainability designs. This starts by iden... | ['Yi-Zhe Song', 'Tao Xiang', 'Kaiyue Pang', 'Ke Li', 'Yulia Gryaditskaya', 'Zhiyu Qu'] | 2023-04-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Qu_SketchXAI_A_First_Look_at_Explainability_for_Human_Sketches_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Qu_SketchXAI_A_First_Look_at_Explainability_for_Human_Sketches_CVPR_2023_paper.pdf | cvpr-2023-1 | ['sketch-recognition'] | ['computer-vision'] | [ 1.35531902e-01 3.52562994e-01 -3.76115710e-01 -2.61514843e-01
-1.02310635e-01 -1.08144355e+00 9.92205203e-01 -4.87061560e-01
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-1.71194300e-02 4.12675291e-01 -4.30592984e-01 -9.04797241... | [11.704503059387207, 0.369618684053421] |
822760e6-fa58-48b4-8e32-3bb2be2b4e8f | fine-mixing-mitigating-backdoors-in-fine | 2210.09545 | null | https://arxiv.org/abs/2210.09545v1 | https://arxiv.org/pdf/2210.09545v1.pdf | Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models | Deep Neural Networks (DNNs) are known to be vulnerable to backdoor attacks. In Natural Language Processing (NLP), DNNs are often backdoored during the fine-tuning process of a large-scale Pre-trained Language Model (PLM) with poisoned samples. Although the clean weights of PLMs are readily available, existing methods h... | ['Xu sun', 'Chenguang Wang', 'Xingjun Ma', 'Lingjuan Lyu', 'Zhiyuan Zhang'] | 2022-10-18 | null | null | null | null | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 7.29490370e-02 -7.59707540e-02 -3.58026326e-02 -1.55828670e-01
-1.08227015e+00 -1.36268926e+00 7.20864832e-01 3.76392573e-01
-7.23622262e-01 4.86165822e-01 4.56568718e-01 -8.34947705e-01
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1.05391666e-01 -1.37332782e-01 2.18723565e-01 -4.43252981... | [6.105504989624023, 7.7920355796813965] |
f55f1113-c922-4705-a5cd-b761c2922a93 | continual-learning-for-anomaly-detection-in | 2004.07941 | null | https://arxiv.org/abs/2004.07941v1 | https://arxiv.org/pdf/2004.07941v1.pdf | Continual Learning for Anomaly Detection in Surveillance Videos | Anomaly detection in surveillance videos has been recently gaining attention. A challenging aspect of high-dimensional applications such as video surveillance is continual learning. While current state-of-the-art deep learning approaches perform well on existing public datasets, they fail to work in a continual learnin... | ['Yasin Yilmaz', 'Keval Doshi'] | 2020-04-15 | null | null | null | null | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 9.57111493e-02 -2.47334376e-01 -2.06239909e-01 -1.75713792e-01
-4.96944219e-01 -2.36376122e-01 4.00813043e-01 1.01382129e-01
-3.78687143e-01 5.07745326e-01 -1.92558050e-01 -5.02239347e-01
-1.09324411e-01 -6.06943905e-01 -9.82720792e-01 -6.36333704e-01
-5.14096379e-01 2.01471169e-02 5.62652290e-01 9.92516428... | [7.862951755523682, 1.6220593452453613] |
467c83ce-ac9c-42c8-9e16-b9f34e8c533e | 190910363 | 1909.10363 | null | https://arxiv.org/abs/1909.10363v2 | https://arxiv.org/pdf/1909.10363v2.pdf | Shadow Transfer: Single Image Relighting For Urban Road Scenes | Illumination effects in images, specifically cast shadows and shading, have been shown to decrease the performance of deep neural networks on a large number of vision-based detection, recognition and segmentation tasks in urban driving scenes. A key factor that contributes to this performance gap is the lack of `time-o... | ['Matthew Johnson-Roberson', 'Ram Vasudevan', 'Alexandra Carlson'] | 2019-09-23 | null | null | null | null | ['image-relighting'] | ['computer-vision'] | [ 7.25961208e-01 -1.58524126e-01 1.12910785e-01 -7.11511552e-01
-3.12309712e-01 -6.04820371e-01 7.62990832e-01 -4.49371755e-01
-4.05785531e-01 8.38310361e-01 -2.52367705e-01 -3.54156584e-01
3.26240480e-01 -7.88652003e-01 -1.08490443e+00 -7.14197993e-01
3.65449816e-01 2.54575551e-01 5.01170516e-01 -4.05422837... | [8.852160453796387, -1.7563341856002808] |
9717152d-3e55-4c60-8afa-549acae716a3 | a-dataset-of-inertial-measurement-units-for | 2307.02480 | null | https://arxiv.org/abs/2307.02480v1 | https://arxiv.org/pdf/2307.02480v1.pdf | A Dataset of Inertial Measurement Units for Handwritten English Alphabets | This paper presents an end-to-end methodology for collecting datasets to recognize handwritten English alphabets by utilizing Inertial Measurement Units (IMUs) and leveraging the diversity present in the Indian writing style. The IMUs are utilized to capture the dynamic movement patterns associated with handwriting, en... | ['Rahul Mishra', 'Hari Prabhat Gupta'] | 2023-07-05 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 1.98101044e-01 -7.45025337e-01 -5.52534401e-01 -3.32820237e-01
1.20420298e-02 -8.49755466e-01 4.82872754e-01 -6.27233028e-01
-2.56842017e-01 4.04126942e-01 2.90369630e-01 -1.39130905e-01
-4.16141778e-01 -4.45688963e-01 -3.73788327e-01 -4.94081676e-01
3.37951750e-01 2.68041402e-01 -5.12920730e-02 -1.46471947... | [11.857401847839355, 2.5644547939300537] |
475b5549-f7fb-44a2-b15c-4334ac53f7ad | ttpp-temporal-transformer-with-progressive | 2003.03530 | null | https://arxiv.org/abs/2003.03530v1 | https://arxiv.org/pdf/2003.03530v1.pdf | TTPP: Temporal Transformer with Progressive Prediction for Efficient Action Anticipation | Video action anticipation aims to predict future action categories from observed frames. Current state-of-the-art approaches mainly resort to recurrent neural networks to encode history information into hidden states, and predict future actions from the hidden representations. It is well known that the recurrent pipeli... | ['Xiaojiang Peng', 'Yu Qiao', 'Wen Wang', 'Jian Cheng', 'Yanzhou Su'] | 2020-03-07 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 5.09791791e-01 -2.69546099e-02 -5.50603151e-01 -4.35555667e-01
-5.69464922e-01 7.88245052e-02 7.59669960e-01 -2.82222867e-01
-1.43656343e-01 5.86021721e-01 7.91807055e-01 1.95610411e-02
1.73923910e-01 -6.22497141e-01 -4.54883516e-01 -4.25695479e-01
-4.50872421e-01 1.61367450e-02 6.12991214e-01 -3.42348628... | [8.193334579467773, 0.5084204077720642] |
ba6d3455-dfbd-4f4f-aa46-bef4901ba88b | guided-table-structure-recognition-through | 2104.10538 | null | https://arxiv.org/abs/2104.10538v1 | https://arxiv.org/pdf/2104.10538v1.pdf | Guided Table Structure Recognition through Anchor Optimization | This paper presents the novel approach towards table structure recognition by leveraging the guided anchors. The concept differs from current state-of-the-art approaches for table structure recognition that naively apply object detection methods. In contrast to prior techniques, first, we estimate the viable anchors fo... | ['Muhammad Zeshan Afzal', 'Muhammad Noman Afzal', 'Marcus Liwicki', 'Didier Stricker', 'Khurram Azeem Hashmi'] | 2021-04-21 | null | null | null | null | ['table-recognition'] | ['computer-vision'] | [ 1.78940520e-01 1.84603687e-02 -3.17399174e-01 -3.02569926e-01
-1.21309352e+00 -7.53899813e-01 3.14176530e-01 7.32416987e-01
-1.87455434e-02 6.40220642e-01 5.93720423e-03 -3.70587528e-01
-2.09832579e-01 -9.69080150e-01 -8.84627342e-01 -4.07136917e-01
-4.13522929e-01 7.07262099e-01 1.18049182e-01 -1.56409994... | [11.696083068847656, 3.036895990371704] |
979fd3de-0988-4413-b2d8-04db8ec53506 | online-3d-bin-packing-reinforcement-learning | 2208.07123 | null | https://arxiv.org/abs/2208.07123v1 | https://arxiv.org/pdf/2208.07123v1.pdf | Online 3D Bin Packing Reinforcement Learning Solution with Buffer | The 3D Bin Packing Problem (3D-BPP) is one of the most demanded yet challenging problems in industry, where an agent must pack variable size items delivered in sequence into a finite bin with the aim to maximize the space utilization. It represents a strongly NP-Hard optimization problem such that no solution has been ... | ['Sukhan Lee', 'Aaron Valero Puche'] | 2022-08-15 | null | null | null | null | ['3d-bin-packing'] | ['miscellaneous'] | [-4.74448800e-02 6.52216822e-02 -2.87691534e-01 1.25180930e-01
-5.76104701e-01 -3.17514271e-01 2.16395892e-02 3.65999162e-01
-6.45597100e-01 9.25359666e-01 -3.11511517e-01 -3.73382747e-01
-6.35156870e-01 -1.01742709e+00 -8.17761123e-01 -8.15404892e-01
-3.78439814e-01 9.89875436e-01 3.55281740e-01 -4.45628554... | [4.928703308105469, 2.6695752143859863] |
ae1bf041-cf54-4cd2-a5ae-215f809d08e0 | efficient-neural-net-approaches-in-metal | 2208.04150 | null | https://arxiv.org/abs/2208.04150v1 | https://arxiv.org/pdf/2208.04150v1.pdf | Efficient Neural Net Approaches in Metal Casting Defect Detection | One of the most pressing challenges prevalent in the steel manufacturing industry is the identification of surface defects. Early identification of casting defects can help boost performance, including streamlining production processes. Though, deep learning models have helped bridge this gap and automate most of these... | ['Sabeesh Ethiraj', 'Bharath Kumar Bolla', 'Rohit Lal'] | 2022-08-08 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [-2.96284765e-01 1.93498611e-01 5.05910397e-01 -2.61938334e-01
-3.91407646e-02 -1.91560969e-01 3.53109807e-01 -1.52767375e-01
-5.39583385e-01 1.42251328e-01 -1.51078105e-01 -6.99057996e-01
-2.66528666e-01 -1.03392339e+00 -5.80844939e-01 -5.92281640e-01
1.95419267e-01 2.52595603e-01 3.54460537e-01 -3.77214551... | [7.830989360809326, 2.045464515686035] |
55c55c8d-9ce9-45b1-822f-08e447a29730 | generative-aspect-based-sentiment-analysis | 2211.07743 | null | https://arxiv.org/abs/2211.07743v1 | https://arxiv.org/pdf/2211.07743v1.pdf | Generative Aspect-Based Sentiment Analysis with Contrastive Learning and Expressive Structure | Generative models have demonstrated impressive results on Aspect-based Sentiment Analysis (ABSA) tasks, particularly for the emerging task of extracting Aspect-Category-Opinion-Sentiment (ACOS) quadruples. However, these models struggle with implicit sentiment expressions, which are commonly observed in opinionated con... | ['Lu Wang', 'Joseph J. Peper'] | 2022-11-14 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 4.41530824e-01 4.74091440e-01 -2.42391571e-01 -7.69624949e-01
-1.29172182e+00 -7.03817546e-01 5.64946055e-01 1.06452471e-02
1.27838969e-01 6.14236534e-01 5.00995815e-01 -3.76451969e-01
4.31067228e-01 -8.39607835e-01 -6.80658519e-01 -4.78403687e-01
2.58288413e-01 5.43615222e-01 -5.91933370e-01 -6.18564427... | [11.485977172851562, 6.717613220214844] |
17fb37bf-482a-42c7-a7aa-a51ec1a5e038 | corpus-driven-lexical-analysis-norms-and | null | null | https://aclanthology.org/W13-3807 | https://aclanthology.org/W13-3807.pdf | Corpus-driven Lexical Analysis: Norms and Exploitations in Word Use | null | ['Patrick Hanks'] | 2013-11-01 | null | null | null | ws-2013-11 | ['lexical-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.403311252593994, 3.7471799850463867] |
63f1375b-6d71-44ff-823e-64df86dfdaa3 | a-unified-compression-framework-for-efficient | 2304.00471 | null | https://arxiv.org/abs/2304.00471v2 | https://arxiv.org/pdf/2304.00471v2.pdf | A Unified Compression Framework for Efficient Speech-Driven Talking-Face Generation | Virtual humans have gained considerable attention in numerous industries, e.g., entertainment and e-commerce. As a core technology, synthesizing photorealistic face frames from target speech and facial identity has been actively studied with generative adversarial networks. Despite remarkable results of modern talking-... | ['Hyoung-Kyu Song', 'Sungsu Lim', 'Hyungshin Kim', 'Shinkook Choi', 'Hancheol Park', 'Daeun Seo', 'Jaemin Kang', 'Bo-Kyeong Kim'] | 2023-04-02 | null | null | null | null | ['talking-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision'] | [ 3.65426987e-01 3.35064858e-01 1.68472111e-01 -1.82587162e-01
-8.83311033e-01 -4.49348062e-01 5.97050011e-01 -7.08714604e-01
-1.54494688e-01 7.61411965e-01 -4.47245352e-02 -4.38525915e-01
5.08718610e-01 -1.02654636e+00 -6.77051246e-01 -8.71098638e-01
3.76586735e-01 -6.98646083e-02 -6.25230223e-02 -2.38991871... | [12.146272659301758, -0.3871088922023773] |
e56a06bb-619a-4fd7-8c3f-bb8ba838586e | phyaat-physiology-of-auditory-attention-to | 2005.11577 | null | https://arxiv.org/abs/2005.11577v1 | https://arxiv.org/pdf/2005.11577v1.pdf | PhyAAt: Physiology of Auditory Attention to Speech Dataset | Auditory attention to natural speech is a complex brain process. Its quantification from physiological signals can be valuable to improving and widening the range of applications of current brain-computer-interface systems, however it remains a challenging task. In this article, we present a dataset of physiological si... | ['Jesús Requena Carrión', 'Nikesh Bajaj', 'Francesco Bellotti'] | 2020-05-23 | null | null | null | null | ['lwr-classification', 'semanticity-prediction', 'attention-score-prediction', 'noise-level-prediction'] | ['time-series', 'time-series', 'time-series', 'time-series'] | [ 1.87854990e-01 -2.40713239e-01 4.23441559e-01 -2.87659705e-01
-4.56242770e-01 -1.84891358e-01 1.34614855e-01 7.52741564e-03
-5.05899012e-01 8.28103244e-01 2.57821977e-01 -5.82190305e-02
-7.00694025e-02 -1.67971864e-01 -2.32936993e-01 -7.07722306e-01
-4.72790860e-02 -2.00026453e-01 -1.73047781e-02 7.86397383... | [13.218949317932129, 3.382521390914917] |
c2cbf74e-293c-45ce-b2c8-299a6fa4473c | magvlt-masked-generative-vision-and-language | 2303.12208 | null | https://arxiv.org/abs/2303.12208v1 | https://arxiv.org/pdf/2303.12208v1.pdf | MAGVLT: Masked Generative Vision-and-Language Transformer | While generative modeling on multimodal image-text data has been actively developed with large-scale paired datasets, there have been limited attempts to generate both image and text data by a single model rather than a generation of one fixed modality conditioned on the other modality. In this paper, we explore a unif... | ['Jongmin Kim', 'Donghoon Lee', 'DaeJin Jo', 'Sungwoong Kim'] | 2023-03-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Kim_MAGVLT_Masked_Generative_Vision-and-Language_Transformer_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kim_MAGVLT_Masked_Generative_Vision-and-Language_Transformer_CVPR_2023_paper.pdf | cvpr-2023-1 | ['text-infilling'] | ['natural-language-processing'] | [ 7.02348173e-01 2.29206994e-01 2.83367813e-01 -4.89813685e-01
-1.19329476e+00 -3.34983468e-01 1.15614903e+00 -4.99002606e-01
-3.47649992e-01 7.49663174e-01 2.00014740e-01 -3.37571919e-01
4.51623231e-01 -7.14074910e-01 -1.17524898e+00 -9.02888238e-01
7.54591048e-01 8.30549181e-01 4.83895056e-02 7.89474547... | [11.210923194885254, 0.48760393261909485] |
66c3c5b9-14f7-4881-843b-fe799767228c | safe-continuous-control-with-constrained | 2104.06922 | null | https://arxiv.org/abs/2104.06922v1 | https://arxiv.org/pdf/2104.06922v1.pdf | Safe Continuous Control with Constrained Model-Based Policy Optimization | The applicability of reinforcement learning (RL) algorithms in real-world domains often requires adherence to safety constraints, a need difficult to address given the asymptotic nature of the classic RL optimization objective. In contrast to the traditional RL objective, safe exploration considers the maximization of ... | ['J. Marius Zöllner', 'Karam Daaboul', 'Moritz A. Zanger'] | 2021-04-14 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 2.80403912e-01 3.07895243e-01 -4.68848139e-01 9.26060900e-02
-1.03774202e+00 -4.79941100e-01 5.66091955e-01 1.07838124e-01
-9.63989258e-01 1.55539286e+00 -1.66371614e-01 -4.65472609e-01
-6.31853282e-01 -4.90859687e-01 -6.70762122e-01 -6.18679523e-01
-7.77167320e-01 5.84114134e-01 8.89530703e-02 -9.03986022... | [4.400390625, 2.225665330886841] |
228cfd0a-f11a-483c-88a0-4511d8831400 | adv-bot-realistic-adversarial-botnet-attacks | 2303.06664 | null | https://arxiv.org/abs/2303.06664v1 | https://arxiv.org/pdf/2303.06664v1.pdf | Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems | Due to the numerous advantages of machine learning (ML) algorithms, many applications now incorporate them. However, many studies in the field of image classification have shown that MLs can be fooled by a variety of adversarial attacks. These attacks take advantage of ML algorithms' inherent vulnerability. This raises... | ['Wim Mees', 'Jean-Michel Dricot', 'Thibault Debatty', 'Tayeb Kenaza', 'Benjamin Cochez', 'Islam Debicha'] | 2023-03-12 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 3.17841560e-01 3.44136246e-02 3.83895822e-02 -7.31737763e-02
2.27077037e-01 -1.11991870e+00 7.87622511e-01 -1.94058672e-01
-4.77469653e-01 4.15336937e-01 -8.13337326e-01 -1.05101573e+00
2.49819476e-02 -1.06965160e+00 -4.95778531e-01 -5.19821584e-01
-4.35821533e-01 1.07710779e-01 5.00186682e-01 -3.04209262... | [5.50906229019165, 7.526325225830078] |
77aa180c-6328-452a-8c84-74be21525c38 | supervised-fine-tuning-evaluation-for-long | 2211.07696 | null | https://arxiv.org/abs/2211.07696v1 | https://arxiv.org/pdf/2211.07696v1.pdf | Supervised Fine-tuning Evaluation for Long-term Visual Place Recognition | In this paper, we present a comprehensive study on the utility of deep convolutional neural networks with two state-of-the-art pooling layers which are placed after convolutional layers and fine-tuned in an end-to-end manner for visual place recognition task in challenging conditions, including seasonal and illuminatio... | ['Esa Rahtu', 'Farid Alijani'] | 2022-11-14 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-1.86176389e-01 -3.87550682e-01 2.43378058e-01 -7.97808290e-01
-4.18875962e-01 -8.44224811e-01 5.82315028e-01 1.25089079e-01
-7.86549330e-01 8.79071772e-01 -3.98488902e-02 -5.55842277e-03
-3.56239229e-01 -8.11729014e-01 -1.27988780e+00 -5.59999049e-01
-5.14620483e-01 -1.06181763e-01 1.88242570e-01 -2.11611718... | [7.776882648468018, -1.845916509628296] |
aab4ac39-583c-4e7b-97db-0b7b8f618c74 | challenges-and-opportunities-for-computer | 2004.06180 | null | https://arxiv.org/abs/2004.06180v1 | https://arxiv.org/pdf/2004.06180v1.pdf | Challenges and Opportunities for Computer Vision in Real-life Soccer Analytics | In this paper, we explore some of the applications of computer vision to sports analytics. Sport analytics deals with understanding and discovering patterns from a corpus of sports data. Analysing such data provides important performance metrics for the players, for instance in soccer matches, that could be useful for ... | ['Neha Bhargava', 'Fabio Cuzzolin'] | 2020-04-13 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [-1.45942941e-02 -2.31486574e-01 -2.14857221e-01 -1.81732461e-01
-4.58627045e-01 -5.28402686e-01 3.88281971e-01 6.21145606e-01
-7.04151452e-01 2.61379898e-01 2.52205908e-01 3.85988802e-01
-3.72755736e-01 -5.46304286e-01 -5.32192409e-01 -6.98608637e-01
-2.63447583e-01 4.32358801e-01 4.57445174e-01 -6.72629237... | [7.405196189880371, 0.22444649040699005] |
789a04f6-759d-4fbc-9516-1e04ce024a9d | machine-learning-for-synthetic-data | 2302.04062 | null | https://arxiv.org/abs/2302.04062v4 | https://arxiv.org/pdf/2302.04062v4.pdf | Machine Learning for Synthetic Data Generation: A Review | Machine learning heavily relies on data, but real-world applications often encounter various data-related issues. These include data of poor quality, insufficient data points leading to under-fitting of machine learning models, and difficulties in data access due to concerns surrounding privacy, safety, and regulations... | ['Wenqi Wei', 'Huazheng Wang', 'Minjie Shen', 'Yingzhou Lu'] | 2023-02-08 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 3.96234870e-01 4.72610533e-01 -4.75936949e-01 -4.68049645e-01
-7.90663719e-01 -3.97264391e-01 5.98800838e-01 4.78959709e-01
-4.97884542e-01 9.44528818e-01 2.54969478e-01 -3.09002817e-01
-2.04778202e-02 -1.02547622e+00 -4.38073605e-01 -5.58927834e-01
1.99189126e-01 2.96342194e-01 -8.28148365e-01 -9.58538651... | [6.333499908447266, 6.723089694976807] |
4fc129b6-c36c-4f82-8b5e-fd6001193439 | physics-informed-machine-learning-for-sensor | 2006.13380 | null | http://arxiv.org/abs/2006.13380v1 | http://arxiv.org/pdf/2006.13380v1.pdf | Physics-informed machine learning for sensor fault detection with flight test data | We develop data-driven algorithms to fully automate sensor fault detection in
systems governed by underlying physics. The proposed machine learning method
uses a time series of typical behavior to approximate the evolution of
measurements of interest by a linear time-invariant system. Given additional
data from related... | [] | 2020-06-23 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 2.95699984e-01 -1.21191852e-01 1.84751838e-01 -9.10649374e-02
-4.42015320e-01 -4.25471365e-01 4.17125046e-01 7.05724776e-01
2.22750992e-01 7.63699949e-01 -5.43481946e-01 -4.00790781e-01
-6.24917626e-01 -4.61884379e-01 -5.69685400e-01 -8.99312735e-01
-4.03629005e-01 4.08323765e-01 5.07852554e-01 -3.36678892... | [6.588922023773193, 2.490638494491577] |
fbdcbf09-0146-47ad-8b37-97f9544bb0a5 | logical-story-representations-via-framenet | null | null | https://openreview.net/forum?id=76qMNL5EUjq | https://openreview.net/pdf?id=76qMNL5EUjq | Logical Story Representations via FrameNet + Semantic Parsing | We present a means of obtaining rich semantic representations of stories by combining neural FrameNet identification, a formal logic-based semantic parser, and a hierarchical event schema representation. The final schematic representation of the story abstracts constants to variables, preserving their types and relatio... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['formal-logic'] | ['reasoning'] | [ 8.58870894e-02 6.89273775e-01 -5.48259020e-01 -6.23236716e-01
-2.70413250e-01 -7.19416380e-01 9.64008093e-01 6.70822978e-01
-3.64851594e-01 1.15166152e+00 7.87383676e-01 1.88595816e-01
-3.44542593e-01 -1.29123354e+00 -7.15076149e-01 -2.93044388e-01
-1.83315098e-01 6.54490829e-01 8.29097867e-01 -4.12553847... | [10.291892051696777, 9.145818710327148] |
fb675ea2-d7b1-455f-bcd8-46946d2dbcbf | tapir-tracking-any-point-with-per-frame | 2306.08637 | null | https://arxiv.org/abs/2306.08637v1 | https://arxiv.org/pdf/2306.08637v1.pdf | TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement | We present a novel model for Tracking Any Point (TAP) that effectively tracks any queried point on any physical surface throughout a video sequence. Our approach employs two stages: (1) a matching stage, which independently locates a suitable candidate point match for the query point on every other frame, and (2) a ref... | ['Andrew Zisserman', 'Joao Carreira', 'Yusuf Aytar', 'Ankush Gupta', 'Dilara Gokay', 'Mel Vecerik', 'Yi Yang', 'Carl Doersch'] | 2023-06-14 | null | null | null | null | ['visual-tracking', 'motion-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.55891255e-01 -5.70344806e-01 -3.97721797e-01 -4.98744175e-02
-1.15277314e+00 -6.87167883e-01 5.89958549e-01 5.21489322e-01
-5.09784639e-01 2.69341081e-01 1.10968478e-01 3.41029488e-03
-8.62719398e-03 -7.42403090e-01 -7.47965455e-01 -4.60208982e-01
-4.97745693e-01 5.07158220e-01 1.03240502e+00 1.23886749... | [8.062812805175781, -1.3626630306243896] |
09c34f1c-ef95-4e51-adb2-7f23c4055101 | safe-exploration-of-state-and-action-spaces | 1402.0560 | null | http://arxiv.org/abs/1402.0560v1 | http://arxiv.org/pdf/1402.0560v1.pdf | Safe Exploration of State and Action Spaces in Reinforcement Learning | In this paper, we consider the important problem of safe exploration in
reinforcement learning. While reinforcement learning is well-suited to domains
with complex transition dynamics and high-dimensional state-action spaces, an
additional challenge is posed by the need for safe and efficient exploration.
Traditional e... | ['Javier Garcia', 'Fernando Fernandez'] | 2014-02-04 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 2.62517810e-01 1.88255653e-01 -1.20843984e-01 1.85211912e-01
-2.92352498e-01 -3.24258566e-01 5.12962520e-01 4.19949442e-01
-6.45525217e-01 1.19495356e+00 -4.13730323e-01 -4.65293348e-01
-5.33456624e-01 -6.13121152e-01 -5.31777978e-01 -8.26307535e-01
-5.70871770e-01 4.56493020e-01 1.87997282e-01 -3.87671471... | [4.537845134735107, 2.008737802505493] |
ab406c8d-1418-4111-b22c-d24f693c9a20 | improved-tree-search-for-automatic-program | 2303.07166 | null | https://arxiv.org/abs/2303.07166v1 | https://arxiv.org/pdf/2303.07166v1.pdf | Improved Tree Search for Automatic Program Synthesis | In the task of automatic program synthesis, one obtains pairs of matching inputs and outputs and generates a computer program, in a particular domain-specific language (DSL), which given each sample input returns the matching output. A key element is being able to perform an efficient search in the space of valid progr... | ['Lior Wolf', 'Aran Carmon'] | 2023-03-13 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 5.71008801e-01 1.70284927e-01 -7.21410215e-01 -2.58525938e-01
-9.17219281e-01 -8.42899203e-01 5.59643388e-01 2.71255821e-01
-9.31869820e-02 7.25321412e-01 -1.20092764e-01 -8.91011298e-01
2.54133970e-01 -1.34572101e+00 -9.70867991e-01 -1.00720078e-01
-2.09359098e-02 2.72650093e-01 5.89190483e-01 3.90308239... | [8.202512741088867, 7.350993633270264] |
5ba4befc-c283-42be-837f-51846c4cd35c | openceres-when-open-information-extraction | null | null | https://aclanthology.org/N19-1309 | https://aclanthology.org/N19-1309.pdf | OpenCeres: When Open Information Extraction Meets the Semi-Structured Web | Open Information Extraction (OpenIE), the problem of harvesting triples from natural language text whose predicate relations are not aligned to any pre-defined ontology, has been a popular subject of research for the last decade. However, this research has largely ignored the vast quantity of facts available in semi-st... | ['Prashant Shiralkar', 'Xin Luna Dong', 'Colin Lockard'] | 2019-06-01 | null | null | null | naacl-2019-6 | ['open-information-extraction'] | ['natural-language-processing'] | [ 1.66579977e-01 9.63868678e-01 -6.65437579e-01 -3.27813596e-01
-8.17912579e-01 -7.70172954e-01 4.48933333e-01 5.31778872e-01
-1.76689476e-01 1.28013992e+00 1.99709475e-01 -2.25164428e-01
-1.17961712e-01 -1.16268587e+00 -8.10369074e-01 3.31127696e-04
-1.85669497e-01 7.26942956e-01 5.71931541e-01 -2.35244125... | [9.353689193725586, 8.574577331542969] |
edbfce72-de9b-4fdc-a8cb-d2bbde79e031 | towards-a-general-deep-feature-extractor-for | 2201.07781 | null | https://arxiv.org/abs/2201.07781v1 | https://arxiv.org/pdf/2201.07781v1.pdf | Towards a General Deep Feature Extractor for Facial Expression Recognition | The human face conveys a significant amount of information. Through facial expressions, the face is able to communicate numerous sentiments without the need for verbalisation. Visual emotion recognition has been extensively studied. Recently several end-to-end trained deep neural networks have been proposed for this ta... | ['Alice Othmani', 'Liam Schoneveld'] | 2022-01-19 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [-7.51465410e-02 -1.36723043e-02 9.15329009e-02 -9.46076095e-01
-6.30570352e-02 -3.52171361e-01 8.79377723e-01 -3.57325584e-01
-4.31211650e-01 5.27980387e-01 7.24816844e-02 2.97281265e-01
3.01538467e-01 -4.18092519e-01 -3.85368675e-01 -7.87889123e-01
-1.49651691e-01 1.08073935e-01 -6.39716446e-01 -5.79491317... | [13.572559356689453, 1.8235673904418945] |
1f844c08-94bd-461e-99a4-cdb578857b75 | multi-scale-multi-band-densenets-for-audio | 1706.09588 | null | http://arxiv.org/abs/1706.09588v1 | http://arxiv.org/pdf/1706.09588v1.pdf | Multi-scale Multi-band DenseNets for Audio Source Separation | This paper deals with the problem of audio source separation. To handle the
complex and ill-posed nature of the problems of audio source separation, the
current state-of-the-art approaches employ deep neural networks to obtain
instrumental spectra from a mixture. In this study, we propose a novel network
architecture t... | ['Naoya Takahashi', 'Yuki Mitsufuji'] | 2017-06-29 | null | null | null | null | ['audio-source-separation', 'music-source-separation'] | ['audio', 'music'] | [ 2.18941793e-01 -3.99567604e-01 1.65570781e-01 -2.30784491e-02
-1.22639787e+00 -2.02333286e-01 2.72059679e-01 -1.30389139e-01
-3.94820690e-01 7.27566719e-01 2.07928032e-01 5.31391539e-02
-3.16475123e-01 -2.92638391e-01 -5.31216323e-01 -9.03931201e-01
-1.41888291e-01 -1.52180925e-01 1.40327409e-01 -1.73722759... | [15.34446907043457, 5.578188419342041] |
d85e40a3-77de-45d9-84cb-62a2d4f92c71 | generalizing-to-unseen-elements-a-survey-on | 2302.01859 | null | https://arxiv.org/abs/2302.01859v1 | https://arxiv.org/pdf/2302.01859v1.pdf | Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs | Knowledge graphs (KGs) have become effective knowledge resources in diverse applications, and knowledge graph embedding (KGE) methods have attracted increasing attention in recent years. However, it's still challenging for conventional KGE methods to handle unseen entities or relations during the model test. Much effor... | ['Huajun Chen', 'Jeff Z. Pan', 'Zezhong Xu', 'Yuxia Geng', 'Wen Zhang', 'Mingyang Chen'] | 2023-02-03 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-2.35522702e-01 3.89462441e-01 -6.69159114e-01 -1.81586310e-01
5.03643043e-02 -5.56710124e-01 4.32050407e-01 1.85994238e-01
-5.81065379e-02 9.48626518e-01 1.22461602e-01 -4.07108277e-01
-5.37716329e-01 -1.13797474e+00 -4.41703737e-01 -2.94841558e-01
-2.45256066e-01 2.72674292e-01 4.30911750e-01 -2.17612401... | [8.824671745300293, 7.8840250968933105] |
87a37de1-c187-4e97-a4f0-5e7f6c7a1b8a | a-new-spatio-temporal-loss-function-for-3d | 2210.08562 | null | https://arxiv.org/abs/2210.08562v1 | https://arxiv.org/pdf/2210.08562v1.pdf | A New Spatio-Temporal Loss Function for 3D Motion Reconstruction and Extended Temporal Metrics for Motion Evaluation | We propose a new loss function that we call Laplacian loss, based on spatio-temporal Laplacian representation of the motion as a graph. This loss function is intended to be used in training models for motion reconstruction through 3D human pose estimation from videos. It compares the differential coordinates of the joi... | ['Thibaut Le Naour', 'Sylvie Gibet', 'Mansour Tchenegnon'] | 2022-10-16 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [-4.8832834e-01 -2.6032138e-01 -3.6575812e-01 -3.8297787e-02
-3.8539934e-01 -3.2114398e-01 6.0624206e-01 -3.6184177e-01
-7.6105845e-01 4.9151459e-01 3.7582350e-01 1.9078265e-01
1.8672766e-01 -4.3532246e-01 -8.6366874e-01 -4.5619324e-01
-6.5156722e-01 1.8097784e-01 3.8511699e-01 -2.7055455e-02
-4.7604896e-02... | [7.268174171447754, -0.4094148278236389] |
d37b61ea-46b6-415a-8155-9b8ed329c9a4 | dexart-benchmarking-generalizable-dexterous | 2305.05706 | null | https://arxiv.org/abs/2305.05706v1 | https://arxiv.org/pdf/2305.05706v1.pdf | DexArt: Benchmarking Generalizable Dexterous Manipulation with Articulated Objects | To enable general-purpose robots, we will require the robot to operate daily articulated objects as humans do. Current robot manipulation has heavily relied on using a parallel gripper, which restricts the robot to a limited set of objects. On the other hand, operating with a multi-finger robot hand will allow better a... | ['Xiaolong Wang', 'Yuzhe Qin', 'Helin Xu', 'Chen Bao'] | 2023-05-09 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Bao_DexArt_Benchmarking_Generalizable_Dexterous_Manipulation_With_Articulated_Objects_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Bao_DexArt_Benchmarking_Generalizable_Dexterous_Manipulation_With_Articulated_Objects_CVPR_2023_paper.pdf | cvpr-2023-1 | ['robot-manipulation'] | ['robots'] | [-3.33856881e-01 1.71710867e-02 -1.86298251e-01 3.36889215e-02
-3.93401086e-02 -7.89846957e-01 1.15924433e-01 -5.79966247e-01
-3.00789446e-01 6.41224861e-01 -3.17737490e-01 -7.54409805e-02
-3.53503555e-01 -4.58581924e-01 -8.25202525e-01 -5.99976420e-01
-2.94218421e-01 8.89168143e-01 1.63695499e-01 -5.16358376... | [4.828277587890625, 0.4440566301345825] |
f841248e-7f68-4e8c-9e68-1b3f35165a6f | personalized-popular-music-generation-using | 2105.04709 | null | https://arxiv.org/abs/2105.04709v1 | https://arxiv.org/pdf/2105.04709v1.pdf | Personalized Popular Music Generation Using Imitation and Structure | Many practices have been presented in music generation recently. While stylistic music generation using deep learning techniques has became the main stream, these models still struggle to generate music with high musicality, different levels of music structure, and controllability. In addition, more application scenari... | ['Roger B. Dannenberg', 'Ye Wang', 'Xichu Ma', 'Shuqi Dai'] | 2021-05-10 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 2.06885651e-01 -2.51125749e-02 1.20730504e-01 -1.24256276e-02
-5.45668364e-01 -8.67586911e-01 4.45369482e-01 -2.40928322e-01
1.73356935e-01 5.62186897e-01 4.79246587e-01 2.71349490e-01
-3.98277074e-01 -8.20482671e-01 -4.96133655e-01 -3.59443575e-01
1.56383723e-01 7.60939717e-01 -3.10320735e-01 -5.91299891... | [16.061080932617188, 5.578774452209473] |
3bdf13a8-488f-4b8b-90ed-f490a2654fc9 | spark-deficient-gabor-frames-for-inverse | 2110.09296 | null | https://arxiv.org/abs/2110.09296v1 | https://arxiv.org/pdf/2110.09296v1.pdf | Spark Deficient Gabor Frames for Inverse Problems | In this paper, we apply star-Digital Gabor Transform in analysis Compressed Sensing and speech denoising. Based on assumptions on the ambient dimension, we produce a window vector that generates a spark deficient Gabor frame with many linear dependencies among its elements. We conduct computational experiments on both ... | ['Holger Rauhut', 'Vasiliki Kouni'] | 2021-10-13 | null | null | null | null | ['speech-denoising'] | ['speech'] | [ 3.99292648e-01 -4.85099584e-01 2.57708758e-01 1.28556192e-01
-8.34744573e-01 -5.69229484e-01 2.92209923e-01 -4.12160754e-01
-2.32564941e-01 5.51388919e-01 6.19863331e-01 -2.40795016e-01
-2.10189134e-01 -7.36918449e-01 -4.80290473e-01 -8.88945520e-01
-5.54451406e-01 -5.12618423e-01 2.84554094e-01 -2.49982432... | [15.386903762817383, 5.584723949432373] |
5dfbe222-fe8f-4a3d-b5ac-2228496bbd8b | graphsearchnet-enhancing-gnns-via-capturing | 2111.02671 | null | https://arxiv.org/abs/2111.02671v5 | https://arxiv.org/pdf/2111.02671v5.pdf | GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code Search | Code search aims to retrieve accurate code snippets based on a natural language query to improve software productivity and quality. With the massive amount of available programs such as (on GitHub or Stack Overflow), identifying and localizing the precise code is critical for the software developers. In addition, Deep ... | ['Guozhu Meng', 'Yang Liu', 'JingKai Siow', 'Lei Ma', 'Xiaofei Xie', 'Shangqing Liu'] | 2021-11-04 | null | null | null | null | ['code-summarization', 'code-search', 'code-search', 'vulnerability-detection'] | ['computer-code', 'computer-code', 'computer-vision', 'miscellaneous'] | [-2.2315912e-01 -2.3898175e-01 -5.9157133e-01 -1.8462820e-01
-7.0156968e-01 -5.5486184e-01 3.9371572e-02 4.9484310e-01
-7.5344056e-02 9.6469551e-02 2.1643403e-01 -5.5435288e-01
-1.2760081e-02 -8.4436893e-01 -8.1894964e-01 -2.1682234e-01
2.4324618e-03 -7.7395737e-02 4.9049994e-01 -1.5836746e-01
4.8876905e-01... | [7.482914924621582, 8.041744232177734] |
75fc935d-408c-4128-bd89-f31fbe418f5e | demographic-influences-on-contemporary-art | 2009.14545 | null | https://arxiv.org/abs/2009.14545v2 | https://arxiv.org/pdf/2009.14545v2.pdf | Demographic Influences on Contemporary Art with Unsupervised Style Embeddings | Computational art analysis has, through its reliance on classification tasks, prioritised historical datasets in which the artworks are already well sorted with the necessary annotations. Art produced today, on the other hand, is numerous and easily accessible, through the internet and social networks that are used by ... | ['Yuta Nakashima', 'Noa Garcia', 'Nikolai Huckle'] | 2020-09-30 | null | null | null | null | ['art-analysis'] | ['computer-vision'] | [ 1.10563971e-01 1.51114585e-02 7.96537772e-02 -2.76919544e-01
-6.76769838e-02 -9.58422899e-01 1.23764026e+00 6.25364959e-01
-3.60867292e-01 7.11940587e-01 6.34698987e-01 2.66236197e-02
-4.86449242e-01 -1.09307992e+00 2.74422183e-03 -5.34387887e-01
1.09442011e-01 7.10446179e-01 -5.62537797e-02 -3.97245616... | [11.302689552307129, 0.29054874181747437] |
dffee6cb-7a6a-4798-a53b-b4c23f70699e | enabling-classifiers-to-make-judgements | 2210.07652 | null | https://arxiv.org/abs/2210.07652v1 | https://arxiv.org/pdf/2210.07652v1.pdf | Enabling Classifiers to Make Judgements Explicitly Aligned with Human Values | Many NLP classification tasks, such as sexism/racism detection or toxicity detection, are based on human values. Yet, human values can vary under diverse cultural conditions. Therefore, we introduce a framework for value-aligned classification that performs prediction based on explicitly written human values in the com... | ['Pascale Fung', 'Mona Diab', 'Zhaojiang Lin', 'Andrea Madotto', 'Tiezheng Yu', 'Yejin Bang'] | 2022-10-14 | null | null | null | null | ['text-augmentation'] | ['natural-language-processing'] | [ 4.19614017e-01 5.48122585e-01 -8.74447107e-01 -6.69742107e-01
-7.22603023e-01 -1.69646934e-01 8.40637028e-01 2.81255007e-01
-6.31617963e-01 1.03065634e+00 8.49139631e-01 -1.15140513e-01
6.30727485e-02 -7.34656692e-01 -5.46067417e-01 -5.98403253e-02
5.04671395e-01 8.26891720e-01 -3.22269291e-01 -6.60735786... | [10.929702758789062, 8.248601913452148] |
78948d15-bc27-4285-881a-ae7200bcf7d9 | dynamic-programming-in-rank-space-scaling-1 | 2205.00484 | null | https://arxiv.org/abs/2205.00484v1 | https://arxiv.org/pdf/2205.00484v1.pdf | Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGs | Hidden Markov Models (HMMs) and Probabilistic Context-Free Grammars (PCFGs) are widely used structured models, both of which can be represented as factor graph grammars (FGGs), a powerful formalism capable of describing a wide range of models. Recent research found it beneficial to use large state spaces for HMMs and P... | ['Kewei Tu', 'Wei Liu', 'Songlin Yang'] | 2022-05-01 | null | https://aclanthology.org/2022.naacl-main.353 | https://aclanthology.org/2022.naacl-main.353.pdf | naacl-2022-7 | ['constituency-grammar-induction'] | ['natural-language-processing'] | [ 1.44746631e-01 3.42377514e-01 -1.69695884e-01 -2.69411087e-01
-9.07204866e-01 -6.82257950e-01 6.77759647e-01 -1.38808697e-01
5.70537113e-02 5.12135983e-01 3.89459044e-01 -9.75130916e-01
1.99371446e-02 -7.52938271e-01 -7.12355196e-01 -6.92899704e-01
-2.56228298e-01 5.81289232e-01 6.63544595e-01 1.24201074... | [10.362467765808105, 9.574357032775879] |
f6bef3cb-b9e7-41a1-8a2a-65fb5aac37ee | adaptive-graph-based-total-variation-for | 1610.00893 | null | http://arxiv.org/abs/1610.00893v3 | http://arxiv.org/pdf/1610.00893v3.pdf | Adaptive Graph-based Total Variation for Tomographic Reconstructions | Sparsity exploiting image reconstruction (SER) methods have been extensively
used with Total Variation (TV) regularization for tomographic reconstructions.
Local TV methods fail to preserve texture details and often create additional
artefacts due to over-smoothing. Non-Local TV (NLTV) methods have been proposed
as a s... | ['Pierre Vandergheynst', 'Faisal Mahmood', 'Ulf Skoglund', 'Nauman Shahid'] | 2016-10-04 | null | null | null | null | ['tomographic-reconstructions'] | ['medical'] | [ 5.53672791e-01 7.81673193e-02 8.02049786e-03 -4.38856445e-02
-4.98872906e-01 -5.36807440e-02 4.54388112e-01 3.18507731e-01
-2.38625795e-01 7.69971371e-01 1.47383705e-01 4.83299121e-02
-4.30983275e-01 -8.48901391e-01 -5.14667571e-01 -9.27206993e-01
1.03125975e-01 1.92118108e-01 6.42249882e-01 -1.43073902... | [11.675834655761719, -2.4191482067108154] |
acbbbae0-6cd3-474a-8285-539ca4002c2f | online-categorical-subspace-learning-for | 1609.08235 | null | http://arxiv.org/abs/1609.08235v1 | http://arxiv.org/pdf/1609.08235v1.pdf | Online Categorical Subspace Learning for Sketching Big Data with Misses | With the scale of data growing every day, reducing the dimensionality (a.k.a.
sketching) of high-dimensional data has emerged as a task of paramount
importance. Relevant issues to address in this context include the sheer volume
of data that may consist of categorical samples, the typically streaming format
of acquisit... | ['Georgios B. Giannakis', 'Yanning Shen', 'Morteza Mardani'] | 2016-09-27 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [ 2.52678782e-01 -2.12182164e-01 -3.60839725e-01 -8.96898508e-02
-9.67062712e-01 -9.69195545e-01 5.60053468e-01 -8.98555666e-02
-2.77661145e-01 5.89780331e-01 3.28891158e-01 -9.58807245e-02
-7.87288070e-01 -3.31800640e-01 -5.61402440e-01 -1.00604212e+00
-1.28034741e-01 6.16258800e-01 -3.32652301e-01 1.50985435... | [7.3801703453063965, 4.349123477935791] |
06b149ed-0a9f-4c03-8efb-2d00448de775 | hand-pose-estimation-in-the-task-of | null | null | https://ieeexplore.ieee.org/document/9319235 | https://ieeexplore.ieee.org/document/9319235 | Hand Pose Estimation in the Task of Egocentric Actions | In this article we tackle the problem of hand pose estimation when the hand is interacting with various objects from egocentric viewpoint. This entails a frequent occlusion of parts of the hand by the object and also self-occlusions of the hand. We use a Voxel-to-Voxel approach to obtain hypotheses of the hand joint lo... | ['Zdenĕk Krňoul', 'Jakub Kanis', 'Marek Hrúz'] | 2021-01-11 | null | null | null | ieee-access-2021-1 | ['3d-hand-pose-estimation', 'pose-estimation', 'hand-pose-estimation', '3d-hand-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'graphs'] | [-8.76260027e-02 5.72695099e-02 4.78658170e-01 -1.02420747e-01
-6.14650071e-01 -6.60872400e-01 4.50177580e-01 -2.52773672e-01
-6.83403850e-01 5.74244320e-01 3.57496828e-01 1.56663403e-01
-1.78862527e-01 -4.44128029e-02 -4.47366804e-01 -6.61298275e-01
-3.76005881e-02 1.12990224e+00 5.07393241e-01 4.18123566... | [6.555068016052246, -0.7934607267379761] |
f9e51745-974e-4fc3-b6a8-46f6304c7cba | roberta-a-robustly-optimized-bert-pretraining | 1907.11692 | null | https://arxiv.org/abs/1907.11692v1 | https://arxiv.org/pdf/1907.11692v1.pdf | RoBERTa: A Robustly Optimized BERT Pretraining Approach | Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We ... | ['Myle Ott', 'Mike Lewis', 'Luke Zettlemoyer', 'Mandar Joshi', 'Danqi Chen', 'Yinhan Liu', 'Omer Levy', 'Veselin Stoyanov', 'Naman Goyal', 'Jingfei Du'] | 2019-07-26 | null | https://openreview.net/forum?id=SyxS0T4tvS | https://openreview.net/pdf?id=SyxS0T4tvS | null | ['type-prediction', 'document-image-classification', 'multi-task-language-understanding', 'lexical-simplification', 'riddle-sense', 'linguistic-acceptability'] | ['computer-code', 'computer-vision', 'methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-2.58225113e-01 -1.10528871e-01 -4.61472213e-01 -3.21743190e-01
-1.03267276e+00 -8.09951127e-01 5.23106098e-01 -3.06222495e-02
-7.31344700e-01 8.07517231e-01 2.88638026e-01 -8.09655786e-01
-7.49247223e-02 -3.05749804e-01 -1.00077283e+00 -3.47730219e-01
-2.08568141e-01 6.59354508e-01 2.94545978e-01 -2.44537875... | [10.643017768859863, 8.235651969909668] |
087dd54a-e04d-4b87-ae36-b39350f79330 | mice-mixture-of-contrastive-experts-for-1 | 2105.01899 | null | https://arxiv.org/abs/2105.01899v1 | https://arxiv.org/pdf/2105.01899v1.pdf | MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering | We present Mixture of Contrastive Experts (MiCE), a unified probabilistic clustering framework that simultaneously exploits the discriminative representations learned by contrastive learning and the semantic structures captured by a latent mixture model. Motivated by the mixture of experts, MiCE employs a gating functi... | ['Jun Zhu', 'Chongxuan Li', 'Tsung Wei Tsai'] | 2021-05-05 | mice-mixture-of-contrastive-experts-for | https://openreview.net/forum?id=gV3wdEOGy_V | https://openreview.net/pdf?id=gV3wdEOGy_V | iclr-2021-1 | ['image-clustering'] | ['computer-vision'] | [-6.41524121e-02 -1.68338284e-01 -3.18924218e-01 -6.21992528e-01
-9.17305052e-01 -6.65440798e-01 6.95411861e-01 -2.93783605e-01
-4.80779976e-01 4.35905099e-01 2.49499515e-01 1.31827772e-01
-2.67829597e-01 -4.06864166e-01 -3.82789999e-01 -1.02131140e+00
-3.99691500e-02 9.81109321e-01 2.17505395e-01 4.55202758... | [9.387797355651855, 2.926084280014038] |
566803ac-807d-4fb4-96fd-206e5759c1f4 | inter-brain-substrates-of-role-switching | 2303.04902 | null | https://arxiv.org/abs/2303.04902v1 | https://arxiv.org/pdf/2303.04902v1.pdf | Inter-brain substrates of role switching during mother-child interaction | Mother-child interaction is highly dynamic and reciprocal. Switching roles in these back-and-forth interactions serves as a crucial feature of reciprocal behaviors while the underlying neural entrainment is still not well-studied. Here, we designed a role-controlled cooperative task with dual EEG recording to study how... | ['Xiaoli Guo', 'Yunting Zhang', 'Shanbao Tong', 'Fan Jiang', 'Yue Fang', 'Wen Shi', 'Qi Zhu', 'Haiwa Wang', 'Jiayang Xu', 'Saishuang Wu', 'Yamin Li'] | 2023-03-08 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [-2.29021594e-01 2.11690724e-01 1.13471178e-02 -5.89548588e-01
5.94483197e-01 -3.46503854e-01 4.11393166e-01 3.57526183e-01
-1.19579688e-01 4.64891255e-01 1.23476714e-01 6.79866791e-01
-1.52182430e-01 -6.50475264e-01 -4.91529882e-01 -1.31290066e+00
-4.57236499e-01 7.36264363e-02 -1.00665383e-01 -2.91380525... | [12.913161277770996, 3.3674049377441406] |
c92f4136-0e18-4def-8457-f0bdc940d4e3 | the-second-place-solution-for-cvpr-vision-23 | 2306.14116 | null | https://arxiv.org/abs/2306.14116v1 | https://arxiv.org/pdf/2306.14116v1.pdf | The Second-place Solution for CVPR VISION 23 Challenge Track 1 -- Data Effificient Defect Detection | The Vision Challenge Track 1 for Data-Effificient Defect Detection requires competitors to instance segment 14 industrial inspection datasets in a data-defificient setting. This report introduces the technical details of the team Aoi-overfifitting-Team for this challenge. Our method focuses on the key problem of segmen... | ['Zhengtao Zhang', 'Fei Shen', 'Chengkan Lv', 'Danfeng Liu', 'Yonghao He', 'Jianwen Han', 'Hengliang Luo', 'Zhen Qu', 'Xian Tao'] | 2023-06-25 | null | null | null | null | ['instance-segmentation', 'defect-detection'] | ['computer-vision', 'computer-vision'] | [ 2.79573381e-01 3.00143510e-01 2.23374456e-01 -3.53791505e-01
-1.21476638e+00 -3.97389084e-01 6.11683726e-02 -1.69710979e-01
-7.81370178e-02 1.14042714e-01 -4.03981537e-01 -3.00794423e-01
2.54992023e-02 -6.26516998e-01 -7.89191067e-01 -5.88736355e-01
2.61132240e-01 4.65579331e-01 3.11017811e-01 -1.04425639... | [7.678824424743652, 1.8586496114730835] |
c3709261-581c-49d5-9809-5cb6cfffeb90 | select-label-and-mix-learning-discriminative | 2012.03358 | null | https://arxiv.org/abs/2012.03358v2 | https://arxiv.org/pdf/2012.03358v2.pdf | Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptation | Partial domain adaptation which assumes that the unknown target label space is a subset of the source label space has attracted much attention in computer vision. Despite recent progress, existing methods often suffer from three key problems: negative transfer, lack of discriminability, and domain invariance in the lat... | ['Abir Das', 'Kate Saenko', 'Rogerio Feris', 'Rameswar Panda', 'Aadarsh Sahoo'] | 2020-12-06 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 3.96433055e-01 -3.66048723e-01 -5.13930023e-01 -5.87226689e-01
-7.67238915e-01 -7.65397370e-01 4.51119512e-01 -2.15364903e-01
-2.11702004e-01 8.53027940e-01 2.76072137e-02 1.35160521e-01
-5.05808331e-02 -4.23872739e-01 -4.80579257e-01 -1.06470764e+00
5.40885568e-01 4.96184409e-01 2.61312842e-01 3.24980170... | [10.363417625427246, 3.1160271167755127] |
64eece2b-ed16-4893-86e4-41f73c71c629 | metoomaastricht-building-a-chatbot-to-assist | 1909.02809 | null | https://arxiv.org/abs/1909.02809v1 | https://arxiv.org/pdf/1909.02809v1.pdf | #MeTooMaastricht: Building a chatbot to assist survivors of sexual harassment | Inspired by the recent social movement of #MeToo, we are building a chatbot to assist survivors of sexual harassment cases (designed for the city of Maastricht but can easily be extended). The motivation behind this work is twofold: properly assist survivors of such events by directing them to appropriate institutions ... | ['Gerasimos Spanakis', 'William Lopez Jaramillo', 'Misha Glazunov', 'Tobias Bauer', 'Emre Devrim', 'Balaganesh Mohan'] | 2019-09-06 | null | null | null | null | ['temporal-information-extraction'] | ['natural-language-processing'] | [-3.41675192e-01 4.88149494e-01 -4.40223701e-02 -2.64249563e-01
-8.05678129e-01 -5.18230915e-01 5.59115291e-01 7.15991020e-01
-9.70895231e-01 9.29092824e-01 3.81864935e-01 -3.72859925e-01
-5.07903516e-01 -9.56102014e-01 -8.44305977e-02 -3.68722290e-01
-1.80807307e-01 8.57004285e-01 2.49152958e-01 -3.22262019... | [8.710012435913086, 10.516627311706543] |
64aa8c4a-a919-4da9-bf0c-a12875821973 | multi-hop-question-answering-via-reasoning | 1910.02610 | null | https://arxiv.org/abs/1910.02610v2 | https://arxiv.org/pdf/1910.02610v2.pdf | Multi-hop Question Answering via Reasoning Chains | Multi-hop question answering requires models to gather information from different parts of a text to answer a question. Most current approaches learn to address this task in an end-to-end way with neural networks, without maintaining an explicit representation of the reasoning process. We propose a method to extract a ... | ['Shih-ting Lin', 'Jifan Chen', 'Greg Durrett'] | 2019-10-07 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 8.85324627e-02 7.13636816e-01 -1.25726432e-01 -5.78678370e-01
-1.37158859e+00 -9.16849673e-01 3.91686887e-01 4.83957708e-01
-6.31183207e-01 9.87293363e-01 4.07873183e-01 -6.66089892e-01
-1.97123691e-01 -9.25382257e-01 -9.29964185e-01 7.66876712e-02
2.56840020e-01 1.20706427e+00 8.70085299e-01 -6.26525998... | [10.91812515258789, 7.9351983070373535] |
ecd6df1f-c7a8-46ef-ad07-cb4217c66121 | wildtrack-a-multi-camera-hd-dataset-for-dense | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Chavdarova_WILDTRACK_A_Multi-Camera_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Chavdarova_WILDTRACK_A_Multi-Camera_CVPR_2018_paper.pdf | WILDTRACK: A Multi-Camera HD Dataset for Dense Unscripted Pedestrian Detection | People detection methods are highly sensitive to occlusions between pedestrians, which are extremely frequent in many situations where cameras have to be mounted at a limited height. The reduction of camera prices allows for the generalization of static multi-camera set-ups. Using joint visual information from multiple... | ['François Fleuret', 'Luc van Gool', 'Stéphane Bouquet', 'Pierre Baqué', 'Pascal Fua', 'Louis Lettry', 'Cijo Jose', 'Tatjana Chavdarova', 'Timur Bagautdinov', 'Andrii Maksai'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['multiview-detection'] | ['computer-vision'] | [-7.07109496e-02 -4.97757107e-01 2.58465648e-01 -2.72033632e-01
-5.10641217e-01 -6.64514422e-01 5.32508731e-01 -1.35851488e-01
-9.36500609e-01 6.93339765e-01 5.00838794e-02 4.38627511e-01
6.25023842e-01 -7.38309145e-01 -7.42063344e-01 -5.74772477e-01
-2.16460899e-01 6.06969059e-01 7.25666225e-01 9.07419398... | [7.373427867889404, -1.0006755590438843] |
b2447fbc-382c-4647-bfd3-a29a6db9f95b | fusing-wearable-imus-with-multi-view-images | 2003.11163 | null | https://arxiv.org/abs/2003.11163v2 | https://arxiv.org/pdf/2003.11163v2.pdf | Fusing Wearable IMUs with Multi-View Images for Human Pose Estimation: A Geometric Approach | We propose to estimate 3D human pose from multi-view images and a few IMUs attached at person's limbs. It operates by firstly detecting 2D poses from the two signals, and then lifting them to the 3D space. We present a geometric approach to reinforce the visual features of each pair of joints based on the IMUs. This no... | ['Wen-Jun Zeng', 'Zhe Zhang', 'Wenhu Qin', 'Chunyu Wang'] | 2020-03-25 | fusing-wearable-imus-with-multi-view-images-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Fusing_Wearable_IMUs_With_Multi-View_Images_for_Human_Pose_Estimation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Fusing_Wearable_IMUs_With_Multi-View_Images_for_Human_Pose_Estimation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-absolute-human-pose-estimation'] | ['computer-vision'] | [-2.47778609e-01 1.82972610e-01 -2.22436979e-01 -1.93006113e-01
-4.97143984e-01 -4.81764108e-01 5.47774732e-01 -6.68940425e-01
-3.68386000e-01 4.08790976e-01 6.23773634e-01 3.47732127e-01
1.67969048e-01 -4.32724744e-01 -7.32242465e-01 -1.89447135e-01
8.33074003e-03 5.95045090e-01 -1.66488394e-01 -1.18138991... | [7.0166239738464355, -0.941461980342865] |
c43773c0-3b35-4e57-9269-eee37073e563 | generative-hierarchical-features-from | 2007.10379 | null | https://arxiv.org/abs/2007.10379v2 | https://arxiv.org/pdf/2007.10379v2.pdf | Generative Hierarchical Features from Synthesizing Images | Generative Adversarial Networks (GANs) have recently advanced image synthesis by learning the underlying distribution of the observed data. However, how the features learned from solving the task of image generation are applicable to other vision tasks remains seldom explored. In this work, we show that learning to syn... | ['Yujun Shen', 'Ceyuan Yang', 'Bolei Zhou', 'Yinghao Xu', 'Jiapeng Zhu'] | 2020-07-20 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Xu_Generative_Hierarchical_Features_From_Synthesizing_Images_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Xu_Generative_Hierarchical_Features_From_Synthesizing_Images_CVPR_2021_paper.pdf | cvpr-2021-1 | ['image-harmonization'] | ['computer-vision'] | [ 6.29336417e-01 2.34620765e-01 4.10603248e-02 -5.55595219e-01
-7.96034873e-01 -5.61267614e-01 7.74638057e-01 -6.24753416e-01
2.96938747e-01 7.96383739e-01 2.49095961e-01 -6.58535063e-02
5.76366112e-02 -8.38842630e-01 -1.15625203e+00 -9.59497035e-01
2.77199060e-01 4.14501168e-02 -4.15036947e-01 -1.81028083... | [11.702592849731445, -0.3892733156681061] |
a400ff64-76c4-487d-adba-42ca9497a89e | improving-hospital-mortality-prediction-with | 1811.12276 | null | http://arxiv.org/abs/1811.12276v2 | http://arxiv.org/pdf/1811.12276v2.pdf | Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning | Clinical text provides essential information to estimate the acuity of a
patient during hospital stays in addition to structured clinical data. In this
study, we explore how clinical text can complement a clinical predictive
learning task. We leverage an internal medical natural language processing
service to perform n... | ['Taha Kass-Hout', 'Borui Zhang', 'Mohammed Khalilia', 'Mohammad Taha Bahadori', 'Daniel Navarro', 'Ram Bhakta', 'Mengqi Jin', 'Arun Ravi', 'Parminder Bhatia', 'Aaron Colak', 'Tiberiu Doman', 'Selvan Senthivel', 'Matthieu Liger', 'Busra Celikkaya'] | 2018-11-29 | null | null | null | null | ['negation-detection'] | ['natural-language-processing'] | [ 3.25773656e-01 2.13281482e-01 -2.79093266e-01 -5.22458553e-01
-8.04884911e-01 -3.44977677e-01 -2.76794434e-02 1.04736960e+00
-6.48234546e-01 8.43762100e-01 7.93633223e-01 -6.07647121e-01
-3.59820098e-01 -7.41831005e-01 -1.20304130e-01 -2.70897686e-01
-3.59786600e-01 7.85697222e-01 -5.70166945e-01 1.91009521... | [7.960072994232178, 6.527502536773682] |
1f3e2377-15fe-4e2c-a9ed-b665171174ac | a-billion-scale-foundation-model-for-remote | 2304.05215 | null | https://arxiv.org/abs/2304.05215v1 | https://arxiv.org/pdf/2304.05215v1.pdf | A Billion-scale Foundation Model for Remote Sensing Images | As the potential of foundation models in visual tasks has garnered significant attention, pretraining these models before downstream tasks has become a crucial step. The three key factors in pretraining foundation models are the pretraining method, the size of the pretraining dataset, and the number of model parameters... | ['Taekyung Lee', 'Junghoon Seo', 'Keumgang Cha'] | 2023-04-11 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 1.34540170e-01 -4.12319303e-01 1.16209805e-01 -3.95045489e-01
-4.10315573e-01 -6.57402813e-01 3.60349089e-01 -2.11503506e-01
-7.16454327e-01 9.09937471e-02 -2.30121642e-01 -7.70311832e-01
-9.43637639e-02 -7.94676363e-01 -7.55644202e-01 -5.48496068e-01
-8.13087299e-02 2.80862778e-01 4.90908533e-01 -2.14050204... | [9.345931053161621, -0.99358731508255] |
e157efc8-68e7-4951-9d20-6c09a81ee474 | method-for-specifying-location-data | 2304.11926 | null | https://arxiv.org/abs/2304.11926v1 | https://arxiv.org/pdf/2304.11926v1.pdf | Method for Specifying Location Data Requirements for Intralogistics Applications | Various applications leverage location data to increase transparency, efficiency, and safety in intralogistics. There are several properties of location data, such as the data's degrees of freedom, system latency, update rate, or accuracy. To select a suitable indoor localization system, corresponding data requirements... | ['Jochen Kreutzfeldt', 'Johannes Hinckeldeyn', 'Markus Knitt', 'Jakob Schyga'] | 2023-04-24 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [-2.82745570e-01 -4.28374112e-01 -9.64172930e-02 -4.45859164e-01
-3.83065224e-01 -7.58700132e-01 3.79220068e-01 1.13339078e+00
-3.59064370e-01 7.59784043e-01 -1.62042186e-01 -4.72286105e-01
-9.84223545e-01 -9.12638068e-01 -3.88372749e-01 -4.83791262e-01
-1.93429098e-01 3.66492093e-01 1.81791261e-01 -1.10888466... | [6.374463081359863, 1.6939343214035034] |
6233931a-78ad-4b46-a007-5f6ea1374345 | unixlong-at-semeval-2020-task-6-a-joint-model | null | null | https://aclanthology.org/2020.semeval-1.96 | https://aclanthology.org/2020.semeval-1.96.pdf | UNIXLONG at SemEval-2020 Task 6: A Joint Model for Definition Extraction | Definition Extraction is the task to automatically extract terms and their definitions from text. In recent years, it attracts wide interest from NLP researchers. This paper describes the unixlong team{'}s system for the SemEval 2020 task6: DeftEval: Extracting term-definition pairs in free text. The goal of this task ... | ['Jianping Shen', 'Mo Yang', 'Jiang Lianxin', 'Haiqin Yang', 'Jian Ma', 'Shuyi Xie'] | 2020-12-01 | null | null | null | semeval-2020 | ['definition-extraction'] | ['natural-language-processing'] | [ 3.63278896e-01 3.42792064e-01 -4.12186801e-01 -4.55861241e-01
-8.01995814e-01 -7.20448732e-01 7.32410431e-01 4.09468323e-01
-6.32608831e-01 1.22894001e+00 3.33580017e-01 -4.53188330e-01
-2.86034733e-01 -8.94974709e-01 -3.76678318e-01 -4.31063920e-01
-4.08412665e-02 7.24979043e-01 1.81429222e-01 -1.77952856... | [9.49594783782959, 8.825020790100098] |
79fae7a2-ddb5-4731-9ea3-7a6ace7d26d7 | benchmarking-graphormer-on-large-scale | 2203.04810 | null | https://arxiv.org/abs/2203.04810v2 | https://arxiv.org/pdf/2203.04810v2.pdf | Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets | This technical note describes the recent updates of Graphormer, including architecture design modifications, and the adaption to 3D molecular dynamics simulation. With these simple modifications, Graphormer could attain better results on large-scale molecular modeling datasets than the vanilla one, and the performance ... | ['Tie-Yan Liu', 'Di He', 'Chang Liu', 'Shengjie Luo', 'Jiyan He', 'Jiacheng You', 'Yifei Shen', 'Guolin Ke', 'Shuxin Zheng', 'Yu Shi'] | 2022-03-09 | null | null | null | null | ['graph-regression'] | ['graphs'] | [-1.67723030e-01 4.81787659e-02 -1.82649329e-01 1.77811552e-02
-4.16935831e-01 -5.04639685e-01 6.04109108e-01 4.94075298e-01
-5.40677547e-01 9.66404021e-01 -2.17625901e-01 -7.35144317e-01
-4.15925384e-02 -7.35405505e-01 -9.65329230e-01 -8.42747867e-01
-3.46191198e-01 8.73458147e-01 1.20214343e-01 -4.60744292... | [5.341142654418945, 5.724493980407715] |
711b321e-9e72-4d5d-9c93-ea77b7d20e77 | objects-as-context-for-detecting-their | 1703.09529 | null | http://arxiv.org/abs/1703.09529v3 | http://arxiv.org/pdf/1703.09529v3.pdf | Objects as context for detecting their semantic parts | We present a semantic part detection approach that effectively leverages
object information.We use the object appearance and its class as indicators of
what parts to expect. We also model the expected relative location of parts
inside the objects based on their appearance. We achieve this with a new
network module, cal... | ['Vittorio Ferrari', 'Abel Gonzalez-Garcia', 'Davide Modolo'] | 2017-03-28 | objects-as-context-for-detecting-their-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Gonzalez-Garcia_Objects_as_Context_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Gonzalez-Garcia_Objects_as_Context_CVPR_2018_paper.pdf | cvpr-2018-6 | ['semantic-part-detection'] | ['computer-vision'] | [ 2.10441321e-01 2.73160666e-01 -1.20153219e-01 -6.06211603e-01
-4.56708699e-01 -6.88515544e-01 7.22762406e-01 2.27244701e-02
-2.33495355e-01 3.23594272e-01 9.67017487e-02 2.71103144e-01
3.00185025e-01 -7.48293877e-01 -1.20188773e+00 -2.09981367e-01
-1.48735708e-03 6.90185666e-01 8.42562079e-01 -3.76718529... | [9.276906967163086, 0.8759414553642273] |
edc27c25-38ab-42e6-900c-0388db7ffba5 | deploying-a-robust-active-preference | 2306.04061 | null | https://arxiv.org/abs/2306.04061v1 | https://arxiv.org/pdf/2306.04061v1.pdf | Deploying a Robust Active Preference Elicitation Algorithm: Experiment Design, Interface, and Evaluation for COVID-19 Patient Prioritization | Preference elicitation leverages AI or optimization to learn stakeholder preferences in settings ranging from marketing to public policy. The online robust preference elicitation procedure of arXiv:2003.01899 has been shown in simulation to outperform various other elicitation procedures in terms of effectively learnin... | ['Phebe Vayanos', 'Simon Blessenohl', 'Patrick Vossler', 'Caroline M. Johnston'] | 2023-06-06 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 4.88696955e-02 2.55551815e-01 -3.70149910e-01 -4.51931953e-01
-1.13692963e+00 -1.17554355e+00 2.08345264e-01 2.77541131e-01
-7.60507941e-01 1.08922827e+00 4.93914574e-01 -7.99892604e-01
-5.80604732e-01 -5.35656631e-01 -4.80722964e-01 -3.92018557e-01
-3.52542341e-01 9.95917201e-01 -5.51462352e-01 3.37403007... | [9.149348258972168, 5.441460132598877] |
9d322119-8627-4ae0-9fb8-f176d4f8c845 | deep-tractable-probabilistic-models-for-moral | 1810.03736 | null | https://arxiv.org/abs/1810.03736v3 | https://arxiv.org/pdf/1810.03736v3.pdf | Learning Tractable Probabilistic Models for Moral Responsibility and Blame | Moral responsibility is a major concern in autonomous systems, with applications ranging from self-driving cars to kidney exchanges. Although there have been recent attempts to formalise responsibility and blame, among similar notions, the problem of learning within these formalisms has been unaddressed. From the viewp... | ['Lewis Hammond', 'Vaishak Belle'] | 2018-10-08 | null | null | null | null | ['moral-scenarios'] | ['miscellaneous'] | [ 3.76692206e-01 1.11672008e+00 -1.09363817e-01 -8.47679198e-01
-4.74487185e-01 -3.30596775e-01 8.27997029e-01 3.79085511e-01
-5.59175491e-01 9.28971410e-01 2.61140198e-01 -6.11686647e-01
-6.79174006e-01 -5.00624418e-01 -1.85929835e-01 -5.20931780e-01
1.05619967e-01 7.92978525e-01 1.18488364e-01 -2.69095778... | [8.816980361938477, 6.131811618804932] |
a83f279a-ee30-4498-ad2b-792676dffb65 | motiontrack-end-to-end-transformer-based | 2306.17000 | null | https://arxiv.org/abs/2306.17000v1 | https://arxiv.org/pdf/2306.17000v1.pdf | MotionTrack: End-to-End Transformer-based Multi-Object Tracing with LiDAR-Camera Fusion | Multiple Object Tracking (MOT) is crucial to autonomous vehicle perception. End-to-end transformer-based algorithms, which detect and track objects simultaneously, show great potential for the MOT task. However, most existing methods focus on image-based tracking with a single object category. In this paper, we propose... | ['Michael Happold', 'Lingji Chen', 'Yiluan Guo', 'Chengjie Zhang', 'Ce Zhang'] | 2023-06-29 | null | null | null | null | ['object-tracking', 'multiple-object-tracking'] | ['computer-vision', 'computer-vision'] | [ 1.66621823e-02 -4.30140525e-01 -1.69712305e-01 -3.12938899e-01
-1.00777686e+00 -4.09072727e-01 7.96195984e-01 -5.05331494e-02
-6.08993649e-01 3.35131556e-01 -2.75702745e-01 -9.47477110e-03
-1.40347049e-01 -7.14798272e-01 -9.52757359e-01 -9.69792068e-01
3.12661380e-01 6.32008851e-01 1.21709156e+00 -2.55416214... | [6.635846138000488, -2.1578176021575928] |
1bc92cab-f067-4811-9dfa-0acfe0f5eab8 | a-cognitively-driven-weighted-entropy-model | 2112.06876 | null | https://arxiv.org/abs/2112.06876v2 | https://arxiv.org/pdf/2112.06876v2.pdf | A cognitively driven weighted-entropy model for embedding semantic categories in hyperbolic geometry | In this paper, an unsupervised and cognitively driven weighted-entropy method for embedding semantic categories in hyperbolic geometry is proposed. The model is driven by two fields of research in cognitive linguistics: the first is the statistical learning theory of language acquisition and the proposal of using high-... | ['Eugene Yu Ji'] | 2021-12-13 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [-1.74688518e-01 6.34777367e-01 1.68707147e-02 -3.95216078e-01
2.66724676e-01 -5.23906291e-01 7.72998273e-01 5.70626855e-01
-9.69329178e-01 3.16117227e-01 4.25866246e-01 -3.25435430e-01
-9.79420960e-01 -1.22214222e+00 3.46880890e-02 -6.64103031e-01
-3.42285782e-01 8.05772364e-01 3.55481803e-01 -4.58286047... | [10.370676040649414, 8.964204788208008] |
c7bdda06-9bc8-4394-a359-c67282492d5f | enhancing-face-recognition-with-self | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/He_Enhancing_Face_Recognition_With_Self-Supervised_3D_Reconstruction_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/He_Enhancing_Face_Recognition_With_Self-Supervised_3D_Reconstruction_CVPR_2022_paper.pdf | Enhancing Face Recognition With Self-Supervised 3D Reconstruction | Attributed to both the development of deep networks and abundant data, automatic face recognition (FR) has quickly reached human-level capacity in the past few years. However, the FR problem is not perfectly solved in case of uncontrolled illumination and pose. In this paper, we propose to enhance face recognition ... | ['Xilin Chen', 'Shiguang Shan', 'Jie Zhang', 'Mingjie He'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['3d-face-reconstruction', 'face-identification', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.08414799e-01 1.67578638e-01 5.05187025e-04 -7.91352272e-01
-2.38046452e-01 -2.99963981e-01 5.24526656e-01 -5.33141673e-01
-2.27478072e-01 3.97915244e-01 -1.49722457e-01 1.43581539e-01
-6.60682917e-02 -7.55533457e-01 -8.73096466e-01 -1.11868727e+00
2.57178426e-01 2.81713128e-01 -5.39357185e-01 -4.59573828... | [13.199358940124512, 0.42939144372940063] |
34aa37d8-eb10-4e8b-bd11-08e9f322c88c | eliciting-knowledge-from-large-pre-trained | 2211.01587 | null | https://arxiv.org/abs/2211.01587v2 | https://arxiv.org/pdf/2211.01587v2.pdf | Eliciting Knowledge from Large Pre-Trained Models for Unsupervised Knowledge-Grounded Conversation | Recent advances in large-scale pre-training provide large models with the potential to learn knowledge from the raw text. It is thus natural to ask whether it is possible to leverage these large models as knowledge bases for downstream tasks. In this work, we answer the aforementioned question in unsupervised knowledge... | ['LiWei Wang', 'Michael R. Lyu', 'Jianqiao Zhao', 'Yanyang Li'] | 2022-11-03 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 0.12401599 1.0696228 -0.31426755 -0.02625161 -1.1972302 -0.62504375
0.96481156 0.05355621 -0.22130537 1.4139161 0.92314297 -0.22910546
0.04435662 -0.810803 -0.60695755 -0.41430327 0.33736685 0.6906473
0.16510262 -0.56291443 0.31405312 -0.04192489 -1.1532507 0.7088551
1.0745888 0.64839184 0.35... | [12.158616065979004, 8.205634117126465] |
592ed164-44cb-4a67-ba4a-98add45a36f7 | deep-learning-for-energy-time-series-analysis | 2306.09129 | null | https://arxiv.org/abs/2306.09129v2 | https://arxiv.org/pdf/2306.09129v2.pdf | Deep Learning for Energy Time-Series Analysis and Forecasting | Energy time-series analysis describes the process of analyzing past energy observations and possibly external factors so as to predict the future. Different tasks are involved in the general field of energy time-series analysis and forecasting, with electric load demand forecasting, personalized energy consumption fore... | ['Anastasios Tefas', 'Nikos Nikolaidis', 'Pavlos Tosidis', 'Theodoros Manousis', 'Efstratios Kakaletsis', 'Paraskevi Nousi', 'Charalampos Symeonidis', 'Maria Tzelepi'] | 2023-06-15 | null | null | null | null | ['time-series'] | ['time-series'] | [-2.23796055e-01 -5.01102269e-01 -1.65311322e-01 -1.38102859e-01
-1.51298359e-01 -5.76908290e-01 8.74754727e-01 2.53369153e-01
-8.22875947e-02 4.98072803e-01 1.43570319e-01 -3.91978890e-01
-3.56832117e-01 -9.65137243e-01 -2.70948291e-01 -9.56697047e-01
-2.31808186e-01 1.44477487e-01 -6.49277985e-01 -2.86596924... | [6.161076545715332, 2.77988862991333] |
27ad5a6a-d0a3-45cb-96d2-4f92263b108c | aesthetic-driven-image-enhancement-by | 1707.05251 | null | http://arxiv.org/abs/1707.05251v2 | http://arxiv.org/pdf/1707.05251v2.pdf | Aesthetic-Driven Image Enhancement by Adversarial Learning | We introduce EnhanceGAN, an adversarial learning based model that performs
automatic image enhancement. Traditional image enhancement frameworks typically
involve training models in a fully-supervised manner, which require expensive
annotations in the form of aligned image pairs. In contrast to these
approaches, our pr... | ['Yubin Deng', 'Chen Change Loy', 'Xiaoou Tang'] | 2017-07-17 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 6.34045184e-01 2.62705266e-01 2.26357251e-01 -5.09309351e-01
-8.65365267e-01 -5.38009048e-01 4.58472133e-01 -1.58625841e-01
-4.94715899e-01 3.12601835e-01 -4.48026843e-02 -2.03775242e-01
1.49727449e-01 -8.00389349e-01 -1.06638730e+00 -5.56903780e-01
4.91621383e-02 -2.66924530e-01 -4.00268197e-01 -6.76670969... | [11.40692138671875, -1.5529546737670898] |
95811514-c479-4c8b-829b-8af546065f85 | learning-representations-for-incomplete-time | null | null | https://ojs.aaai.org/index.php/AAAI/article/view/17070 | https://ojs.aaai.org/index.php/AAAI/article/view/17070/16877 | Learning Representations for Incomplete Time Series Clustering | Time-series clustering is an essential unsupervised technique for data analysis, applied to many real-world fields, such as medical analysis and DNA microarray. Existing clustering methods are usually based on the assumption that the data is complete. However, time series in real-world applications often contain missin... | ['Garrison W. Cottrell', 'Sen Li', 'Chuxin Chen', 'Qianli Ma'] | 2021-05-18 | null | null | null | aaai-2021-5 | ['time-series-clustering'] | ['time-series'] | [ 1.99678123e-01 -6.51593149e-01 -1.55579448e-01 -6.07576132e-01
-7.47933269e-01 -4.74984616e-01 6.03705794e-02 9.97967571e-02
-2.40221307e-01 8.00664246e-01 1.70205474e-01 -9.14631039e-02
-6.18463874e-01 -7.88591743e-01 -7.08279312e-01 -1.23971474e+00
-1.17852159e-01 6.57294393e-01 -4.36162859e-01 2.02418536... | [7.297471046447754, 3.576587438583374] |
bf71a71d-d56c-4903-bec5-7e6e97805341 | shikra-unleashing-multimodal-llm-s | 2306.15195 | null | https://arxiv.org/abs/2306.15195v2 | https://arxiv.org/pdf/2306.15195v2.pdf | Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic | In human conversations, individuals can indicate relevant regions within a scene while addressing others. In turn, the other person can then respond by referring to specific regions if necessary. This natural referential ability in dialogue remains absent in current Multimodal Large Language Models (MLLMs). To fill thi... | ['Rui Zhao', 'Feng Zhu', 'Richong Zhang', 'Weili Zeng', 'Zhao Zhang', 'Keqin Chen'] | 2023-06-27 | null | null | null | null | ['image-captioning'] | ['computer-vision'] | [-1.54922545e-01 2.46006578e-01 -6.15657344e-02 -5.89956164e-01
-9.79572773e-01 -7.95506477e-01 9.44774866e-01 1.70054153e-01
-4.90913659e-01 4.56481874e-01 5.91146111e-01 -2.14469939e-01
3.99233729e-01 -5.12088537e-01 -4.47949231e-01 -3.95150363e-01
5.59994757e-01 7.40317047e-01 4.23381180e-02 -4.05967861... | [10.868599891662598, 1.489579439163208] |
c63387ad-7451-477d-9c8e-67e39fe1970f | referring-to-screen-texts-with-voice | 2306.07298 | null | https://arxiv.org/abs/2306.07298v1 | https://arxiv.org/pdf/2306.07298v1.pdf | Referring to Screen Texts with Voice Assistants | Voice assistants help users make phone calls, send messages, create events, navigate, and do a lot more. However, assistants have limited capacity to understand their users' context. In this work, we aim to take a step in this direction. Our work dives into a new experience for users to refer to phone numbers, addresse... | ['Vincent Renkens', 'Hong Yu', 'Alkesh Patel', 'Hoang Long Nguyen', 'Ing-Marie Jonsson', 'Anand Dhoot', 'Shruti Bhargava'] | 2023-06-10 | null | null | null | null | ['visual-grounding', 'navigate'] | ['computer-vision', 'reasoning'] | [ 4.07730728e-01 8.97530094e-02 -1.92874730e-01 -2.92775214e-01
-3.73616070e-01 -8.02011967e-01 6.57986701e-01 3.72075826e-01
-2.92181432e-01 4.30034399e-01 6.63249910e-01 -5.91682673e-01
1.41946925e-02 -6.67705059e-01 -9.22285095e-02 6.99069649e-02
2.98647881e-01 2.48114944e-01 2.15179175e-01 -5.64059205... | [11.794472694396973, 2.7496795654296875] |
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