paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
98204de0-25d6-48df-bcb2-440aa9c3cf17 | code-attention-translating-code-to-comments | 1709.07642 | null | http://arxiv.org/abs/1709.07642v2 | http://arxiv.org/pdf/1709.07642v2.pdf | Code Attention: Translating Code to Comments by Exploiting Domain Features | Appropriate comments of code snippets provide insight for code functionality,
which are helpful for program comprehension. However, due to the great cost of
authoring with the comments, many code projects do not contain adequate
comments. Automatic comment generation techniques have been proposed to
generate comments f... | ['Hong-Yu Zhou', 'Wenhao Zheng', 'Ming Li', 'Jianxin Wu'] | 2017-09-22 | null | null | null | null | ['comment-generation'] | ['natural-language-processing'] | [-8.79984349e-02 1.20053999e-01 -8.01637024e-02 -4.09040660e-01
-5.42389691e-01 -6.66713119e-01 1.08394802e-01 3.36957455e-01
-5.45737259e-02 4.44558561e-01 2.93403059e-01 -4.42603827e-01
4.51688468e-01 -5.34265399e-01 -4.95958328e-01 -5.04914224e-02
2.19142810e-01 -2.29370415e-01 1.93297818e-01 -1.72560841... | [7.650936126708984, 7.919658184051514] |
8244a763-ad86-4ac2-bf88-99c599990942 | edge-representation-learning-with-hypergraphs | 2106.15845 | null | https://arxiv.org/abs/2106.15845v2 | https://arxiv.org/pdf/2106.15845v2.pdf | Edge Representation Learning with Hypergraphs | Graph neural networks have recently achieved remarkable success in representing graph-structured data, with rapid progress in both the node embedding and graph pooling methods. Yet, they mostly focus on capturing information from the nodes considering their connectivity, and not much work has been done in representing ... | ['Sung Ju Hwang', 'Minki Kang', 'DongKi Kim', 'Seul Lee', 'Jinheon Baek', 'Jaehyeong Jo'] | 2021-06-30 | null | http://proceedings.neurips.cc/paper/2021/hash/3def184ad8f4755ff269862ea77393dd-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/3def184ad8f4755ff269862ea77393dd-Paper.pdf | neurips-2021-12 | ['graph-reconstruction'] | ['graphs'] | [ 2.53954619e-01 2.35309482e-01 -3.87543172e-01 -9.42775421e-03
-3.64657551e-01 -5.43510854e-01 4.94412512e-01 6.58134639e-01
2.17297357e-02 3.85018855e-01 2.25695267e-01 -3.43971699e-01
-1.00902043e-01 -1.58644354e+00 -5.87360084e-01 -7.18940794e-01
-3.60475093e-01 3.04420620e-01 4.46670055e-02 -1.27771214... | [7.0993804931640625, 6.303421497344971] |
fd248292-2b1e-4a88-951e-c1935c3ce209 | unsupervised-multi-view-pedestrian-detection | 2305.12457 | null | https://arxiv.org/abs/2305.12457v1 | https://arxiv.org/pdf/2305.12457v1.pdf | Unsupervised Multi-view Pedestrian Detection | With the prosperity of the video surveillance, multiple visual sensors have been applied for an accurate localization of pedestrians in a specific area, which facilitate various applications like intelligent safety or new retailing. However, previous methods rely on the supervision from the human annotated pedestrian p... | ['Xu-Cheng Yin', 'Shiqi Ren', 'Chao Zhu', 'Mengyin Liu'] | 2023-05-21 | null | null | null | null | ['camera-calibration', 'pedestrian-detection'] | ['computer-vision', 'computer-vision'] | [-8.03340077e-02 -3.93657207e-01 1.10357277e-01 -2.68923581e-01
-3.40173572e-01 -4.90866393e-01 5.52217126e-01 -8.55548754e-02
-7.09396780e-01 3.60049903e-01 -2.10441381e-01 5.57707883e-02
5.36830783e-01 -7.79969275e-01 -7.38749444e-01 -8.34756613e-01
3.60964447e-01 4.67755169e-01 9.27774787e-01 -8.84342764... | [7.692292213439941, -0.8056875467300415] |
831cf9ce-2a2b-4a43-9a0f-24b17709d51b | a-comparison-of-strategies-for-source-free-1 | null | null | https://aclanthology.org/2022.acl-long.572 | https://aclanthology.org/2022.acl-long.572.pdf | A Comparison of Strategies for Source-Free Domain Adaptation | Data sharing restrictions are common in NLP, especially in the clinical domain, but there is limited research on adapting models to new domains without access to the original training data, a setting known as source-free domain adaptation. We take algorithms that traditionally assume access to the source-domain trainin... | ['Steven Bethard', 'Yiyun Zhao', 'Xin Su'] | null | null | null | null | acl-2022-5 | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 5.60842812e-01 7.70167887e-01 -9.88948882e-01 -6.40237570e-01
-1.23215008e+00 -6.69982672e-01 5.92095494e-01 5.15504599e-01
-9.20258701e-01 1.42309070e+00 5.01308382e-01 -3.51508230e-01
-2.29868278e-01 -4.46470350e-01 -6.53123677e-01 -4.74216998e-01
4.32587042e-02 1.03620410e+00 8.55912864e-02 -1.17437176... | [10.678529739379883, 7.971100807189941] |
30c66ee3-b1ce-46e9-bed9-ece29a903661 | improving-text-matching-in-e-commerce-search | 2307.00370 | null | https://arxiv.org/abs/2307.00370v1 | https://arxiv.org/pdf/2307.00370v1.pdf | Improving Text Matching in E-Commerce Search with A Rationalizable, Intervenable and Fast Entity-Based Relevance Model | Discovering the intended items of user queries from a massive repository of items is one of the main goals of an e-commerce search system. Relevance prediction is essential to the search system since it helps improve performance. When online serving a relevance model, the model is required to perform fast and accurate ... | ['Kewei Tu', 'Fei Huang', 'Pengjun Xie', 'Zhongqiang Huang', 'Tao Wang', 'Haihong Tang', 'Rong Xiao', 'Jianhui Ji', 'Ke Yu', 'Chenyue Jiang', 'Yue Zhang', 'Yong Jiang', 'Jiong Cai'] | 2023-07-01 | null | null | null | null | ['text-matching'] | ['natural-language-processing'] | [-1.41991228e-01 -4.49763574e-02 -6.09670937e-01 -5.17023027e-01
-1.02584517e+00 -4.43459302e-01 1.98873922e-01 2.70194620e-01
-1.62644163e-01 5.21692693e-01 -4.88683730e-02 -3.34892392e-01
-3.88085902e-01 -1.02847278e+00 -9.64287221e-01 7.19205886e-02
7.77932405e-02 8.26023519e-01 4.99737620e-01 -4.04702157... | [10.961050033569336, 7.316137790679932] |
ccb602de-0428-4ccf-82ed-1a557b1323e3 | learning-to-infer-from-unlabeled-data-a-semi | 2211.02971 | null | https://arxiv.org/abs/2211.02971v1 | https://arxiv.org/pdf/2211.02971v1.pdf | Learning to Infer from Unlabeled Data: A Semi-supervised Learning Approach for Robust Natural Language Inference | Natural Language Inference (NLI) or Recognizing Textual Entailment (RTE) aims at predicting the relation between a pair of sentences (premise and hypothesis) as entailment, contradiction or semantic independence. Although deep learning models have shown promising performance for NLI in recent years, they rely on large ... | ['Cornelia Caragea', 'Mobashir Sadat'] | 2022-11-05 | null | null | null | null | ['sentence-classification'] | ['natural-language-processing'] | [ 3.31263244e-01 2.89985418e-01 -3.82849276e-01 -9.06214595e-01
-8.05307508e-01 -7.28687048e-01 7.99565077e-01 1.61330283e-01
-3.51066887e-01 1.05694771e+00 2.51191467e-01 -7.80787468e-01
2.50900835e-01 -4.89243388e-01 -9.16996241e-01 -3.13600421e-01
3.99523675e-01 6.74682856e-01 -2.14752495e-01 -5.97666390... | [10.229240417480469, 8.596946716308594] |
1f0aaa27-c201-450d-97c3-e9aac31ef393 | facial-makeup-transfer-combining-illumination | 1907.03398 | null | https://arxiv.org/abs/1907.03398v1 | https://arxiv.org/pdf/1907.03398v1.pdf | Facial Makeup Transfer Combining Illumination Transfer | To meet the women appearance needs, we present a novel virtual experience approach of facial makeup transfer, developed into windows platform application software. The makeup effects could present on the user's input image in real time, with an only single reference image. The input image and reference image are divide... | ['Xiao-Dong Li', 'Xin Jin', 'Xiaokun Zhang', 'Rui Han', 'Ning Ning'] | 2019-07-08 | null | null | null | null | ['facial-makeup-transfer'] | ['computer-vision'] | [ 2.02689007e-01 2.86072046e-01 2.35324308e-01 -2.07658976e-01
-1.19766043e-02 -4.62354779e-01 4.07347172e-01 -5.93680739e-01
-1.92608945e-02 3.73318106e-01 -9.14637744e-02 -5.29748797e-02
5.06251574e-01 -9.86788929e-01 -7.40249336e-01 -4.55621392e-01
2.83458352e-01 -2.49274269e-01 2.28361249e-01 -2.96991915... | [12.802413940429688, -0.13666968047618866] |
f4a2a5cf-dcbb-4a97-a61a-ebc9ab2c285b | flexible-portrait-image-editing-with-fine | 2204.01318 | null | https://arxiv.org/abs/2204.01318v1 | https://arxiv.org/pdf/2204.01318v1.pdf | Flexible Portrait Image Editing with Fine-Grained Control | We develop a new method for portrait image editing, which supports fine-grained editing of geometries, colors, lights and shadows using a single neural network model. We adopt a novel asymmetric conditional GAN architecture: the generators take the transformed conditional inputs, such as edge maps, color palette, slide... | ['Ying He', 'Fei Hou', 'QiAn Fu', 'Linlin Liu'] | 2022-04-04 | null | null | null | null | ['sketch-to-image-translation'] | ['computer-vision'] | [ 5.82611859e-01 -9.45117697e-02 1.25325367e-01 -3.90866727e-01
-2.12867796e-01 -9.85668838e-01 5.51690936e-01 -4.89413768e-01
-1.29014552e-01 5.93397796e-01 -2.78562456e-02 -2.35622630e-01
3.93033206e-01 -1.09332466e+00 -7.37829149e-01 -5.79126239e-01
3.86705637e-01 1.47562027e-01 1.87188119e-01 -3.21512580... | [11.658341407775879, -0.6035923361778259] |
541ce0e6-9bef-4281-921d-52371fced18b | parameter-inference-in-a-computational-model | 2101.06266 | null | https://arxiv.org/abs/2101.06266v2 | https://arxiv.org/pdf/2101.06266v2.pdf | Parameter inference in a computational model of hemodynamics in pulmonary hypertension | Pulmonary hypertension (PH), defined by a mean pulmonary arterial pressure (mPAP) $>$ 20 mmHg, is characterized by increased pulmonary vascular resistance and decreased pulmonary arterial compliance. There are few measurable biomarkers of PH progression, but a conclusive diagnosis of the disease requires invasive right... | ['REU Program', 'Amanda L. Colunga', 'Mette S. Olufsen', 'Mitchel J. Colebank'] | 2021-01-15 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 1.06770433e-01 -4.49182279e-02 -1.25223622e-01 9.07704607e-02
-3.58404070e-01 -6.54534280e-01 1.56964853e-01 1.70410797e-01
-7.16193244e-02 7.61459172e-01 3.80844444e-01 -8.28331769e-01
-7.38506556e-01 -5.48823178e-01 -5.71659431e-02 -4.00334775e-01
-4.03984427e-01 9.86333966e-01 1.29373912e-02 5.17793179... | [14.103777885437012, 3.038763999938965] |
32bf654d-9c0e-4a5e-b9dd-02e27ec0c2da | fashionpedia-ontology-segmentation-and-an | 2004.12276 | null | https://arxiv.org/abs/2004.12276v2 | https://arxiv.org/pdf/2004.12276v2.pdf | Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset | In this work we explore the task of instance segmentation with attribute localization, which unifies instance segmentation (detect and segment each object instance) and fine-grained visual attribute categorization (recognize one or multiple attributes). The proposed task requires both localizing an object and describin... | ['Serge Belongie', 'Mengyun Shi', 'Menglin Jia', 'Mikhail Sirotenko', 'Yin Cui', 'Hartwig Adam', 'Claire Cardie', 'Bharath Hariharan'] | 2020-04-26 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1203_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460307.pdf | eccv-2020-8 | ['fine-grained-visual-recognition', 'fine-grained-visual-categorization'] | ['computer-vision', 'computer-vision'] | [ 1.50511339e-01 -8.22860003e-02 -3.82305443e-01 -7.95983672e-01
-7.07344770e-01 -9.41505373e-01 4.08526510e-01 2.99816102e-01
-2.13426799e-01 3.98582757e-01 4.61106300e-02 1.55703068e-01
-2.99378559e-02 -7.46620119e-01 -8.63680959e-01 -5.26554525e-01
5.17586805e-02 9.18563426e-01 -2.27940045e-02 8.93859118... | [9.759133338928223, 0.4427202045917511] |
008f1c48-bcc9-4e28-b4ed-43a9ee1c91eb | multi-spectral-vehicle-re-identification-with | 2208.00632 | null | https://arxiv.org/abs/2208.00632v2 | https://arxiv.org/pdf/2208.00632v2.pdf | Multi-spectral Vehicle Re-identification with Cross-directional Consistency Network and a High-quality Benchmark | To tackle the challenge of vehicle re-identification (Re-ID) in complex lighting environments and diverse scenes, multi-spectral sources like visible and infrared information are taken into consideration due to their excellent complementary advantages. However, multi-spectral vehicle Re-ID suffers cross-modality discre... | ['Chenglong Li', 'Zhiqi Ma', 'Jixin Ma', 'Jin Tang', 'Xianpeng Zhu', 'Aihua Zheng'] | 2022-08-01 | null | null | null | null | ['vehicle-re-identification'] | ['computer-vision'] | [-9.57108513e-02 -8.52167666e-01 -3.67112160e-02 -4.77502555e-01
-8.04160237e-01 -5.45271873e-01 6.52220309e-01 -3.96043390e-01
-4.37681526e-01 3.93460989e-01 1.10263951e-01 1.13560095e-01
-6.43876716e-02 -4.94095057e-01 -7.51903534e-01 -1.01248455e+00
6.97162926e-01 1.70154795e-01 1.35506183e-01 -3.44471693... | [14.503548622131348, 0.8088288903236389] |
3ddb2599-5591-4727-8c6d-f394aaadd51f | long-context-language-decision-transformers | 2302.05507 | null | https://arxiv.org/abs/2302.05507v1 | https://arxiv.org/pdf/2302.05507v1.pdf | Long-Context Language Decision Transformers and Exponential Tilt for Interactive Text Environments | Text-based game environments are challenging because agents must deal with long sequences of text, execute compositional actions using text and learn from sparse rewards. We address these challenges by proposing Long-Context Language Decision Transformers (LLDTs), a framework that is based on long transformer language ... | ['Christopher Pal', 'David Vazquez', 'Issam Laradji', 'Pau Rodriguez', 'Nicolas Gontier'] | 2023-02-10 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [ 4.71078828e-02 1.63682774e-01 -1.94739833e-01 -8.16112235e-02
-1.06553280e+00 -6.18840814e-01 1.07600188e+00 -9.87899825e-02
-6.71939552e-01 9.35974121e-01 4.97814804e-01 -7.37859964e-01
-3.29428539e-02 -7.76465416e-01 -4.52719390e-01 -3.04740936e-01
-4.70000833e-01 9.64194894e-01 3.45688909e-01 -7.69178331... | [3.8423376083374023, 1.4546858072280884] |
4a357936-9366-4cc8-ba75-d6da432d89f8 | guiding-text-to-image-diffusion-model-towards | 2301.05221 | null | https://arxiv.org/abs/2301.05221v1 | https://arxiv.org/pdf/2301.05221v1.pdf | Guiding Text-to-Image Diffusion Model Towards Grounded Generation | The goal of this paper is to augment a pre-trained text-to-image diffusion model with the ability of open-vocabulary objects grounding, i.e., simultaneously generating images and segmentation masks for the corresponding visual entities described in the text prompt. We make the following contributions: (i) we insert a g... | ['Weidi Xie', 'Yanfeng Wang', 'Ya zhang', 'Xiaoyun Zhang', 'Qinye Zhou', 'Ziyi Li'] | 2023-01-12 | null | null | null | null | ['zero-shot-segmentation'] | ['computer-vision'] | [ 4.77764010e-01 6.28073573e-01 -1.70874611e-01 -4.44980592e-01
-8.41181219e-01 -7.61004210e-01 8.10342014e-01 7.54542351e-02
-3.66793483e-01 -6.11912459e-02 9.04006660e-02 -7.37671107e-02
1.79396003e-01 -8.60828817e-01 -7.79845059e-01 -4.07781333e-01
3.31182808e-01 9.09660399e-01 9.72145557e-01 -1.74465761... | [9.72454833984375, 0.8254665732383728] |
17c0d985-8a79-49f3-9c58-67a0d466eda0 | mutual-and-self-prototype-alignment-for-semi | 2206.01739 | null | https://arxiv.org/abs/2206.01739v1 | https://arxiv.org/pdf/2206.01739v1.pdf | Mutual- and Self- Prototype Alignment for Semi-supervised Medical Image Segmentation | Semi-supervised learning methods have been explored in medical image segmentation tasks due to the scarcity of pixel-level annotation in the real scenario. Proto-type alignment based consistency constraint is an intuitional and plausible solu-tion to explore the useful information in the unlabeled data. In this paper, ... | ['Zhicheng Jiao', 'Chunna Tian', 'Zhenxi Zhang'] | 2022-06-03 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 2.04932421e-01 5.32003403e-01 -7.97191858e-01 -7.05819130e-01
-8.05899084e-01 -3.57234716e-01 1.93607315e-01 3.20718698e-02
-4.19915825e-01 4.67575073e-01 9.10570696e-02 -2.02326011e-02
-1.21371165e-01 -4.21845645e-01 -2.28428811e-01 -1.06208980e+00
1.76134989e-01 4.18593228e-01 3.05767238e-01 2.39247233... | [14.748893737792969, -2.080825090408325] |
f7ec971f-5479-4c1d-9dfd-37c48c0ceded | part-stacked-cnn-for-fine-grained-visual | 1512.08086 | null | http://arxiv.org/abs/1512.08086v1 | http://arxiv.org/pdf/1512.08086v1.pdf | Part-Stacked CNN for Fine-Grained Visual Categorization | In the context of fine-grained visual categorization, the ability to
interpret models as human-understandable visual manuals is sometimes as
important as achieving high classification accuracy. In this paper, we propose
a novel Part-Stacked CNN architecture that explicitly explains the fine-grained
recognition process ... | ['Ya zhang', 'DaCheng Tao', 'Shaoli Huang', 'Zhe Xu'] | 2015-12-26 | part-stacked-cnn-for-fine-grained-visual-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Huang_Part-Stacked_CNN_for_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Huang_Part-Stacked_CNN_for_CVPR_2016_paper.pdf | cvpr-2016-6 | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 2.39895552e-01 8.42111558e-02 -2.46062115e-01 -6.26002371e-01
-3.25743198e-01 -5.18127799e-01 6.22703791e-01 2.58401304e-01
-1.82095379e-01 2.08459198e-01 -1.70512825e-01 -4.15378422e-01
-1.66927576e-01 -5.84341764e-01 -1.03720605e+00 -3.98556620e-01
2.34958738e-01 4.88100648e-01 3.18603814e-01 2.12529659... | [9.564166069030762, 1.5992463827133179] |
46655ffb-cdf9-461b-8286-3e814c02ce48 | better-exploiting-motion-for-better-action | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Jain_Better_Exploiting_Motion_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Jain_Better_Exploiting_Motion_2013_CVPR_paper.pdf | Better Exploiting Motion for Better Action Recognition | Several recent works on action recognition have attested the importance of explicitly integrating motion characteristics in the video description. This paper establishes that adequately decomposing visual motion into dominant and residual motions, both in the extraction of the space-time trajectories and for the comput... | ['Herve Jegou', 'Patrick Bouthemy', 'Mihir Jain'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['video-description'] | ['computer-vision'] | [ 2.59497285e-01 -6.83382392e-01 -6.66549861e-01 -3.40791419e-02
-6.26998782e-01 -6.78326070e-01 9.73539531e-01 7.09115155e-03
-4.52560812e-01 5.46349525e-01 6.88245714e-01 2.83252656e-01
-3.83087844e-01 -2.60227233e-01 -6.32638857e-02 -9.72748399e-01
-4.18740720e-01 -1.37241870e-01 4.18747097e-01 -7.83400685... | [8.040848731994629, 0.3208381235599518] |
1c1d6da4-4caf-4f40-9557-278c70b73cee | star-ris-enabled-simultaneous-indoor-and | 2302.03342 | null | https://arxiv.org/abs/2302.03342v1 | https://arxiv.org/pdf/2302.03342v1.pdf | STAR-RIS-Enabled Simultaneous Indoor and Outdoor 3D Localization: Theoretical Analysis and Algorithmic Design | Recent research and development interests deal with metasurfaces for wireless systems beyond their consideration as intelligent tunable reflectors. Among the latest proposals is the simultaneously transmitting (a.k.a. refracting) and reflecting reconfigurable intelligent surface (STAR-RIS) which intends to enable bidir... | ['George C. Alexandropoulos', 'Aymen Fakhreddine', 'Jiguang He'] | 2023-02-07 | null | null | null | null | ['compressive-sensing'] | ['computer-vision'] | [ 4.22545135e-01 2.84421682e-01 4.37115014e-01 1.52820900e-01
-8.79421353e-01 -4.19693381e-01 2.71861643e-01 -1.63342357e-01
-8.66950527e-02 6.04545176e-01 5.83621711e-02 -4.08149987e-01
-7.90862918e-01 -8.97187591e-01 -5.89748919e-01 -1.24523151e+00
-4.74453688e-01 8.77538696e-02 -1.75418735e-01 -2.93496907... | [6.27695369720459, 1.2458192110061646] |
5d8b2dc8-0a56-478c-89da-01c732403812 | towards-high-performance-one-stage-human-pose | 2301.04842 | null | https://arxiv.org/abs/2301.04842v1 | https://arxiv.org/pdf/2301.04842v1.pdf | Towards High Performance One-Stage Human Pose Estimation | Making top-down human pose estimation method present both good performance and high efficiency is appealing. Mask RCNN can largely improve the efficiency by conducting person detection and pose estimation in a single framework, as the features provided by the backbone are able to be shared by the two tasks. However, th... | ['Jie Xu', 'Linhao Xu', 'Lin Zhao', 'Ling Li'] | 2023-01-12 | null | null | null | null | ['keypoint-detection', 'human-detection'] | ['computer-vision', 'computer-vision'] | [-1.90357387e-01 -9.87714082e-02 1.50008827e-01 -1.19831063e-01
-7.49318480e-01 -3.52490038e-01 3.86904001e-01 -1.76705986e-01
-9.60840821e-01 6.03247762e-01 3.61962557e-01 2.43652806e-01
1.49266317e-01 -6.71982825e-01 -6.12098694e-01 -5.80729127e-01
-5.45855202e-02 3.10466409e-01 4.35569137e-01 -2.85398483... | [7.141965866088867, -0.7796465158462524] |
5793d2c7-6105-43f0-9617-95c3439d3583 | utterance-to-utterance-interactive-matching | 1911.06940 | null | https://arxiv.org/abs/1911.06940v1 | https://arxiv.org/pdf/1911.06940v1.pdf | Utterance-to-Utterance Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots | This paper proposes an utterance-to-utterance interactive matching network (U2U-IMN) for multi-turn response selection in retrieval-based chatbots. Different from previous methods following context-to-response matching or utterance-to-response matching frameworks, this model treats both contexts and responses as sequen... | ['Zhen-Hua Ling', 'Jia-Chen Gu', 'Quan Liu'] | 2019-11-16 | null | null | null | null | ['conversational-response-selection'] | ['natural-language-processing'] | [ 3.01692307e-01 -1.65666789e-02 -3.04505140e-01 -9.53624666e-01
-1.34099483e+00 -1.94027156e-01 6.08415306e-01 1.30666465e-01
-4.10544306e-01 2.66553491e-01 6.81071043e-01 -1.67556480e-01
-7.78106824e-02 -5.46626031e-01 -1.16033517e-01 -3.83709490e-01
2.34532073e-01 7.57686853e-01 1.65264353e-01 -6.29028320... | [12.500687599182129, 7.829361438751221] |
4e4b800a-e147-4dd6-9dab-136cd9c7405f | the-combination-of-context-information-to | 1810.04000 | null | http://arxiv.org/abs/1810.04000v1 | http://arxiv.org/pdf/1810.04000v1.pdf | The combination of context information to enhance simple question answering | With the rapid development of knowledge base,question answering based on
knowledge base has been a hot research issue. In this paper, we focus on
answering singlerelation factoid questions based on knowledge base. We build a
question answering system and study the effect of context information on fact
selection, such a... | ['Lin Li', 'Zhaohui Chao'] | 2018-10-09 | null | null | null | null | ['knowledge-base-question-answering', 'fact-selection'] | ['natural-language-processing', 'natural-language-processing'] | [-7.84411669e-01 2.64699787e-01 -3.14643502e-01 -3.34876120e-01
-4.64277864e-01 -4.29946274e-01 3.46165895e-01 4.03256923e-01
-4.42469180e-01 1.24821258e+00 4.50843811e-01 -4.35647786e-01
-4.30395752e-01 -1.53946102e+00 -3.20683390e-01 1.78132132e-01
2.04621524e-01 3.77597004e-01 1.17086697e+00 -8.40550840... | [10.539799690246582, 7.932443618774414] |
4575156f-7386-4c0d-a207-bd4eca7c45c4 | social-honeypot-for-humans-luring-people | 2303.17946 | null | https://arxiv.org/abs/2303.17946v1 | https://arxiv.org/pdf/2303.17946v1.pdf | Social Honeypot for Humans: Luring People through Self-managed Instagram Pages | Social Honeypots are tools deployed in Online Social Networks (OSN) to attract malevolent activities performed by spammers and bots. To this end, their content is designed to be of maximum interest to malicious users. However, by choosing an appropriate content topic, this attractive mechanism could be extended to any ... | ['Pier Paolo Tricomi', 'Luca Pajola', 'Mauro Conti', 'Sara Bardi'] | 2023-03-31 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [-2.60940760e-01 1.00615434e-01 -4.14333373e-01 9.05724522e-03
-5.70049621e-02 -8.63689542e-01 1.08203065e+00 1.37569413e-01
-4.31519210e-01 8.45534027e-01 1.48577066e-02 -1.15672266e-02
-1.04932643e-01 -1.21738398e+00 1.52569547e-01 -2.56443024e-01
-1.09834045e-01 5.57451844e-01 6.20739579e-01 -4.25849050... | [8.122415542602539, 10.106975555419922] |
bbb150b1-1000-4592-a49c-b62e47147daa | real-time-simultaneous-multi-object-3d-shape | 2305.09510 | null | https://arxiv.org/abs/2305.09510v1 | https://arxiv.org/pdf/2305.09510v1.pdf | Real-time Simultaneous Multi-Object 3D Shape Reconstruction, 6DoF Pose Estimation and Dense Grasp Prediction | Robotic manipulation systems operating in complex environments rely on perception systems that provide information about the geometry (pose and 3D shape) of the objects in the scene along with other semantic information such as object labels. This information is then used for choosing the feasible grasps on relevant ob... | ['Volkan Isler', 'Jinwook Huh', 'Selim Engin', 'Isaac Kasahara', 'Nikhil Chavan-Dafle', 'Shubham Agrawal'] | 2023-05-16 | null | null | null | null | ['3d-shape-reconstruction'] | ['computer-vision'] | [ 4.08436209e-02 -1.69773951e-01 -1.10115223e-01 -3.28641772e-01
-4.29652959e-01 -8.51200044e-01 2.53434777e-01 7.17267334e-01
-2.75295913e-01 2.53323168e-01 -1.45013139e-01 1.81970045e-01
-3.76665533e-01 -7.77404964e-01 -8.79990935e-01 -4.87498999e-01
-2.83711791e-01 1.17515147e+00 7.08527625e-01 -1.38409331... | [5.802702903747559, -0.8396283984184265] |
23e60ffa-6e8f-4554-9736-d5660658578d | dual-side-feature-fusion-3d-pose-transfer | 2305.14951 | null | https://arxiv.org/abs/2305.14951v1 | https://arxiv.org/pdf/2305.14951v1.pdf | Dual-Side Feature Fusion 3D Pose Transfer | 3D pose transfer solves the problem of additional input and correspondence of traditional deformation transfer, only the source and target meshes need to be input, and the pose of the source mesh can be transferred to the target mesh. Some lightweight methods proposed in recent years consume less memory but cause spike... | ['Feipeng Da', 'Jue Liu'] | 2023-05-24 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 2.27883771e-01 4.67708372e-02 3.43522951e-02 -3.56525660e-01
-7.45554030e-01 -3.99122834e-01 1.60906240e-01 -3.01246583e-01
-4.30446386e-01 6.20592058e-01 -7.33875623e-03 3.56139660e-01
1.59414336e-01 -1.06803882e+00 -1.27036452e+00 -7.57965505e-01
2.07089618e-01 6.04354084e-01 4.16567028e-01 -4.06756878... | [7.235569000244141, -1.4395400285720825] |
1c905f8f-d743-4722-9dd5-06b26475f556 | neural-character-based-composition-models-for | 1809.00378 | null | http://arxiv.org/abs/1809.00378v1 | http://arxiv.org/pdf/1809.00378v1.pdf | Neural Character-based Composition Models for Abuse Detection | The advent of social media in recent years has fed into some highly
undesirable phenomena such as proliferation of offensive language, hate speech,
sexist remarks, etc. on the Internet. In light of this, there have been several
efforts to automate the detection and moderation of such abusive content.
However, deliberat... | ['Pushkar Mishra', 'Ekaterina Shutova', 'Helen Yannakoudakis'] | 2018-09-02 | neural-character-based-composition-models-for-1 | https://aclanthology.org/W18-5101 | https://aclanthology.org/W18-5101.pdf | ws-2018-10 | ['abuse-detection'] | ['natural-language-processing'] | [-6.00028858e-02 -9.48704332e-02 -1.80424377e-01 -1.31832911e-02
-4.92387652e-01 -7.81719208e-01 1.05728018e+00 5.86715698e-01
-5.29927552e-01 7.46613324e-01 3.60479563e-01 -5.25418162e-01
4.10636902e-01 -7.83270419e-01 -2.53316641e-01 -3.62188429e-01
8.46318528e-02 -9.28020924e-02 1.66618526e-01 -4.94843155... | [8.673258781433105, 10.441415786743164] |
d7d00c71-cdf6-4b5a-aef8-a6a47b882f4c | membership-inference-attack-on-graph-neural | 2101.06570 | null | https://arxiv.org/abs/2101.06570v3 | https://arxiv.org/pdf/2101.06570v3.pdf | Membership Inference Attack on Graph Neural Networks | Graph Neural Networks (GNNs), which generalize traditional deep neural networks on graph data, have achieved state-of-the-art performance on several graph analytical tasks. We focus on how trained GNN models could leak information about the \emph{member} nodes that they were trained on. We introduce two realistic setti... | ['Megha Khosla', 'Wolfgang Nejdl', 'Iyiola E. Olatunji'] | 2021-01-17 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 1.5290016e-01 5.8838421e-01 -3.4060284e-01 1.2030529e-01
-2.0261668e-01 -9.3640667e-01 6.3089454e-01 2.9664722e-01
-2.6338547e-01 5.0099993e-01 -1.0090885e-01 -1.2302306e+00
-9.1287173e-02 -1.1935782e+00 -1.0021166e+00 -4.7076058e-01
-4.6428928e-01 3.7774333e-01 2.1297553e-01 -1.9678906e-01
2.0999315e-01... | [6.058763027191162, 7.307633876800537] |
1f98599d-98de-43f0-aa58-1cc3cd3aab44 | fsitm-a-feature-similarity-index-for-tone | 1704.05624 | null | http://arxiv.org/abs/1704.05624v1 | http://arxiv.org/pdf/1704.05624v1.pdf | FSITM: A Feature Similarity Index For Tone-Mapped Images | In this work, based on the local phase information of images, an objective
index, called the feature similarity index for tone-mapped images (FSITM), is
proposed. To evaluate a tone mapping operator (TMO), the proposed index
compares the locally weighted mean phase angle map of an original high dynamic
range (HDR) to t... | ['Atena Shahkolaei', 'Hossein Ziaei Nafchi', 'Reza Farrahi Moghaddam', 'Mohamed Cheriet'] | 2017-04-19 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 5.35640836e-01 -4.19123143e-01 -4.82945852e-02 -1.09989718e-01
-7.50979602e-01 -1.71872973e-01 3.47406447e-01 -2.24601496e-02
-1.59758031e-01 4.58094358e-01 -2.43983399e-02 5.52038923e-02
-6.17208242e-01 -8.43466759e-01 -2.47048020e-01 -7.26148546e-01
-4.61190268e-02 8.83766823e-03 5.89280307e-01 -1.65883422... | [11.67646598815918, -1.951554775238037] |
b0077d2a-9d21-48a4-89b3-eda63335f098 | hyperspherical-embedding-for-point-cloud | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Hyperspherical_Embedding_for_Point_Cloud_Completion_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Hyperspherical_Embedding_for_Point_Cloud_Completion_CVPR_2023_paper.pdf | Hyperspherical Embedding for Point Cloud Completion | Most real-world 3D measurements from depth sensors are incomplete, and to address this issue the point cloud completion task aims to predict the complete shapes of objects from partial observations. Previous works often adapt an encoder-decoder architecture, where the encoder is trained to extract embeddings that a... | ['Matthew Johnson-Roberson', 'Ram Vasudevan', 'Haomeng Zhang', 'Junming Zhang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['point-cloud-completion'] | ['computer-vision'] | [ 3.28074954e-02 1.84944078e-01 -7.94525295e-02 -5.98907113e-01
-2.25488946e-01 -3.20957899e-01 5.55708289e-01 -1.60068050e-01
-1.75161675e-01 5.10261118e-01 2.64747977e-01 -2.43139379e-02
-3.42198201e-02 -8.19873273e-01 -9.66873229e-01 -7.49603629e-01
1.01521805e-01 5.14642596e-01 6.82484061e-02 1.37723163... | [8.221424102783203, -3.4285433292388916] |
82243d90-af11-4415-a76e-c56f6457cd71 | ai-security-for-geoscience-and-remote-sensing | 2212.09360 | null | https://arxiv.org/abs/2212.09360v2 | https://arxiv.org/pdf/2212.09360v2.pdf | AI Security for Geoscience and Remote Sensing: Challenges and Future Trends | Recent advances in artificial intelligence (AI) have significantly intensified research in the geoscience and remote sensing (RS) field. AI algorithms, especially deep learning-based ones, have been developed and applied widely to RS data analysis. The successful application of AI covers almost all aspects of Earth obs... | ['Pedram Ghamisi', 'Peter M. Atkinson', 'Shizhen Chang', 'Weikang Yu', 'Tao Bai', 'Yonghao Xu'] | 2022-12-19 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 4.12806779e-01 6.09569177e-02 2.92066205e-03 -1.09818161e-01
-2.50621080e-01 -6.00380540e-01 6.46405935e-01 3.02637845e-01
-3.10061812e-01 5.31197906e-01 -3.19874316e-01 -5.64023316e-01
-2.78022438e-01 -1.16917753e+00 -5.65092623e-01 -9.69400644e-01
-5.33220172e-01 8.01621303e-02 -1.28510743e-01 -3.89042258... | [5.544480800628662, 7.858702182769775] |
8ca331f3-1268-403f-b126-3ec200a2f074 | progressive-learning-with-cross-window | 2211.12425 | null | https://arxiv.org/abs/2211.12425v2 | https://arxiv.org/pdf/2211.12425v2.pdf | Progressive Learning with Cross-Window Consistency for Semi-Supervised Semantic Segmentation | Semi-supervised semantic segmentation focuses on the exploration of a small amount of labeled data and a large amount of unlabeled data, which is more in line with the demands of real-world image understanding applications. However, it is still hindered by the inability to fully and effectively leverage unlabeled image... | ['Jiayi Ma', 'Yongjun Zhang', 'Yansheng Li', 'Bo Dang'] | 2022-11-22 | null | null | null | null | ['semi-supervised-semantic-segmentation'] | ['computer-vision'] | [ 3.79828632e-01 2.74658591e-01 -6.05483472e-01 -9.90795016e-01
-8.85673702e-01 -4.36086535e-01 3.31450552e-02 -1.46305025e-01
-6.47445142e-01 6.50251329e-01 -9.75859687e-02 -3.16918194e-01
-2.79711671e-02 -3.32237363e-01 -7.26877511e-01 -7.65344501e-01
1.47209555e-01 4.79404658e-01 4.42083299e-01 1.55721635... | [9.519834518432617, 1.0385048389434814] |
4026c0e4-3735-4acb-a36d-b7a0fade09b9 | exploiting-more-information-in-sparse-point | 2210.00519 | null | https://arxiv.org/abs/2210.00519v1 | https://arxiv.org/pdf/2210.00519v1.pdf | Exploiting More Information in Sparse Point Cloud for 3D Single Object Tracking | 3D single object tracking is a key task in 3D computer vision. However, the sparsity of point clouds makes it difficult to compute the similarity and locate the object, posing big challenges to the 3D tracker. Previous works tried to solve the problem and improved the tracking performance in some common scenarios, but ... | ['Zheng Fang', 'Zhiheng Li', 'Zuoxu Gu', 'Jiayao Shan', 'Yubo Cui'] | 2022-10-02 | null | null | null | null | ['3d-single-object-tracking'] | ['computer-vision'] | [-1.92415059e-01 -5.11426210e-01 -1.36593446e-01 -1.25651091e-01
-5.08754134e-01 -4.21305567e-01 4.83917505e-01 -2.53770381e-01
-1.10324211e-01 1.75397575e-01 1.28696501e-01 4.51578246e-03
-6.77379742e-02 -4.06386346e-01 -7.75681674e-01 -6.86114192e-01
1.62403226e-01 3.37410033e-01 5.19513905e-01 1.26580656... | [6.626049041748047, -2.375286102294922] |
c239c50c-9cf7-4011-bda0-5499d0d925b1 | human-guided-ground-truth-generation-for | 2303.13069 | null | https://arxiv.org/abs/2303.13069v1 | https://arxiv.org/pdf/2303.13069v1.pdf | Human Guided Ground-truth Generation for Realistic Image Super-resolution | How to generate the ground-truth (GT) image is a critical issue for training realistic image super-resolution (Real-ISR) models. Existing methods mostly take a set of high-resolution (HR) images as GTs and apply various degradations to simulate their low-resolution (LR) counterparts. Though great progress has been achi... | ['Lei Zhang', 'Hui Zeng', 'Ming Liu', 'Xindong Zhang', 'Jie Liang', 'Du Chen'] | 2023-03-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_Human_Guided_Ground-Truth_Generation_for_Realistic_Image_Super-Resolution_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_Human_Guided_Ground-Truth_Generation_for_Realistic_Image_Super-Resolution_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-super-resolution', 'image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 4.60705161e-01 1.49721906e-01 -1.93858948e-02 -1.84720531e-01
-1.06212795e+00 -9.48109031e-02 1.60512865e-01 -5.30165136e-01
-5.29134944e-02 7.99164712e-01 1.39694169e-01 -9.24568344e-03
3.21824461e-01 -8.37183654e-01 -5.46692550e-01 -8.23212206e-01
3.00391972e-01 -2.59243697e-01 2.30932489e-01 -4.00281101... | [11.12280559539795, -2.0237152576446533] |
23f2f05c-a33a-48b3-b4bd-8b99f34912b3 | knowledge-acquisition-strategies-for-goal | null | null | https://aclanthology.org/W14-4326 | https://aclanthology.org/W14-4326.pdf | Knowledge Acquisition Strategies for Goal-Oriented Dialog Systems | null | ['er', 'Aasish Pappu', 'Alex Rudnicky'] | 2014-06-01 | null | null | null | ws-2014-6 | ['goal-oriented-dialog'] | ['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... | [-7.505391597747803, 3.6524617671966553] |
57a63624-9f68-40c7-8462-5b79aefb36f7 | global-local-context-network-for-person | 2112.02500 | null | https://arxiv.org/abs/2112.02500v4 | https://arxiv.org/pdf/2112.02500v4.pdf | MovieNet-PS: A Large-Scale Person Search Dataset in the Wild | Person search aims to jointly localize and identify a query person from natural, uncropped images, which has been actively studied over the past few years. In this paper, we delve into the rich context information globally and locally surrounding the target person, which we refer to as scene and group context, respecti... | ['Bingbing Ni', 'Rong Quan', 'Peng Zheng', 'Jie Qin', 'Xiaogang Cheng', 'Yichao Yan'] | 2021-12-05 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-3.75318862e-02 -7.05033422e-01 -9.49037820e-02 -4.08252686e-01
-5.66224277e-01 -4.00374621e-01 7.54281104e-01 4.26396020e-02
-8.05826664e-01 4.77340579e-01 6.24382973e-01 2.89242774e-01
-1.69351101e-01 -4.55030650e-01 -2.30923027e-01 -5.26877284e-01
1.87103912e-01 4.11114603e-01 6.93695024e-02 4.66027968... | [14.76541805267334, 0.8342705368995667] |
9cc2e657-2cd3-47f2-827b-e8d1266c2482 | uncertainty-guided-label-denoising-for | 2305.11029 | null | https://arxiv.org/abs/2305.11029v2 | https://arxiv.org/pdf/2305.11029v2.pdf | Uncertainty Guided Label Denoising for Document-level Distant Relation Extraction | Document-level relation extraction (DocRE) aims to infer complex semantic relations among entities in a document. Distant supervision (DS) is able to generate massive auto-labeled data, which can improve DocRE performance. Recent works leverage pseudo labels generated by the pre-denoising model to reduce noise in DS da... | ['Soujanya Poria', 'Kun Zhang', 'Pengfei Hong', 'Xiaocui Yang', 'Kun Huang', 'Qi Sun'] | 2023-05-18 | null | null | null | null | ['document-level-relation-extraction', 'relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-1.20171243e-02 4.00059968e-01 -2.53341466e-01 -6.92173898e-01
-1.27213717e+00 -7.66415238e-01 6.89137280e-01 3.87733668e-01
-2.64359534e-01 1.02244437e+00 3.24394017e-01 -3.50837968e-02
-3.72052103e-01 -9.14556146e-01 -8.44425917e-01 -6.11723304e-01
1.68947950e-01 7.44302392e-01 2.19171152e-01 -2.33687591... | [9.272506713867188, 8.604736328125] |
6b32e621-dd5f-4b3e-8f23-fe5b37783f77 | learnable-theory-vs-applications | 1807.10681 | null | http://arxiv.org/abs/1807.10681v1 | http://arxiv.org/pdf/1807.10681v1.pdf | Learnable: Theory vs Applications | Two different views on machine learning problem: Applied learning (machine
learning with business applications) and Agnostic PAC learning are formalized
and compared here. I show that, under some conditions, the theory of PAC
Learnable provides a way to solve the Applied learning problem. However, the
theory requires t... | ['Marina Sapir'] | 2018-07-27 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [-3.50143877e-03 6.05505466e-01 -6.54687345e-01 -3.82378578e-01
-6.47690058e-01 -8.40203822e-01 5.81661046e-01 -2.31727719e-01
-3.94581288e-01 8.37633491e-01 9.75247622e-02 -6.89537287e-01
-4.47009325e-01 -6.38433039e-01 -7.81052411e-01 -1.08643866e+00
5.25303185e-02 5.95454156e-01 1.06075957e-01 -1.54363051... | [8.356961250305176, 4.754675388336182] |
c50d2252-bdad-488e-866e-a955bfe5c186 | paramnet-a-parameter-variable-network-for | 2305.06511 | null | https://arxiv.org/abs/2305.06511v1 | https://arxiv.org/pdf/2305.06511v1.pdf | ParamNet: A Parameter-variable Network for Fast Stain Normalization | In practice, digital pathology images are often affected by various factors, resulting in very large differences in color and brightness. Stain normalization can effectively reduce the differences in color and brightness of digital pathology images, thus improving the performance of computer-aided diagnostic systems. C... | ['Xiuli Liu', 'Shaoqun Zeng', 'Tingwei Quan', 'Shenghua Cheng', 'Junbo Hu', 'Li Chen', 'Die Luo', 'Hongtao Kang'] | 2023-05-11 | null | null | null | null | ['parameter-prediction'] | ['miscellaneous'] | [ 1.27581075e-01 -3.61180365e-01 -5.43792136e-02 -3.05087745e-01
-4.27459568e-01 -4.72140461e-01 7.95220435e-02 9.93086919e-02
-6.12933517e-01 5.10097623e-01 -4.73000020e-01 -5.21486104e-01
1.74094662e-01 -1.02590406e+00 -2.39082292e-01 -1.18232000e+00
2.48509809e-01 1.55969784e-01 5.92587531e-01 4.34448896... | [14.99111557006836, -2.9552969932556152] |
6e1b50f6-7446-423a-9c9e-c6af5e675165 | idtrackerai-tracking-all-individuals-in-large | 1803.04351 | null | http://arxiv.org/abs/1803.04351v1 | http://arxiv.org/pdf/1803.04351v1.pdf | idtracker.ai: Tracking all individuals in large collectives of unmarked animals | Our understanding of collective animal behavior is limited by our ability to
track each of the individuals. We describe an algorithm and software,
idtracker.ai, that extracts from video all trajectories with correct identities
at a high accuracy for collectives of up to 100 individuals. It uses two deep
networks, one d... | ['Francisco Romero-Ferrero', 'Gonzalo G. de Polavieja', 'Francisco J. H. Heras', 'Robert Hinz', 'Mattia G. Bergomi'] | 2018-03-12 | null | null | null | null | ['multi-animal-tracking-with-identification'] | ['computer-vision'] | [-2.52415210e-01 -4.72934574e-01 1.35156661e-01 -1.36621222e-01
5.54455854e-02 -1.05446529e+00 4.22993213e-01 2.18418390e-01
-8.01232755e-01 6.78877056e-01 -2.32512623e-01 4.27698195e-02
-1.35678351e-01 -7.00601459e-01 -5.79745054e-01 -5.20728886e-01
-8.81656170e-01 7.98604608e-01 6.03807569e-01 1.41607448... | [7.856321811676025, -0.919897735118866] |
e5007608-a31f-47e2-ad03-2812b82328bc | framing-the-news-from-human-perception-to | 2304.14456 | null | https://arxiv.org/abs/2304.14456v1 | https://arxiv.org/pdf/2304.14456v1.pdf | Framing the News:From Human Perception to Large Language Model Inferences | Identifying the frames of news is important to understand the articles' vision, intention, message to be conveyed, and which aspects of the news are emphasized. Framing is a widely studied concept in journalism, and has emerged as a new topic in computing, with the potential to automate processes and facilitate the wor... | ['Daniel Gatica-Perez', 'David Alonso del Barrio'] | 2023-04-27 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [ 3.03113014e-01 3.10347140e-01 -5.80002487e-01 -1.63949266e-01
-6.27693474e-01 -8.89910758e-01 1.16307902e+00 7.45551109e-01
-5.57586432e-01 6.60900831e-01 1.04311323e+00 -7.84533441e-01
-1.17603995e-01 -7.29409814e-01 -8.19552898e-01 -1.85094282e-01
2.75967866e-01 5.62400520e-01 2.59714663e-01 -4.17395324... | [8.967133522033691, 9.768510818481445] |
724eaa2a-072b-418b-9cf3-849cdece2f80 | segflow-joint-learning-for-video-object | 1709.06750 | null | http://arxiv.org/abs/1709.06750v1 | http://arxiv.org/pdf/1709.06750v1.pdf | SegFlow: Joint Learning for Video Object Segmentation and Optical Flow | This paper proposes an end-to-end trainable network, SegFlow, for
simultaneously predicting pixel-wise object segmentation and optical flow in
videos. The proposed SegFlow has two branches where useful information of
object segmentation and optical flow is propagated bidirectionally in a unified
framework. The segmenta... | ['Ming-Hsuan Yang', 'Yi-Hsuan Tsai', 'Shengjin Wang', 'Jingchun Cheng'] | 2017-09-20 | segflow-joint-learning-for-video-object-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Cheng_SegFlow_Joint_Learning_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Cheng_SegFlow_Joint_Learning_ICCV_2017_paper.pdf | iccv-2017-10 | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 3.17573957e-02 -2.35219523e-01 -4.79570508e-01 -3.68627548e-01
-1.86300501e-02 -5.07662416e-01 2.13931680e-01 -4.94817615e-01
-4.09130573e-01 6.80439472e-01 1.31383482e-02 -1.80611417e-01
4.90678139e-02 -7.67522454e-01 -7.06736147e-01 -5.33182144e-01
-2.54638016e-01 2.69605458e-01 5.87115347e-01 1.52602181... | [9.137460708618164, -0.24043810367584229] |
e68cfc4d-09ee-456d-abb2-e8aefd716783 | improved-neural-relation-detection-for | 1704.06194 | null | http://arxiv.org/abs/1704.06194v2 | http://arxiv.org/pdf/1704.06194v2.pdf | Improved Neural Relation Detection for Knowledge Base Question Answering | Relation detection is a core component for many NLP applications including
Knowledge Base Question Answering (KBQA). In this paper, we propose a
hierarchical recurrent neural network enhanced by residual learning that
detects KB relations given an input question. Our method uses deep residual
bidirectional LSTMs to com... | ['Bo-Wen Zhou', 'Bing Xiang', 'Mo Yu', 'Cicero dos Santos', 'Wenpeng Yin', 'Kazi Saidul Hasan'] | 2017-04-20 | improved-neural-relation-detection-for-1 | https://aclanthology.org/P17-1053 | https://aclanthology.org/P17-1053.pdf | acl-2017-7 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-1.33041620e-01 6.40592515e-01 -2.81219631e-01 -2.36046761e-01
-1.18485248e+00 -3.68090749e-01 4.57457066e-01 2.98801929e-01
-3.54333103e-01 1.14886129e+00 2.30316967e-01 -7.99003899e-01
-3.15729558e-01 -1.33088708e+00 -8.86500597e-01 -1.74070641e-01
1.75041720e-01 8.99888754e-01 8.12269986e-01 -8.25798631... | [10.544983863830566, 7.958098411560059] |
a09a0a66-2463-44c1-ac7d-0ba786612e38 | hybrid-rnn-at-semeval-2019-task-9-blending | null | null | https://aclanthology.org/S19-2210 | https://aclanthology.org/S19-2210.pdf | Hybrid RNN at SemEval-2019 Task 9: Blending Information Sources for Domain-Independent Suggestion Mining | Social media has an increasing amount of information that both customers and companies can benefit from. These social media posts can include Tweets or be in the form of vocalization of complements and complaints (e.g., reviews) of a product or service. Researchers have been actively mining this invaluable information ... | ['Aysu Ezen-Can', 'Ethem F. Can'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['suggestion-mining'] | ['natural-language-processing'] | [ 5.07569164e-02 3.48875731e-01 -4.81942832e-01 -7.45280325e-01
-5.60609460e-01 -5.13072908e-01 5.23991346e-01 6.04983807e-01
-4.10237968e-01 6.53431237e-01 5.36110580e-01 -6.72631800e-01
3.27565938e-01 -1.02413237e+00 -2.34278128e-01 -3.95580947e-01
4.81853962e-01 -4.02292609e-03 -2.31985509e-01 -7.62865663... | [11.137450218200684, 6.793140411376953] |
86f9cddd-612f-4102-8103-b805d04c5162 | punctuation-restoration-in-spanish-customer | 2205.13961 | null | https://arxiv.org/abs/2205.13961v1 | https://arxiv.org/pdf/2205.13961v1.pdf | Punctuation Restoration in Spanish Customer Support Transcripts using Transfer Learning | Automatic Speech Recognition (ASR) systems typically produce unpunctuated transcripts that have poor readability. In addition, building a punctuation restoration system is challenging for low-resource languages, especially for domain-specific applications. In this paper, we propose a Spanish punctuation restoration sys... | ['Simon Corston-Oliver', 'Tere Roldán', 'David Rossouw', 'Shayna Gardiner', 'Xiliang Zhu'] | 2022-05-27 | null | https://aclanthology.org/2022.deeplo-1.9 | https://aclanthology.org/2022.deeplo-1.9.pdf | deeplo-2022-7 | ['punctuation-restoration'] | ['natural-language-processing'] | [ 3.34760517e-01 -5.80354854e-02 -1.80750847e-01 -6.81684911e-01
-1.60010171e+00 -4.58237231e-01 1.65371865e-01 -1.36393681e-01
-3.09753180e-01 7.26212025e-01 5.53896606e-01 -5.99701226e-01
3.79729807e-01 -3.03016990e-01 -6.34894550e-01 -4.15842652e-01
5.82622826e-01 5.83724439e-01 -2.22213447e-01 -3.84798437... | [14.384541511535645, 6.900758266448975] |
893f2551-ca76-489a-9397-005f9fb2f798 | kiu-net-overcomplete-convolutional | 2010.01663 | null | https://arxiv.org/abs/2010.01663v2 | https://arxiv.org/pdf/2010.01663v2.pdf | KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation | Most methods for medical image segmentation use U-Net or its variants as they have been successful in most of the applications. After a detailed analysis of these "traditional" encoder-decoder based approaches, we observed that they perform poorly in detecting smaller structures and are unable to segment boundary regio... | ['Vishal M. Patel', 'Ilker Hacihaliloglu', 'Vishwanath A. Sindagi', 'Jeya Maria Jose Valanarasu'] | 2020-10-04 | null | null | null | null | ['3d-medical-imaging-segmentation', 'volumetric-medical-image-segmentation', 'liver-segmentation', 'ultrasound'] | ['medical', 'medical', 'medical', 'medical'] | [ 8.07596222e-02 3.27823073e-01 -1.36593699e-01 -3.07588130e-01
-5.91379225e-01 -2.31663480e-01 8.53227526e-02 5.97857928e-04
-3.96383226e-01 6.63091660e-01 2.15185449e-01 -3.99055809e-01
2.26854578e-01 -9.72314775e-01 -9.90402460e-01 -4.83884275e-01
-3.42358589e-01 9.84118730e-02 4.97152030e-01 1.07545787... | [14.633065223693848, -2.6099634170532227] |
45ce20d3-3602-46e6-bfef-e0062bb9bd1f | appearance-based-gaze-estimation-using | 1903.07296 | null | http://arxiv.org/abs/1903.07296v1 | http://arxiv.org/pdf/1903.07296v1.pdf | Appearance-Based Gaze Estimation Using Dilated-Convolutions | Appearance-based gaze estimation has attracted more and more attention
because of its wide range of applications. The use of deep convolutional neural
networks has improved the accuracy significantly. In order to improve the
estimation accuracy further, we focus on extracting better features from eye
images. Relatively... | ['Zhaokang Chen', 'Bertram E. Shi'] | 2019-03-18 | null | null | null | null | ['contact-detection'] | ['robots'] | [ 2.07059950e-01 -6.95944354e-02 7.29694888e-02 -7.49446213e-01
-6.31429479e-02 -2.00045228e-01 2.39209980e-01 -3.46504360e-01
-5.58116674e-01 4.57941800e-01 -5.03558926e-02 -1.02489263e-01
1.40777349e-01 -2.52653509e-01 -6.32936716e-01 -5.56930482e-01
-3.96537706e-02 -5.65089524e-01 1.98608398e-01 -1.24379024... | [14.124470710754395, 0.06657512485980988] |
19cd41c1-6e46-428c-b8da-922110d06872 | dgc-net-dense-geometric-correspondence | 1810.08393 | null | http://arxiv.org/abs/1810.08393v2 | http://arxiv.org/pdf/1810.08393v2.pdf | DGC-Net: Dense Geometric Correspondence Network | This paper addresses the challenge of dense pixel correspondence estimation
between two images. This problem is closely related to optical flow estimation
task where ConvNets (CNNs) have recently achieved significant progress. While
optical flow methods produce very accurate results for the small pixel
translation and ... | ['Esa Rahtu', 'Marc Pollefeys', 'Torsten Sattler', 'Juho Kannala', 'Iaroslav Melekhov', 'Aleksei Tiulpin'] | 2018-10-19 | null | null | null | null | ['dense-pixel-correspondence-estimation'] | ['computer-vision'] | [ 1.27716571e-01 -2.61105806e-01 -1.83380499e-01 -1.38505742e-01
-4.55238581e-01 -5.74038565e-01 5.91917217e-01 -4.24586236e-01
-4.91080284e-01 8.85271192e-01 3.08321357e-01 -1.28212675e-01
1.78270251e-01 -6.02905810e-01 -7.21776247e-01 -2.10937262e-01
3.11519891e-01 3.52924109e-01 3.20766509e-01 -2.08097428... | [8.728808403015137, -1.9220070838928223] |
529db013-a0e6-4aba-890a-145abd3d3834 | signal-processing-with-optical-quadratic | 2212.00660 | null | https://arxiv.org/abs/2212.00660v2 | https://arxiv.org/pdf/2212.00660v2.pdf | Signal processing with optical quadratic random sketches | Random data sketching (or projection) is now a classical technique enabling, for instance, approximate numerical linear algebra and machine learning algorithms with reduced computational complexity and memory. In this context, the possibility of performing data processing (such as pattern detection or classification) d... | ['Laurent Jacques', 'Laurent Daudet', 'Vincent Schellekens', 'Rémi Delogne'] | 2022-12-01 | null | null | null | null | ['compressive-sensing'] | ['computer-vision'] | [ 6.99913144e-01 1.09505311e-01 2.60622978e-01 -5.02152974e-03
-2.21888274e-01 -6.70520067e-01 6.86163604e-01 -2.52920449e-01
-5.62324226e-01 7.01907218e-01 -1.20255873e-01 -3.74170184e-01
-2.65453368e-01 -8.74810159e-01 -5.73165357e-01 -7.99410462e-01
-1.48989350e-01 4.58460748e-01 -1.55349776e-01 1.68520302... | [11.491315841674805, -2.284080743789673] |
8d7a2440-8ab6-41d4-a162-96075b94df6e | fast-refacing-of-mr-images-with-a-generative | 2305.16922 | null | https://arxiv.org/abs/2305.16922v1 | https://arxiv.org/pdf/2305.16922v1.pdf | Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency | With the rise of open data, identifiability of individuals based on 3D renderings obtained from routine structural magnetic resonance imaging (MRI) scans of the head has become a growing privacy concern. To protect subject privacy, several algorithms have been developed to de-identify imaging data using blurring, defac... | ['Jonas Richiardi', 'Till Huelnhagen', 'Tobias Kober', 'Jean-Philippe Thiran', 'Bénédicte Maréchal', 'Nataliia Molchanova'] | 2023-05-26 | null | null | null | null | ['face-generation', 'brain-morphometry', 'de-identification'] | ['computer-vision', 'medical', 'natural-language-processing'] | [ 3.86063427e-01 3.10261279e-01 6.33111060e-01 -4.74332154e-01
-5.41815579e-01 -5.53278506e-01 4.81377482e-01 2.09054202e-01
-7.13127851e-01 5.97013652e-01 2.04726998e-02 1.57857407e-02
-3.29429328e-01 -6.30260110e-01 -5.01090646e-01 -6.50898695e-01
-1.48527145e-01 6.34616137e-01 6.41952753e-02 2.85403103... | [14.136228561401367, -1.9231795072555542] |
8946d903-a858-4aba-86fd-1fab5fe78054 | early-prediction-of-the-risk-of-icu-mortality | 2212.00554 | null | https://arxiv.org/abs/2212.00554v2 | https://arxiv.org/pdf/2212.00554v2.pdf | Early prediction of the risk of ICU mortality with Deep Federated Learning | Intensive Care Units usually carry patients with a serious risk of mortality. Recent research has shown the ability of Machine Learning to indicate the patients' mortality risk and point physicians toward individuals with a heightened need for care. Nevertheless, healthcare data is often subject to privacy regulations ... | ['Korbinian Randl', 'Ioanna Miliou', 'Lena Mondrejevski', 'Núria Lladós Armengol'] | 2022-12-01 | null | null | null | null | ['mortality-prediction', 'icu-mortality'] | ['medical', 'medical'] | [-2.48840258e-01 1.95210353e-01 -3.84315550e-01 -5.27029037e-01
-9.29656327e-01 -3.67476434e-01 9.17383805e-02 8.05321574e-01
-6.99650526e-01 7.56219029e-01 4.89838004e-01 -5.29160678e-01
-5.10066330e-01 -6.00568295e-01 -1.86109155e-01 -6.54641032e-01
-3.56854558e-01 5.06422281e-01 -4.46696579e-01 3.01868230... | [6.199338912963867, 6.511131763458252] |
f1d2aee9-006e-4a59-bbb7-796250fbf85f | an-analytics-of-culture-modeling-subjectivity | 2211.07460 | null | https://arxiv.org/abs/2211.07460v1 | https://arxiv.org/pdf/2211.07460v1.pdf | An Analytics of Culture: Modeling Subjectivity, Scalability, Contextuality, and Temporality | There is a bidirectional relationship between culture and AI; AI models are increasingly used to analyse culture, thereby shaping our understanding of culture. On the other hand, the models are trained on collections of cultural artifacts thereby implicitly, and not always correctly, encoding expressions of culture. Th... | ['Marcel Worring', 'Julia Noordegraaf', 'Tobias Blanke', 'Melvin Wevers', 'Nanne van Noord'] | 2022-11-14 | null | null | null | null | ['culture'] | ['speech'] | [ 2.46373773e-01 -3.87308970e-02 -1.53495744e-01 -4.40536886e-02
1.40111148e-01 -8.04002583e-01 9.02645111e-01 2.80449092e-01
-3.93357456e-01 5.51088750e-01 8.56165946e-01 -1.84384227e-01
8.15896224e-03 -5.63856661e-01 -2.99582362e-01 -4.53788340e-01
3.10385853e-01 1.66351661e-01 -1.38594136e-01 -4.51525092... | [9.087401390075684, 6.320952892303467] |
f5f21622-7b74-4c08-ad65-43ee9db26759 | learning-a-target-sample-re-generator-for | 1707.08645 | null | http://arxiv.org/abs/1707.08645v1 | http://arxiv.org/pdf/1707.08645v1.pdf | Learning a Target Sample Re-Generator for Cross-Database Micro-Expression Recognition | In this paper, we investigate the cross-database micro-expression recognition
problem, where the training and testing samples are from two different
micro-expression databases. Under this setting, the training and testing
samples would have different feature distributions and hence the performance of
most existing micr... | ['Yuan Zong', 'Zhen Cui', 'Xiaohua Huang', 'Wenming Zheng', 'Guoying Zhao'] | 2017-07-26 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 1.83166847e-01 -3.83879930e-01 -2.00521842e-01 -6.63081288e-01
-9.16959047e-01 -3.32825273e-01 3.10727537e-01 3.22081894e-02
-2.28902414e-01 7.30805993e-01 -2.83712864e-01 3.66798729e-01
3.60890418e-01 -7.25917578e-01 -2.45896012e-01 -8.52085888e-01
4.27230269e-01 1.87379166e-01 -2.89474577e-01 -1.48148477... | [13.660445213317871, 1.783782958984375] |
4bd9949a-8514-460b-a365-84ceac9d0da7 | axm-net-cross-modal-context-sharing-attention | 2101.08238 | null | https://arxiv.org/abs/2101.08238v3 | https://arxiv.org/pdf/2101.08238v3.pdf | AXM-Net: Implicit Cross-Modal Feature Alignment for Person Re-identification | Cross-modal person re-identification (Re-ID) is critical for modern video surveillance systems. The key challenge is to align cross-modality representations induced by the semantic information present for a person and ignore background information. This work presents a novel convolutional neural network (CNN) based arc... | ['Syed Safwan Khalid', 'Josef Kittler', 'Muhammad Awais', 'Ammarah Farooq'] | 2021-01-19 | null | null | null | null | ['cross-view-person-re-identification', 'nlp-based-person-retrival', 'person-search'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.28166854e-01 -4.19570148e-01 -3.05400282e-01 -3.64399880e-01
-7.75625706e-01 -4.46637481e-01 7.70495832e-01 -3.02796423e-01
-7.92543709e-01 3.97330254e-01 5.14446557e-01 2.27620780e-01
6.32333308e-02 -3.86335820e-01 -7.14836061e-01 -3.84017318e-01
2.36979425e-01 4.85214025e-01 1.28555104e-01 -2.08853438... | [14.675957679748535, 0.9285708069801331] |
a44eb307-f0fd-4d74-9d77-08a4108ad7f7 | minimum-latency-deep-online-video | 2212.02073 | null | https://arxiv.org/abs/2212.02073v1 | https://arxiv.org/pdf/2212.02073v1.pdf | Minimum Latency Deep Online Video Stabilization | We present a novel camera path optimization framework for the task of online video stabilization. Typically, a stabilization pipeline consists of three steps: motion estimating, path smoothing, and novel view rendering. Most previous methods concentrate on motion estimation, proposing various global or local motion mod... | ['Shuaicheng Liu', 'Bing Zeng', 'Zhen Liu', 'Zhuofan Zhang'] | 2022-12-05 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [-1.93567008e-01 -4.45101410e-01 -4.79366153e-01 -1.21677659e-01
-6.12426460e-01 -5.29451132e-01 4.31563973e-01 -1.74045324e-01
-4.55726773e-01 4.36101735e-01 3.12851638e-01 -1.74603626e-01
3.81871849e-01 -2.81990409e-01 -8.41101706e-01 -8.42508972e-01
4.99102287e-02 -1.36465907e-01 6.33081138e-01 -7.59551898... | [10.557856559753418, -1.374179482460022] |
b161b879-13b6-4786-9b2a-2e571e1a6a45 | physion-evaluating-physical-scene | 2306.15668 | null | https://arxiv.org/abs/2306.15668v1 | https://arxiv.org/pdf/2306.15668v1.pdf | Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties | General physical scene understanding requires more than simply localizing and recognizing objects -- it requires knowledge that objects can have different latent properties (e.g., mass or elasticity), and that those properties affect the outcome of physical events. While there has been great progress in physical and vi... | ['Kevin A. Smith', 'Judith E Fan', 'Daniel LK Yamins', 'Joshua B. Tenenbaum', 'Chuang Gan', 'Daniel Bear', 'Zhenfang Chen', 'Mingyu Ding', 'Hsiao-Yu Tung'] | 2023-06-27 | null | null | null | null | ['video-prediction', 'scene-understanding'] | ['computer-vision', 'computer-vision'] | [ 1.81869984e-01 -4.42374796e-02 -3.67378712e-01 -1.88125625e-01
1.44122494e-02 -4.53464955e-01 9.07474399e-01 2.76477516e-01
9.05642286e-02 7.76515484e-01 3.58846784e-02 -2.07414180e-01
-2.32876822e-01 -9.60749626e-01 -1.07669926e+00 -8.55725706e-01
-9.94388536e-02 4.63555187e-01 6.77647710e-01 -3.62955071... | [8.384525299072266, 0.8642739653587341] |
ad84a1c4-2505-47c5-aab5-9f8331e629b7 | effcrn-an-efficient-convolutional-recurrent | 2306.02778 | null | https://arxiv.org/abs/2306.02778v1 | https://arxiv.org/pdf/2306.02778v1.pdf | EffCRN: An Efficient Convolutional Recurrent Network for High-Performance Speech Enhancement | Fully convolutional recurrent neural networks (FCRNs) have shown state-of-the-art performance in single-channel speech enhancement. However, the number of parameters and the FLOPs/second of the original FCRN are restrictively high. A further important class of efficient networks is the CRUSE topology, serving as refere... | ['Tim Fingscheidt', 'Wouter Tirry', 'Maximilian Strake', 'Kristoff Fluyt', 'Bruno Defraene', 'Jan Franzen', 'Marvin Sach'] | 2023-06-05 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 4.80156578e-02 -6.01321645e-02 9.84107330e-02 3.75028178e-02
-3.56268018e-01 -3.19673479e-01 3.93095523e-01 -1.97267801e-01
-8.76725376e-01 8.06639135e-01 1.90739527e-01 -7.89235413e-01
-4.07780439e-01 -5.67719460e-01 -6.52092516e-01 -7.75644004e-01
-3.36851776e-01 -2.53195792e-01 4.54308599e-01 -6.97780132... | [14.946374893188477, 5.9444580078125] |
51b92982-d843-4de2-be8a-c250198e5270 | physics-driven-fire-modeling-from-multi-view | 1804.05261 | null | http://arxiv.org/abs/1804.05261v1 | http://arxiv.org/pdf/1804.05261v1.pdf | Physics-driven Fire Modeling from Multi-view Images | Fire effects are widely used in various computer graphics applications such
as visual effects and video games. Modeling the shape and appearance of fire
phenomenon is challenging as the underlying effects are driven by complex laws
of physics. State-of-the-art fire modeling techniques rely on sophisticated
physical sim... | ['Yong-Liang Yang', 'Garoe Dorta', 'Luca Benedetti', 'Dmitry Kit'] | 2018-04-14 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [ 3.08135778e-01 -7.63580322e-01 5.07125199e-01 1.55045643e-01
1.32191852e-01 -7.02573597e-01 8.42427850e-01 -1.64360538e-01
-2.26656243e-01 9.68843400e-01 -2.90026724e-01 -2.05562115e-01
-1.59383267e-02 -1.23702371e+00 -5.86087227e-01 -8.99021149e-01
8.41174871e-02 3.08675319e-01 9.01487172e-01 -3.01293075... | [9.50438404083252, -3.1039605140686035] |
56680ce2-7e91-4cf2-9a58-a5d2142fc4b0 | fa3l-at-semeval-2017-task-3-a-three | null | null | https://aclanthology.org/S17-2048 | https://aclanthology.org/S17-2048.pdf | FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering | In this paper we present ThReeNN, a model for Community Question Answering, Task 3, of SemEval-2017. The proposed model exploits both syntactic and semantic information to build a single and meaningful embedding space. Using a dependency parser in combination with word embeddings, the model creates sequences of inputs ... | ['Ludovica Pannitto', 'Giuseppe Attardi', 'Antonio Carta', 'Andrea Madotto', 'Federico Errica'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['question-similarity'] | ['natural-language-processing'] | [ 1.78085137e-02 2.19596863e-01 7.73085281e-02 -2.13938579e-01
-6.32660508e-01 -3.35544705e-01 5.51027536e-01 5.67556500e-01
-6.74234569e-01 3.60087395e-01 6.75089419e-01 -5.15794337e-01
-1.28068253e-01 -6.88723564e-01 -2.69317299e-01 -7.20557943e-02
6.11774959e-02 6.10226989e-01 6.08816803e-01 -4.27083492... | [11.252071380615234, 8.031338691711426] |
f88ddd76-c362-4828-b325-d4b12825eee9 | semantic-scene-completion-via-integrating | 2104.03640 | null | https://arxiv.org/abs/2104.03640v2 | https://arxiv.org/pdf/2104.03640v2.pdf | Semantic Scene Completion via Integrating Instances and Scene in-the-Loop | Semantic Scene Completion aims at reconstructing a complete 3D scene with precise voxel-wise semantics from a single-view depth or RGBD image. It is a crucial but challenging problem for indoor scene understanding. In this work, we present a novel framework named Scene-Instance-Scene Network (\textit{SISNet}), which ta... | ['Hongsheng Li', 'Xiaogang Wang', 'Kwan-Yee Lin', 'Chao Zhang', 'Xuesong Chen', 'Yingjie Cai'] | 2021-04-08 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Cai_Semantic_Scene_Completion_via_Integrating_Instances_and_Scene_In-the-Loop_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Cai_Semantic_Scene_Completion_via_Integrating_Instances_and_Scene_In-the-Loop_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 2.9006752e-01 2.9319942e-02 2.5665006e-01 -7.2168189e-01
-6.5325296e-01 -4.3884712e-01 5.1090491e-01 -7.3251552e-03
4.2914860e-02 4.9273080e-01 1.8658106e-01 8.0360234e-02
-9.4116807e-02 -1.1288859e+00 -8.3978844e-01 -4.4304064e-01
4.1119438e-01 4.4685897e-01 5.0005239e-01 -1.5089756e-01
4.6979137e-02... | [8.536799430847168, -2.824820041656494] |
f38eac06-0f51-4ef9-ac9e-11353038743a | patient-independent-epileptic-seizure | 2011.09581 | null | https://arxiv.org/abs/2011.09581v1 | https://arxiv.org/pdf/2011.09581v1.pdf | Patient-independent Epileptic Seizure Prediction using Deep Learning Models | Objective: Epilepsy is one of the most prevalent neurological diseases among humans and can lead to severe brain injuries, strokes, and brain tumors. Early detection of seizures can help to mitigate injuries, and can be used to aid the treatment of patients with epilepsy. The purpose of a seizure prediction system is t... | ['Clinton Fookes', 'Sridha Sridharan', 'Simon Denman', 'Tharindu Fernando', 'Theekshana Dissanayake'] | 2020-11-18 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [-6.19902415e-03 -3.76527846e-01 1.40851825e-01 -4.70946521e-01
-9.40174460e-01 -1.95782542e-01 2.89602935e-01 1.72533356e-02
-3.13914299e-01 6.49851739e-01 3.07825267e-01 1.35186523e-01
-6.06468737e-01 -2.26677313e-01 -3.49242479e-01 -8.21901381e-01
-7.19426215e-01 5.76322317e-01 -1.54438755e-02 -1.98297828... | [13.222028732299805, 3.551008701324463] |
9cf1ff9f-b535-4abd-ac4e-f328e2678d89 | unbiased-scene-graph-generation-from-biased | 2002.11949 | null | https://arxiv.org/abs/2002.11949v3 | https://arxiv.org/pdf/2002.11949v3.pdf | Unbiased Scene Graph Generation from Biased Training | Today's scene graph generation (SGG) task is still far from practical, mainly due to the severe training bias, e.g., collapsing diverse "human walk on / sit on / lay on beach" into "human on beach". Given such SGG, the down-stream tasks such as VQA can hardly infer better scene structures than merely a bag of objects. ... | ['Jiaxin Shi', 'Jianqiang Huang', 'Hanwang Zhang', 'Yulei Niu', 'Kaihua Tang'] | 2020-02-27 | unbiased-scene-graph-generation-from-biased-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Tang_Unbiased_Scene_Graph_Generation_From_Biased_Training_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Tang_Unbiased_Scene_Graph_Generation_From_Biased_Training_CVPR_2020_paper.pdf | cvpr-2020-6 | ['unbiased-scene-graph-generation'] | ['computer-vision'] | [ 4.00270551e-01 5.28213918e-01 -2.20839947e-01 -3.96090835e-01
-6.33732259e-01 -3.89671415e-01 8.73772442e-01 8.08279514e-02
1.07143797e-01 1.01135445e+00 7.33652830e-01 -4.95800108e-01
-8.91207829e-02 -1.07246375e+00 -1.25037754e+00 -7.38787055e-01
3.15621197e-01 5.69661260e-01 1.31803483e-01 -1.73727617... | [10.333995819091797, 1.8191927671432495] |
e0785a77-06ee-4113-aff5-c8894d641f0f | a-dataless-faceswap-detection-approach-using | 2212.02571 | null | https://arxiv.org/abs/2212.02571v1 | https://arxiv.org/pdf/2212.02571v1.pdf | A Dataless FaceSwap Detection Approach Using Synthetic Images | Face swapping technology used to create "Deepfakes" has advanced significantly over the past few years and now enables us to create realistic facial manipulations. Current deep learning algorithms to detect deepfakes have shown promising results, however, they require large amounts of training data, and as we show they... | ['Julian Togelius', 'Nasir Memon', 'Anubhav Jain'] | 2022-12-05 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [-1.20568303e-02 4.11417991e-01 2.54874304e-02 -5.76954365e-01
-3.29594672e-01 -4.80044842e-01 8.66470218e-01 -8.25629711e-01
-2.85585821e-01 8.69123459e-01 2.64409602e-01 -1.90867181e-03
3.03401798e-01 -9.90910590e-01 -5.67280233e-01 -6.45654857e-01
7.04673082e-02 4.27094311e-01 -1.31497711e-01 -4.62302715... | [12.831899642944336, 0.651921272277832] |
22e17121-977b-43ea-bfda-c22dc4953a80 | spiking-neural-networks-for-visual-place | 2109.06452 | null | https://arxiv.org/abs/2109.06452v2 | https://arxiv.org/pdf/2109.06452v2.pdf | Spiking Neural Networks for Visual Place Recognition via Weighted Neuronal Assignments | Spiking neural networks (SNNs) offer both compelling potential advantages, including energy efficiency and low latencies and challenges including the non-differentiable nature of event spikes. Much of the initial research in this area has converted deep neural networks to equivalent SNNs, but this conversion approach p... | ['Tobias Fischer', 'Michael Milford', 'Somayeh Hussaini'] | 2021-09-14 | null | null | null | null | ['template-matching', 'visual-place-recognition'] | ['computer-vision', 'computer-vision'] | [ 3.73967260e-01 -3.56551319e-01 8.84991363e-02 -1.66687384e-01
-6.61112130e-01 -5.15566647e-01 6.12373471e-01 1.17243282e-01
-8.09739947e-01 7.56528735e-01 -1.90828498e-02 2.01010585e-01
-2.18597814e-01 -7.08749592e-01 -1.03464639e+00 -7.56944239e-01
-2.14563742e-01 4.01484221e-01 6.83018565e-01 -4.20386374... | [7.666865348815918, -1.7744299173355103] |
942ce375-c716-4696-b963-2bc50dd94c8c | mononeuralfusion-online-monocular-neural-3d | 2209.15153 | null | https://arxiv.org/abs/2209.15153v1 | https://arxiv.org/pdf/2209.15153v1.pdf | MonoNeuralFusion: Online Monocular Neural 3D Reconstruction with Geometric Priors | High-fidelity 3D scene reconstruction from monocular videos continues to be challenging, especially for complete and fine-grained geometry reconstruction. The previous 3D reconstruction approaches with neural implicit representations have shown a promising ability for complete scene reconstruction, while their results ... | ['Hongbo Fu', 'Ying Shan', 'Tai-Jiang Mu', 'Yan-Pei Cao', 'Shi-Sheng Huang', 'Zi-Xin Zou'] | 2022-09-30 | null | null | null | null | ['3d-scene-reconstruction'] | ['computer-vision'] | [-1.25754505e-01 8.47566202e-02 3.08802575e-01 -4.86098647e-01
-3.22168320e-01 -3.04669440e-01 5.85877120e-01 -3.81704062e-01
-1.99675217e-01 7.24416077e-01 1.54368147e-01 -4.25677225e-02
8.83715972e-02 -1.04955566e+00 -1.02108896e+00 -6.69258654e-01
8.54698271e-02 5.54689407e-01 3.76215838e-02 -1.24442190... | [9.01490592956543, -3.1224000453948975] |
0165520c-3a65-4f52-9590-52e8a7d4a5a6 | 07120194 | 0712.0194 | null | http://arxiv.org/abs/0712.0194v1 | http://arxiv.org/pdf/0712.0194v1.pdf | Computing High Accuracy Power Spectra with Pico | This paper presents the second release of Pico (Parameters for the Impatient
COsmologist). Pico is a general purpose machine learning code which we have
applied to computing the CMB power spectra and the WMAP likelihood. For this
release, we have made improvements to the algorithm as well as the data sets
used to train... | ['William A. Fendt', 'Benjamin D. Wandelt'] | 2007-12-02 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [-6.75661325e-01 -3.12626183e-01 1.31288916e-01 -3.40081900e-02
-7.54694998e-01 -6.33092701e-01 9.64239478e-01 -2.99078584e-01
-4.01526511e-01 8.83921504e-01 -6.18855096e-02 -6.48741424e-01
3.27867791e-02 -9.01743829e-01 -3.97328228e-01 -9.72869754e-01
-2.26749390e-01 1.06296432e+00 4.24289465e-01 -6.68121725... | [7.043764591217041, 3.5328826904296875] |
8db2ffff-237e-45ff-b5b4-1c40f6aca994 | learning-to-count-everything | 2104.08391 | null | https://arxiv.org/abs/2104.08391v1 | https://arxiv.org/pdf/2104.08391v1.pdf | Learning To Count Everything | Existing works on visual counting primarily focus on one specific category at a time, such as people, animals, and cells. In this paper, we are interested in counting everything, that is to count objects from any category given only a few annotated instances from that category. To this end, we pose counting as a few-sh... | ['Minh Hoai', 'Thu Nguyen', 'Udbhav Sharma', 'Viresh Ranjan'] | 2021-04-16 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Ranjan_Learning_To_Count_Everything_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Ranjan_Learning_To_Count_Everything_CVPR_2021_paper.pdf | cvpr-2021-1 | ['object-counting'] | ['computer-vision'] | [-3.10669392e-02 -4.87974286e-01 -9.23803598e-02 -4.28825647e-01
-3.74240279e-01 -4.61257279e-01 6.53350234e-01 4.85235214e-01
-1.01495767e+00 6.79221630e-01 -2.56186724e-01 1.37841493e-01
4.26006407e-01 -8.76616955e-01 -7.76738167e-01 -4.62998211e-01
9.53413546e-02 6.90201402e-01 7.17601120e-01 2.68817425... | [8.990392684936523, 0.5606868267059326] |
b53b3c1d-8840-4a10-b376-5463313f2ba3 | a-general-framework-combining-generative | 2101.10553 | null | https://arxiv.org/abs/2101.10553v1 | https://arxiv.org/pdf/2101.10553v1.pdf | A General Framework Combining Generative Adversarial Networks and Mixture Density Networks for Inverse Modeling in Microstructural Materials Design | Microstructural materials design is one of the most important applications of inverse modeling in materials science. Generally speaking, there are two broad modeling paradigms in scientific applications: forward and inverse. While the forward modeling estimates the observations based on known parameters, the inverse mo... | ['Ankit Agrawal', 'Alok Choudhary', 'Wei-keng Liao', 'Arindam Paul', 'Dipendra Jha', 'Zijiang Yang'] | 2021-01-26 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [ 3.39665651e-01 -8.02808404e-02 2.44361386e-01 -1.10199004e-01
-4.23586100e-01 -4.47234064e-01 5.59781313e-01 -2.50811845e-01
-1.61401540e-01 8.45302403e-01 -6.81296065e-02 -3.13462973e-01
-3.88104767e-01 -9.20585871e-01 -8.71137559e-01 -1.02974796e+00
4.14400220e-01 8.09432805e-01 -1.44030321e-02 -2.17383325... | [6.844232559204102, 3.3001317977905273] |
138cb551-d44d-436d-8c5a-3c5f03e4435f | learning-material-aware-local-descriptors-for | 1810.08729 | null | http://arxiv.org/abs/1810.08729v1 | http://arxiv.org/pdf/1810.08729v1.pdf | Learning Material-Aware Local Descriptors for 3D Shapes | Material understanding is critical for design, geometric modeling, and
analysis of functional objects. We enable material-aware 3D shape analysis by
employing a projective convolutional neural network architecture to learn
material- aware descriptors from view-based representations of 3D points for
point-wise material ... | ['Kavita Bala', 'Siddhartha Chaudhuri', 'Siddhant Ranade', 'Balazs Kovacs', 'Melinos Averkiou', 'Hubert Lin', 'Vladimir G. Kim', 'Evangelos Kalogerakis'] | 2018-10-20 | null | null | null | null | ['material-classification'] | ['computer-vision'] | [-2.45645016e-01 -2.76515007e-01 5.92209287e-02 -2.29585603e-01
-1.00150132e+00 -9.02772963e-01 5.75881600e-01 4.63834763e-01
4.29762214e-01 2.74492264e-01 1.80941999e-01 -6.06210297e-03
-4.00323212e-01 -1.16841340e+00 -1.18814504e+00 -3.56516659e-01
-1.21907435e-01 9.19781208e-01 1.32174967e-02 -2.35474512... | [8.610685348510742, -3.6465096473693848] |
8920b1e7-1462-4f42-92c3-1fdc4fd557e6 | hyperexpan-taxonomy-expansion-with-hyperbolic | 2109.10500 | null | https://arxiv.org/abs/2109.10500v1 | https://arxiv.org/pdf/2109.10500v1.pdf | HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning | Taxonomies are valuable resources for many applications, but the limited coverage due to the expensive manual curation process hinders their general applicability. Prior works attempt to automatically expand existing taxonomies to improve their coverage by learning concept embeddings in Euclidean space, while taxonomie... | ['Nanyun Peng', 'Te-Lin Wu', 'Muhao Chen', 'Mingyu Derek Ma'] | 2021-09-22 | null | https://aclanthology.org/2021.findings-emnlp.353 | https://aclanthology.org/2021.findings-emnlp.353.pdf | findings-emnlp-2021-11 | ['taxonomy-expansion'] | ['natural-language-processing'] | [-9.56551954e-02 4.27145213e-01 -3.74514014e-01 -2.56042510e-01
2.21052747e-02 -8.81053388e-01 4.85494018e-01 5.72271109e-01
-2.56349087e-01 5.15020117e-02 4.90899980e-01 -6.70117795e-01
-6.25329196e-01 -1.15827596e+00 -3.00249439e-02 -5.55245757e-01
-3.06268334e-01 8.72401178e-01 2.06542656e-01 -2.72301704... | [9.241908073425293, 7.919550895690918] |
07bac234-e0d8-48c0-ac61-ca4fb65850fd | chemlambda-universality-and-self | 1403.8046 | null | http://arxiv.org/abs/1403.8046v1 | http://arxiv.org/pdf/1403.8046v1.pdf | Chemlambda, universality and self-multiplication | We present chemlambda (or the chemical concrete machine), an artificial
chemistry with the following properties: (a) is Turing complete, (b) has a
model of decentralized, distributed computing associated to it, (c) works at
the level of individual (artificial) molecules, subject of reversible, but
otherwise determinist... | ['Marius Buliga', 'Louis H. Kauffman'] | 2014-03-31 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 6.25374243e-02 2.86002696e-01 2.27563158e-01 1.64765239e-01
2.67869323e-01 -1.25223148e+00 1.34363985e+00 4.82548237e-01
-2.07684666e-01 1.14104450e+00 9.31886807e-02 -5.68804920e-01
-1.45653576e-01 -1.14725780e+00 -7.35328197e-01 -9.43444192e-01
-6.46114290e-01 1.04919934e+00 3.30319673e-01 -2.31347397... | [5.618916034698486, 4.253814697265625] |
90b93f3b-c70d-4a7b-b782-988ca9c79647 | human-from-blur-human-pose-tracking-from | 2303.17209 | null | https://arxiv.org/abs/2303.17209v1 | https://arxiv.org/pdf/2303.17209v1.pdf | Human from Blur: Human Pose Tracking from Blurry Images | We propose a method to estimate 3D human poses from substantially blurred images. The key idea is to tackle the inverse problem of image deblurring by modeling the forward problem with a 3D human model, a texture map, and a sequence of poses to describe human motion. The blurring process is then modeled by a temporal i... | ['Martin R. Oswald', 'Marc Pollefeys', 'Otmar Hilliges', 'Jie Song', 'Denys Rozumnyi', 'Yiming Zhao'] | 2023-03-30 | null | null | null | null | ['pose-tracking', 'deblurring'] | ['computer-vision', 'computer-vision'] | [ 4.86690521e-01 1.10687412e-01 3.47015858e-01 -8.14686622e-03
-4.45362478e-01 -5.01748443e-01 5.36685646e-01 -6.46678090e-01
-3.10632825e-01 5.74016690e-01 3.60905349e-01 1.24102272e-01
-5.13026444e-03 -2.38979220e-01 -9.03391480e-01 -4.88221318e-01
4.36694086e-01 2.88788050e-01 1.56722814e-01 -7.49864653... | [11.44452953338623, -2.547445058822632] |
0f16d9ae-0f1a-42c4-a852-f01642c7e0fb | iterative-correlation-based-feature | 2201.08959 | null | https://arxiv.org/abs/2201.08959v5 | https://arxiv.org/pdf/2201.08959v5.pdf | Few-shot Object Counting with Similarity-Aware Feature Enhancement | This work studies the problem of few-shot object counting, which counts the number of exemplar objects (i.e., described by one or several support images) occurring in the query image. The major challenge lies in that the target objects can be densely packed in the query image, making it hard to recognize every single o... | ['Xinyi Le', 'Wenhan Luo', 'Kai Yang', 'Lei Cui', 'Xin Lu', 'Zhiyuan You'] | 2022-01-22 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 3.02180022e-01 -4.36678350e-01 -2.19792336e-01 -2.46605188e-01
-8.20863128e-01 -4.43725675e-01 6.17955387e-01 4.27700907e-01
-6.93999052e-01 4.60511893e-01 -3.11206765e-02 2.28385359e-01
-1.49665579e-01 -7.85611272e-01 -6.45882130e-01 -7.74100482e-01
4.88604568e-02 4.03750181e-01 7.00505614e-01 4.49949019... | [9.296103477478027, 0.9511836171150208] |
552ac85e-7eb7-4358-b2b7-8117ed2f96a1 | camera-intrinsic-blur-kernel-estimation-a | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Mosleh_Camera_Intrinsic_Blur_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Mosleh_Camera_Intrinsic_Blur_2015_CVPR_paper.pdf | Camera Intrinsic Blur Kernel Estimation: A Reliable Framework | This paper presents a reliable non-blind method to measure intrinsic lens blur. We first introduce an accurate camera-scene alignment framework that avoids erroneous homography estimation and camera tone curve estimation. This alignment is used to generate a sharp correspondence of a target pattern captured by the came... | ['J. M. Pierre Langlois', 'Emmanuel Onzon', 'Ali Mosleh', 'Paul Green', 'Isabelle Begin'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['homography-estimation'] | ['computer-vision'] | [ 5.53188503e-01 -4.60931391e-01 5.96342683e-01 -6.58260062e-02
-6.42374516e-01 -6.02393389e-01 6.53721988e-01 -2.74734288e-01
-3.98701191e-01 7.87385583e-01 2.78077126e-01 5.45592643e-02
-1.58135071e-01 -4.08192784e-01 -6.86964393e-01 -6.21139526e-01
2.89537549e-01 -6.76986121e-04 4.47579563e-01 1.89507052... | [11.552196502685547, -2.67510986328125] |
dacffbb5-4ec3-41f1-b8a3-543786fd0e64 | visual-discourse-parsing | 1903.02252 | null | https://arxiv.org/abs/1903.02252v4 | https://arxiv.org/pdf/1903.02252v4.pdf | Discourse Parsing in Videos: A Multi-modal Appraoch | Text-level discourse parsing aims to unmask how two sentences in the text are related to each other. We propose the task of Visual Discourse Parsing, which requires understanding discourse relations among scenes in a video. Here we use the term scene to refer to a subset of video frames that can better summarize the vi... | ['Arjun R. Akula', 'Song-Chun Zhu'] | 2019-03-06 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [ 4.96202558e-01 1.19860910e-01 -8.39062929e-02 -4.15848255e-01
-7.30510652e-01 -8.15998316e-01 7.33319581e-01 3.27625483e-01
-1.34725437e-01 5.17894804e-01 6.34531796e-01 -2.34582081e-01
3.70742798e-01 -3.01815540e-01 -8.74852836e-01 -5.89356482e-01
4.06255014e-02 1.92318261e-01 3.58146340e-01 -1.22006655... | [10.642853736877441, 0.8940608501434326] |
5845c567-22ae-4e27-acbc-184e07680371 | a-contextual-combinatorial-semi-bandit | 2206.08144 | null | https://arxiv.org/abs/2206.08144v2 | https://arxiv.org/pdf/2206.08144v2.pdf | A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification | Bottleneck identification is a challenging task in network analysis, especially when the network is not fully specified. To address this task, we develop a unified online learning framework based on combinatorial semi-bandits that performs bottleneck identification in parallel with learning the specifications of the un... | ['Morteza Haghir Chehreghani', 'Niklas Åkerblom', 'Fazeleh Hoseini'] | 2022-06-16 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 6.50998801e-02 -1.31820679e-01 -9.92837548e-01 -6.63958266e-02
-6.54045343e-01 -5.79831481e-01 2.93815643e-01 -1.87209055e-01
-3.02831829e-01 1.31472266e+00 1.81171000e-01 -1.02107418e+00
-8.23972285e-01 -6.95113659e-01 -9.88248050e-01 -3.55806828e-01
1.43392580e-02 6.81816101e-01 4.13795561e-01 1.65822744... | [4.537153244018555, 3.268362283706665] |
8bd56555-a12a-4142-8907-e72f49202e15 | learning-compositional-shape-priors-for-few | 2106.06440 | null | https://arxiv.org/abs/2106.06440v2 | https://arxiv.org/pdf/2106.06440v2.pdf | Learning Compositional Shape Priors for Few-Shot 3D Reconstruction | The impressive performance of deep convolutional neural networks in single-view 3D reconstruction suggests that these models perform non-trivial reasoning about the 3D structure of the output space. Recent work has challenged this belief, showing that, on standard benchmarks, complex encoder-decoder architectures perfo... | ['Eugene Belilovsky', 'Anders Eriksson', 'Mahsa Baktashmotlagh', 'Sarah Parisot', 'Stavros Tsogkas', 'Mateusz Michalkiewicz'] | 2021-06-11 | null | null | null | null | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 2.86665678e-01 3.78088415e-01 -1.40847206e-01 -5.97297490e-01
-8.27363372e-01 -7.93176651e-01 1.03126609e+00 1.76824120e-04
-1.83444545e-01 2.26230606e-01 5.22799969e-01 -7.76112750e-02
1.20843731e-01 -8.87368023e-01 -1.10796452e+00 -5.41875124e-01
1.50950059e-01 9.25265074e-01 3.87820154e-01 -9.85168070... | [8.496231079101562, -3.142235517501831] |
d796108a-a3e6-430d-9e02-5a8ab0249a54 | digital-and-physical-world-attacks-on-remote | 2110.11525 | null | https://arxiv.org/abs/2110.11525v1 | https://arxiv.org/pdf/2110.11525v1.pdf | Digital and Physical-World Attacks on Remote Pulse Detection | Remote photoplethysmography (rPPG) is a technique for estimating blood volume changes from reflected light without the need for a contact sensor. We present the first examples of presentation attacks in the digital and physical domains on rPPG from face video. Digital attacks are easily performed by adding imperceptibl... | ['Adam Czajka', 'Kevin W. Bowyer', 'Patrick Flynn', 'Nathan Vance', 'Jeremy Speth'] | 2021-10-21 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 6.02204680e-01 3.47495556e-01 6.21118784e-01 -7.61028752e-02
-3.68643522e-01 -6.54263675e-01 3.15191299e-01 -5.24384737e-01
-1.60773963e-01 7.33210266e-01 1.70897901e-01 -8.60281140e-02
2.91572273e-01 -4.35981989e-01 -4.59895372e-01 -8.46383393e-01
-1.45528480e-01 -3.39442581e-01 1.49133489e-01 1.21683329... | [13.883305549621582, 2.7539944648742676] |
d3b82271-17bd-48b1-b6ab-8c9a13498012 | knowledge-aware-neural-collective-matrix | 2206.13255 | null | https://arxiv.org/abs/2206.13255v1 | https://arxiv.org/pdf/2206.13255v1.pdf | Knowledge-aware Neural Collective Matrix Factorization for Cross-domain Recommendation | Cross-domain recommendation (CDR) can help customers find more satisfying items in different domains. Existing CDR models mainly use common users or mapping functions as bridges between domains but have very limited exploration in fully utilizing extra knowledge across domains. In this paper, we propose to incorporate ... | ['Haiping Lu', 'Jianmo Ni', 'Jun Ma', 'Yan Ge', 'Li Zhang'] | 2022-06-27 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [-6.37496471e-01 -1.32992044e-01 -8.46330941e-01 -5.11392832e-01
-2.66124070e-01 -5.86670101e-01 8.08393061e-02 3.15784276e-01
-1.16958544e-01 6.11470222e-01 5.46475708e-01 3.65439896e-03
-5.16291142e-01 -1.08571517e+00 -8.17087471e-01 1.47658130e-02
-5.73814549e-02 4.82066005e-01 9.83398110e-02 -4.09466296... | [10.262395858764648, 5.636030197143555] |
caedcf28-1f5a-425f-a4f6-fedd6a192e96 | deep-diacritization-efficient-hierarchical | 2011.00538 | null | https://arxiv.org/abs/2011.00538v1 | https://arxiv.org/pdf/2011.00538v1.pdf | Deep Diacritization: Efficient Hierarchical Recurrence for Improved Arabic Diacritization | We propose a novel architecture for labelling character sequences that achieves state-of-the-art results on the Tashkeela Arabic diacritization benchmark. The core is a two-level recurrence hierarchy that operates on the word and character levels separately---enabling faster training and inference than comparable tradi... | ['Mohamed Gabr', 'Muhammad N. ElNokrashy', 'Badr AlKhamissi'] | 2020-11-01 | null | https://aclanthology.org/2020.wanlp-1.4 | https://aclanthology.org/2020.wanlp-1.4.pdf | coling-wanlp-2020-12 | ['arabic-text-diacritization'] | ['natural-language-processing'] | [ 3.91959727e-01 3.86956960e-01 -2.03902990e-01 -5.59789956e-01
-9.22580183e-01 -6.26045048e-01 4.30373728e-01 2.39524320e-01
-7.31088519e-01 6.32862628e-01 2.47308746e-01 -7.66632438e-01
5.15468657e-01 -6.88179612e-01 -6.27863169e-01 -7.63483405e-01
-5.19155376e-02 4.52505559e-01 2.91152835e-01 -5.83251595... | [10.829487800598145, 10.311607360839844] |
956514ee-0154-4852-a7c3-7274f1ba6f3d | surgical-gesture-recognition-based-on | 2105.00460 | null | https://arxiv.org/abs/2105.00460v1 | https://arxiv.org/pdf/2105.00460v1.pdf | Surgical Gesture Recognition Based on Bidirectional Multi-Layer Independently RNN with Explainable Spatial Feature Extraction | Minimally invasive surgery mainly consists of a series of sub-tasks, which can be decomposed into basic gestures or contexts. As a prerequisite of autonomic operation, surgical gesture recognition can assist motion planning and decision-making, and build up context-aware knowledge to improve the surgical robot control ... | ['Benny Lo', 'Ruoxi Wang', 'Dandan Zhang'] | 2021-05-02 | null | null | null | null | ['surgical-gesture-recognition'] | ['medical'] | [ 1.98101372e-01 1.92556456e-01 -4.97450083e-01 -3.26314479e-01
-3.11718702e-01 -1.40312046e-01 3.46570939e-01 -4.86593992e-01
-6.82942986e-01 4.97407198e-01 5.61783075e-01 -4.50164795e-01
-4.86351073e-01 -3.62972170e-01 -4.25477028e-01 -1.12541473e+00
-5.72245521e-03 -3.86771224e-02 -1.74272433e-01 -1.77877679... | [14.096013069152832, -3.3569281101226807] |
6f073397-cc9f-4d33-8bd1-55a1cfcc0e6a | probabilistic-color-constancy | 2005.02730 | null | https://arxiv.org/abs/2005.02730v1 | https://arxiv.org/pdf/2005.02730v1.pdf | Probabilistic Color Constancy | In this paper, we propose a novel unsupervised color constancy method, called Probabilistic Color Constancy (PCC). We define a framework for estimating the illumination of a scene by weighting the contribution of different image regions using a graph-based representation of the image. To estimate the weight of each (su... | ['Jarno Nikkanen', 'Alexandros Iosifidis', 'Uygar Tuna', 'Moncef Gabbouj', 'Jenni Raitoharju', 'Firas Laakom'] | 2020-05-06 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 3.24591309e-01 -4.11905110e-01 5.92392571e-02 -4.01921749e-01
-3.64226401e-01 -5.14047265e-01 3.28190833e-01 6.55193850e-02
-4.42063540e-01 5.93507051e-01 -1.74513564e-01 -2.97435876e-02
3.35534513e-01 -4.89319921e-01 -4.26051319e-01 -9.97259617e-01
1.03449315e-01 6.63324399e-03 4.35292333e-01 9.68754813... | [10.464164733886719, -2.557286262512207] |
ae4bd6e1-f5ef-46d8-9c9d-0f4a99698abb | robust-learning-through-cross-task-1 | 2006.04096 | null | https://arxiv.org/abs/2006.04096v1 | https://arxiv.org/pdf/2006.04096v1.pdf | Robust Learning Through Cross-Task Consistency | Visual perception entails solving a wide set of tasks, e.g., object detection, depth estimation, etc. The predictions made for multiple tasks from the same image are not independent, and therefore, are expected to be consistent. We propose a broadly applicable and fully computational method for augmenting learning with... | ['Zhangjie Cao', 'Oğuzhan Kar', 'Teresa Yeo', 'Rohan Suri', 'Nikhil Cheerla', 'Leonidas Guibas', 'Alexander Sax', 'Jitendra Malik', 'Amir Zamir'] | 2020-06-07 | null | null | null | null | ['surface-normals-estimation'] | ['computer-vision'] | [ 6.85841665e-02 -2.22368225e-01 8.22926015e-02 -4.94257331e-01
-7.92082369e-01 -4.10995275e-01 7.44224966e-01 2.37053514e-01
-4.37705427e-01 9.13036644e-01 -3.60683888e-01 1.16073474e-01
-3.79349113e-01 -3.14969599e-01 -8.20601463e-01 -7.51975954e-01
5.53333312e-02 2.45113686e-01 5.61680138e-01 3.31745774... | [9.512909889221191, 1.7529308795928955] |
0a0d6929-4e52-48ef-8225-8e5e91f33f6e | ma-copie-adore-le-v-elo-analyse-des-besoins-r | null | null | https://aclanthology.org/2019.jeptalnrecital-court.19 | https://aclanthology.org/2019.jeptalnrecital-court.19.pdf | Ma copie adore le v\'elo : analyse des besoins r\'eels en correction orthographique sur un corpus de dict\'ees d'enfants (A corpus analysis to define the needs of dyslexic children in terms of spelling correction) | Cet article pr{\'e}sente la constitution d{'}un corpus de textes produits, sur des donn{\'e}es lors de dict{\'e}es, par des enfants paralys{\'e}s c{\'e}r{\'e}braux (PC) ou dysorthographiques, son annotation en termes d{'}erreurs orthographiques, et enfin son analyse quantitative. Cette analyse de corpus a pour objectif... | ['Jean-Yves Antoine', 'Marion Crochetet', 'Emmanuelle Lopez', 'Samuel Pouplin', 'Celine Arbizu'] | 2019-07-01 | null | null | null | jeptalnrecital-2019-7 | ['spelling-correction'] | ['natural-language-processing'] | [ 8.96762013e-02 3.57125163e-01 6.14037275e-01 -4.15109217e-01
-4.11473304e-01 -9.87864435e-01 7.78790951e-01 7.42401123e-01
-6.90562665e-01 8.10152590e-01 9.63205919e-02 -7.89861530e-02
-2.79148251e-01 -1.01518166e+00 -8.36066484e-01 -3.38888615e-01
1.11247420e-01 4.59555507e-01 5.61686680e-02 -7.81007409... | [14.102850914001465, 13.317082405090332] |
7eb156c9-4a15-409c-995d-1d6739e6f493 | using-multiple-losses-for-accurate-facial-age | 2106.09393 | null | https://arxiv.org/abs/2106.09393v1 | https://arxiv.org/pdf/2106.09393v1.pdf | using multiple losses for accurate facial age estimation | Age estimation is an essential challenge in computer vision. With the advances of convolutional neural networks, the performance of age estimation has been dramatically improved. Existing approaches usually treat age estimation as a classification problem. However, the age labels are ambiguous, thus make the classifica... | ['Tapio Elomaa', 'Heikki Huttunen', 'Yi Zhou'] | 2021-06-17 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-2.06121802e-01 -6.60129860e-02 -3.53484124e-01 -7.19525218e-01
-4.96197551e-01 -3.77433514e-03 5.98371029e-01 5.25343895e-01
-6.20470047e-01 8.12810481e-01 7.01511651e-02 -1.53371328e-02
-7.84626417e-03 -8.71225417e-01 -4.06417638e-01 -7.15656459e-01
4.49840091e-02 3.34968686e-01 -9.47021991e-02 3.54339898... | [13.627915382385254, 0.8772023916244507] |
9ae6d909-7da7-40d4-ba4c-94205c1f21eb | pix2vex-image-to-geometry-reconstruction | 1903.11149 | null | https://arxiv.org/abs/1903.11149v2 | https://arxiv.org/pdf/1903.11149v2.pdf | Pix2Vex: Image-to-Geometry Reconstruction using a Smooth Differentiable Renderer | The long-coveted task of reconstructing 3D geometry from images is still a standing problem. In this paper, we build on the power of neural networks and introduce Pix2Vex, a network trained to convert camera-captured images into 3D geometry. We present a novel differentiable renderer ($DR$) as a forward validation mean... | ['Daniel Cohen-Or', 'Oliver Deussen', 'Amit H. Bermano', 'Felix Petersen'] | 2019-03-26 | null | null | null | null | ['3d-object-reconstruction'] | ['computer-vision'] | [ 3.17317635e-01 4.08566564e-01 2.52431184e-01 -4.77247894e-01
-3.81009430e-01 -6.67449355e-01 5.15304029e-01 -4.37913448e-01
-1.84203699e-01 4.04590517e-01 -4.65131044e-01 -5.22362769e-01
2.76305139e-01 -9.93276834e-01 -1.36981535e+00 -3.19000304e-01
8.83634761e-02 3.42375576e-01 1.86510593e-01 -2.15480343... | [9.250553131103516, -3.04294753074646] |
e459717e-89e9-4eb5-b058-118b59e935ad | accurate-urban-road-centerline-extraction | 1508.06163 | null | http://arxiv.org/abs/1508.06163v2 | http://arxiv.org/pdf/1508.06163v2.pdf | Accurate Urban Road Centerline Extraction from VHR Imagery via Multiscale Segmentation and Tensor Voting | It is very useful and increasingly popular to extract accurate road
centerlines from very-high-resolution (VHR) re- mote sensing imagery for
various applications, such as road map generation and updating etc. There are
three shortcomings of current methods: (a) Due to the noise and occlusions
(owing to vehicles and tre... | ['Guangliang Cheng', 'Feiyun Zhu', 'Shiming Xiang', 'Chunhong Pan'] | 2015-08-25 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 3.18421125e-01 -4.57102031e-01 -1.42937496e-01 -1.09630562e-01
-3.65759373e-01 -2.69137353e-01 6.00850642e-01 5.94804548e-02
-3.30630094e-01 6.66696608e-01 4.41269353e-02 -4.79668856e-01
-2.89907336e-01 -1.46606469e+00 -4.13718492e-01 -4.77350354e-01
1.50706589e-01 1.64441317e-01 7.78081000e-01 -4.76541370... | [8.6122465133667, -1.6918890476226807] |
a80ab26b-37c2-418b-99b3-87faca635a67 | clear-memory-augmented-auto-encoder-for | 2208.03879 | null | https://arxiv.org/abs/2208.03879v2 | https://arxiv.org/pdf/2208.03879v2.pdf | Clear Memory-Augmented Auto-Encoder for Surface Defect Detection | In surface defect detection, due to the extreme imbalance in the number of positive and negative samples, positive-samples-based anomaly detection methods have received more and more attention. Specifically, reconstruction-based methods are the most popular. However, existing methods are either difficult to repair abno... | ['Bin Li', 'Wenyong Yu', 'Lixin Tang', 'Tongzhi Niu', 'Wei Luo'] | 2022-08-08 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 4.42767680e-01 -3.15531850e-01 3.78403425e-01 -5.38829677e-02
-4.55912828e-01 2.85191774e-01 2.72715420e-01 2.04099864e-01
-5.71951568e-02 4.15241539e-01 -3.42299759e-01 7.35430885e-03
1.99137971e-01 -1.13228631e+00 -5.25916576e-01 -9.64649975e-01
3.01914155e-01 3.81109267e-02 8.35160315e-01 -1.77551210... | [7.517828941345215, 1.9540423154830933] |
c5cdfbd7-f759-4322-bc02-3f03f4e11434 | stochastic-compartmental-models-of-covid-19 | 2206.13907 | null | https://arxiv.org/abs/2206.13907v1 | https://arxiv.org/pdf/2206.13907v1.pdf | Stochastic compartmental models of COVID-19 pandemic must have temporally correlated uncertainties | Compartmental models are an important quantitative tool in epidemiology, enabling us to forecast the course of a communicable disease. However, the model parameters, such as the infectivity rate of the disease, are riddled with uncertainties, which has motivated the development and use of stochastic compartmental model... | ['Mohammad Farazmand', 'Konstantinos Mamis'] | 2022-06-23 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 4.65056896e-02 -2.75012225e-01 2.17641637e-01 2.10428536e-01
-6.71772733e-02 -3.86699289e-01 5.43776035e-01 1.99853629e-01
-4.46589828e-01 1.02451634e+00 1.17313173e-02 -6.69867456e-01
-5.07502139e-01 -7.80961335e-01 -5.07090092e-01 -1.00261140e+00
-2.57678926e-01 9.24830198e-01 1.39044169e-02 -7.61510432... | [5.976702690124512, 4.341594219207764] |
e8067ae7-1e73-4922-8625-bbf536432aa6 | scenario-discovery-via-rule-extraction | 1910.01713 | null | https://arxiv.org/abs/1910.01713v2 | https://arxiv.org/pdf/1910.01713v2.pdf | REDS: Rule Extraction for Discovering Scenarios | Scenario discovery is the process of finding areas of interest, known as scenarios, in data spaces resulting from simulations. For instance, one might search for conditions, i.e., inputs of the simulation model, where the system is unstable. Subgroup discovery methods are commonly used for scenario discovery. They find... | ['Klemens Böhm', 'Vadim Arzamasov'] | 2019-10-03 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 1.27403855e-01 -2.08752621e-02 -2.03800842e-01 -2.77645499e-01
-7.02876985e-01 -6.80334508e-01 7.66824126e-01 6.16447330e-01
-1.70190230e-01 1.04103029e+00 -4.71267253e-02 -9.05352712e-01
-4.64139462e-01 -9.89188731e-01 -8.28101814e-01 -6.56809390e-01
-4.59678173e-01 5.26310802e-01 8.39524791e-02 6.51963474... | [7.312282562255859, 4.136934757232666] |
1167403f-59ed-4421-bfa9-1f91fe0f232b | bandwidth-scalable-fully-mask-based-deep-fcrn | 2205.04276 | null | https://arxiv.org/abs/2205.04276v2 | https://arxiv.org/pdf/2205.04276v2.pdf | Bandwidth-Scalable Fully Mask-Based Deep FCRN Acoustic Echo Cancellation and Postfiltering | Although today's speech communication systems support various bandwidths from narrowband to super-wideband and beyond, state-of-the art DNN methods for acoustic echo cancellation (AEC) are lacking modularity and bandwidth scalability. Our proposed DNN model builds upon a fully convolutional recurrent network (FCRN) and... | ['Tim Fingscheidt', 'Pejman Mowlaee', 'Zhengyang Li', 'Karim Haddad', 'Rasmus Kongsgaard Olsson', 'Ernst Seidel'] | 2022-05-09 | null | null | null | null | ['bandwidth-extension', 'acoustic-echo-cancellation', 'bandwidth-extension', 'acoustic-echo-cancellation'] | ['audio', 'medical', 'speech', 'speech'] | [ 1.47024289e-01 -4.96573523e-02 5.09710491e-01 -2.42923334e-01
-8.83416593e-01 -4.56614435e-01 4.78388190e-01 -3.02406073e-01
-7.23288059e-01 2.03408509e-01 6.39328122e-01 -6.24888361e-01
-1.78325981e-01 -1.38141975e-01 -3.30814928e-01 -6.07843697e-01
-5.24454355e-01 -1.06955700e-01 3.98541391e-01 -3.44890773... | [15.073112487792969, 5.949801921844482] |
b908ce39-1a80-4a54-84cb-40ed4d891d5d | robustness-of-convolutional-neural-networks | 2108.01995 | null | https://arxiv.org/abs/2108.01995v1 | https://arxiv.org/pdf/2108.01995v1.pdf | Robustness of convolutional neural networks to physiological ECG noise | The electrocardiogram (ECG) is one of the most widespread diagnostic tools in healthcare and supports the diagnosis of cardiovascular disorders. Deep learning methods are a successful and popular technique to detect indications of disorders from an ECG signal. However, there are open questions around the robustness of ... | ['P. J. Aston', 'N. A. S. Smith', 'A. Sundar', 'P. M. Harris', 'J. Venton'] | 2021-08-02 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 4.00318950e-01 -2.33188663e-02 6.02836370e-01 -3.94095123e-01
-8.28305185e-01 -5.53474486e-01 4.46369871e-02 9.20275152e-02
-4.49081779e-01 5.39507747e-01 2.14759100e-04 -3.37968826e-01
-9.79583412e-02 -5.87083697e-01 -5.45114934e-01 -8.40191245e-01
-2.25612655e-01 1.08016161e-02 -3.75474423e-01 -8.49431679... | [14.352516174316406, 3.2539567947387695] |
464e600d-1e2d-48da-8533-2db3d73c5c6d | cold-concurrent-loads-disaggregator-for-non | 2106.02352 | null | https://arxiv.org/abs/2106.02352v1 | https://arxiv.org/pdf/2106.02352v1.pdf | COLD: Concurrent Loads Disaggregator for Non-Intrusive Load Monitoring | The modern artificial intelligence techniques show the outstanding performances in the field of Non-Intrusive Load Monitoring (NILM). However, the problem related to the identification of a large number of appliances working simultaneously is underestimated. One of the reasons is the absence of a specific data. In this... | ['Elena Gryazina', 'Dmitrii Kriukov', 'Ilia Kamyshev'] | 2021-06-04 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [-6.54915646e-02 -1.77328855e-01 -1.96565047e-01 -5.35464466e-01
-3.38679612e-01 -3.62184912e-01 4.41998214e-01 -9.06875283e-02
-7.42271319e-02 7.17730641e-01 -4.97155637e-02 -6.49916530e-02
-2.48164311e-01 -8.00929904e-01 -4.91682202e-01 -8.01944673e-01
-1.78290635e-01 7.08817303e-01 -1.57761768e-01 -5.36416955... | [6.006244659423828, 2.5967018604278564] |
c99e501c-2df1-477f-97e2-b98a4edc43ca | fmas-fast-multi-objective-supernet | 2303.16322 | null | https://arxiv.org/abs/2303.16322v1 | https://arxiv.org/pdf/2303.16322v1.pdf | FMAS: Fast Multi-Objective SuperNet Architecture Search for Semantic Segmentation | We present FMAS, a fast multi-objective neural architecture search framework for semantic segmentation. FMAS subsamples the structure and pre-trained parameters of DeepLabV3+, without fine-tuning, dramatically reducing training time during search. To further reduce candidate evaluation time, we use a subset of the vali... | ['Brett H. Meyer', 'Warren J. Gross', 'Olivier Therrien', 'Marihan Amein', 'Zhuoran Xiong'] | 2023-03-28 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 3.73518988e-02 -5.78755438e-02 -1.55628040e-01 -4.59698588e-01
-8.88743341e-01 -6.73884094e-01 -3.42994720e-01 1.64232165e-01
-8.79427731e-01 7.64425457e-01 -6.76896036e-01 -4.40321118e-01
-3.89263660e-01 -7.40510106e-01 -9.92214799e-01 -3.90576690e-01
1.17773704e-01 6.16450369e-01 3.54523510e-01 2.58334786... | [8.571768760681152, 3.0673108100891113] |
b1d8d801-43ef-49d2-b8dc-c248d875fa8f | an-end-to-end-and-accurate-ppg-based | 2105.00594 | null | https://arxiv.org/abs/2105.00594v2 | https://arxiv.org/pdf/2105.00594v2.pdf | An End-to-End and Accurate PPG-based Respiratory Rate Estimation Approach Using Cycle Generative Adversarial Networks | Respiratory rate (RR) is a clinical sign representing ventilation. An abnormal change in RR is often the first sign of health deterioration as the body attempts to maintain oxygen delivery to its tissues. There has been a growing interest in remotely monitoring of RR in everyday settings which has made photoplethysmogr... | ['Amir M. Rahmani', 'Amir Hosein Afandizadeh Zargari', 'Rui Cao', 'Seyed Amir Hossein Aqajari'] | 2021-05-03 | null | null | null | null | ['photoplethysmography-ppg', 'respiratory-rate-estimation'] | ['medical', 'medical'] | [ 2.15194821e-01 5.74593283e-02 1.86552212e-01 -1.41434640e-01
-8.48853528e-01 -3.49315733e-01 7.03175217e-02 -2.96518683e-01
-2.77958870e-01 8.53958845e-01 4.19385470e-02 -1.49222314e-01
1.62230313e-01 -6.09907925e-01 -4.50489014e-01 -8.69509816e-01
-8.08291808e-02 -9.85583887e-02 -1.71180084e-01 1.53611019... | [13.941012382507324, 2.9520440101623535] |
3e14f19f-44c0-4d90-8d2c-7bb7c2fb84f4 | meta-referential-games-to-learn-compositional-1 | 2207.08012 | null | https://arxiv.org/abs/2207.08012v1 | https://arxiv.org/pdf/2207.08012v1.pdf | Meta-Referential Games to Learn Compositional Learning Behaviours | Human beings use compositionality to generalise from past experiences to actual or fictive, novel experiences. To do so, we separate our experiences into fundamental atomic components. These atomic components can then be recombined in novel ways to support our ability to imagine and engage with novel experiences. We fr... | ['James Alfred Walker', 'Sondess Missaoui', 'Kevin Denamganaï'] | 2022-07-16 | meta-referential-games-to-learn-compositional | https://openreview.net/forum?id=ffS_Y258dZs | https://openreview.net/pdf?id=ffS_Y258dZs | null | ['systematic-generalization'] | ['reasoning'] | [ 2.76753694e-01 4.91851479e-01 2.60020286e-01 6.17438667e-02
-1.40550643e-01 -6.23547852e-01 1.18201125e+00 1.07959114e-01
-6.09685302e-01 9.96248662e-01 9.48273912e-02 -2.77911305e-01
-4.21146154e-01 -1.01904893e+00 -7.24744260e-01 -5.98917186e-01
-3.27512801e-01 6.33055806e-01 4.16164190e-01 -9.30848718... | [4.237679481506348, 1.395906686782837] |
a25d14df-9836-494c-907e-95fb567179bf | deblurring-masked-autoencoder-is-better | 2306.08249 | null | https://arxiv.org/abs/2306.08249v2 | https://arxiv.org/pdf/2306.08249v2.pdf | Deblurring Masked Autoencoder is Better Recipe for Ultrasound Image Recognition | Masked autoencoder (MAE) has attracted unprecedented attention and achieves remarkable performance in many vision tasks. It reconstructs random masked image patches (known as proxy task) during pretraining and learns meaningful semantic representations that can be transferred to downstream tasks. However, MAE has not b... | ['Qicheng Lao', 'Kang Li', 'Jun Gao', 'Qingbo Kang'] | 2023-06-14 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 6.28027439e-01 9.15580802e-03 3.33850354e-01 -3.58054966e-01
-9.67136145e-01 -2.21375972e-01 4.08581585e-01 -4.29921120e-01
-2.44767189e-01 3.46040845e-01 5.01107395e-01 -1.34644032e-01
-2.02889830e-01 -4.75271851e-01 -8.92822146e-01 -1.24094093e+00
-1.24009714e-01 -2.33002752e-01 4.92512994e-02 5.52233048... | [11.427007675170898, -2.4527745246887207] |
d0f0c158-f247-4abc-8564-1412d2f631ae | iterative-adversarial-attack-on-image-guided | 2305.13208 | null | https://arxiv.org/abs/2305.13208v1 | https://arxiv.org/pdf/2305.13208v1.pdf | Iterative Adversarial Attack on Image-guided Story Ending Generation | Multimodal learning involves developing models that can integrate information from various sources like images and texts. In this field, multimodal text generation is a crucial aspect that involves processing data from multiple modalities and outputting text. The image-guided story ending generation (IgSEG) is a partic... | ['Richang Hong', 'WenBo Hu', 'Youze Wang'] | 2023-05-16 | null | null | null | null | ['adversarial-attack', 'adversarial-text', 'multimodal-machine-translation'] | ['adversarial', 'adversarial', 'natural-language-processing'] | [ 5.89192390e-01 1.25047728e-01 4.55272317e-01 -1.54066645e-02
-1.29381120e+00 -1.08292818e+00 1.32363641e+00 -1.83506742e-01
-8.65648016e-02 5.51613092e-01 2.77894616e-01 -2.40681082e-01
3.11208963e-01 -7.88507462e-01 -1.03516233e+00 -7.57469118e-01
6.12950742e-01 5.59975922e-01 1.31537132e-02 -6.16792977... | [11.16129207611084, 1.2483168840408325] |
c35f0bfc-d3ca-42bb-b6d8-237bbf2ad556 | incorporating-word-and-subword-units-in | 1908.05925 | null | https://arxiv.org/abs/1908.05925v2 | https://arxiv.org/pdf/1908.05925v2.pdf | Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring | This paper describes CAiRE's submission to the unsupervised machine translation track of the WMT'19 news shared task from German to Czech. We leverage a phrase-based statistical machine translation (PBSMT) model and a pre-trained language model to combine word-level neural machine translation (NMT) and subword-level NM... | ['Yan Xu', 'Pascale Fung', 'Zihan Liu', 'Genta Indra Winata'] | 2019-08-16 | incorporating-word-and-subword-units-in-1 | https://aclanthology.org/W19-5327 | https://aclanthology.org/W19-5327.pdf | ws-2019-8 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 5.11821806e-01 -1.22788228e-01 -4.91831690e-01 -4.07221586e-01
-1.13337445e+00 -6.22937262e-01 7.54642248e-01 2.41464265e-02
-6.30153775e-01 8.21735859e-01 5.34480333e-01 -8.14146578e-01
4.12738025e-01 -6.33989990e-01 -7.45565891e-01 -3.79147917e-01
3.44683826e-01 7.38932550e-01 -3.58644813e-01 -3.35937083... | [11.634339332580566, 10.334495544433594] |
79e57172-b4c1-44d0-914e-8f3574b69e6b | amplitude-modulated-video-camera-light | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Kolaman_Amplitude_Modulated_Video_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Kolaman_Amplitude_Modulated_Video_CVPR_2016_paper.pdf | Amplitude Modulated Video Camera - Light Separation in Dynamic Scenes | Controlled light conditions improve considerably the performance of most computer vision algorithms. Dynamic light conditions create varying spatial changes in color and intensity across the scene. These condition, caused by a moving shadow for example, force developers to create algorithms which are robust to such var... | ['Maxim Lvov', 'Rami Hagege', 'Amir Kolaman', 'Hugo Guterman'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['shadow-removal', 'color-constancy'] | ['computer-vision', 'computer-vision'] | [ 5.65453649e-01 -6.56119525e-01 4.97571468e-01 -1.25172868e-01
1.97617710e-01 -6.90177143e-01 4.24660742e-01 -4.80616331e-01
-4.73407954e-01 8.31921577e-01 -3.30975354e-01 -1.49700969e-01
4.08418357e-01 -6.07008874e-01 -7.09383130e-01 -9.19800699e-01
9.18218568e-02 -3.74868721e-01 7.69946754e-01 -9.83972698... | [10.514522552490234, -2.5910017490386963] |
edd0fa45-9eed-4797-a844-92e146f40dab | debiased-mapping-for-full-reference-image | 2302.11464 | null | https://arxiv.org/abs/2302.11464v2 | https://arxiv.org/pdf/2302.11464v2.pdf | Debiased Mapping for Full-Reference Image Quality Assessment | Mapping images to deep feature space for comparisons has been wildly adopted in recent learning-based full-reference image quality assessment (FR-IQA) models. Analogous to the classical classification task, the ideal mapping space for quality regression should possess both inter-class separability and intra-class compa... | ['Lingyu Zhu', 'Shiqi Wang', 'Hanwei Zhu', 'Baoliang Chen'] | 2023-02-22 | null | null | null | null | ['image-quality-assessment'] | ['computer-vision'] | [-1.21907435e-01 -3.28100622e-01 -1.08233035e-01 -3.77704978e-01
-5.90640366e-01 -2.98106164e-01 4.36256617e-01 -2.29810774e-01
-1.67519584e-01 4.28376645e-01 3.19148600e-01 3.89124416e-02
-6.51380301e-01 -7.82210410e-01 -4.28587824e-01 -9.78762388e-01
1.48430809e-01 -2.84852535e-01 -1.99684292e-01 -2.49390110... | [11.913718223571777, -1.8095849752426147] |
00656b1b-24af-4c58-870c-179d47e77439 | ttt-ucdr-test-time-training-for-universal | 2208.09198 | null | https://arxiv.org/abs/2208.09198v3 | https://arxiv.org/pdf/2208.09198v3.pdf | Test-time Training for Data-efficient UCDR | Image retrieval under generalized test scenarios has gained significant momentum in literature, and the recently proposed protocol of Universal Cross-domain Retrieval is a pioneer in this direction. A common practice in any such generalized classification or retrieval algorithm is to exploit samples from many domains d... | ['Soma Biswas', 'Aheli Saha', 'Titir Dutta', 'Abhishek Samanta', 'Soumava Paul'] | 2022-08-19 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 1.54296547e-01 -5.55707753e-01 -3.38491648e-01 -3.84681523e-01
-1.25719225e+00 -8.67037773e-01 7.55298257e-01 2.32433796e-01
-4.67496455e-01 7.64135838e-01 -1.09491348e-01 1.16003267e-01
-7.35574782e-01 -6.16880178e-01 -4.31786239e-01 -7.12732792e-01
1.56127810e-02 7.61809349e-01 1.93970844e-01 -2.31863141... | [11.307955741882324, 1.0303281545639038] |
81f6a3d8-4a6e-420f-a595-52e2085f631a | duplex-conversation-towards-human-like | 2205.15060 | null | https://arxiv.org/abs/2205.15060v4 | https://arxiv.org/pdf/2205.15060v4.pdf | Duplex Conversation: Towards Human-like Interaction in Spoken Dialogue Systems | In this paper, we present Duplex Conversation, a multi-turn, multimodal spoken dialogue system that enables telephone-based agents to interact with customers like a human. We use the concept of full-duplex in telecommunication to demonstrate what a human-like interactive experience should be and how to achieve smooth t... | ['Yongbin Li', 'Jian Sun', 'Luo Si', 'Fei Huang', 'Yuchuan Wu', 'Ting-En Lin'] | 2022-05-30 | null | null | null | null | ['spoken-dialogue-systems'] | ['speech'] | [ 7.92073831e-02 5.48853219e-01 -1.90236405e-01 -8.96335721e-01
-1.20438647e+00 -8.21235418e-01 7.95051634e-01 -4.61318046e-01
-2.75630981e-01 6.95925176e-01 3.79375994e-01 -8.19464922e-01
4.77708548e-01 -4.84374240e-02 -2.32067034e-01 -3.36108804e-01
-9.73463207e-02 9.06512499e-01 -1.60441816e-01 -8.67954731... | [12.867080688476562, 7.947339057922363] |
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