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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1d11cfa4-77d2-4fb6-9d5a-de0a07c2e7f5 | boosting-fast-and-high-quality-speech | 2306.05708 | null | https://arxiv.org/abs/2306.05708v2 | https://arxiv.org/pdf/2306.05708v2.pdf | Boosting Fast and High-Quality Speech Synthesis with Linear Diffusion | Denoising Diffusion Probabilistic Models have shown extraordinary ability on various generative tasks. However, their slow inference speed renders them impractical in speech synthesis. This paper proposes a linear diffusion model (LinDiff) based on an ordinary differential equation to simultaneously reach fast inferenc... | ['JianHua Tao', 'Ran He', 'Jie Cao', 'Tao Wang', 'Haogeng Liu'] | 2023-06-09 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [ 1.76916465e-01 2.43906826e-01 2.11107865e-01 9.76486877e-02
-1.22201931e+00 -6.64713383e-01 6.42880857e-01 -3.78198594e-01
6.00116141e-02 5.91028154e-01 5.11213064e-01 -3.74205410e-01
-3.54459658e-02 -8.96246970e-01 -6.63856447e-01 -9.57926452e-01
1.80787891e-01 5.16248588e-03 2.35375479e-01 -2.34627351... | [15.23721981048584, 6.103432655334473] |
b78f240c-e0b4-4c26-a974-ea3cda6d25db | towards-reducing-manual-workload-in | 2201.05648 | null | https://arxiv.org/abs/2201.05648v1 | https://arxiv.org/pdf/2201.05648v1.pdf | Towards Reducing Manual Workload in Technology-Assisted Reviews: Estimating Ranking Performance | Conducting a systematic review (SR) is comprised of multiple tasks: (i) collect documents (studies) that are likely to be relevant from digital libraries (eg., PubMed), (ii) manually read and label the documents as relevant or irrelevant, (iii) extract information from the relevant studies, and (iv) analyze and synthes... | ['Aixin Sun', 'Grace E. Lee'] | 2022-01-14 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [ 5.11278391e-01 8.69738907e-02 -6.00495517e-01 -1.49705395e-01
-1.05009007e+00 -7.99595296e-01 4.95357066e-01 7.04259217e-01
-4.85696018e-01 7.86093771e-01 5.02256095e-01 -5.04833341e-01
-6.15572333e-01 -7.25226700e-01 -5.23695469e-01 -5.24050713e-01
4.81401592e-01 4.65961456e-01 3.26474041e-01 4.40203488... | [8.873350143432617, 8.495345115661621] |
b489fea7-3b17-4ba8-b359-ba6144a2bafe | uob-at-semeval-2020-task-12-boosting-bert | 2008.08547 | null | https://arxiv.org/abs/2008.08547v1 | https://arxiv.org/pdf/2008.08547v1.pdf | UoB at SemEval-2020 Task 12: Boosting BERT with Corpus Level Information | Pre-trained language model word representation, such as BERT, have been extremely successful in several Natural Language Processing tasks significantly improving on the state-of-the-art. This can largely be attributed to their ability to better capture semantic information contained within a sentence. Several tasks, ho... | ['Harish Tayyar Madabushi', 'Wah Meng Lim'] | 2020-08-19 | null | https://aclanthology.org/2020.semeval-1.295 | https://aclanthology.org/2020.semeval-1.295.pdf | semeval-2020 | ['abuse-detection'] | ['natural-language-processing'] | [ 1.20629549e-01 -1.93634316e-01 -1.55229755e-02 -3.51315856e-01
-1.10766411e+00 -2.58335561e-01 6.91045284e-01 1.03872371e+00
-1.10223424e+00 5.39106429e-01 6.86203480e-01 3.25827748e-02
-1.61361381e-01 -4.77367610e-01 -3.61889839e-01 -8.34562853e-02
-3.42943907e-01 4.74514067e-01 1.34647712e-01 -4.80064631... | [8.890907287597656, 10.528313636779785] |
5dad9383-f590-4924-971a-cd0db0509d48 | doubleu-net-a-deep-convolutional-neural | 2006.04868 | null | https://arxiv.org/abs/2006.04868v2 | https://arxiv.org/pdf/2006.04868v2.pdf | DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation | Semantic image segmentation is the process of labeling each pixel of an image with its corresponding class. An encoder-decoder based approach, like U-Net and its variants, is a popular strategy for solving medical image segmentation tasks. To improve the performance of U-Net on various segmentation tasks, we propose a ... | ['Håvard D. Johansen', 'Pål Halvorsen', 'Dag Johansen', 'Michael A. Riegler', 'Debesh Jha'] | 2020-06-08 | null | null | null | null | ['skin-cancer-segmentation'] | ['medical'] | [ 5.10967791e-01 4.74618047e-01 -3.38332027e-01 -4.25006121e-01
-8.15785885e-01 -3.39251667e-01 1.51449606e-01 2.25111961e-01
-5.80814421e-01 3.63920718e-01 1.13255382e-01 -4.92594391e-01
2.43520290e-01 -8.60416114e-01 -1.08498096e+00 -5.98178864e-01
8.40436071e-02 3.27534556e-01 6.52585745e-01 2.07534898... | [14.621026039123535, -2.5738823413848877] |
fa8877d7-63e8-4f75-9387-7ff4d2185d59 | object-detection-based-on-the-collection-of | 2306.14120 | null | https://arxiv.org/abs/2306.14120v1 | https://arxiv.org/pdf/2306.14120v1.pdf | Object Detection based on the Collection of Geometric Evidence | Artificial objects usually have very stable shape features, which are stable, persistent properties in geometry. They can provide evidence for object recognition. Shape features are more stable and more distinguishing than appearance features, color features, grayscale features, or gradient features. The difficulty wit... | ['Fu-yu Tang', 'Hui Wei'] | 2023-06-25 | null | null | null | null | ['object-recognition', 'combinatorial-optimization'] | ['computer-vision', 'methodology'] | [ 1.38948843e-01 -6.62645936e-01 -4.22257222e-02 -3.77375752e-01
1.86348483e-01 -4.90366280e-01 2.88812220e-01 1.28076956e-01
-3.89506429e-01 5.16921461e-01 -5.48838079e-01 -1.00469291e-01
-6.27052784e-01 -9.47350502e-01 -2.19816074e-01 -8.83816063e-01
6.52869279e-03 3.83787155e-01 4.82015789e-01 -2.35323831... | [10.055181503295898, -0.7918177843093872] |
7cacc213-a731-494e-b65e-a7d56bd5f258 | apb2face-audio-guided-face-reenactment-with | 2004.14569 | null | https://arxiv.org/abs/2004.14569v1 | https://arxiv.org/pdf/2004.14569v1.pdf | APB2Face: Audio-guided face reenactment with auxiliary pose and blink signals | Audio-guided face reenactment aims at generating photorealistic faces using audio information while maintaining the same facial movement as when speaking to a real person. However, existing methods can not generate vivid face images or only reenact low-resolution faces, which limits the application value. To solve thos... | ['Zhu-Cun Xue', 'Yong liu', 'Liang Liu', 'Jiangning Zhang'] | 2020-04-30 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 4.78108972e-02 5.63785613e-01 2.62984157e-01 -4.44358557e-01
-4.47969168e-01 -3.44652444e-01 5.93953371e-01 -1.04526818e+00
4.62967217e-01 5.06485343e-01 2.54516572e-01 3.59015077e-01
2.54125059e-01 -7.04350591e-01 -7.46054709e-01 -7.17418849e-01
1.14169367e-01 -1.31227942e-02 -6.55600488e-01 -3.25163633... | [13.077743530273438, -0.39704012870788574] |
fb12542e-b7fb-4112-b823-0b86f3ab7d9b | deep-ensemble-learning-with-frame-skipping | 2307.02858 | null | https://arxiv.org/abs/2307.02858v2 | https://arxiv.org/pdf/2307.02858v2.pdf | Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing | Face presentation attacks (PA), also known as spoofing attacks, pose a substantial threat to biometric systems that rely on facial recognition systems, such as access control systems, mobile payments, and identity verification systems. To mitigate the spoofing risk, several video-based methods have been presented in th... | ['Jorma Laaksonen', 'Mourad Oussalah', 'Md Ziaul Hoque', 'Usman Muhammad'] | 2023-07-06 | null | null | null | null | ['motion-prediction', 'ensemble-learning', 'face-anti-spoofing', 'ensemble-learning'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology'] | [ 5.21949768e-01 -3.05883050e-01 -2.42233127e-01 -1.62807062e-01
-4.13733572e-01 -3.85250062e-01 4.45309281e-01 -6.10225320e-01
-2.90778279e-01 3.47740412e-01 -6.86103925e-02 -3.75023633e-01
1.88489422e-01 -4.80301589e-01 -5.80676973e-01 -9.59563494e-01
-1.94288939e-01 -4.01244015e-01 2.37837285e-02 -8.16149116... | [13.081833839416504, 1.2233806848526] |
fdacc5fc-8c8b-4153-a5a7-32c58b863d74 | overview-of-the-medvidqa-2022-shared-task-on | null | null | https://aclanthology.org/2022.bionlp-1.25 | https://aclanthology.org/2022.bionlp-1.25.pdf | Overview of the MedVidQA 2022 Shared Task on Medical Video Question-Answering | In this paper, we present an overview of the MedVidQA 2022 shared task, collocated with the 21st BioNLP workshop at ACL 2022. The shared task addressed two of the challenges faced by medical video question answering: (I) a video classification task that explores new approaches to medical video understanding (labeling),... | ['Dina Demner-Fushman', 'Deepak Gupta'] | null | null | null | null | bionlp-acl-2022-5 | ['video-question-answering'] | ['computer-vision'] | [ 2.50016540e-01 6.55993372e-02 -1.75436407e-01 -4.02960420e-01
-1.31963813e+00 -7.78358817e-01 5.27206779e-01 5.27725935e-01
-5.41930258e-01 4.46965754e-01 5.65189183e-01 -1.11469835e-01
-4.54740226e-02 7.73597062e-02 -5.29197812e-01 -3.76206249e-01
-2.46107146e-01 5.17978549e-01 3.11147541e-01 2.58827537... | [10.262028694152832, 0.9121875166893005] |
b4191672-7a91-4500-b051-2a67c45bd3aa | highly-confident-local-structure-based | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wen_Highly_Confident_Local_Structure_Based_Consensus_Graph_Learning_for_Incomplete_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wen_Highly_Confident_Local_Structure_Based_Consensus_Graph_Learning_for_Incomplete_CVPR_2023_paper.pdf | Highly Confident Local Structure Based Consensus Graph Learning for Incomplete Multi-View Clustering | Graph-based multi-view clustering has attracted extensive attention because of the powerful clustering-structure representation ability and noise robustness. Considering the reality of a large amount of incomplete data, in this paper, we propose a simple but effective method for incomplete multi-view clustering bas... | ['Yong Xu', 'Lunke Fei', 'Chao Huang', 'Zhihao Wu', 'Gehui Xu', 'Chengliang Liu', 'Jie Wen'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['incomplete-multi-view-clustering'] | ['computer-vision'] | [-3.77852738e-01 -3.89121249e-02 -2.96073407e-01 -4.40729588e-01
-7.39491999e-01 -6.51635826e-01 2.29803324e-01 1.47512853e-01
3.44543844e-01 3.23258013e-01 3.70955944e-01 -3.90990153e-02
-3.14670384e-01 -6.76165104e-01 -4.93564129e-01 -7.87992358e-01
8.72967839e-02 3.38450909e-01 2.29432389e-01 1.19154088... | [8.032177925109863, 4.802638530731201] |
8667a03a-2dbe-43d8-8648-b7bbb3bcfd49 | unsupervised-learning-algorithms-for-keyword | 2206.12016 | null | https://arxiv.org/abs/2206.12016v1 | https://arxiv.org/pdf/2206.12016v1.pdf | Unsupervised Learning Algorithms for Keyword Extraction in an Undergraduate Thesis | The amount of data managed in many academic institutions has increased in recent years, particularly in all the research work done by undergraduate students, who simply use empirical techniques for keyword selection, forgetting existing technical methods to assist their students in this process. Information and communi... | ['Marga I. Ingaluque', 'Irenio L. Chagua', 'William E. Arcaya', 'Edelfre Flores', 'Fred Torres-Cruz'] | 2022-06-23 | null | null | null | null | ['keyword-extraction'] | ['natural-language-processing'] | [ 3.47617194e-02 1.18383430e-01 -3.25009435e-01 1.15224846e-01
-4.18412387e-01 -6.07887506e-01 4.90924060e-01 6.28693223e-01
-7.13875651e-01 8.58029842e-01 1.31954076e-02 -7.03421354e-01
-7.50912070e-01 -9.60854173e-01 -3.67612690e-01 -4.23291326e-01
3.79138768e-01 5.79360723e-01 2.96347421e-02 2.15024278... | [9.65237045288086, 8.326278686523438] |
d49fee98-229a-4b53-b6e1-35e2eb3d91a9 | pyramid-a-layered-model-for-nested-named | null | null | https://aclanthology.org/2020.acl-main.525 | https://aclanthology.org/2020.acl-main.525.pdf | Pyramid: A Layered Model for Nested Named Entity Recognition | This paper presents Pyramid, a novel layered model for Nested Named Entity Recognition (nested NER). In our approach, token or text region embeddings are recursively inputted into L flat NER layers, from bottom to top, stacked in a pyramid shape. Each time an embedding passes through a layer of the pyramid, its length ... | ['Jue Wang', 'Lidan Shou', 'Ke Chen', 'Gang Chen'] | 2020-07-01 | null | null | null | acl-2020-6 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-5.13477981e-01 2.18208313e-01 -4.51912023e-02 -2.47435600e-01
-8.57686818e-01 -7.81492412e-01 2.45421603e-01 4.81642812e-01
-9.49276686e-01 7.38555431e-01 4.54788297e-01 -2.93283582e-01
3.92460555e-01 -8.60711336e-01 -5.84364653e-01 -3.23918819e-01
-2.44555324e-01 1.84182838e-01 2.55761743e-01 -4.55706865... | [9.648161888122559, 9.562028884887695] |
39b0253b-3b87-41b7-9164-2f7e75f07b06 | a-unified-generative-framework-for-various | 2106.01223 | null | https://arxiv.org/abs/2106.01223v1 | https://arxiv.org/pdf/2106.01223v1.pdf | A Unified Generative Framework for Various NER Subtasks | Named Entity Recognition (NER) is the task of identifying spans that represent entities in sentences. Whether the entity spans are nested or discontinuous, the NER task can be categorized into the flat NER, nested NER, and discontinuous NER subtasks. These subtasks have been mainly solved by the token-level sequence la... | ['Xipeng Qiu', 'Zheng Zhang', 'Qipeng Guo', 'Junqi Dai', 'Tao Gui', 'Hang Yan'] | 2021-06-02 | null | https://aclanthology.org/2021.acl-long.451 | https://aclanthology.org/2021.acl-long.451.pdf | acl-2021-5 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [ 2.44005471e-02 1.28890887e-01 1.82715073e-01 -3.58532995e-01
-1.03078485e+00 -8.64871740e-01 2.69411296e-01 6.02213107e-02
-7.53070474e-01 9.37036276e-01 5.70886970e-01 -4.45555359e-01
1.75981075e-01 -7.49971986e-01 -5.62736034e-01 -2.92892337e-01
9.13841277e-02 1.85048357e-02 2.02431560e-01 -2.15150639... | [9.6334810256958, 9.505621910095215] |
001a51dc-3bf5-454a-8abd-726bea57e8b7 | maskgit-masked-generative-image-transformer | 2202.04200 | null | https://arxiv.org/abs/2202.04200v1 | https://arxiv.org/pdf/2202.04200v1.pdf | MaskGIT: Masked Generative Image Transformer | Generative transformers have experienced rapid popularity growth in the computer vision community in synthesizing high-fidelity and high-resolution images. The best generative transformer models so far, however, still treat an image naively as a sequence of tokens, and decode an image sequentially following the raster ... | ['William T. Freeman', 'Ce Liu', 'Lu Jiang', 'Han Zhang', 'Huiwen Chang'] | 2022-02-08 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chang_MaskGIT_Masked_Generative_Image_Transformer_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chang_MaskGIT_Masked_Generative_Image_Transformer_CVPR_2022_paper.pdf | cvpr-2022-1 | ['image-outpainting'] | ['computer-vision'] | [ 7.91096270e-01 2.43760929e-01 1.05267458e-01 -2.57956058e-01
-8.85104120e-01 -3.64656866e-01 8.22854757e-01 -5.32955885e-01
-1.20353498e-01 6.83122277e-01 1.40185714e-01 -2.38511860e-01
3.64135534e-01 -8.91349912e-01 -1.22627664e+00 -7.31073022e-01
3.90990883e-01 6.30663931e-01 -2.34191697e-02 -5.22921942... | [11.325806617736816, -0.5696256160736084] |
4dbfb8c1-c4d2-42f3-81c5-ee0cf3d834a2 | boosting-image-outpainting-with-semantic | 2110.09267 | null | https://arxiv.org/abs/2110.09267v1 | https://arxiv.org/pdf/2110.09267v1.pdf | Boosting Image Outpainting with Semantic Layout Prediction | The objective of image outpainting is to extend image current border and generate new regions based on known ones. Previous methods adopt generative adversarial networks (GANs) to synthesize realistic images. However, the lack of explicit semantic representation leads to blurry and abnormal image pixels when the outpai... | ['Tong Lin', 'Yuning Jiang', 'Tiezheng Ge', 'Quan Chen', 'Min Zhou', 'Jin Ma', 'Ye Ma'] | 2021-10-18 | null | null | null | null | ['image-outpainting'] | ['computer-vision'] | [ 6.81132972e-01 4.07028824e-01 2.65379399e-02 -3.41732591e-01
-5.43273449e-01 -6.13991082e-01 4.90744054e-01 -2.50888169e-01
-4.64418754e-02 9.05755937e-01 -3.92879508e-02 3.15401776e-05
3.64688665e-01 -1.11883605e+00 -1.01671016e+00 -6.27105653e-01
5.91046274e-01 4.29119647e-01 4.75048929e-01 -1.80277497... | [11.384275436401367, -0.5201571583747864] |
2c680cfe-6fcc-4c90-b4cc-5ce40b5481e3 | heuristically-guided-compilation-for-multi | 2212.06940 | null | https://arxiv.org/abs/2212.06940v1 | https://arxiv.org/pdf/2212.06940v1.pdf | Heuristically Guided Compilation for Multi-Agent Path Finding | Multi-agent path finding (MAPF) is a task of finding non-conflicting paths connecting agents' specified initial and goal positions in a shared environment. We focus on compilation-based solvers in which the MAPF problem is expressed in a different well established formalism such as mixed-integer linear programming (MIL... | ['Pavel Surynek'] | 2022-12-13 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 1.13940261e-01 7.92759717e-01 -2.17601165e-01 -2.10915536e-01
-5.22002339e-01 -9.04346764e-01 3.56927842e-01 4.40131575e-01
6.72859773e-02 1.57456744e+00 -4.11426760e-02 -4.55355853e-01
-7.77881265e-01 -1.35246766e+00 -6.70862079e-01 -4.77005392e-01
-6.08790815e-01 1.32529700e+00 3.08657438e-01 -4.41995621... | [4.98881196975708, 1.9042932987213135] |
62d1b365-a292-4cc0-b2fa-9c58810515bd | an-audio-visual-speech-separation-model | 2212.10744 | null | https://arxiv.org/abs/2212.10744v1 | https://arxiv.org/pdf/2212.10744v1.pdf | An Audio-Visual Speech Separation Model Inspired by Cortico-Thalamo-Cortical Circuits | Audio-visual approaches involving visual inputs have laid the foundation for recent progress in speech separation. However, the optimization of the concurrent usage of auditory and visual inputs is still an active research area. Inspired by the cortico-thalamo-cortical circuit, in which the sensory processing mechanism... | ['Xiaolin Hu', 'Kexin Yuan', 'Hang Chen', 'Fenghua Xie', 'Kai Li'] | 2022-12-21 | null | null | null | null | ['speech-separation'] | ['speech'] | [-1.45446390e-01 -6.32608980e-02 1.08958751e-01 -6.37745410e-02
-3.17788571e-01 -5.72221816e-01 5.21735668e-01 -1.68307319e-01
-3.39458674e-01 3.25469196e-01 2.77401745e-01 -1.05456479e-01
1.38532639e-01 -2.71935314e-01 -4.68684584e-01 -9.85788465e-01
2.67201722e-01 1.09499887e-01 3.92645031e-01 -3.90483737... | [14.46849536895752, 5.267792224884033] |
5d0b2b87-c60e-4710-8624-94935cb26631 | modelling-sentence-pairs-with-tree-structured | 1610.02806 | null | http://arxiv.org/abs/1610.02806v1 | http://arxiv.org/pdf/1610.02806v1.pdf | Modelling Sentence Pairs with Tree-structured Attentive Encoder | We describe an attentive encoder that combines tree-structured recursive
neural networks and sequential recurrent neural networks for modelling sentence
pairs. Since existing attentive models exert attention on the sequential
structure, we propose a way to incorporate attention into the tree topology.
Specially, given ... | ['Yan Pan', 'Cong Liu', 'Yao Zhou'] | 2016-10-10 | modelling-sentence-pairs-with-tree-structured-1 | https://aclanthology.org/C16-1274 | https://aclanthology.org/C16-1274.pdf | coling-2016-12 | ['question-selection'] | ['natural-language-processing'] | [ 4.32608217e-01 4.81871367e-01 9.38497111e-02 -7.28101432e-01
-8.38776708e-01 -2.18055457e-01 2.81328142e-01 3.31204236e-01
-4.53724921e-01 4.27066684e-01 7.02566087e-01 -5.36297917e-01
1.27846450e-01 -7.99688220e-01 -6.48531795e-01 6.53389245e-02
1.76883146e-01 5.32346129e-01 2.50905901e-01 -3.63431126... | [11.234530448913574, 8.351404190063477] |
44df836c-e5fc-4e2a-b4cd-619fbb81dd72 | online-gaussian-lda-for-unsupervised-pattern | 1910.11599 | null | https://arxiv.org/abs/1910.11599v1 | https://arxiv.org/pdf/1910.11599v1.pdf | Online Gaussian LDA for Unsupervised Pattern Mining from Utility Usage Data | Non-intrusive load monitoring (NILM) aims at separating a whole-home energy signal into its appliance components. Such method can be harnessed to provide various services to better manage and control energy consumption (optimal planning and saving). NILM has been traditionally approached from signal processing and elec... | ['Abdelhamid Bouchachia', 'Saad Mohamad'] | 2019-10-25 | null | null | null | null | ['non-intrusive-load-monitoring', 'electrical-engineering', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'miscellaneous', 'time-series'] | [ 1.85722783e-01 1.39859673e-02 -3.02318156e-01 -3.30284148e-01
-5.01124144e-01 -2.26389721e-01 7.17292786e-01 2.26435289e-01
-2.35744212e-02 4.33606386e-01 2.83334926e-02 5.30735813e-02
-4.15176272e-01 -1.12889063e+00 -1.00157350e-01 -1.14897192e+00
6.83930516e-02 5.63501596e-01 -5.27329780e-02 -7.14380443... | [6.010283470153809, 2.5889101028442383] |
2144ddba-ee63-4beb-ba64-32385f0ce9c9 | mean-variance-efficient-collaborative | 2306.06590 | null | https://arxiv.org/abs/2306.06590v1 | https://arxiv.org/pdf/2306.06590v1.pdf | Mean-Variance Efficient Collaborative Filtering for Stock Recommendation | The rise of FinTech has transformed financial services onto online platforms, yet stock investment recommender systems have received limited attention compared to other industries. Personalized stock recommendations can significantly impact customer engagement and satisfaction within the industry. However, traditional ... | ['Woo Chang Kim', 'YongJae lee', 'Munki Chung'] | 2023-06-11 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-6.39306009e-01 -6.78577870e-02 -5.27364910e-01 -4.13013667e-01
-1.84858292e-01 -7.42731810e-01 1.44368663e-01 -2.81245392e-02
-1.25168368e-01 3.18332493e-01 4.21273857e-01 -6.47360086e-01
-9.35321927e-01 -1.19374418e+00 -2.63487726e-01 -1.91474855e-01
-6.59481734e-02 4.04858291e-01 1.18101731e-01 -5.56510091... | [9.882128715515137, 5.6867146492004395] |
dbf103d8-65e0-4e13-bce7-2065ca7dcf45 | large-scale-representation-learning-from | 1909.08782 | null | https://arxiv.org/abs/1909.08782v1 | https://arxiv.org/pdf/1909.08782v1.pdf | Large-scale representation learning from visually grounded untranscribed speech | Systems that can associate images with their spoken audio captions are an important step towards visually grounded language learning. We describe a scalable method to automatically generate diverse audio for image captioning datasets. This supports pretraining deep networks for encoding both audio and images, which we ... | ['Yuan Zhang', 'Gabriel Ilharco', 'Jason Baldridge'] | 2019-09-19 | large-scale-representation-learning-from-2 | https://aclanthology.org/K19-1006 | https://aclanthology.org/K19-1006.pdf | conll-2019-11 | ['grounded-language-learning'] | ['natural-language-processing'] | [ 5.14704287e-01 1.89782679e-01 -8.02201629e-02 -5.01982450e-01
-2.00341940e+00 -8.19541931e-01 7.27474034e-01 -4.99074981e-02
-4.65591699e-01 5.97281575e-01 8.28523576e-01 2.68492065e-02
2.97800481e-01 -3.94167572e-01 -1.23053241e+00 -3.90508115e-01
-1.41627938e-01 5.34225941e-01 -1.94056913e-01 1.13285340... | [10.996772766113281, 1.176615834236145] |
31074625-d2df-4cb1-aaab-b4228acf22a4 | blip-bootstrapping-language-image-pre | 2201.12086 | null | https://arxiv.org/abs/2201.12086v2 | https://arxiv.org/pdf/2201.12086v2.pdf | BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation | Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-te... | ['Steven Hoi', 'Caiming Xiong', 'Dongxu Li', 'Junnan Li'] | 2022-01-28 | null | null | null | null | ['open-vocabulary-attribute-detection'] | ['computer-vision'] | [ 2.05028504e-01 -1.40011981e-01 -1.10825278e-01 -3.60455483e-01
-1.23246193e+00 -5.95591962e-01 9.52079296e-01 -3.42046350e-01
-5.21361113e-01 5.92919350e-01 1.33116394e-01 -1.86019346e-01
7.02426255e-01 -6.13685369e-01 -1.16185224e+00 -4.34008539e-01
5.17039180e-01 5.81188798e-01 2.37613931e-01 -3.03626627... | [10.948837280273438, 1.3718061447143555] |
2f85705b-b1e5-4ae8-a4a2-ba56672e30f8 | a-relation-specific-attention-network-for | null | null | https://www.ijcai.org/Proceedings/2020/561 | https://www.ijcai.org/Proceedings/2020/0561.pdf | A Relation-Specific Attention Network for Joint Entity and Relation Extraction | Joint extraction of entities and relations is an important task in natural language processing (NLP), which aims to capture all relational triplets from plain texts. This is a big challenge due to some of the triplets extracted from one sentence may have overlapping entities. Most existing methods perform entity recogn... | ['Li Guo', 'Zeliang Song', 'Qiannan Zhu', 'Shirui Pan', 'Xiaofei Zhou', 'Yue Yuan'] | 2020-07-01 | null | null | null | null | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-1.15252724e-02 2.09428906e-01 -2.59317398e-01 -4.13621813e-01
-8.53641570e-01 -5.64019084e-01 2.98664212e-01 4.85260665e-01
-3.58761579e-01 8.36000681e-01 3.49171221e-01 -4.67088401e-01
-2.56003235e-02 -9.78672266e-01 -7.89663911e-01 -2.24142298e-01
-8.95136446e-02 6.59761071e-01 1.47926122e-01 -2.65552849... | [9.209691047668457, 8.674110412597656] |
d49fbcb5-a0c1-4f6a-87c9-ddde23fd7bd3 | geometer-graph-few-shot-class-incremental | 2205.13954 | null | https://arxiv.org/abs/2205.13954v2 | https://arxiv.org/pdf/2205.13954v2.pdf | Geometer: Graph Few-Shot Class-Incremental Learning via Prototype Representation | With the tremendous expansion of graphs data, node classification shows its great importance in many real-world applications. Existing graph neural network based methods mainly focus on classifying unlabeled nodes within fixed classes with abundant labeling. However, in many practical scenarios, graph evolves with emer... | ['Xinbing Wang', 'Luoyi Fu', 'Weinan Zhang', 'Lina Yang', 'Xiaoying Gan', 'Bin Lu'] | 2022-05-27 | null | null | null | null | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 2.23627165e-01 6.22174621e-01 -2.60044366e-01 -3.50921452e-01
-1.08449236e-02 -4.07226861e-01 3.49787623e-01 6.62086248e-01
-1.89777866e-01 6.40504003e-01 -2.20352933e-01 -1.71033636e-01
-5.13334759e-02 -1.14617586e+00 -6.29134655e-01 -6.32149458e-01
-2.48405740e-01 6.86721146e-01 3.77938926e-01 -1.10879928... | [9.608907699584961, 3.6794302463531494] |
124f9d42-94cb-47ad-bb21-a8c157453cd1 | physics-informed-machine-learning-of-1 | 2209.09349 | null | https://arxiv.org/abs/2209.09349v1 | https://arxiv.org/pdf/2209.09349v1.pdf | Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian Inference | Although the no-u-turn sampler (NUTS) is a widely adopted method for performing Bayesian inference, it requires numerous posterior gradients which can be expensive to compute in practice. Recently, there has been a significant interest in physics-based machine learning of dynamical (or Hamiltonian) systems and Hamilton... | ['Michael D. Shields', 'Yifeng Che', 'Somayajulu L. N. Dhulipala'] | 2022-09-19 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-1.32247239e-01 1.62844099e-02 5.54582365e-02 -2.87183762e-01
-7.35732496e-01 -8.85024369e-02 5.64407110e-01 -2.16302037e-01
-4.45707202e-01 1.20359361e+00 -1.42364830e-01 -3.28066379e-01
-2.87469923e-01 -9.95941222e-01 -7.24523842e-01 -1.02154768e+00
-5.53089976e-02 6.86700642e-01 3.76667321e-01 1.74477413... | [6.9452080726623535, 3.878100872039795] |
469c6139-735b-488b-846c-62114cc04cb0 | ssp-based-construction-of-evaluation | null | null | https://aclanthology.org/2022.pandl-1.5 | https://aclanthology.org/2022.pandl-1.5.pdf | SSP-Based Construction of Evaluation-Annotated Data for Fine-Grained Aspect-Based Sentiment Analysis | We report the construction of a Korean evaluation-annotated corpus, hereafter called ‘Evaluation Annotated Dataset (EVAD)’, and its use in Aspect-Based Sentiment Analysis (ABSA) extended in order to cover e-commerce reviews containing sentiment and non-sentiment linguistic patterns. The annotation process uses Semi-Aut... | ['Jeesun Nam', 'Eric Laporte', 'Gwanghoon Yoo', 'Changhoe Hwang', 'Shinwoo Kim', 'Suwon Choi'] | null | null | null | null | pandl-coling-2022-10 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 1.61744520e-01 2.84322351e-01 -3.11064869e-01 -8.89823914e-01
-1.01033461e+00 -1.13111591e+00 4.16811019e-01 4.70210105e-01
-2.77828902e-01 5.38715959e-01 1.86721325e-01 -3.92428249e-01
2.44875271e-02 -7.80504763e-01 -3.67880762e-01 -3.25310647e-01
1.56619057e-01 6.60728157e-01 1.21206149e-01 -4.75441664... | [11.347665786743164, 6.754476070404053] |
5d579f5d-75a8-4816-bba3-b9b07c3a1ef1 | texture-text-guided-texturing-of-3d-shapes | 2302.01721 | null | https://arxiv.org/abs/2302.01721v1 | https://arxiv.org/pdf/2302.01721v1.pdf | TEXTure: Text-Guided Texturing of 3D Shapes | In this paper, we present TEXTure, a novel method for text-guided generation, editing, and transfer of textures for 3D shapes. Leveraging a pretrained depth-to-image diffusion model, TEXTure applies an iterative scheme that paints a 3D model from different viewpoints. Yet, while depth-to-image models can create plausib... | ['Daniel Cohen-Or', 'Raja Giryes', 'Yuval Alaluf', 'Gal Metzer', 'Elad Richardson'] | 2023-02-03 | null | null | null | null | ['text-guided-generation'] | ['computer-vision'] | [ 8.49918187e-01 2.23062679e-01 4.94625062e-01 -2.11341187e-01
-5.41294098e-01 -9.24190223e-01 8.07784021e-01 -1.63904816e-01
4.00143266e-01 2.74909347e-01 -7.65154045e-03 -1.87433481e-01
1.61928777e-02 -1.19521129e+00 -7.56159663e-01 -5.96121252e-01
5.11195064e-01 9.01812017e-01 4.69280601e-01 -3.02747905... | [9.330604553222656, -3.200638771057129] |
efe865f7-2d3f-4d31-8343-c8c7c157d935 | a-multi-task-dual-tree-network-for-aspect | null | null | https://aclanthology.org/2022.coling-1.616 | https://aclanthology.org/2022.coling-1.616.pdf | A Multi-Task Dual-Tree Network for Aspect Sentiment Triplet Extraction | Aspect Sentiment Triplet Extraction (ASTE) aims at extracting triplets from a given sentence, where each triplet includes an aspect, its sentiment polarity, and a corresponding opinion explaining the polarity. Existing methods are poor at detecting complicated relations between aspects and opinions as well as classifyi... | ['Huijia Zhu', 'Jintao Du', 'Gongshen Liu', 'Kui Meng', 'Yichun Zhao'] | null | null | null | null | coling-2022-10 | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [ 1.56919867e-01 -3.51227000e-02 -4.03940767e-01 -7.80672908e-01
-9.11806107e-01 -7.77796686e-01 5.81248999e-01 1.34052664e-01
-8.55341032e-02 6.02080166e-01 5.88020504e-01 -5.45633674e-01
2.81525552e-01 -6.80423975e-01 -4.63155121e-01 -4.82281178e-01
3.67468208e-01 3.93500030e-01 8.94781128e-02 -1.59231365... | [11.503494262695312, 6.623701095581055] |
50760e37-3c5c-46c0-8a9b-c20be995850f | measuring-uncertainty-during-respiratory-rate | 1805.00082 | null | http://arxiv.org/abs/1805.00082v1 | http://arxiv.org/pdf/1805.00082v1.pdf | Measuring uncertainty during respiratory rate estimation using pressure-sensitive mats | We develop and evaluate a respiratory rate estimation algorithm that utilizes
data from pressure-sensitive mat (PSM) technology for continuous patient
monitoring in neonatal intensive care units (NICU). An analysis of the random
effect of drift and systematic effect of creep in the PSM data is presented,
showing that t... | [] | 2018-04-30 | null | null | null | null | ['respiratory-rate-estimation'] | ['medical'] | [ 4.45136815e-01 1.62180364e-01 2.47181371e-01 -3.63482744e-01
-4.58099037e-01 -5.85206211e-01 -7.21046701e-02 4.55170214e-01
-5.77283859e-01 8.35364342e-01 6.44529015e-02 -6.20762706e-01
-3.95330995e-01 -3.37191075e-01 -9.90836740e-01 -6.07132673e-01
-5.46181463e-02 3.92201513e-01 4.85166430e-01 5.54112852... | [14.049521446228027, 3.016875743865967] |
41568b12-c0da-4f54-a030-4aba593310b9 | eeg-based-epileptic-seizure-prediction-using | 2209.11172 | null | https://arxiv.org/abs/2209.11172v1 | https://arxiv.org/pdf/2209.11172v1.pdf | EEG-Based Epileptic Seizure Prediction Using Temporal Multi-Channel Transformers | Epilepsy is one of the most common neurological diseases, characterized by transient and unprovoked events called epileptic seizures. Electroencephalogram (EEG) is an auxiliary method used to perform both the diagnosis and the monitoring of epilepsy. Given the unexpected nature of an epileptic seizure, its prediction w... | ['Glauco A. P. Caurin', 'Marcelo Becker', 'Americo C. Sakamoto', 'Helio R. Machado', 'Frederico N. Nakano', 'Ricardo L. Saute', 'Gustavo J. G. Lahr', 'Paulo H. Polegato', 'Tharik J. S. Reis', 'Ricardo V. Godoy'] | 2022-09-18 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [-5.58557399e-02 -2.32253760e-01 3.94765139e-01 -1.58739582e-01
-6.34024143e-01 -2.83692747e-01 3.84082019e-01 -1.02357417e-01
-2.05751464e-01 7.55266309e-01 2.79485732e-01 -4.14865911e-01
-5.34240723e-01 -4.49228346e-01 -5.22880018e-01 -8.06689739e-01
-4.36408192e-01 6.25908554e-01 -1.66988783e-02 9.80495512... | [13.193907737731934, 3.4969141483306885] |
1edac68a-dc94-4a16-8bec-f58674c8f550 | integration-of-reinforcement-learning-based | 2304.08280 | null | https://arxiv.org/abs/2304.08280v1 | https://arxiv.org/pdf/2304.08280v1.pdf | Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Driving | Reinforcement learning has received high research interest for developing planning approaches in automated driving. Most prior works consider the end-to-end planning task that yields direct control commands and rarely deploy their algorithm to real vehicles. In this work, we propose a method to employ a trained deep re... | ['Michael Buchholz', 'Benjamin Völz', 'Marvin Klimke'] | 2023-04-17 | null | null | null | null | ['motion-planning'] | ['robots'] | [-3.12941857e-02 5.72057009e-01 -2.81589866e-01 -2.34063193e-01
-6.88687563e-01 -4.26776022e-01 7.80161560e-01 -1.47868991e-01
-4.51785117e-01 8.91466618e-01 -6.92440495e-02 -7.67663658e-01
-2.48222977e-01 -9.90399718e-01 -8.47756028e-01 -5.53062081e-01
-4.13324058e-01 8.70040536e-01 6.16299272e-01 -8.08281898... | [5.057991981506348, 1.275649905204773] |
b8f9eda2-4cae-4911-8159-b96b7204a5a0 | magnet-motif-agnostic-generation-of-molecules | 2305.19303 | null | https://arxiv.org/abs/2305.19303v1 | https://arxiv.org/pdf/2305.19303v1.pdf | MAGNet: Motif-Agnostic Generation of Molecules from Shapes | Recent advances in machine learning for molecules exhibit great potential for facilitating drug discovery from in silico predictions. Most models for molecule generation rely on the decomposition of molecules into frequently occurring substructures (motifs), from which they generate novel compounds. While motif represe... | ['Stephan Günnemann', 'Fabian Theis', 'Bastian Rieck', 'Johanna Sommer', 'Leon Hetzel'] | 2023-05-30 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 3.89434338e-01 7.19364583e-02 -5.58159292e-01 -2.23099723e-01
-4.89950091e-01 -9.63511705e-01 7.64053822e-01 8.00326765e-01
1.53203860e-01 1.04895902e+00 2.67938137e-01 -6.84101403e-01
-1.22301042e-01 -1.06515920e+00 -8.65661025e-01 -7.83914983e-01
-2.40575328e-01 8.68296385e-01 4.38653119e-02 -8.29417482... | [5.1285176277160645, 5.733266353607178] |
81e4596b-b367-4b46-8dac-f4b74009ca4d | an-anatomy-based-v1-model-extraction-of-low | 2302.09074 | null | https://arxiv.org/abs/2302.09074v1 | https://arxiv.org/pdf/2302.09074v1.pdf | An anatomy-based V1 model: Extraction of Low-level Features, Reduction of distortion and a V1-inspired SOM | We present a model of the primary visual cortex V1, guided by anatomical experiments. Unlike most machine learning systems our goal is not to maximize accuracy but to realize a system more aligned to biological systems. Our model consists of the V1 layers 4, 2/3, and 5, with inter-layer connections between them in acco... | ['Nikhil Ranjan Pal', 'Suvam Roy'] | 2023-02-18 | null | null | null | null | ['contour-detection', 'anatomy'] | ['computer-vision', 'miscellaneous'] | [ 1.10257573e-01 4.18976963e-01 3.35470960e-02 -1.20645151e-01
5.50808050e-02 -5.02438068e-01 6.29909873e-01 1.24377057e-01
-6.82221889e-01 5.00814915e-01 4.23820466e-01 1.33026034e-01
-1.28880784e-01 -8.91670585e-01 -8.05187166e-01 -8.79597425e-01
-4.47009020e-02 1.97095871e-01 8.17521632e-01 -1.27205685... | [9.495361328125, 2.4275732040405273] |
2adcbbe6-ca01-4a54-82ec-4d27d92d8212 | high-order-tensor-pooling-with-attention-for | 2110.05216 | null | https://arxiv.org/abs/2110.05216v1 | https://arxiv.org/pdf/2110.05216v1.pdf | High-order Tensor Pooling with Attention for Action Recognition | We aim at capturing high-order statistics of feature vectors formed by a neural network, and propose end-to-end second- and higher-order pooling to form a tensor descriptor. Tensor descriptors require a robust similarity measure due to low numbers of aggregated vectors and the burstiness phenomenon, when a given featur... | ['Ke Sun', 'Lei Wang', 'Piotr Koniusz'] | 2021-10-11 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [-7.19699562e-02 5.80389462e-02 1.30882159e-01 1.60366789e-01
2.56007332e-02 -7.55516648e-01 7.72209466e-01 4.15001690e-01
-3.30486178e-01 -5.32426611e-02 4.39497381e-01 -8.91966522e-02
-4.97751951e-01 -7.33012974e-01 -6.80947304e-01 -9.44485664e-01
-1.03868461e+00 -9.41001624e-02 1.40940949e-01 -3.20539266... | [7.69040584564209, 4.205646991729736] |
bccf7059-53ac-4045-9900-67e50c2526a9 | hunting-group-clues-with-transformers-for | 2207.05254 | null | https://arxiv.org/abs/2207.05254v1 | https://arxiv.org/pdf/2207.05254v1.pdf | Hunting Group Clues with Transformers for Social Group Activity Recognition | This paper presents a novel framework for social group activity recognition. As an expanded task of group activity recognition, social group activity recognition requires recognizing multiple sub-group activities and identifying group members. Most existing methods tackle both tasks by refining region features and then... | ['Ravigopal Vennelakanti', 'Rahul Vishwakarma', 'Masato Tamura'] | 2022-07-12 | null | null | null | null | ['group-activity-recognition'] | ['computer-vision'] | [ 2.67991930e-01 1.00901462e-01 -2.01412424e-01 -4.30693448e-01
-2.60247648e-01 -2.69568861e-01 8.38036120e-01 3.72325331e-01
-6.00716293e-01 5.20987570e-01 8.35430801e-01 4.10264432e-01
-4.52863902e-01 -1.04481792e+00 -3.61892879e-01 -6.62370086e-01
-2.91128486e-01 2.62242705e-01 9.18222442e-02 -7.21210614... | [8.121682167053223, 0.6159323453903198] |
84d01233-da5f-490f-a9e5-33b95b64f559 | plan-then-seam-towards-efficient-table-to | 2302.05138 | null | https://arxiv.org/abs/2302.05138v2 | https://arxiv.org/pdf/2302.05138v2.pdf | Plan-then-Seam: Towards Efficient Table-to-Text Generation | Table-to-text generation aims at automatically generating text to help people conveniently obtain salient information in tables. Recent works explicitly decompose the generation process into content planning and surface generation stages, employing two autoregressive networks for them respectively. However, they are co... | ['Yongbin Li', 'Binhua Li', 'Can Ma', 'Bing Li', 'Chengyang Fang', 'Ruiying Geng', 'Liang Li'] | 2023-02-10 | null | null | null | null | ['table-to-text-generation'] | ['natural-language-processing'] | [ 5.23801386e-01 6.34904206e-01 -2.38512516e-01 -3.85328859e-01
-1.35510278e+00 -5.17770350e-01 5.83397329e-01 1.40408054e-01
-1.88426852e-01 8.96119237e-01 7.15392292e-01 -5.84524870e-01
4.53438729e-01 -1.01820350e+00 -1.07040441e+00 -4.01538730e-01
2.24275395e-01 9.97929275e-01 5.33799231e-02 -2.45226339... | [11.575788497924805, 8.792526245117188] |
c29620b8-b7fe-46ff-9bfb-eabef93daa73 | question-answering-on-scholarly-knowledge | 2006.01527 | null | https://arxiv.org/abs/2006.01527v1 | https://arxiv.org/pdf/2006.01527v1.pdf | Question Answering on Scholarly Knowledge Graphs | Answering questions on scholarly knowledge comprising text and other artifacts is a vital part of any research life cycle. Querying scholarly knowledge and retrieving suitable answers is currently hardly possible due to the following primary reason: machine inactionable, ambiguous and unstructured content in publicatio... | ['Sören Auer', 'Markus Stocker', 'Mohamad Yaser Jaradeh'] | 2020-06-02 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-2.41007298e-01 1.52464405e-01 -3.30373347e-01 -1.02410158e-02
-1.35011518e+00 -1.32999814e+00 5.76037526e-01 6.27629697e-01
4.98747304e-02 9.58725274e-01 4.84192789e-01 -6.60924792e-01
-5.51374137e-01 -8.63322914e-01 -6.95518672e-01 1.90141618e-01
5.72676778e-01 7.69331574e-01 6.39371157e-01 -2.44375393... | [9.818024635314941, 8.040340423583984] |
c6a18cee-82bf-4373-aafa-c645ed1bde80 | controlvideo-training-free-controllable-text | 2305.13077 | null | https://arxiv.org/abs/2305.13077v1 | https://arxiv.org/pdf/2305.13077v1.pdf | ControlVideo: Training-free Controllable Text-to-Video Generation | Text-driven diffusion models have unlocked unprecedented abilities in image generation, whereas their video counterpart still lags behind due to the excessive training cost of temporal modeling. Besides the training burden, the generated videos also suffer from appearance inconsistency and structural flickers, especial... | ['Qi Tian', 'WangMeng Zuo', 'Xiaopeng Zhang', 'Dongsheng Jiang', 'Yuxiang Wei', 'Yabo Zhang'] | 2023-05-22 | null | null | null | null | ['video-generation', 'text-to-video-generation'] | ['computer-vision', 'natural-language-processing'] | [ 1.45971417e-01 -8.57386366e-02 -2.21129850e-01 7.30170235e-02
-7.80307829e-01 -5.81257045e-01 8.64124238e-01 -5.62936485e-01
-8.18986632e-03 7.06245601e-01 4.75962400e-01 8.14074837e-03
2.30658591e-01 -5.07800639e-01 -7.98548996e-01 -7.04653978e-01
1.43153310e-01 -1.82182431e-01 1.26893222e-01 -1.07725255... | [10.903190612792969, -0.6808971762657166] |
fcb3ab91-484c-4062-93c3-531aa9e0e36d | let-graph-be-the-go-board-gradient-free-node | 2211.10782 | null | https://arxiv.org/abs/2211.10782v2 | https://arxiv.org/pdf/2211.10782v2.pdf | Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning | Graph Neural Networks (GNNs) have drawn significant attentions over the years and been broadly applied to essential applications requiring solid robustness or vigorous security standards, such as product recommendation and user behavior modeling. Under these scenarios, exploiting GNN's vulnerabilities and further downg... | ['Yanfang Ye', 'Chuxu Zhang', 'Yujie Fan', 'Mingxuan Ju'] | 2022-11-19 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 1.79446280e-01 5.22036672e-01 -4.38699901e-01 2.23860577e-01
-3.68587375e-01 -9.31067348e-01 3.96800131e-01 4.11149412e-02
-7.84808472e-02 4.96242464e-01 -3.01327884e-01 -9.99913812e-01
1.53031703e-02 -1.00317037e+00 -8.33411396e-01 -6.57949209e-01
-3.22803020e-01 1.28448859e-01 1.09366179e-01 -4.29464579... | [6.099552154541016, 7.312499523162842] |
e97d4f28-1330-4721-bb0c-224fc6cb451b | kashmir-a-computational-analysis-of-the-voice | 1909.12940 | null | https://arxiv.org/abs/1909.12940v4 | https://arxiv.org/pdf/1909.12940v4.pdf | Hope Speech Detection: A Computational Analysis of the Voice of Peace | The recent Pulwama terror attack (February 14, 2019, Pulwama, Kashmir) triggered a chain of escalating events between India and Pakistan adding another episode to their 70-year-old dispute over Kashmir. The present era of ubiquitious social media has never seen nuclear powers closer to war. In this paper, we analyze th... | ['Jaime G. Carbonell', 'Ashiqur R. KhudaBukhsh', 'Shriphani Palakodety'] | 2019-09-11 | null | null | null | null | ['hope-speech-detection'] | ['natural-language-processing'] | [-2.55033791e-01 -1.38871104e-01 -2.66310006e-01 9.22013074e-02
-8.54982078e-01 -1.03008449e+00 1.13077068e+00 2.96971262e-01
-7.76516318e-01 5.54539084e-01 9.60864365e-01 -6.59108102e-01
-1.91883761e-02 -4.90235150e-01 -1.02566034e-01 -3.81751925e-01
-4.30512905e-01 4.23146933e-01 -2.52133429e-01 -7.37074614... | [8.895218849182129, 10.496238708496094] |
3f6e3e99-c253-40aa-8061-503ba5ed5781 | ma-vit-modality-agnostic-vision-transformers | 2304.07549 | null | https://arxiv.org/abs/2304.07549v1 | https://arxiv.org/pdf/2304.07549v1.pdf | MA-ViT: Modality-Agnostic Vision Transformers for Face Anti-Spoofing | The existing multi-modal face anti-spoofing (FAS) frameworks are designed based on two strategies: halfway and late fusion. However, the former requires test modalities consistent with the training input, which seriously limits its deployment scenarios. And the latter is built on multiple branches to process different ... | ['Yanyan Liang', 'Ajian Liu'] | 2023-04-15 | null | null | null | null | ['face-anti-spoofing'] | ['computer-vision'] | [ 3.90108585e-01 -4.42024678e-01 -2.31560975e-01 -4.57931235e-02
-9.02275085e-01 -6.17640793e-01 6.76521659e-01 -6.47944391e-01
-1.68536395e-01 3.56323689e-01 1.10213384e-01 -4.34087187e-01
-4.08933610e-02 -6.33751810e-01 -5.38758397e-01 -9.41235185e-01
4.72735375e-01 2.50298530e-01 5.14755607e-01 -3.64400327... | [13.065021514892578, 1.203995704650879] |
6d44f01d-1d1c-490d-8839-d68948fe072b | residual-attention-a-simple-but-effective | 2108.02456 | null | https://arxiv.org/abs/2108.02456v2 | https://arxiv.org/pdf/2108.02456v2.pdf | Residual Attention: A Simple but Effective Method for Multi-Label Recognition | Multi-label image recognition is a challenging computer vision task of practical use. Progresses in this area, however, are often characterized by complicated methods, heavy computations, and lack of intuitive explanations. To effectively capture different spatial regions occupied by objects from different categories, ... | ['Jianxin Wu', 'Ke Zhu'] | 2021-08-05 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhu_Residual_Attention_A_Simple_but_Effective_Method_for_Multi-Label_Recognition_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhu_Residual_Attention_A_Simple_but_Effective_Method_for_Multi-Label_Recognition_ICCV_2021_paper.pdf | iccv-2021-1 | ['multi-label-image-classification'] | ['computer-vision'] | [ 2.98599124e-01 -2.35930651e-01 -1.32858694e-01 -6.42472386e-01
-8.94783318e-01 -5.68319142e-01 6.30951285e-01 3.44276279e-01
-3.81604165e-01 5.66390574e-01 6.74068779e-02 -2.54608780e-01
-5.11952080e-02 -4.48435098e-01 -6.76696181e-01 -9.24658358e-01
4.04198259e-01 2.52194703e-01 -1.82522070e-02 1.71261188... | [9.829909324645996, 3.82525897026062] |
38d9a8ce-8d18-4c39-9059-62789e6d7580 | pitch-preservation-in-singing-voice-synthesis | 2110.05033 | null | https://arxiv.org/abs/2110.05033v2 | https://arxiv.org/pdf/2110.05033v2.pdf | Pitch Preservation In Singing Voice Synthesis | Suffering from limited singing voice corpus, existing singing voice synthesis (SVS) methods that build encoder-decoder neural networks to directly generate spectrogram could lead to out-of-tune issues during the inference phase. To attenuate these issues, this paper presents a novel acoustic model with independent pitc... | ['Huajun Wang', 'Kun Wang', 'Hai Zhu', 'Shujun Liu'] | 2021-10-11 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [ 5.43889292e-02 9.32872519e-02 -2.52337512e-02 -1.83462705e-02
-6.93669617e-01 -6.24636769e-01 3.87123413e-02 -4.90134060e-01
1.16607539e-01 6.30906224e-01 5.05049706e-01 7.76673630e-02
5.09795398e-02 -5.91894329e-01 -6.33449733e-01 -8.72904539e-01
1.79419234e-01 -6.54134154e-02 -1.21131510e-01 -2.22577453... | [15.498559951782227, 6.169124126434326] |
c7607d58-de6b-484d-b3c7-19972c32933e | illumination-estimation-challenge-experience | 2012.15779 | null | https://arxiv.org/abs/2012.15779v1 | https://arxiv.org/pdf/2012.15779v1.pdf | Illumination Estimation Challenge: experience of past two years | Illumination estimation is the essential step of computational color constancy, one of the core parts of various image processing pipelines of modern digital cameras. Having an accurate and reliable illumination estimation is important for reducing the illumination influence on the image colors. To motivate the generat... | ['Dmitry Nikolaev', 'Sven Lončarić', 'Raimondo Schettini', 'Simone Bianco', 'Riccardo Riva', 'Marco Buzzelli', 'Yanlin Qian', 'Artem Nikonorov', 'Daria Senshina', 'Arseniy Terekhin', 'Zhihao LI', 'Alexander Belokopytov', 'Marko Subašić', 'Karlo Koscević', 'Nikola Banić', 'Ilya Semenkov', 'Alex Savchik', 'Egor Ershov'] | 2020-12-31 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 2.78548062e-01 -6.94363475e-01 4.68090475e-01 -5.68329036e-01
-4.53058869e-01 -8.13045859e-01 4.69430029e-01 -4.17249709e-01
-5.31788886e-01 6.63131654e-01 -2.06167892e-01 7.74792358e-02
3.72557193e-02 -3.65053654e-01 -7.50102699e-01 -9.88939106e-01
-2.08579795e-03 1.75292522e-01 2.38474205e-01 -3.35536897... | [10.399166107177734, -2.495224952697754] |
256577b5-41a8-4c8b-82e4-d0fab0b81b56 | a-new-paradigm-for-device-free-indoor | 2304.06490 | null | https://arxiv.org/abs/2304.06490v1 | https://arxiv.org/pdf/2304.06490v1.pdf | A New Paradigm for Device-free Indoor Localization: Deep Learning with Error Vector Spectrum in Wi-Fi Systems | The demand for device-free indoor localization using commercial Wi-Fi devices has rapidly increased in various fields due to its convenience and versatile applications. However, random frequency offset (RFO) in wireless channels poses challenges to the accuracy of indoor localization when using fluctuating channel stat... | ['Kai-Ten Feng', 'Li-Hsiang Shen', 'An-Hung Hsiao', 'Wen Liu'] | 2023-03-25 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 1.65330112e-01 -4.23645556e-01 -1.73510715e-01 -4.23934877e-01
-9.83518779e-01 -2.56989449e-01 3.14853787e-01 -3.04429293e-01
-2.63300747e-01 1.15942943e+00 1.31167889e-01 -2.55210578e-01
-6.36843562e-01 -7.03708887e-01 -5.91957271e-01 -9.37457204e-01
-5.34948528e-01 -2.51280397e-01 -2.00896621e-01 -1.73243508... | [6.425297260284424, 0.8838650584220886] |
6604f7eb-1072-479c-b663-712b784bab5a | renderdiffusion-image-diffusion-for-3d | 2211.09869 | null | https://arxiv.org/abs/2211.09869v2 | https://arxiv.org/pdf/2211.09869v2.pdf | RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generation | Diffusion models currently achieve state-of-the-art performance for both conditional and unconditional image generation. However, so far, image diffusion models do not support tasks required for 3D understanding, such as view-consistent 3D generation or single-view object reconstruction. In this paper, we present Rende... | ['Titas Anciukevicius', 'Paul Guerrero', 'Niloy J. Mitra', 'Hakan Bilen', 'Paul Henderson', 'Matthew Fisher', 'Zexiang Xu'] | 2022-11-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Anciukevicius_RenderDiffusion_Image_Diffusion_for_3D_Reconstruction_Inpainting_and_Generation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Anciukevicius_RenderDiffusion_Image_Diffusion_for_3D_Reconstruction_Inpainting_and_Generation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['object-reconstruction'] | ['computer-vision'] | [ 1.23001963e-01 3.85351986e-01 2.17544556e-01 -5.47204137e-01
-6.74448669e-01 -7.16591299e-01 9.81221497e-01 -3.42944205e-01
-7.38747343e-02 4.72647101e-01 2.04319358e-01 -2.49486893e-01
1.80747226e-01 -1.08929718e+00 -1.04581428e+00 -5.03404081e-01
4.85105157e-01 6.25358164e-01 1.20575473e-01 -1.32104769... | [9.221192359924316, -3.141237497329712] |
ad82a5e7-da45-4efe-9b6a-13a33a2cd322 | fast-multi-layer-laplacian-enhancement | 1606.07396 | null | http://arxiv.org/abs/1606.07396v1 | http://arxiv.org/pdf/1606.07396v1.pdf | Fast Multi-Layer Laplacian Enhancement | A novel, fast and practical way of enhancing images is introduced in this
paper. Our approach builds on Laplacian operators of well-known edge-aware
kernels, such as bilateral and nonlocal means, and extends these filter's
capabilities to perform more effective and fast image smoothing, sharpening and
tone manipulation... | ['Peyman Milanfar', 'Hossein Talebi'] | 2016-06-23 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [ 4.93470371e-01 -1.04872286e-01 2.91868269e-01 -3.23061049e-01
-2.12581500e-01 -6.34560764e-01 6.06825769e-01 2.81158984e-01
-6.85689092e-01 4.55178499e-01 1.12266228e-01 -1.22166589e-01
-1.92072377e-01 -8.07530880e-01 -3.92821193e-01 -5.08031726e-01
1.08638100e-01 -3.08348358e-01 8.90380740e-01 -3.26346427... | [11.046388626098633, -2.4550387859344482] |
11d7202a-9ebe-41d5-8cc5-af66cb10f826 | artificial-neural-network-based-breast-cancer | 2006.01767 | null | https://arxiv.org/abs/2006.01767v1 | https://arxiv.org/pdf/2006.01767v1.pdf | Artificial Neural Network Based Breast Cancer Screening: A Comprehensive Review | Breast cancer is a common fatal disease for women. Early diagnosis and detection is necessary in order to improve the prognosis of breast cancer affected people. For predicting breast cancer, several automated systems are already developed using different medical imaging modalities. This paper provides a systematic rev... | ['Subrato Bharati', 'Prajoy Podder', 'M. Rubaiyat Hossain Mondal'] | 2020-05-29 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 1.96201980e-01 2.22080991e-01 -1.03817917e-02 -2.84898877e-01
1.99798375e-01 2.02879593e-01 4.55213249e-01 2.07530603e-01
-2.90700316e-01 8.16791117e-01 1.66545838e-01 -2.91536987e-01
-3.53757113e-01 -1.00657952e+00 -3.93185854e-01 -8.30703437e-01
-1.11030675e-01 1.69539690e-01 2.66011626e-01 -2.58823454... | [15.227286338806152, -2.69459867477417] |
9f8433db-a295-4b73-bfad-479345c90f45 | the-loss-of-the-property-of-locality-of-the | 2211.11170 | null | https://arxiv.org/abs/2211.11170v1 | https://arxiv.org/pdf/2211.11170v1.pdf | The loss of the property of locality of the kernel in high-dimensional Gaussian process regression on the example of the fitting of molecular potential energy surfaces | Kernel based methods including Gaussian process regression (GPR) and generally kernel ridge regression (KRR) have been finding increasing use in computational chemistry, including the fitting of potential energy surfaces and density functionals in high-dimensional feature spaces. Kernels of the Matern family such as Ga... | ['Manabu Ihara', 'Sergei Manzhos'] | 2022-11-21 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-1.57569833e-02 -8.69879201e-02 1.14669621e-01 -3.19550574e-01
-6.76008761e-01 -1.97106615e-01 5.04662871e-01 4.97428447e-01
-5.44542253e-01 8.11380148e-01 -4.53494117e-02 -5.29103339e-01
-5.10375679e-01 -8.52036357e-01 -6.55999184e-01 -1.20471609e+00
-1.78394750e-01 2.63391882e-01 2.70940423e-01 -1.16521373... | [7.499190330505371, 4.108118534088135] |
5dcdd2d4-f4c9-46c1-80bd-6f1c662128f8 | efficient-bayesian-physics-informed-neural | 2303.07392 | null | https://arxiv.org/abs/2303.07392v1 | https://arxiv.org/pdf/2303.07392v1.pdf | Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion | Bayesian Physics Informed Neural Networks (B-PINNs) have gained significant attention for inferring physical parameters and learning the forward solutions for problems based on partial differential equations. However, the overparameterized nature of neural networks poses a computational challenge for high-dimensional p... | ['Xueyu Zhu', 'Andrew Pensoneault'] | 2023-03-13 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 1.18514776e-01 -1.13917939e-01 1.57724127e-01 -4.29361105e-01
-1.04020894e+00 -1.77421197e-01 6.80264056e-01 -2.50427723e-01
-5.54280341e-01 1.56096721e+00 -2.29692146e-01 -4.49269176e-01
-7.31589735e-01 -9.55647528e-01 -8.16980720e-01 -1.10414994e+00
4.65836230e-04 1.01139343e+00 1.81586444e-01 4.08149153... | [6.910454273223877, 3.840543031692505] |
c8942559-49c0-4b8e-80a4-dc03e4ff966a | lico-net-linearized-convolution-network-for | 2211.04635 | null | https://arxiv.org/abs/2211.04635v1 | https://arxiv.org/pdf/2211.04635v1.pdf | LiCo-Net: Linearized Convolution Network for Hardware-efficient Keyword Spotting | This paper proposes a hardware-efficient architecture, Linearized Convolution Network (LiCo-Net) for keyword spotting. It is optimized specifically for low-power processor units like microcontrollers. ML operators exhibit heterogeneous efficiency profiles on power-efficient hardware. Given the exact theoretical computa... | ['Vikas Chandra', 'Raghuraman Krishnamoorthi', 'Xin Lei', 'Ming Sun', 'Raziel Alvarez', 'Limin Tang', 'Ivaylo Enchev', 'Yiteng Huang', 'Yangyang Shi', 'Biqiao Zhang', 'Li Wan', 'Zhaojun Yang', 'Haichuan Yang'] | 2022-11-09 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [-9.39291567e-02 -3.02007586e-01 -4.65187997e-01 -1.48234218e-01
-1.17887288e-01 -2.65964270e-01 2.11460978e-01 -3.23474854e-02
-8.25276852e-01 2.14674160e-01 1.07822316e-02 -8.27327609e-01
-7.70755392e-03 -8.40372086e-01 -7.25166619e-01 -4.11163032e-01
-2.42953613e-01 -1.62484959e-01 3.32078636e-01 -2.50374917... | [8.469861030578613, 2.899280071258545] |
61f79ded-70fe-4e97-87a4-67e371e7511d | graph-based-features-for-automatic-online | 1708.01060 | null | https://arxiv.org/abs/1708.01060v1 | https://arxiv.org/pdf/1708.01060v1.pdf | Graph-based Features for Automatic Online Abuse Detection | While online communities have become increasingly important over the years, the moderation of user-generated content is still performed mostly manually. Automating this task is an important step in reducing the financial cost associated with moderation, but the majority of automated approaches strictly based on message... | ['Georges Linares', 'Richard Dufour', 'Vincent Labatut', 'Etienne Papegnies'] | 2017-08-03 | null | null | null | null | ['abuse-detection'] | ['natural-language-processing'] | [ 2.50376403e-01 4.04149294e-01 3.82204615e-02 -1.44154847e-01
-5.03382206e-01 -8.01192164e-01 1.00748217e+00 7.24130809e-01
-3.62405807e-01 7.14806616e-01 4.22607630e-01 -5.96172214e-01
1.57844290e-01 -9.45745111e-01 1.40577301e-01 -3.01968753e-01
-2.22597882e-01 4.06829566e-01 3.82521629e-01 -2.95209020... | [8.381038665771484, 10.2698335647583] |
61e48ce6-00df-46ee-8dc1-705fa4dc143f | mpchat-towards-multimodal-persona-grounded | 2305.17388 | null | https://arxiv.org/abs/2305.17388v1 | https://arxiv.org/pdf/2305.17388v1.pdf | MPCHAT: Towards Multimodal Persona-Grounded Conversation | In order to build self-consistent personalized dialogue agents, previous research has mostly focused on textual persona that delivers personal facts or personalities. However, to fully describe the multi-faceted nature of persona, image modality can help better reveal the speaker's personal characteristics and experien... | ['Gunhee Kim', 'Sangdoo Yun', 'Yeda Song', 'Jaewoo Ahn'] | 2023-05-27 | null | null | null | null | ['speaker-identification'] | ['speech'] | [-1.12914048e-01 5.31265974e-01 -4.34049591e-03 -5.42932093e-01
-8.39887381e-01 -4.88808572e-01 1.21012807e+00 2.58066952e-01
-2.56062061e-01 7.20545828e-01 9.61143434e-01 3.89670491e-01
-2.36586984e-02 -6.54955328e-01 -3.51312518e-01 -3.95363152e-01
7.43268803e-03 7.96152115e-01 -1.96834922e-01 -5.53823829... | [12.796562194824219, 7.876481533050537] |
2fc8915f-ecb7-4bbf-9aac-295986e23853 | llm-itself-can-read-and-generate-cxr-images | 2305.11490 | null | https://arxiv.org/abs/2305.11490v2 | https://arxiv.org/pdf/2305.11490v2.pdf | LLM Itself Can Read and Generate CXR Images | Building on the recent remarkable development of large language models (LLMs), active attempts are being made to extend the utility of LLMs to multimodal tasks. There have been previous efforts to link language and visual information, and attempts to add visual capabilities to LLMs are ongoing as well. However, existin... | ['Jong Chul Ye', 'Won Jun Kim', 'Suhyeon Lee'] | 2023-05-19 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [ 7.15230703e-01 5.28948724e-01 -6.92612603e-02 -4.93455887e-01
-1.03074729e+00 -4.85156506e-01 8.82737219e-01 -7.04954192e-02
-3.15481812e-01 6.67836249e-01 3.97582412e-01 -6.53332889e-01
5.68363309e-01 -7.57423043e-01 -8.60860467e-01 -3.22542191e-01
4.80063558e-01 3.12359512e-01 2.56742351e-02 -2.19608620... | [11.168100357055664, 0.88862144947052] |
4093f185-d317-4533-b715-d606b1fd8946 | tu-wien-trec-deep-learning-19-simple | 1912.01385 | null | https://arxiv.org/abs/1912.01385v1 | https://arxiv.org/pdf/1912.01385v1.pdf | TU Wien @ TREC Deep Learning '19 -- Simple Contextualization for Re-ranking | The usage of neural network models puts multiple objectives in conflict with each other: Ideally we would like to create a neural model that is effective, efficient, and interpretable at the same time. However, in most instances we have to choose which property is most important to us. We used the opportunity of the TR... | ['Markus Zlabinger', 'Sebastian Hofstätter', 'Allan Hanbury'] | 2019-12-03 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [-2.12144107e-01 -1.71801001e-01 2.48206034e-02 -4.24599916e-01
-9.88313973e-01 -7.66950488e-01 9.37696457e-01 7.11471200e-01
-1.05974805e+00 4.93679821e-01 7.50367403e-01 -4.60310251e-01
-6.52562559e-01 -6.41500294e-01 -7.81423211e-01 -3.00590038e-01
-3.72673362e-01 5.42828858e-01 3.81554723e-01 -6.01373076... | [11.459564208984375, 7.642856121063232] |
5072e2ba-7d23-48f7-9c51-b56986e65e20 | multilingual-negation-scope-resolution-for | null | null | https://aclanthology.org/2021.louhi-1.2 | https://aclanthology.org/2021.louhi-1.2.pdf | Multilingual Negation Scope Resolution for Clinical Text | Negation scope resolution is key to high-quality information extraction from clinical texts, but so far, efforts to make encoders used for information extraction negation-aware have been limited to English. We present a universal approach to multilingual negation scope resolution, that overcomes the lack of training da... | ['Anders Søgaard', 'Mareike Hartmann'] | null | null | null | null | eacl-louhi-2021-4 | ['negation-scope-resolution'] | ['natural-language-processing'] | [ 4.39172477e-01 4.09669578e-01 -8.05017233e-01 -3.20169538e-01
-1.74021208e+00 -4.65536565e-01 1.19294515e-02 7.82830060e-01
-9.10232663e-01 1.27141082e+00 8.55954468e-01 -3.01862150e-01
-1.62623599e-01 -4.01444107e-01 -5.09951055e-01 -1.67192463e-02
2.69694954e-01 6.63408697e-01 2.64096260e-01 -5.33021390... | [8.488519668579102, 8.78680419921875] |
7995cf05-5e39-4b56-94d7-3547015c9f23 | genpose-generative-category-level-object-pose | 2306.10531 | null | https://arxiv.org/abs/2306.10531v1 | https://arxiv.org/pdf/2306.10531v1.pdf | GenPose: Generative Category-level Object Pose Estimation via Diffusion Models | Object pose estimation plays a vital role in embodied AI and computer vision, enabling intelligent agents to comprehend and interact with their surroundings. Despite the practicality of category-level pose estimation, current approaches encounter challenges with partially observed point clouds, known as the multihypoth... | ['Hao Dong', 'Mingdong Wu', 'Jiyao Zhang'] | 2023-06-18 | null | null | null | null | ['pose-tracking', 'pose-estimation', '6d-pose-estimation-using-rgbd', '6d-pose-estimation-1'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 1.37787044e-01 -9.76922214e-02 4.40696441e-02 -3.69501710e-01
-9.87317383e-01 -5.88961661e-01 8.24637890e-01 1.69156656e-01
-7.03461885e-01 3.36667448e-01 1.13178445e-02 2.16002986e-01
-3.69902216e-02 -4.19160157e-01 -9.40780759e-01 -6.88397229e-01
-1.44433513e-01 8.32511127e-01 5.25325179e-01 1.86736017... | [7.614870548248291, -2.5294976234436035] |
4cbcd469-149b-45a8-a185-ca500bc1696a | asner-annotated-dataset-and-baseline-for-1 | null | null | https://aclanthology.org/2022.lrec-1.706 | https://aclanthology.org/2022.lrec-1.706.pdf | AsNER - Annotated Dataset and Baseline for Assamese Named Entity recognition | We present the AsNER, a named entity annotation dataset for low resource Assamese language with a baseline Assamese NER model. The dataset contains about 99k tokens comprised of text from the speech of the Prime Minister of India and Assamese play. It also contains person names, location names and addresses. The propos... | ['Priyankoo Sarmah', 'Sukumar Nandi', 'Dhrubajyoti Pathak'] | null | null | null | null | lrec-2022-6 | ['xlm-r'] | ['natural-language-processing'] | [-3.21324021e-01 1.91120803e-01 -9.57401991e-02 -3.61480981e-01
-8.30534101e-01 -8.50874424e-01 8.51391077e-01 3.81367773e-01
-1.37166142e+00 1.19395566e+00 7.22448409e-01 -4.24551278e-01
4.53550696e-01 -1.01780558e+00 -5.31911373e-01 -1.14822961e-01
-1.73608229e-01 7.83644438e-01 2.75494337e-01 -3.83954853... | [9.7786865234375, 9.806588172912598] |
7177700b-521a-491f-9c0d-7b9012ca0e69 | consistent-open-ended-question-generation | 2210.11536 | null | https://arxiv.org/abs/2210.11536v1 | https://arxiv.org/pdf/2210.11536v1.pdf | CONSISTENT: Open-Ended Question Generation From News Articles | Recent work on question generation has largely focused on factoid questions such as who, what, where, when about basic facts. Generating open-ended why, how, what, etc. questions that require long-form answers have proven more difficult. To facilitate the generation of open-ended questions, we propose CONSISTENT, a new... | ['Smaranda Muresan', 'Justin Lewis', 'Tuhin Chakrabarty'] | 2022-10-20 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [-7.74821192e-02 1.11213446e+00 1.33648902e-01 -5.32218456e-01
-1.59079170e+00 -9.82825160e-01 8.85386646e-01 3.63431960e-01
-6.35567978e-02 1.29486561e+00 9.07819867e-01 -7.42398441e-01
-8.12717751e-02 -9.40337956e-01 -5.45110822e-01 5.95640838e-01
4.49386388e-01 7.52838731e-01 2.13664278e-01 -8.14442515... | [11.523414611816406, 8.14305305480957] |
bd9fab31-86a3-4c97-b389-f6e3bf198af2 | learning-to-rectify-for-robust-learning-with | 2111.04239 | null | https://arxiv.org/abs/2111.04239v1 | https://arxiv.org/pdf/2111.04239v1.pdf | Learning to Rectify for Robust Learning with Noisy Labels | Label noise significantly degrades the generalization ability of deep models in applications. Effective strategies and approaches, \textit{e.g.} re-weighting, or loss correction, are designed to alleviate the negative impact of label noise when training a neural network. Those existing works usually rely on the pre-spe... | ['Yilong Yin', 'Zhongyi Han', 'Qi Wei', 'Chenhui Guo', 'Haoliang Sun'] | 2021-11-08 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 3.51710737e-01 2.68440306e-01 -2.84484863e-01 -6.96609437e-01
-9.24425185e-01 -2.96889007e-01 4.61065888e-01 -1.83404595e-01
-6.67587399e-01 7.52024114e-01 -1.53865770e-01 -1.23219602e-01
-5.16152263e-01 -8.27038348e-01 -9.62927282e-01 -1.23072529e+00
3.88443261e-01 3.65163773e-01 -1.98377594e-01 1.78987592... | [9.302663803100586, 3.8150343894958496] |
cbc6a177-4aa8-4de5-aecf-14d8e56b72a8 | a-low-rank-approximation-approach-to-learning | null | null | https://aclanthology.org/N16-1008 | https://aclanthology.org/N16-1008.pdf | A Low-Rank Approximation Approach to Learning Joint Embeddings of News Stories and Images for Timeline Summarization | null | ['a', 'William Yang Wang', 'Yashar Mehdad', 'Dragomir R. Radev', 'Am Stent'] | 2016-06-01 | null | null | null | naacl-2016-6 | ['timeline-summarization'] | ['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... | [-7.298902988433838, 3.6974704265594482] |
26253a9d-086f-4bd7-bf08-957a7fa2433b | spectral-image-clustering-on-dual-energy-ct | 2201.13398 | null | https://arxiv.org/abs/2201.13398v1 | https://arxiv.org/pdf/2201.13398v1.pdf | Spectral image clustering on dual-energy CT scans using functional regression mixtures | Dual-energy computed tomography (DECT) is an advanced CT scanning technique enabling material characterization not possible with conventional CT scans. It allows the reconstruction of energy decay curves at each 3D image voxel, representing varying image attenuation at different effective scanning energy levels. In thi... | ['Peter Savadjiev', 'Reza Forghani', 'Mark Coates', 'Faicel Chamroukhi', 'Segolene Brivet'] | 2022-01-31 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 2.08813488e-01 -1.59169659e-02 -3.06589454e-01 -2.68589497e-01
-1.14373374e+00 -4.61390555e-01 3.04477662e-01 4.35689121e-01
-5.32072484e-01 2.74838060e-01 3.79405737e-01 -5.20171523e-01
-4.67252016e-01 -4.56845939e-01 -1.20986804e-01 -1.06374550e+00
-2.26274088e-01 1.09971118e+00 3.70980173e-01 3.04198682... | [14.292433738708496, -2.4305405616760254] |
557366f7-b8c8-424d-80e4-f940d8badafa | clevrtex-a-texture-rich-benchmark-for | 2111.10265 | null | https://arxiv.org/abs/2111.10265v1 | https://arxiv.org/pdf/2111.10265v1.pdf | ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation | There has been a recent surge in methods that aim to decompose and segment scenes into multiple objects in an unsupervised manner, i.e., unsupervised multi-object segmentation. Performing such a task is a long-standing goal of computer vision, offering to unlock object-level reasoning without requiring dense annotation... | ['Christian Rupprecht', 'Iro Laina', 'Laurynas Karazija'] | 2021-11-19 | null | null | null | null | ['unsupervised-object-segmentation'] | ['computer-vision'] | [ 5.57857513e-01 -1.08167984e-01 1.78998828e-01 -3.66765499e-01
-8.89357030e-01 -9.29499209e-01 6.23947382e-01 -1.15022749e-01
-2.43846267e-01 5.11387467e-01 -1.21481419e-01 -1.58270866e-01
-1.59582704e-01 -4.81709898e-01 -1.00747252e+00 -6.40536070e-01
9.34397504e-02 9.43646312e-01 4.98497754e-01 -9.86746047... | [9.734793663024902, 0.38331952691078186] |
6e54e172-5383-449c-a095-38afe2d60970 | knowledge-based-multilingual-language-model-1 | 2111.10962 | null | https://arxiv.org/abs/2111.10962v4 | https://arxiv.org/pdf/2111.10962v4.pdf | Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples | Knowledge-enhanced language representation learning has shown promising results across various knowledge-intensive NLP tasks. However, prior methods are limited in efficient utilization of multilingual knowledge graph (KG) data for language model (LM) pretraining. They often train LMs with KGs in indirect ways, relying... | ['Luo Si', 'Shafiq Joty', 'Lidong Bing', 'Ruidan He', 'Xin Li', 'Linlin Liu'] | 2021-11-22 | knowledge-based-multilingual-language-model | https://openreview.net/forum?id=SCSonHu4p0W | https://openreview.net/pdf?id=SCSonHu4p0W | null | ['relation-classification'] | ['natural-language-processing'] | [-4.12186801e-01 4.86699551e-01 -5.78583419e-01 -3.88777673e-01
-7.33267426e-01 -5.91936290e-01 4.04742807e-01 3.15919489e-01
-6.11720979e-01 1.12139523e+00 3.33708584e-01 -5.37817299e-01
-3.26628797e-02 -1.12872994e+00 -1.06184137e+00 5.06043993e-02
-4.15809713e-02 5.79918504e-01 6.25338480e-02 -5.15717149... | [9.42414379119873, 8.492165565490723] |
23e5d5d1-2bf7-475d-8290-9d06a74341c2 | fast-spatially-varying-indoor-lighting-1 | 1906.03799 | null | https://arxiv.org/abs/1906.03799v1 | https://arxiv.org/pdf/1906.03799v1.pdf | Fast Spatially-Varying Indoor Lighting Estimation | We propose a real-time method to estimate spatiallyvarying indoor lighting from a single RGB image. Given an image and a 2D location in that image, our CNN estimates a 5th order spherical harmonic representation of the lighting at the given location in less than 20ms on a laptop mobile graphics card. While existing app... | ['Jean-François Lalonde', 'Sunil Hadap', 'Mathieu Garon', 'Kalyan Sunkavalli', 'Nathan Carr'] | 2019-06-10 | fast-spatially-varying-indoor-lighting | http://openaccess.thecvf.com/content_CVPR_2019/html/Garon_Fast_Spatially-Varying_Indoor_Lighting_Estimation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Garon_Fast_Spatially-Varying_Indoor_Lighting_Estimation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['lighting-estimation'] | ['computer-vision'] | [ 3.37473564e-02 -1.36470914e-01 3.62088174e-01 -5.09311020e-01
-5.64977765e-01 -8.60987723e-01 2.42210999e-01 -2.15156451e-01
-3.37340027e-01 3.91570151e-01 -2.12161914e-01 -4.44324344e-01
4.83756036e-01 -7.81009078e-01 -8.22883606e-01 -3.45535249e-01
4.28215265e-01 2.22216144e-01 -2.70472206e-02 5.01142768... | [9.58481216430664, -2.8700969219207764] |
3ad0bc84-53a3-46a3-a0a8-bd78d9e2dece | efficient-multi-domain-dictionary-learning | 1811.00274 | null | http://arxiv.org/abs/1811.00274v1 | http://arxiv.org/pdf/1811.00274v1.pdf | Efficient Multi-Domain Dictionary Learning with GANs | In this paper, we propose the multi-domain dictionary learn- ing (MDDL) to
make dictionary learning-based classification more robust to data representing
in different domains. We use adversarial neural networks to generate data in
different styles, and collect all the generated data into a miscellaneous
dictionary. To ... | ['Ulrich Neumann', 'Cho Ying Wu'] | 2018-11-01 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [ 9.70183685e-02 -7.78587535e-02 -1.81174502e-01 -1.88953325e-01
-5.50992429e-01 -7.73819089e-01 3.41132022e-02 1.64233014e-01
-4.88090664e-01 1.03049433e+00 -9.45632234e-02 -1.34791762e-01
1.19738169e-02 -1.22918940e+00 -7.06888855e-01 -9.21948135e-01
2.07198694e-01 8.21638107e-01 -3.30079794e-01 -4.91802424... | [9.130205154418945, 3.881690263748169] |
c6d457f3-2614-4336-891a-456b50cf419c | document-image-shadow-removal-guided-by-color | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Document_Image_Shadow_Removal_Guided_by_Color-Aware_Background_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Document_Image_Shadow_Removal_Guided_by_Color-Aware_Background_CVPR_2023_paper.pdf | Document Image Shadow Removal Guided by Color-Aware Background | Existing works on document image shadow removal mostly depend on learning and leveraging a constant background (the color of the paper) from the image. However, the constant background is less representative and frequently ignores other background colors, such as the printed colors, resulting in distorted results. ... | ['Chunxia Xiao', 'Xiaolong Zhang', 'Zheng Liu', 'Qing Zhang', 'Yinghao He', 'Ling Zhang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['shadow-removal', 'image-shadow-removal'] | ['computer-vision', 'computer-vision'] | [ 6.71312988e-01 -3.45092654e-01 1.85589001e-01 -3.03534776e-01
-3.73800814e-01 -3.83822709e-01 5.01403093e-01 -2.77568758e-01
-2.04289645e-01 7.07997262e-01 1.45405412e-01 -2.79807508e-01
4.41661328e-01 -5.41044474e-01 -6.77120686e-01 -1.19241393e+00
5.25681734e-01 -2.56122947e-01 7.65312076e-01 -9.65624750... | [10.865912437438965, -4.016974449157715] |
5e4b2cfd-10c6-48dd-b08c-727d9e63677a | leveraging-line-point-consistence-to-preserve | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Jia_Leveraging_Line-Point_Consistence_To_Preserve_Structures_for_Wide_Parallax_Image_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Jia_Leveraging_Line-Point_Consistence_To_Preserve_Structures_for_Wide_Parallax_Image_CVPR_2021_paper.pdf | Leveraging Line-Point Consistence To Preserve Structures for Wide Parallax Image Stitching | Generating high-quality stitched images with natural structures is a challenging task in computer vision. In this paper, we succeed in preserving both local and global geometric structures for wide parallax images, while reducing artifacts and distortions. A projective invariant, Characteristic Number, is used to m... | ['Longin Jan Latecki', 'Xinchen Ye', 'Shiyu Teng', 'Haotian Zhao', 'Xin Fan', 'ZhengJun Li', 'Qi Jia'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['image-stitching'] | ['computer-vision'] | [ 5.16893446e-01 -3.75817776e-01 2.67502256e-02 -5.32838423e-03
-4.84005868e-01 -7.72305727e-01 4.19685841e-01 -1.47836804e-01
1.86972152e-02 3.38138640e-01 1.72312632e-01 2.72524446e-01
-1.09387912e-01 -5.91756403e-01 -6.48852825e-01 -9.12547112e-01
1.49302825e-01 4.07961197e-02 3.13833922e-01 -3.01979303... | [9.349908828735352, -2.337111711502075] |
4a957f4b-c89f-4a3e-a016-df8e4b069e3f | cross-domain-sparse-coding | 1311.7080 | null | http://arxiv.org/abs/1311.7080v1 | http://arxiv.org/pdf/1311.7080v1.pdf | Cross-Domain Sparse Coding | Sparse coding has shown its power as an effective data representation method.
However, up to now, all the sparse coding approaches are limited within the
single domain learning problem. In this paper, we extend the sparse coding to
cross domain learning problem, which tries to learn from a source domain to a
target dom... | ['Jim Jing-Yan Wang'] | 2013-11-27 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 3.01870197e-01 -4.48154688e-01 -3.55507642e-01 -3.92788291e-01
-1.04313564e+00 -3.92842680e-01 6.04650319e-01 -1.55485213e-01
3.01155150e-01 8.11477542e-01 3.71063799e-01 1.27992913e-01
-2.49081627e-01 -4.63331431e-01 -4.51352417e-01 -8.01508546e-01
-1.56277828e-02 2.89309770e-01 2.00790271e-01 2.62258220... | [12.333769798278809, 0.45137685537338257] |
1e1f48dc-4d57-4021-96d2-275ddcaec971 | dimbert-learning-vision-language-grounded | 2210.16431 | null | https://arxiv.org/abs/2210.16431v1 | https://arxiv.org/pdf/2210.16431v1.pdf | DiMBERT: Learning Vision-Language Grounded Representations with Disentangled Multimodal-Attention | Vision-and-language (V-L) tasks require the system to understand both vision content and natural language, thus learning fine-grained joint representations of vision and language (a.k.a. V-L representations) is of paramount importance. Recently, various pre-trained V-L models are proposed to learn V-L representations a... | ['Yuexian Zou', 'Xu sun', 'Wei Fan', 'Xuancheng Ren', 'Shen Ge', 'Xian Wu', 'Fenglin Liu'] | 2022-10-28 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [ 1.56344473e-02 -1.22949421e-01 -2.27576107e-01 -2.00109035e-01
-8.62751842e-01 -6.38463914e-01 1.07675397e+00 -3.21724862e-01
-2.41446584e-01 6.20669186e-01 4.81024623e-01 -2.80516803e-01
2.94283062e-01 -6.14290178e-01 -8.52244377e-01 -7.91505396e-01
4.88152236e-01 1.01608992e-01 -2.36809820e-01 -2.60658294... | [10.814952850341797, 1.5039904117584229] |
ee900e5a-4d48-4eca-8695-e901ae209531 | interactive-error-resolution-strategies-for | null | null | https://aclanthology.org/W13-4022 | https://aclanthology.org/W13-4022.pdf | Interactive Error Resolution Strategies for Speech-to-Speech Translation Systems | null | ['Sanjika Hewavitharana', 'Matthew Roy', 'Frederick Choi', 'Rohit Kumar', 'Sankaranarayanan Ananthakrishnan'] | 2013-08-01 | null | null | null | ws-2013-8 | ['speech-to-speech-translation'] | ['speech'] | [-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... | [-7.378097057342529, 3.8977200984954834] |
7cf6cb56-91e2-4927-98cf-112a378490df | improving-ecg-based-covid-19-diagnosis-and | 2211.10431 | null | https://arxiv.org/abs/2211.10431v2 | https://arxiv.org/pdf/2211.10431v2.pdf | Improving ECG-based COVID-19 diagnosis and mortality predictions using pre-pandemic medical records at population-scale | Pandemic outbreaks such as COVID-19 occur unexpectedly, and need immediate action due to their potential devastating consequences on global health. Point-of-care routine assessments such as electrocardiogram (ECG), can be used to develop prediction models for identifying individuals at risk. However, there is often too... | ['Nariman Sepehrvand', 'Padma Kaul', 'Russell Greiner', 'Abram Hindle', 'Amir Salimi', 'Zihan Wang', 'Luan Manh Chu', 'Sunil Vasu Kalmady', 'Weijie Sun'] | 2022-11-14 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 3.34496051e-01 -5.72696477e-02 -2.49917097e-02 -3.68102044e-01
-9.57548440e-01 -5.76281011e-01 1.06633320e-01 9.97794628e-01
-4.89186049e-01 1.02789903e+00 4.09527630e-01 -7.58422315e-01
-2.59252787e-01 -7.63150811e-01 -4.45340991e-01 -4.84239817e-01
-6.57289386e-01 1.16415322e+00 -2.64743805e-01 -1.33830652... | [7.9329447746276855, 6.1098856925964355] |
675ff0ba-17d7-488f-9566-684f17c144a7 | refining-amortized-posterior-approximations | 2305.08733 | null | https://arxiv.org/abs/2305.08733v1 | https://arxiv.org/pdf/2305.08733v1.pdf | Refining Amortized Posterior Approximations using Gradient-Based Summary Statistics | We present an iterative framework to improve the amortized approximations of posterior distributions in the context of Bayesian inverse problems, which is inspired by loop-unrolled gradient descent methods and is theoretically grounded in maximally informative summary statistics. Amortized variational inference is rest... | ['Felix J. Herrmann', 'Mathias Louboutin', 'Ali Siahkoohi', 'Rafael Orozco'] | 2023-05-15 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 3.40743542e-01 2.47790754e-01 1.67077109e-01 -8.30534175e-02
-1.32497418e+00 -3.81467015e-01 7.59947062e-01 -7.69970268e-02
-6.71116889e-01 1.05585825e+00 3.36601824e-01 -2.97144085e-01
-4.88965571e-01 -5.57425499e-01 -8.70473385e-01 -1.03587317e+00
-1.02511548e-01 7.72994220e-01 -2.30794132e-04 1.01929307... | [6.818777084350586, 3.771951913833618] |
4e021a9f-7f8c-4b33-b2b8-a0dfa2925940 | detectron2-object-detection-manipulating | null | null | https://www.ijert.org/detectron2-object-detection-manipulating-images-using-cartoonization | https://www.ijert.org/research/detectron2-object-detection-manipulating-images-using-cartoonization-IJERTV10IS080122.pdf | Detectron2 Object Detection & Manipulating Images using Cartoonization | In today's world, there is a rapid increase in the autonomous vehicle. There are various levels of autonomous vehicles depending upon the degree of autonomy-for the lower degree of autonomy driver has more power and functionality for managing, on coming to the fully automated vehicle like Tesla are expected to have ful... | ['Sonali Kotni', 'Allena Venkata Sai Abhishek'] | 2021-08-01 | null | null | null | international-journal-of-engineering-research | ['panoptic-segmentation', 'object-recognition', 'image-augmentation', 'real-time-object-detection', 'image-manipulation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-6.93049014e-01 2.34157518e-01 -1.35410890e-01 -5.52000046e-01
-3.61374803e-02 -3.74109894e-01 5.20710707e-01 -2.04454854e-01
-6.26258790e-01 3.57641965e-01 -1.02089427e-01 -5.72934568e-01
9.87939611e-02 -7.13041067e-01 -4.20118660e-01 -2.53948331e-01
-5.76160848e-02 5.52210212e-01 6.29926980e-01 -5.44048190... | [8.011569023132324, -1.1896824836730957] |
95c58cab-f303-4318-bcf3-beca1b1b9448 | ucas-iie-nlp-at-semeval-2023-task-12 | 2306.01093 | null | https://arxiv.org/abs/2306.01093v1 | https://arxiv.org/pdf/2306.01093v1.pdf | UCAS-IIE-NLP at SemEval-2023 Task 12: Enhancing Generalization of Multilingual BERT for Low-resource Sentiment Analysis | This paper describes our system designed for SemEval-2023 Task 12: Sentiment analysis for African languages. The challenge faced by this task is the scarcity of labeled data and linguistic resources in low-resource settings. To alleviate these, we propose a generalized multilingual system SACL-XLMR for sentiment analys... | ['Songlin Hu', 'Wei Zhou', 'Yaxin Liu', 'Lingwei Wei', 'Dou Hu'] | 2023-06-01 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-1.69393614e-01 -3.73371273e-01 -4.20859724e-01 -5.31776428e-01
-1.39387023e+00 -8.33459496e-01 5.09231508e-01 9.49629769e-02
-8.75190973e-01 7.95032501e-01 5.17505825e-01 -4.07613039e-01
5.89322925e-01 -3.46184403e-01 -5.07477164e-01 -2.07499996e-01
2.36451238e-01 3.78361434e-01 -3.78707975e-01 -9.13339555... | [11.333259582519531, 7.187560558319092] |
e7f6c5c2-1fc9-4743-9f46-30001e53651c | combating-confirmation-bias-a-unified-pseudo | 2307.02075 | null | https://arxiv.org/abs/2307.02075v1 | https://arxiv.org/pdf/2307.02075v1.pdf | Combating Confirmation Bias: A Unified Pseudo-Labeling Framework for Entity Alignment | Entity alignment (EA) aims at identifying equivalent entity pairs across different knowledge graphs (KGs) that refer to the same real-world identity. To systematically combat confirmation bias for pseudo-labeling-based entity alignment, we propose a Unified Pseudo-Labeling framework for Entity Alignment (UPL-EA) that e... | ['Junbin Gao', 'Daokun Zhang', 'Jie Yin', 'Qijie Ding'] | 2023-07-05 | null | null | null | null | ['entity-alignment', 'knowledge-graphs', 'pseudo-label', 'entity-alignment'] | ['knowledge-base', 'knowledge-base', 'miscellaneous', 'natural-language-processing'] | [ 2.23843291e-01 3.94472808e-01 -4.98918056e-01 -4.25620824e-01
-9.76767719e-01 -6.28492355e-01 5.54300845e-01 5.18375814e-01
-3.41345072e-01 7.67434716e-01 -1.34707958e-01 -1.11874737e-01
-3.78504038e-01 -8.33547354e-01 -9.12072062e-01 -4.70068604e-01
1.37208298e-01 6.89199567e-01 1.31710529e-01 3.73125933... | [8.745771408081055, 8.001858711242676] |
de444ad5-ac95-4347-9446-15392e05f09b | seqformer-a-frustratingly-simple-model-for | 2112.08275 | null | https://arxiv.org/abs/2112.08275v2 | https://arxiv.org/pdf/2112.08275v2.pdf | SeqFormer: Sequential Transformer for Video Instance Segmentation | In this work, we present SeqFormer for video instance segmentation. SeqFormer follows the principle of vision transformer that models instance relationships among video frames. Nevertheless, we observe that a stand-alone instance query suffices for capturing a time sequence of instances in a video, but attention mechan... | ['Xiang Bai', 'Wenqing Zhang', 'Song Bai', 'Yi Jiang', 'Junfeng Wu'] | 2021-12-15 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 1.43389493e-01 2.61834245e-02 -4.46477801e-01 -2.71095634e-01
-5.94169497e-01 -6.45729244e-01 3.60597759e-01 -2.12122381e-01
-5.08654833e-01 4.34885561e-01 7.26650432e-02 -1.43941596e-01
1.83336899e-01 -5.55256665e-01 -8.68973196e-01 -4.94814992e-01
-9.35503542e-02 2.34562650e-01 7.92501867e-01 9.21855271... | [9.148778915405273, -0.04127845540642738] |
bba4fdeb-a0bf-4708-b19a-92cdeb2fecbf | a-unified-framework-for-slot-based-response | 2305.17433 | null | https://arxiv.org/abs/2305.17433v1 | https://arxiv.org/pdf/2305.17433v1.pdf | A Unified Framework for Slot based Response Generation in a Multimodal Dialogue System | Natural Language Understanding (NLU) and Natural Language Generation (NLG) are the two critical components of every conversational system that handles the task of understanding the user by capturing the necessary information in the form of slots and generating an appropriate response in accordance with the extracted in... | ['Asif Ekbal', 'Avinash Madasu', 'Mauajama Firdaus'] | 2023-05-27 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 3.10528129e-01 4.29851413e-01 2.98299119e-02 -4.30339634e-01
-9.62574005e-01 -4.89572972e-01 7.84196436e-01 4.40944545e-02
-2.82667130e-01 9.84978497e-01 8.03670704e-01 -5.64131886e-02
2.78764546e-01 -6.47517800e-01 -2.24472582e-01 -5.89424312e-01
5.15151441e-01 4.81308281e-01 8.48590955e-03 -5.20023286... | [10.994211196899414, 1.353379726409912] |
537ab7f1-7998-4276-9957-3d1b61f82339 | performance-comparison-of-deep-rl-algorithms | 2208.00728 | null | https://arxiv.org/abs/2208.00728v1 | https://arxiv.org/pdf/2208.00728v1.pdf | Performance Comparison of Deep RL Algorithms for Energy Systems Optimal Scheduling | Taking advantage of their data-driven and model-free features, Deep Reinforcement Learning (DRL) algorithms have the potential to deal with the increasing level of uncertainty due to the introduction of renewable-based generation. To deal simultaneously with the energy systems' operational cost and technical constraint... | ['Peter Palensky', 'Pedro P. Vergara', 'Edgar Mauricio Salazar', 'Hou Shengren'] | 2022-08-01 | null | null | null | null | ['energy-management'] | ['time-series'] | [-3.65093648e-01 7.83633068e-02 -3.80748302e-01 5.51839098e-02
-5.10842323e-01 -5.84116638e-01 4.57516342e-01 3.21396858e-01
-2.07387924e-01 1.27264297e+00 -3.56423467e-01 -3.71632814e-01
-7.13593781e-01 -9.74326134e-01 -4.03424650e-01 -9.74441648e-01
-3.90696645e-01 6.32649720e-01 -4.91765618e-01 -1.40584454... | [5.3930253982543945, 2.475921630859375] |
407ea8c3-b6c7-45d0-a66f-6700a9d85955 | effective-use-of-variational-embedding | 1906.03402 | null | https://arxiv.org/abs/1906.03402v3 | https://arxiv.org/pdf/1906.03402v3.pdf | Effective Use of Variational Embedding Capacity in Expressive End-to-End Speech Synthesis | Recent work has explored sequence-to-sequence latent variable models for expressive speech synthesis (supporting control and transfer of prosody and style), but has not presented a coherent framework for understanding the trade-offs between the competing methods. In this paper, we propose embedding capacity (the amount... | ['RJ Skerry-Ryan', 'Daisy Stanton', 'Soroosh Mariooryad', 'Eric Battenberg', 'David Kao', 'Matt Shannon', 'Tom Bagby'] | 2019-06-08 | null | https://openreview.net/forum?id=SJgBQaVKwH | https://openreview.net/pdf?id=SJgBQaVKwH | null | ['expressive-speech-synthesis'] | ['speech'] | [ 3.60732317e-01 5.25892198e-01 -3.71066153e-01 -3.48538041e-01
-1.05397296e+00 -7.53633916e-01 7.92200625e-01 -3.78513992e-01
-6.70757294e-02 6.41969919e-01 7.66326606e-01 -2.49561295e-02
-2.24131998e-02 -6.51453316e-01 -6.44930005e-01 -7.70848989e-01
2.53949583e-01 6.71617270e-01 4.39328887e-02 -1.71873689... | [15.016096115112305, 6.541414737701416] |
c73c1f39-370f-4c6e-9c02-911542f16d5f | a-graph-based-lattice-dependency-parser-for | null | null | https://aclanthology.org/Q15-1026 | https://aclanthology.org/Q15-1026.pdf | A Graph-based Lattice Dependency Parser for Joint Morphological Segmentation and Syntactic Analysis | Space-delimited words in Turkish and Hebrew text can be further segmented into meaningful units, but syntactic and semantic context is necessary to predict segmentation. At the same time, predicting correct syntactic structures relies on correct segmentation. We present a graph-based lattice dependency parser that oper... | ['{\\"O}zlem {\\c{C}}etino{\\u{g}}lu', 'Wolfgang Seeker'] | 2015-01-01 | null | null | null | tacl-2015-1 | ['morphological-tagging'] | ['natural-language-processing'] | [-4.26746123e-02 3.91946971e-01 -1.85155377e-01 -7.28111863e-01
-7.77327657e-01 -1.04626179e+00 2.57076207e-03 6.49916828e-01
-5.53344011e-01 6.01608336e-01 4.86313432e-01 -8.86040986e-01
3.17771077e-01 -8.17499399e-01 -2.49509960e-01 -1.81838021e-01
1.37889668e-01 6.00170612e-01 4.92220700e-01 -1.63334325... | [10.330690383911133, 9.962845802307129] |
145406cd-1fca-4971-8655-6f884618ad6c | nerf-loc-visual-localization-with-conditional | 2304.07979 | null | https://arxiv.org/abs/2304.07979v1 | https://arxiv.org/pdf/2304.07979v1.pdf | NeRF-Loc: Visual Localization with Conditional Neural Radiance Field | We propose a novel visual re-localization method based on direct matching between the implicit 3D descriptors and the 2D image with transformer. A conditional neural radiance field(NeRF) is chosen as the 3D scene representation in our pipeline, which supports continuous 3D descriptors generation and neural rendering. B... | ['Chengjie Wang', 'Yong liu', 'Qiang Nie', 'Jianlin Liu'] | 2023-04-17 | null | null | null | null | ['neural-rendering', 'visual-localization'] | ['computer-vision', 'computer-vision'] | [-6.03610687e-02 -3.85504901e-01 -2.37453982e-01 -6.65124893e-01
-7.21964896e-01 -6.59267426e-01 5.51013052e-01 -1.07287161e-01
-1.57662034e-01 2.02909019e-02 -7.12353662e-02 -9.16133001e-02
2.20278323e-01 -7.34798789e-01 -8.45706224e-01 -3.14805537e-01
4.07253087e-01 1.62556022e-01 4.47690547e-01 8.09023827... | [7.88304328918457, -2.609022855758667] |
517af0c7-36b5-4853-afd7-1a4cbbb94518 | chatgpt-evaluation-on-sentence-level | 2304.14827 | null | https://arxiv.org/abs/2304.14827v2 | https://arxiv.org/pdf/2304.14827v2.pdf | ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations | This paper aims to quantitatively evaluate the performance of ChatGPT, an interactive large language model, on inter-sentential relations such as temporal relations, causal relations, and discourse relations. Given ChatGPT's promising performance across various tasks, we conduct extensive evaluations on the whole test ... | ['Yangqiu Song', 'Xin Liu', 'Tianqing Fang', 'Yuxin Jiang', 'Weiqi Wang', 'Jiayang Cheng', 'Chunkit Chan'] | 2023-04-28 | null | null | null | null | ['discourse-parsing', 'prompt-engineering', 'relation-classification'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.70916581e-01 7.75433779e-01 -3.59764189e-01 -3.74640942e-01
-8.15467298e-01 -6.80349410e-01 1.05965912e+00 3.93108696e-01
-2.51860488e-02 8.18079770e-01 8.23381662e-01 -8.56073916e-01
-1.94236681e-01 -6.48845255e-01 -2.25184694e-01 -3.48108202e-01
-3.22838992e-01 7.17298150e-01 4.77297038e-01 -6.67675853... | [10.928984642028809, 9.187257766723633] |
cd8c24b8-6bf6-4f25-9817-249c888defc7 | the-application-of-preconditioned-alternating | 1711.07721 | null | http://arxiv.org/abs/1711.07721v1 | http://arxiv.org/pdf/1711.07721v1.pdf | The Application of Preconditioned Alternating Direction Method of Multipliers in Depth from Focal Stack | Post capture refocusing effect in smartphone cameras is achievable by using
focal stacks. However, the accuracy of this effect is totally dependent on the
combination of the depth layers in the stack. The accuracy of the extended
depth of field effect in this application can be improved significantly by
computing an ac... | ['Hossein Javidnia', 'Peter Corcoran'] | 2017-11-21 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 3.85246783e-01 -1.39247045e-01 5.79277039e-01 -2.45459780e-01
-3.40379357e-01 -2.19724774e-01 5.15680671e-01 -1.61153287e-01
-5.74359417e-01 1.22102702e+00 2.58157402e-01 -1.12940939e-02
-3.39707822e-01 -3.97560120e-01 -4.40038681e-01 -8.53262365e-01
2.78388500e-01 2.21262246e-01 4.66952801e-01 1.80888414... | [9.372666358947754, -2.523732900619507] |
0d4e93e6-6108-4252-8346-5ed0bcac4758 | identifying-corresponding-patches-in-sar-and | 1801.08467 | null | http://arxiv.org/abs/1801.08467v1 | http://arxiv.org/pdf/1801.08467v1.pdf | Identifying Corresponding Patches in SAR and Optical Images with a Pseudo-Siamese CNN | In this letter, we propose a pseudo-siamese convolutional neural network
(CNN) architecture that enables to solve the task of identifying corresponding
patches in very-high-resolution (VHR) optical and synthetic aperture radar
(SAR) remote sensing imagery. Using eight convolutional layers each in two
parallel network s... | ['Lloyd H. Hughes', 'Yuanyuan Wang', 'Xiao Xiang Zhu', 'Michael Schmitt', 'Lichao Mou'] | 2018-01-25 | null | null | null | null | ['key-point-matching'] | ['natural-language-processing'] | [ 7.14951098e-01 -3.37249190e-01 2.42880449e-01 -7.14816928e-01
-9.52386737e-01 -2.24256769e-01 7.04733849e-01 3.26627672e-01
-6.12144709e-01 4.48105663e-01 -2.55085289e-01 -3.85213480e-03
-7.21381187e-01 -1.20452690e+00 -7.44717538e-01 -7.50989676e-01
-5.54541111e-01 7.37553418e-01 -2.57509053e-01 -5.17158449... | [9.879616737365723, -1.7468332052230835] |
c23c9c1c-6454-49c4-849c-a7fc01404b91 | boosting-video-super-resolution-with-patch | 2207.08674 | null | https://arxiv.org/abs/2207.08674v3 | https://arxiv.org/pdf/2207.08674v3.pdf | Boosting Video Super Resolution with Patch-Based Temporal Redundancy Optimization | The success of existing video super-resolution (VSR) algorithms stems mainly exploiting the temporal information from the neighboring frames. However, none of these methods have discussed the influence of the temporal redundancy in the patches with stationary objects and background and usually use all the information i... | ['Fei Wang', 'Lean Fu', 'Ding Liu', 'Yu Guo', 'Chao Zhu', 'Jinshan Pan', 'Hang Dong', 'Yuhao Huang'] | 2022-07-18 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 7.50949457e-02 -6.83798552e-01 -2.29297474e-01 -1.63503811e-01
-6.11504972e-01 -2.30440855e-01 2.91371018e-01 -3.80201668e-01
-4.11423109e-02 7.04043567e-01 2.64526486e-01 6.51579648e-02
-1.06623814e-01 -6.69429958e-01 -5.19215465e-01 -7.54455209e-01
-2.74878711e-01 -4.63029593e-01 9.80744362e-01 -4.65605170... | [11.06757640838623, -1.9274219274520874] |
91011176-61a0-4eb0-b6c6-4bf3c85c4966 | targeted-data-generation-finding-and-fixing | 2305.17804 | null | https://arxiv.org/abs/2305.17804v1 | https://arxiv.org/pdf/2305.17804v1.pdf | Targeted Data Generation: Finding and Fixing Model Weaknesses | Even when aggregate accuracy is high, state-of-the-art NLP models often fail systematically on specific subgroups of data, resulting in unfair outcomes and eroding user trust. Additional data collection may not help in addressing these weaknesses, as such challenging subgroups may be unknown to users, and underrepresen... | ['Fereshte Khani', 'Marco Tulio Ribeiro', 'Zexue He'] | 2023-05-28 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [ 4.33056243e-02 6.45949662e-01 -6.84633493e-01 -6.80986822e-01
-1.09813631e+00 -7.02871740e-01 6.03260040e-01 7.29471922e-01
-3.87352794e-01 1.02013195e+00 6.49714530e-01 -4.32402104e-01
9.88008752e-02 -6.32413566e-01 -5.63909352e-01 -1.39853597e-01
2.73606062e-01 7.49913573e-01 -2.87641257e-01 -1.03807628... | [9.802044868469238, 7.998948097229004] |
80ddccdb-f6f1-4f02-a903-5843c245f9c5 | revisiting-and-advancing-fast-adversarial-1 | 2112.12376 | null | https://arxiv.org/abs/2112.12376v6 | https://arxiv.org/pdf/2112.12376v6.pdf | Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization | Adversarial training (AT) is a widely recognized defense mechanism to gain the robustness of deep neural networks against adversarial attacks. It is built on min-max optimization (MMO), where the minimizer (i.e., defender) seeks a robust model to minimize the worst-case training loss in the presence of adversarial exam... | ['Guanhua Zhang', 'Sijia Liu', 'Shiyu Chang', 'Mingyi Hong', 'Prashant Khanduri', 'Yihua Zhang'] | 2021-12-23 | revisiting-and-advancing-fast-adversarial | https://openreview.net/forum?id=gzeruP-0J29 | https://openreview.net/pdf?id=gzeruP-0J29 | null | ['adversarial-defense'] | ['adversarial'] | [ 9.17232856e-02 -5.43247210e-03 1.05656935e-02 -1.99218079e-01
-9.79370117e-01 -1.05354977e+00 4.80537832e-01 -4.24167722e-01
-5.96536458e-01 4.98318613e-01 -2.74879754e-01 -9.15412426e-01
-1.04440255e-02 -5.53406894e-01 -1.10496771e+00 -1.06061649e+00
-1.10981740e-01 -1.06492966e-01 1.97890233e-02 -3.60213250... | [5.694902420043945, 7.795888900756836] |
8e203f4f-33dd-48c5-9142-e69286124e12 | blind-surveillance-image-quality-assessment | 2206.04318 | null | https://arxiv.org/abs/2206.04318v1 | https://arxiv.org/pdf/2206.04318v1.pdf | Blind Surveillance Image Quality Assessment via Deep Neural Network Combined with the Visual Saliency | The intelligent video surveillance system (IVSS) can automatically analyze the content of the surveillance image (SI) and reduce the burden of the manual labour. However, the SIs may suffer quality degradations in the procedure of acquisition, compression, and transmission, which makes IVSS hard to understand the conte... | ['Guangtao Zhai', 'Tao Wang', 'ZiCheng Zhang', 'Xiongkuo Min', 'Wenhan Zhu', 'Wei Sun', 'Wei Lu'] | 2022-06-09 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [ 2.53613681e-01 -3.34721416e-01 -2.28370540e-02 -1.62352517e-01
-3.93996298e-01 -8.54791179e-02 1.00574642e-01 -2.25235030e-01
-1.68197602e-01 1.64487362e-01 3.63048971e-01 1.39372125e-01
-2.43248343e-01 -8.56164753e-01 -5.05322695e-01 -8.29059780e-01
1.07482448e-01 -4.99777824e-01 7.50718892e-01 -1.73959419... | [11.793392181396484, -1.8964407444000244] |
e089db61-4de1-449a-89f2-7124ee33a4c1 | learning-the-prediction-distribution-for-semi | 2007.02745 | null | https://arxiv.org/abs/2007.02745v1 | https://arxiv.org/pdf/2007.02745v1.pdf | Learning the Prediction Distribution for Semi-Supervised Learning with Normalising Flows | As data volumes continue to grow, the labelling process increasingly becomes a bottleneck, creating demand for methods that leverage information from unlabelled data. Impressive results have been achieved in semi-supervised learning (SSL) for image classification, nearing fully supervised performance, with only a fract... | ['Ivana Balažević', 'Timothy Hospedales', 'Carl Allen'] | 2020-07-06 | null | null | null | null | ['normalising-flows'] | ['methodology'] | [ 1.01927125e+00 6.93655372e-01 -6.20932162e-01 -9.67906296e-01
-9.73782420e-01 -6.65770352e-01 1.09446585e+00 3.13641489e-01
-3.69866431e-01 7.00193942e-01 1.28714204e-01 -2.69449651e-01
9.00641382e-02 -3.76454026e-01 -6.69637859e-01 -6.32491052e-01
2.65647233e-01 8.14684451e-01 -7.35348836e-03 3.77909839... | [9.485889434814453, 3.13370418548584] |
64a5773d-86b3-47f8-8893-be7ca63cba92 | a-time-dependent-markovian-model-of-a-limit | 2302.00846 | null | https://arxiv.org/abs/2302.00846v1 | https://arxiv.org/pdf/2302.00846v1.pdf | A time-dependent Markovian model of a limit order book | This paper considers a Markovian model of a limit order book where time-dependent rates are allowed. With the objective of understanding the mechanisms through which a microscopic model of an orderbook can converge to more general diffusion than a Brownian motion with constant coefficient, a simple time-dependent model... | ['Jonathan A. Chávez-Casillas'] | 2023-02-02 | null | null | null | null | ['point-processes'] | ['methodology'] | [-3.57166678e-01 -5.01834452e-01 -4.50585522e-02 9.51274633e-02
1.42581686e-01 -8.83245647e-01 9.18932140e-01 4.78762597e-01
-3.98398966e-01 5.47160685e-01 -6.07520454e-02 -3.58162135e-01
-2.80213416e-01 -9.28283274e-01 -4.52518910e-01 -5.71413994e-01
-3.93646061e-01 7.74447262e-01 4.65368450e-01 -2.90682256... | [4.909128665924072, 4.013772964477539] |
e31efc40-f46e-413a-9548-19fc9b0f43c2 | transfer-learning-for-melanoma-detection | 1703.05235 | null | http://arxiv.org/abs/1703.05235v1 | http://arxiv.org/pdf/1703.05235v1.pdf | Transfer Learning for Melanoma Detection: Participation in ISIC 2017 Skin Lesion Classification Challenge | This manuscript describes our participation in the International Skin Imaging
Collaboration's 2017 Skin Lesion Analysis Towards Melanoma Detection
competition. We participated in Part 3: Lesion Classification. The two stated
goals of this binary image classification challenge were to distinguish between
(a) melanoma an... | ['Dennis H. Murphree', 'Che Ngufor'] | 2017-03-15 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 6.98336780e-01 1.08215429e-01 -2.58252144e-01 -3.28040391e-01
-1.01629531e+00 -5.79974830e-01 8.37583065e-01 2.83123434e-01
-7.75401175e-01 6.51405871e-01 3.48869748e-02 -7.10671008e-01
-2.24218573e-02 -4.23284471e-01 -2.96495557e-01 -9.82774317e-01
1.66997060e-01 -8.57591704e-02 3.10926676e-01 6.72371835... | [15.718647003173828, -3.0154976844787598] |
9235ccc2-95a3-4ad9-9cd5-e1628a18d2d9 | spatio-temporal-person-retrieval-via-natural | 1704.07945 | null | http://arxiv.org/abs/1704.07945v2 | http://arxiv.org/pdf/1704.07945v2.pdf | Spatio-temporal Person Retrieval via Natural Language Queries | In this paper, we address the problem of spatio-temporal person retrieval
from multiple videos using a natural language query, in which we output a tube
(i.e., a sequence of bounding boxes) which encloses the person described by the
query. For this problem, we introduce a novel dataset consisting of videos
containing p... | ['Tatsuya Harada', 'Masataka Yamaguchi', 'Kuniaki Saito', 'Yoshitaka Ushiku'] | 2017-04-26 | spatio-temporal-person-retrieval-via-natural-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Yamaguchi_Spatio-Temporal_Person_Retrieval_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Yamaguchi_Spatio-Temporal_Person_Retrieval_ICCV_2017_paper.pdf | iccv-2017-10 | ['person-retrieval'] | ['computer-vision'] | [ 8.69486015e-03 -5.76581061e-01 -3.11966594e-02 -2.47036487e-01
-1.03302753e+00 -9.44767416e-01 8.98007214e-01 2.44697437e-01
-7.20967412e-01 4.20229584e-01 6.93161190e-01 2.97209829e-01
-9.03450511e-03 -3.16113740e-01 -4.93698001e-01 -4.33633745e-01
-2.21372753e-01 5.11044800e-01 8.66096839e-02 -1.11951083... | [10.236785888671875, 0.9834959506988525] |
bc28f22d-47fa-4167-ae35-73f069996027 | synthesising-clinically-realistic-chest-x | 2010.03975 | null | https://arxiv.org/abs/2010.03975v2 | https://arxiv.org/pdf/2010.03975v2.pdf | Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANs | Chest x-rays are a vital tool in the workup of many patients. Similar to most medical imaging modalities, they are profoundly multi-modal and are capable of visualising a variety of combinations of conditions. There is an ever pressing need for greater quantities of labelled data to develop new diagnostic tools, howeve... | ['Adam Pantanowitz', 'Grace Rubin', 'David M. Rubin', 'Bradley Segal'] | 2020-10-07 | null | null | null | null | ['medical-image-generation'] | ['medical'] | [ 8.25027406e-01 8.46427560e-01 -1.12102017e-01 -3.55047137e-01
-1.38808095e+00 -6.26607299e-01 6.63908660e-01 -2.26959974e-01
-2.43296444e-01 8.86707902e-01 5.94724834e-01 -4.87476587e-01
-1.81169420e-01 -7.48277903e-01 -5.55254459e-01 -7.29766250e-01
2.48382524e-01 7.75667906e-01 -8.69526044e-02 1.59909815... | [14.258280754089355, -1.9190196990966797] |
becbe4b6-9d12-45b5-b012-44dc65ff78d1 | improving-fast-slow-encoder-based-transducer | 2212.07650 | null | https://arxiv.org/abs/2212.07650v1 | https://arxiv.org/pdf/2212.07650v1.pdf | Improving Fast-slow Encoder based Transducer with Streaming Deliberation | This paper introduces a fast-slow encoder based transducer with streaming deliberation for end-to-end automatic speech recognition. We aim to improve the recognition accuracy of the fast-slow encoder based transducer while keeping its latency low by integrating a streaming deliberation model. Specifically, the delibera... | ['Duc Le', 'Michael L. Seltzer', 'Ozlem Kalinli', 'Gil Keren', 'Yangyang Shi', 'Jinxi Guo', 'Jay Mahadeokar', 'Ke Li'] | 2022-12-15 | null | null | null | null | ['text-augmentation'] | ['natural-language-processing'] | [ 6.40336037e-01 5.03050268e-01 2.76392400e-01 -6.45471394e-01
-1.35749435e+00 -2.16259554e-01 5.54616928e-01 1.43468171e-01
-8.64049852e-01 3.25353175e-01 5.31029582e-01 -6.51418507e-01
2.51470357e-01 -4.47170883e-01 -7.08703697e-01 -3.89111102e-01
1.07022956e-01 5.95520079e-01 3.44605297e-01 -1.42505139... | [14.44954776763916, 6.842992782592773] |
8f680d91-a51f-47ce-92cb-7f62bd90ed3f | automatic-distractor-generation-for-multiple | 2011.13100 | null | https://arxiv.org/abs/2011.13100v1 | https://arxiv.org/pdf/2011.13100v1.pdf | Automatic Distractor Generation for Multiple Choice Questions in Standard Tests | To assess the knowledge proficiency of a learner, multiple choice question is an efficient and widespread form in standard tests. However, the composition of the multiple choice question, especially the construction of distractors is quite challenging. The distractors are required to both incorrect and plausible enough... | ['Wei Fan', 'Xian Wu', 'Zhaopeng Qiu'] | 2020-11-26 | null | https://aclanthology.org/2020.coling-main.189 | https://aclanthology.org/2020.coling-main.189.pdf | coling-2020-8 | ['distractor-generation'] | ['natural-language-processing'] | [-1.43240765e-01 7.04883784e-03 2.82759547e-01 -5.51554970e-02
-1.04142129e+00 -7.80093312e-01 6.15309000e-01 3.73222530e-02
-3.80117834e-01 8.61593306e-01 8.25776458e-02 -6.50699973e-01
1.20050438e-01 -8.39927435e-01 -6.96173668e-01 -2.11524859e-01
7.14685738e-01 5.67070782e-01 7.76026070e-01 -6.04223311... | [11.544923782348633, 8.23249340057373] |
818ae85e-b9e3-46eb-9340-379964cfb24c | a-brief-prehistory-of-double-descent | 2004.04328 | null | https://arxiv.org/abs/2004.04328v1 | https://arxiv.org/pdf/2004.04328v1.pdf | A Brief Prehistory of Double Descent | In their thought-provoking paper [1], Belkin et al. illustrate and discuss the shape of risk curves in the context of modern high-complexity learners. Given a fixed training sample size $n$, such curves show the risk of a learner as a function of some (approximate) measure of its complexity $N$. With $N$ the number of ... | ['Jesse H. Krijthe', 'Tom Viering', 'Marco Loog', 'David M. J. Tax', 'Alexander Mey'] | 2020-04-07 | null | null | null | null | ['prehistory'] | ['miscellaneous'] | [ 5.71272783e-02 2.48770043e-01 -2.36726895e-01 -2.97698289e-01
-8.67202878e-01 -6.60007179e-01 4.91370261e-01 6.34806335e-01
-8.18544745e-01 7.51258314e-01 -1.46637326e-02 -7.68516064e-01
-2.14459792e-01 -5.98443627e-01 -8.34635556e-01 -5.35281241e-01
-4.65270847e-01 1.49129987e-01 9.51692760e-02 -1.79197863... | [8.168079376220703, 4.412955284118652] |
5ace8ee5-85d1-404a-8de9-f8a92f12100f | low-dimensional-manifold-constrained | 2007.03882 | null | https://arxiv.org/abs/2007.03882v1 | https://arxiv.org/pdf/2007.03882v1.pdf | Low-dimensional Manifold Constrained Disentanglement Network for Metal Artifact Reduction | Deep neural network based methods have achieved promising results for CT metal artifact reduction (MAR), most of which use many synthesized paired images for training. As synthesized metal artifacts in CT images may not accurately reflect the clinical counterparts, an artifact disentanglement network (ADN) was proposed... | ['Mengzhou Li', 'Fenglei Fan', 'Hongming Shan', 'Ge Wang', 'Jimin Liang', 'Wenxiang Cong', 'Chuang Niu'] | 2020-07-08 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 3.34170252e-01 9.98404692e-04 -1.05564306e-02 -2.95239598e-01
-1.21269000e+00 -1.93382069e-01 1.55207040e-02 -2.66481638e-01
-2.23325700e-01 7.81942844e-01 2.06821561e-01 -1.12714224e-01
-5.33383429e-01 -4.13062006e-01 -7.48600304e-01 -8.68991852e-01
-3.30516659e-02 2.01215908e-01 -3.16911340e-01 1.61530510... | [13.573885917663574, -2.5397636890411377] |
341f5626-1144-4f3a-8392-76b7bebe61e9 | research-and-implementation-of-drug-target | 2306.00041 | null | https://arxiv.org/abs/2306.00041v1 | https://arxiv.org/pdf/2306.00041v1.pdf | Research And Implementation Of Drug Target Interaction Confidence Measurement Method Based On Causal Intervention | The identification and discovery of drug-target Interaction (DTI) is an important step in the field of Drug research and development, which can help scientists discover new drugs and accelerate the development process. KnowledgeGraph and the related knowledge graph Embedding (KGE) model develop rapidly and show good pe... | ['Zaiwen Feng', 'Debo Cheng', 'Yang Xie', 'Bowen Wang', 'Wenting Ye'] | 2023-05-31 | null | null | null | null | ['graph-embedding', 'link-prediction', 'knowledge-graph-embedding', 'drug-discovery'] | ['graphs', 'graphs', 'graphs', 'medical'] | [-1.39672056e-01 -2.51655489e-01 -6.55923069e-01 -7.46564195e-02
2.00735196e-01 6.28720922e-03 2.02310652e-01 5.75951934e-01
-1.36103898e-01 8.53389084e-01 2.14117274e-01 -5.78332961e-01
-6.79953158e-01 -1.04540765e+00 -2.92468101e-01 -5.98567426e-01
-6.26307738e-04 3.92460287e-01 3.88439521e-02 1.01796649... | [5.369332313537598, 5.875895977020264] |
a6bd4e7b-af91-4a34-886f-af474fe64e74 | individual-fairness-in-bayesian-neural | 2304.10828 | null | https://arxiv.org/abs/2304.10828v1 | https://arxiv.org/pdf/2304.10828v1.pdf | Individual Fairness in Bayesian Neural Networks | We study Individual Fairness (IF) for Bayesian neural networks (BNNs). Specifically, we consider the $\epsilon$-$\delta$-individual fairness notion, which requires that, for any pair of input points that are $\epsilon$-similar according to a given similarity metrics, the output of the BNN is within a given tolerance $\... | ['Andrea Patane', 'Luca Laurenti', 'Matthew Wicker', 'Alice Doherty'] | 2023-04-21 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [-2.13267922e-01 3.65698278e-01 -1.72447816e-01 -1.00159454e+00
-7.09745646e-01 -7.12432981e-01 4.28208858e-01 -5.58308959e-02
-7.64027238e-01 1.16589606e+00 -1.40068397e-01 -6.24752462e-01
-4.88176078e-01 -1.06623673e+00 -1.02587354e+00 -5.99372625e-01
-3.61191660e-01 4.69607174e-01 -7.36697540e-02 -2.41385382... | [6.013468265533447, 7.133289337158203] |
451726b1-2a54-4d32-806c-d36dfeb87b18 | automatic-tooth-segmentation-from-3d-dental | 2209.08132 | null | https://arxiv.org/abs/2209.08132v1 | https://arxiv.org/pdf/2209.08132v1.pdf | Automatic Tooth Segmentation from 3D Dental Model using Deep Learning: A Quantitative Analysis of what can be learnt from a Single 3D Dental Model | 3D tooth segmentation is an important task for digital orthodontics. Several Deep Learning methods have been proposed for automatic tooth segmentation from 3D dental models or intraoral scans. These methods require annotated 3D intraoral scans. Manually annotating 3D intraoral scans is a laborious task. One approach is... | ['Dimitris Metaxas', 'Hrebesh Molly Subhash', 'Ananya Jana'] | 2022-09-16 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 2.06147686e-01 9.13573563e-01 -4.41918105e-01 -7.47979760e-01
-9.35436785e-01 -5.40478565e-02 2.83077359e-01 1.72500208e-01
-3.29119444e-01 2.39001155e-01 -1.74555406e-01 -3.31485868e-01
-3.42860594e-02 -6.62085593e-01 -7.55498171e-01 -7.65038073e-01
3.47353891e-02 1.04473019e+00 1.50520250e-01 -7.90569037... | [13.78015422821045, -2.2400858402252197] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.