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98043d1b-b9fe-4d73-a9e3-fb2b23cfb792 | wide-contextual-residual-network-with-active | null | null | https://ieeexplore.ieee.org/document/8517855 | https://www.researchgate.net/publication/328991664_Wide_Contextual_Residual_Network_with_Active_Learning_for_Remote_Sensing_Image_Classification | Wide Contextual Residual Network with Active Learning for Remote Sensing Image Classification | In this paper, we propose a wide contextual residual network (WCRN) with active learning (AL) for remote sensing image (RSI)
classification. Although ResNets have achieved great success in various applications (e.g. RSI classification), its performance is limited by the requirement of abundant labeled samples. As it i... | ['Sheng-Jie Liu', 'Jun Li', 'Zhi He', 'Ying Tu', 'Haowen Luo'] | 2018-07-22 | null | null | null | igarss-2018-2018-ieee-international | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 5.63198209e-01 9.64411721e-02 -4.00043100e-01 -3.84969682e-01
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1e63668a-f63e-4a17-9e65-d5f228ef969c | d2match-leveraging-deep-learning-and | 2306.06380 | null | https://arxiv.org/abs/2306.06380v1 | https://arxiv.org/pdf/2306.06380v1.pdf | D2Match: Leveraging Deep Learning and Degeneracy for Subgraph Matching | Subgraph matching is a fundamental building block for graph-based applications and is challenging due to its high-order combinatorial nature. Existing studies usually tackle it by combinatorial optimization or learning-based methods. However, they suffer from exponential computational costs or searching the matching wi... | ['Haiqin Yang', 'Yujiu Yang', 'Jiaqi Sun', 'Lin Zhang', 'Xuanzhou Liu'] | 2023-06-10 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 0.08602829 0.22749 -0.38833073 -0.14334978 -0.76652294 -0.5917796
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0.4455297 -0.14192326 0.15582256 0.5104274 -1.4168619 -0.01040614
0.92013985 1.0441979 0.046... | [7.097048282623291, 6.217899799346924] |
fcec4166-95db-4239-b9d8-cf55dd1192d8 | standing-on-the-shoulders-of-predecessors | 2110.14170 | null | https://arxiv.org/abs/2110.14170v3 | https://arxiv.org/pdf/2110.14170v3.pdf | Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding | Knowledge graphs (KGs) consisting of a large number of triples have become widespread recently, and many knowledge graph embedding (KGE) methods are proposed to embed entities and relations of a KG into continuous vector spaces. Such embedding methods simplify the operations of conducting various in-KG tasks (e.g., lin... | ['Huajun Chen', 'Changliang Xu', 'Zonggang Yuan', 'Hongting Zhou', 'Yushan Zhu', 'Wen Zhang', 'Mingyang Chen'] | 2021-10-27 | null | null | null | null | ['inductive-relation-prediction'] | ['graphs'] | [-2.86097795e-01 8.25549424e-01 -4.92449582e-01 -3.70414674e-01
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ec2a4364-e483-44cc-96d0-3407cb8b275f | self-supervised-mri-reconstruction-with | 2306.16654 | null | https://arxiv.org/abs/2306.16654v1 | https://arxiv.org/pdf/2306.16654v1.pdf | Self-Supervised MRI Reconstruction with Unrolled Diffusion Models | Magnetic Resonance Imaging (MRI) produces excellent soft tissue contrast, albeit it is an inherently slow imaging modality. Promising deep learning methods have recently been proposed to reconstruct accelerated MRI scans. However, existing methods still suffer from various limitations regarding image fidelity, contextu... | ['Vishal Patel', 'Tolga Cukur', 'Yilmaz Korkmaz'] | 2023-06-29 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 1.75869748e-01 2.37702169e-02 -2.31023535e-01 -8.50855947e-01
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05ce9f47-749a-4db7-8479-7e0e54bb2584 | automated-essay-scoring-via-pairwise | null | null | https://aclanthology.org/2022.coling-1.240 | https://aclanthology.org/2022.coling-1.240.pdf | Automated Essay Scoring via Pairwise Contrastive Regression | Automated essay scoring (AES) involves the prediction of a score relating to the writing quality of an essay. Most existing works in AES utilize regression objectives or ranking objectives respectively. However, the two types of methods are highly complementary. To this end, in this paper we take inspiration from contr... | ['Weiguang Qu', 'Junsheng Zhou', 'Li Kong', 'Kaiwei Cai', 'Jiayi Xie'] | null | null | null | null | coling-2022-10 | ['automated-essay-scoring'] | ['natural-language-processing'] | [ 1.45239830e-02 -2.62804389e-01 -2.95696169e-01 -8.86051595e-01
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4.82960582e-01 2.21118212e-01 -7.41464924e-03 -2.40439653... | [11.348014831542969, 9.357650756835938] |
3c3b7876-8ce4-403a-a22a-3cdc02b4cc1d | improving-accent-identification-and-accented | 2109.07349 | null | https://arxiv.org/abs/2109.07349v1 | https://arxiv.org/pdf/2109.07349v1.pdf | Improving Accent Identification and Accented Speech Recognition Under a Framework of Self-supervised Learning | Recently, self-supervised pre-training has gained success in automatic speech recognition (ASR). However, considering the difference between speech accents in real scenarios, how to identify accents and use accent features to improve ASR is still challenging. In this paper, we employ the self-supervised pre-training me... | ['Long Ma', 'Songjun Cao', 'Keqi Deng'] | 2021-09-15 | null | null | null | null | ['accented-speech-recognition'] | ['speech'] | [ 2.07896575e-01 -4.68937382e-02 1.69772714e-01 -8.01901937e-01
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3.40870798e-01 2.00158343e-01 -1.26346022e-01 -5.42299092... | [14.488929748535156, 6.626343727111816] |
c4ac5d6e-8bbf-492d-b151-9b4a0a969d46 | tuvf-learning-generalizable-texture-uv | 2305.03040 | null | https://arxiv.org/abs/2305.03040v2 | https://arxiv.org/pdf/2305.03040v2.pdf | TUVF: Learning Generalizable Texture UV Radiance Fields | Textures are a vital aspect of creating visually appealing and realistic 3D models. In this paper, we study the problem of generating high-fidelity texture given shapes of 3D assets, which has been relatively less explored compared with generic 3D shape modeling. Our goal is to facilitate a controllable texture generat... | ['Xiaolong Wang', 'Sifei Liu', 'Xueting Li', 'An-Chieh Cheng'] | 2023-05-04 | null | null | null | null | ['texture-synthesis', '3d-shape-modeling'] | ['computer-vision', 'computer-vision'] | [ 3.35620373e-01 1.39407068e-01 2.49485478e-01 -1.95291936e-01
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4.65409011e-01 4.81660932e-01 -1.47400647e-01 -2.48859569... | [9.180418968200684, -3.392587184906006] |
e5b72ad2-5502-4d9d-8111-8326dc089a5f | joint-super-resolution-and-inverse-tone | 2207.03367 | null | https://arxiv.org/abs/2207.03367v3 | https://arxiv.org/pdf/2207.03367v3.pdf | Joint Super-Resolution and Inverse Tone-Mapping: A Feature Decomposition Aggregation Network and A New Benchmark | Joint Super-Resolution and Inverse Tone-Mapping (joint SR-ITM) aims to increase the resolution and dynamic range of low-resolution and standard dynamic range images. Recent networks mainly resort to image decomposition techniques with complex multi-branch architectures. However, the fixed decomposition techniques would... | ['Jun Xu', 'Yu-chen Yang', 'Xian-Tong Zhen', 'Liang Wang', 'Gang Xu'] | 2022-07-07 | null | null | null | null | ['tone-mapping', 'inverse-tone-mapping'] | ['computer-vision', 'computer-vision'] | [ 2.10514680e-01 -6.47342384e-01 -2.62558788e-01 -2.32655108e-01
-9.37892079e-01 -1.64325908e-01 2.37290412e-01 -9.99466181e-01
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4.81828023e-03 -4.24684197e-01 2.59045511e-01 -3.26000720... | [10.966686248779297, -2.02130126953125] |
d6f686a0-6c6f-4f0a-84b5-e7078f719e14 | grnet-gridding-residual-network-for-dense | 2006.03761 | null | https://arxiv.org/abs/2006.03761v4 | https://arxiv.org/pdf/2006.03761v4.pdf | GRNet: Gridding Residual Network for Dense Point Cloud Completion | Estimating the complete 3D point cloud from an incomplete one is a key problem in many vision and robotics applications. Mainstream methods (e.g., PCN and TopNet) use Multi-layer Perceptrons (MLPs) to directly process point clouds, which may cause the loss of details because the structural and context of point clouds a... | ['Shangchen Zhou', 'Wenxiu Sun', 'Jiageng Mao', 'Hongxun Yao', 'Haozhe Xie', 'Shengping Zhang'] | 2020-06-06 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/798_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540341.pdf | eccv-2020-8 | ['point-cloud-completion'] | ['computer-vision'] | [-1.92039609e-01 -2.20110998e-01 3.08554232e-01 -3.43089551e-01
-5.10089815e-01 -2.84889072e-01 5.29005468e-01 -2.02645317e-01
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-4.89140730e-05 -7.82699108e-01 -1.04387712e+00 -6.51901007e-01
2.49380499e-01 4.59645331e-01 1.71382964e-01 3.40267941... | [8.286626815795898, -3.5459306240081787] |
fc4dd3c6-5dc6-4e63-a4ba-c53a571edc4e | physical-model-guided-deep-image-deraining | 2003.13242 | null | https://arxiv.org/abs/2003.13242v1 | https://arxiv.org/pdf/2003.13242v1.pdf | Physical Model Guided Deep Image Deraining | Single image deraining is an urgent task because the degraded rainy image makes many computer vision systems fail to work, such as video surveillance and autonomous driving. So, deraining becomes important and an effective deraining algorithm is needed. In this paper, we propose a novel network based on physical model ... | ['Ya-Jie Zhang', 'Zhixun Su', 'Cong Wang', 'Guohui Zhao', 'Honghe Zhu'] | 2020-03-30 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 6.36151386e-03 -1.11852027e-01 2.66807705e-01 -4.30774629e-01
-4.61300820e-01 5.06135970e-02 5.73370941e-02 -7.61931896e-01
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1.18976966e-01 -2.15466544e-01 3.60529572e-01 -2.54468083... | [10.94882869720459, -3.2821171283721924] |
6735a67f-f148-4bf8-867f-1f11559c81de | eliciting-compatible-demonstrations-for-multi | 2210.08073 | null | https://arxiv.org/abs/2210.08073v1 | https://arxiv.org/pdf/2210.08073v1.pdf | Eliciting Compatible Demonstrations for Multi-Human Imitation Learning | Imitation learning from human-provided demonstrations is a strong approach for learning policies for robot manipulation. While the ideal dataset for imitation learning is homogenous and low-variance -- reflecting a single, optimal method for performing a task -- natural human behavior has a great deal of heterogeneity,... | ['Dorsa Sadigh', 'Madeline Liao', 'Siddharth Karamcheti', 'Kanishk Gandhi'] | 2022-10-14 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 8.41760486e-02 -3.93791646e-02 -5.21198623e-02 -1.31990746e-01
-5.93853116e-01 -1.03315389e+00 7.11728394e-01 -1.60608575e-01
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-3.26404333e-01 2.94472426e-02 -7.60228634e-01 -5.41981101e-01
-4.72679526e-01 5.95776856e-01 2.19613686e-01 -3.59902591... | [4.507364273071289, 0.9751935005187988] |
fb9dbcf7-c9db-4a33-b53e-f0a14df2588d | versatilegait-a-large-scale-synthetic-gait-1 | 2105.14421 | null | https://arxiv.org/abs/2105.14421v2 | https://arxiv.org/pdf/2105.14421v2.pdf | VersatileGait: A Large-Scale Synthetic Gait Dataset Towards in-the-Wild Simulation | Gait recognition has a rapid development in recent years. However, gait recognition in the wild is not well explored yet. An obvious reason could be ascribed to the lack of diverse training data from the perspective of intrinsic and extrinsic factors. To remedy this problem, we propose to construct a large-scale gait d... | ['Xi Li', 'Zequn Qin', 'Songyuan Li', 'Yuhan Zhao', 'Wenhu Zhang', 'Huanzhang Dou', 'Pengyi Zhang'] | 2021-05-30 | null | null | null | null | ['gait-recognition-in-the-wild'] | ['computer-vision'] | [-2.82751352e-01 -6.62084162e-01 4.87443022e-02 -4.35115308e-01
-2.13603437e-01 -4.25504833e-01 1.78880215e-01 -4.45173085e-01
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-1.53614767e-02 4.32127655e-01 3.78496826e-01 -3.46430004... | [14.31860065460205, 1.3783669471740723] |
bf69a911-4d82-4415-ba1a-94d096bcf254 | open-set-action-recognition-via-multi-label | 2303.12698 | null | https://arxiv.org/abs/2303.12698v1 | https://arxiv.org/pdf/2303.12698v1.pdf | Open Set Action Recognition via Multi-Label Evidential Learning | Existing methods for open-set action recognition focus on novelty detection that assumes video clips show a single action, which is unrealistic in the real world. We propose a new method for open set action recognition and novelty detection via MUlti-Label Evidential learning (MULE), that goes beyond previous novel act... | ['Christopher Funk', 'Anthony Hoogs', 'Dawei Du', 'Chen Zhao'] | 2023-02-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhao_Open_Set_Action_Recognition_via_Multi-Label_Evidential_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhao_Open_Set_Action_Recognition_via_Multi-Label_Evidential_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['open-set-action-recognition'] | ['computer-vision'] | [ 5.54217458e-01 6.96689412e-02 -3.88988107e-01 -3.68827164e-01
-1.10248518e+00 -2.31732398e-01 4.27638501e-01 -8.72820243e-02
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-1.79095611e-01 3.99433225e-02 3.18590254e-01 3.60018998... | [8.485969543457031, 0.6902561187744141] |
2a79aceb-babe-491b-849c-449bc550eda1 | complex-word-identification-in-vietnamese | null | null | https://aclanthology.org/2022.mia-1.6 | https://aclanthology.org/2022.mia-1.6.pdf | Complex Word Identification in Vietnamese: Towards Vietnamese Text Simplification | Text Simplification has been an extensively researched problem in English, but has not been investigated in Vietnamese. We focus on the Vietnamese-specific Complex Word Identification task, often the first step in Lexical Simplification (Shardlow, 2013). We examine three different Vietnamese datasets constructed for ot... | ['David Kauchak', 'Phuong Nguyen'] | null | null | null | null | naacl-mia-2022-7 | ['lexical-simplification', 'vietnamese-datasets', 'complex-word-identification'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-8.94041950e-05 -4.75022942e-02 -1.88645989e-01 -2.58849591e-01
-6.98877037e-01 -8.27638149e-01 7.41706431e-01 4.65525478e-01
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5.07874429e-01 6.23518944e-01 -1.17403954e-01 -6.84312642... | [10.815000534057617, 10.349723815917969] |
0417304e-185d-4526-8acf-0a9e18572dfa | a3s-adversarial-learning-of-semantic | 2302.10641 | null | https://arxiv.org/abs/2302.10641v1 | https://arxiv.org/pdf/2302.10641v1.pdf | A3S: Adversarial learning of semantic representations for Scene-Text Spotting | Scene-text spotting is a task that predicts a text area on natural scene images and recognizes its text characters simultaneously. It has attracted much attention in recent years due to its wide applications. Existing research has mainly focused on improving text region detection, not text recognition. Thus, while dete... | ['Masato Fujitake'] | 2023-02-21 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 9.36584294e-01 -5.61319292e-01 -4.58659977e-02 -2.75895476e-01
-5.25421917e-01 -4.62638050e-01 6.69857740e-01 -5.68977296e-02
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7.94824541e-01 3.52359116e-01 6.59246683e-01 1.91804171... | [11.976837158203125, 2.251512289047241] |
4cd16d94-e1f4-42e5-b2ea-5ba23185f12f | formal-ft-based-cause-consequence-reliability | 2101.07174 | null | https://arxiv.org/abs/2101.07174v1 | https://arxiv.org/pdf/2101.07174v1.pdf | Formal FT-based Cause-Consequence Reliability Analysis using Theorem Proving | Cause-consequence Diagram (CCD) is widely used as a deductive safety analysis technique for decision-making at the critical-system design stage. This approach models the causes of subsystem failures in a highly-critical system and their potential consequences using Fault Tree (FT) and Event Tree (ET) methods, which are... | ['Sofiene Tahar', 'Mohamed Abdelghany'] | 2021-01-18 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [-7.05892742e-02 4.42341268e-02 4.18866009e-01 5.82759827e-03
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-5.52221537e-01 2.63675749e-01 3.90568644e-01 -2.42764249... | [5.478420734405518, 2.567106008529663] |
33f2c137-7e77-423d-9bcb-357b8ad679ea | combining-machine-learning-and-agent-based | 2206.01092 | null | https://arxiv.org/abs/2206.01092v2 | https://arxiv.org/pdf/2206.01092v2.pdf | Innovations in Integrating Machine Learning and Agent-Based Modeling of Biomedical Systems | Agent-based modeling (ABM) is a well-established paradigm for simulating complex systems via interactions between constituent entities. Machine learning (ML) refers to approaches whereby statistical algorithms 'learn' from data on their own, without imposing a priori theories of system behavior. Biological systems -- f... | ['Shayn M. Peirce', 'Cameron Mura', 'Nikita Sivakumar'] | 2022-06-02 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 1.66275144e-01 -2.26570576e-01 1.47759229e-01 2.30505720e-01
-2.35869035e-01 -6.45137787e-01 9.50463712e-01 5.76960802e-01
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-5.45627654e-01 -1.04357433e+00 -5.84956706e-01 -1.17559183e+00
-5.55348635e-01 5.79755902e-01 2.98985660e-01 -4.94674116... | [6.068594455718994, 4.296204090118408] |
2db76862-71b8-418b-8179-f2a558a0c4e1 | likert-scoring-with-grade-decoupling-for-long | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Xu_Likert_Scoring_With_Grade_Decoupling_for_Long-Term_Action_Assessment_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Xu_Likert_Scoring_With_Grade_Decoupling_for_Long-Term_Action_Assessment_CVPR_2022_paper.pdf | Likert Scoring With Grade Decoupling for Long-Term Action Assessment | Long-term action quality assessment is a task of evaluating how well an action is performed, namely, estimating a quality score from a long video. Intuitively, longterm actions generally involve parts exhibiting different levels of skill, and we call the levels of skill as performance grades. For example, technical... | ['Wei-Shi Zheng', 'Ling-An Zeng', 'Angchi Xu'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['action-quality-assessment', 'action-assessment'] | ['computer-vision', 'computer-vision'] | [-1.39035910e-01 -3.08604151e-01 -9.73425433e-02 -7.66948402e-01
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4.25930053e-01 3.59309162e-03 2.06742793e-01 -8.65797028... | [8.24856948852539, 0.6501814126968384] |
f9b844ae-d060-4d64-9dcd-f3e6646de854 | cross-domain-graph-anomaly-detection-via | 2212.01096 | null | https://arxiv.org/abs/2212.01096v1 | https://arxiv.org/pdf/2212.01096v1.pdf | Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment | Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph using auxiliary, related source graphs with labelled anomalous and normal nodes. Although it presents a promising approach to address the notoriously high false positive issue in anomaly detect... | ['Christopher Leckie', 'Wray Buntine', 'Mahsa Salehi', 'Guansong Pang', 'Qizhou Wang'] | 2022-12-02 | null | null | null | null | ['graph-anomaly-detection'] | ['graphs'] | [ 4.29307103e-01 3.57726961e-01 9.04740617e-02 -2.71231145e-01
-3.88455451e-01 -5.93673825e-01 5.94928801e-01 5.60499847e-01
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-2.19437405e-01 -8.39688897e-01 -3.85623038e-01 -8.83322179e-01
-4.81191128e-01 8.84293258e-01 5.63990712e-01 -4.71127123... | [6.602664470672607, 5.811122894287109] |
60e3c8d1-b00b-4ab4-8f8b-920447bbd8f7 | an-adaptable-task-oriented-dialog-system-for | null | null | https://aclanthology.org/P19-3009 | https://aclanthology.org/P19-3009.pdf | An adaptable task-oriented dialog system for stand-alone embedded devices | This paper describes a spoken-language end-to-end task-oriented dialogue system for small embedded devices such as home appliances. While the current system implements a smart alarm clock with advanced calendar scheduling functionality, the system is designed to make it easy to port to other application domains (e.g., ... | ['Guy Bashkansky', 'Yu-Heng Hong', 'Vu Cong Duy Hoang', 'Mark Johnson', 'Long Duong', 'Vladislavs Dovgalecs', 'Serge Le Huitouze', 'Jason Black', 'Andrew Bleeker', 'Tuyen Quang Pham'] | 2019-07-01 | null | null | null | acl-2019-7 | ['dialogue-management'] | ['natural-language-processing'] | [-1.23495921e-01 6.24611735e-01 -9.84478858e-04 -7.24621713e-01
-5.53332865e-01 -7.46211052e-01 4.77361798e-01 -5.44192828e-02
-9.76515487e-02 9.73348320e-01 1.27502352e-01 -8.89728606e-01
1.91660076e-01 -4.82295543e-01 2.00732365e-01 -2.74242640e-01
2.18227521e-01 7.65740335e-01 4.34717804e-01 -4.29726005... | [12.953805923461914, 7.909669399261475] |
f0b3dff3-2345-44e9-a28d-35ddda3db3bd | pona-pose-guided-non-local-attention-for | 2012.07049 | null | https://arxiv.org/abs/2012.07049v1 | https://arxiv.org/pdf/2012.07049v1.pdf | PoNA: Pose-guided Non-local Attention for Human Pose Transfer | Human pose transfer, which aims at transferring the appearance of a given person to a target pose, is very challenging and important in many applications. Previous work ignores the guidance of pose features or only uses local attention mechanism, leading to implausible and blurry results. We propose a new human pose tr... | ['Qionghai Dai', 'Yu-Kun Lai', 'Yebin Liu', 'Jinsong Zhang', 'Kun Li'] | 2020-12-13 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 6.79853978e-03 -1.61267668e-01 2.39013165e-01 -5.48259139e-01
-3.94602239e-01 -4.90541577e-01 4.75836247e-01 -5.23653865e-01
-4.71631020e-01 8.28837037e-01 3.47443014e-01 3.23281825e-01
1.82227567e-01 -8.11757207e-01 -8.78872097e-01 -5.63672304e-01
3.82571906e-01 3.45321983e-01 1.26032516e-01 -3.52501124... | [12.012187957763672, -0.8273749351501465] |
e037dc0a-d5e3-4eaf-85c3-240960ab0617 | toward-automatic-discourse-parsing-of-student | null | null | https://aclanthology.org/2022.bea-1.25 | https://aclanthology.org/2022.bea-1.25.pdf | Toward Automatic Discourse Parsing of Student Writing Motivated by Neural Interpretation | Providing effective automatic essay feedback is necessary for offering writing instruction at a massive scale. In particular, feedback for promoting coherent flow of ideas in essays is critical. In this paper we propose a state-of-the-art method for automated analysis of structure and flow of writing, referred to as Rh... | ['Carolyn Rosé', 'David Adamson', 'Shiyan Jiang', 'James Fiacco'] | null | null | null | null | naacl-bea-2022-7 | ['discourse-parsing'] | ['natural-language-processing'] | [ 2.48163968e-01 4.25584733e-01 -5.46599746e-01 -2.40895927e-01
-7.62242436e-01 -9.26865101e-01 6.97317123e-01 7.57466495e-01
-9.39083919e-02 6.87467813e-01 7.63047576e-01 -1.13949466e+00
-2.37566128e-01 -7.31120825e-01 -6.01177514e-01 1.83603644e-01
9.28248107e-01 8.09893161e-02 2.23660886e-01 -5.87529778... | [11.270901679992676, 9.339025497436523] |
0c655511-741b-49a7-a179-487261fef3e2 | open-set-recognition-using-vision-transformer | 2203.08441 | null | https://arxiv.org/abs/2203.08441v1 | https://arxiv.org/pdf/2203.08441v1.pdf | Open Set Recognition using Vision Transformer with an Additional Detection Head | Deep neural networks have demonstrated prominent capacities for image classification tasks in a closed set setting, where the test data come from the same distribution as the training data. However, in a more realistic open set scenario, traditional classifiers with incomplete knowledge cannot tackle test data that are... | ['Xenofon Koutsoukos', 'Jie Liu', 'Zhenkai Zhang', 'Feiyang Cai'] | 2022-03-16 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 3.65928560e-01 -5.99692389e-02 -2.59839714e-01 -1.52177900e-01
-8.74755204e-01 -6.86557353e-01 4.93157238e-01 -3.10857631e-02
-1.01450957e-01 5.52041769e-01 -3.16133559e-01 -2.63536960e-01
-1.22296125e-01 -6.12439871e-01 -8.02859843e-01 -7.35526979e-01
2.11277872e-01 7.85239458e-01 1.41270950e-01 7.99945965... | [9.620771408081055, 2.8335189819335938] |
ddfff171-9f37-4285-95a5-5ae4da7228ad | maptr-structured-modeling-and-learning-for | 2208.14437 | null | https://arxiv.org/abs/2208.14437v2 | https://arxiv.org/pdf/2208.14437v2.pdf | MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction | High-definition (HD) map provides abundant and precise environmental information of the driving scene, serving as a fundamental and indispensable component for planning in autonomous driving system. We present MapTR, a structured end-to-end Transformer for efficient online vectorized HD map construction. We propose a u... | ['Chang Huang', 'Wenyu Liu', 'Qian Zhang', 'Tianheng Cheng', 'Xinggang Wang', 'Shaoyu Chen', 'Bencheng Liao'] | 2022-08-30 | null | null | null | null | ['3d-lane-detection'] | ['computer-vision'] | [-2.39671752e-01 9.02762413e-02 -1.84315637e-01 -5.75824976e-01
-1.08627248e+00 -6.33608282e-01 3.07747900e-01 -9.13896039e-02
-4.18682963e-01 3.80513608e-01 5.13585582e-02 -4.02037442e-01
-3.30391139e-01 -1.35273468e+00 -1.24270129e+00 -5.64480186e-01
6.53483719e-02 8.51734519e-01 4.54190254e-01 -4.56967533... | [7.859837532043457, -1.9426718950271606] |
25eda936-06d6-44cf-bfe6-1de7ecdbe6f7 | st-ddpm-explore-class-clustering-for | null | null | https://openreview.net/forum?id=FuLL40HLCRn | https://openreview.net/pdf?id=FuLL40HLCRn | ST-DDPM: Explore Class Clustering for Conditional Diffusion Probabilistic Models | Score-based generative models involve sequentially corrupting the data distribution with noise and then learns to recover the data distribution based on score matching. In this paper, for the diffusion probabilistic models, we first delve into the changes of data distribution during the forward process of the Markov ch... | ['Zhou Zhao', 'Zijian Zhang', 'Zhijie Lin'] | 2021-09-29 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 2.19312295e-01 -1.15350839e-02 1.04333244e-01 -3.74168307e-01
-9.59558249e-01 -5.91110110e-01 7.74617553e-01 -3.04919720e-01
-2.65416592e-01 7.29703665e-01 2.20389619e-01 -1.36664808e-01
-2.40701750e-01 -8.51863921e-01 -6.79580092e-01 -1.08044136e+00
8.58298317e-02 7.13249981e-01 3.94880354e-01 3.05269927... | [11.230016708374023, -0.10857579857110977] |
bd0aa74e-9cba-49b6-9148-983b8c39ab1b | small-footprint-text-independent-speaker | 2011.01709 | null | https://arxiv.org/abs/2011.01709v2 | https://arxiv.org/pdf/2011.01709v2.pdf | Small footprint Text-Independent Speaker Verification for Embedded Systems | Deep neural network approaches to speaker verification have proven successful, but typical computational requirements of State-Of-The-Art (SOTA) systems make them unsuited for embedded applications. In this work, we present a two-stage model architecture orders of magnitude smaller than common solutions (237.5K learnin... | ['Alice Coucke', 'Mathieu Poumeyrol', 'Raffaele Tavarone', 'Julien Balian'] | 2020-11-03 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [-1.39369685e-02 3.15616429e-01 3.75700325e-01 -5.19306958e-01
-1.25208616e+00 -4.16498840e-01 3.60933542e-01 -2.26357117e-01
-7.52168953e-01 3.71859014e-01 1.07965339e-03 -6.57010317e-01
1.38558391e-02 -7.84813054e-03 -5.90308964e-01 -5.50536275e-01
-4.04118672e-02 2.20064521e-01 -1.12624280e-01 -3.34761143... | [14.4722261428833, 6.0357818603515625] |
0dab5d05-822b-4fd8-bc1f-63098fd8cfb0 | recent-advances-in-neural-program-synthesis | 1802.02353 | null | http://arxiv.org/abs/1802.02353v1 | http://arxiv.org/pdf/1802.02353v1.pdf | Recent Advances in Neural Program Synthesis | In recent years, deep learning has made tremendous progress in a number of
fields that were previously out of reach for artificial intelligence. The
successes in these problems has led researchers to consider the possibilities
for intelligent systems to tackle a problem that humans have only recently
themselves conside... | ['Neel Kant'] | 2018-02-07 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 4.59692746e-01 1.94811508e-01 -3.68540525e-01 -3.86744469e-01
-3.62587571e-01 -6.00849152e-01 8.39369297e-01 3.62824410e-01
-3.72317672e-01 7.10809350e-01 4.31755856e-02 -8.60040545e-01
2.35264394e-02 -8.10719073e-01 -6.64208770e-01 -4.04874206e-01
-1.05895519e-01 2.76840061e-01 -1.06295226e-02 -2.17980906... | [8.887367248535156, 7.115070819854736] |
9fa03403-f623-47d3-a1b4-a48b54b2f232 | heads-headline-generation-as-sequence | null | null | https://aclanthology.info/papers/N15-1014/n15-1014 | https://www.aclweb.org/anthology/N15-1014 | HEADS: Headline Generation as Sequence Prediction Using an Abstract Feature-Rich Space | null | ['Marina Litvak', 'Carlos A. Colmenares', 'Fabrizio Silvestri', 'Amin Mantrach'] | 2015-05-01 | null | null | null | hlt-2015-5 | ['headline-generation'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.5391989946365356, 15.869202613830566] |
0a14ebee-84fe-465d-a0ca-bc9125463901 | towards-metrical-reconstruction-of-human | 2204.06607 | null | https://arxiv.org/abs/2204.06607v2 | https://arxiv.org/pdf/2204.06607v2.pdf | Towards Metrical Reconstruction of Human Faces | Face reconstruction and tracking is a building block of numerous applications in AR/VR, human-machine interaction, as well as medical applications. Most of these applications rely on a metrically correct prediction of the shape, especially, when the reconstructed subject is put into a metrical context (i.e., when there... | ['Justus Thies', 'Timo Bolkart', 'Wojciech Zielonka'] | 2022-04-13 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 1.36064693e-01 1.39497772e-01 6.09120354e-02 -5.53916395e-01
-6.27861500e-01 -4.82342392e-01 4.12741840e-01 -4.49060053e-01
-7.27933273e-02 4.63827372e-01 -8.86825696e-02 7.23840147e-02
-4.61894758e-02 -6.79542601e-01 -7.72710383e-01 -6.41371250e-01
2.17508361e-01 7.05640316e-01 -3.84054892e-02 -1.20521098... | [13.189371109008789, 0.20125479996204376] |
a86f6007-24bb-4d21-96b6-f9d3d2926d32 | hierarchical-memory-learning-for-fine-grained | 2203.06907 | null | https://arxiv.org/abs/2203.06907v4 | https://arxiv.org/pdf/2203.06907v4.pdf | Hierarchical Memory Learning for Fine-Grained Scene Graph Generation | As far as Scene Graph Generation (SGG), coarse and fine predicates mix in the dataset due to the crowd-sourced labeling, and the long-tail problem is also pronounced. Given this tricky situation, many existing SGG methods treat the predicates equally and learn the model under the supervision of mixed-granularity predic... | ['Jiayi Ma', 'Jingdong Chen', 'Jian Wang', 'Xiang Xiang', 'Yongjun Zhang', 'Yansheng Li', 'Youming Deng'] | 2022-03-14 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 3.46016169e-01 4.71720487e-01 -3.65195274e-01 -2.90382385e-01
-3.81849676e-01 -1.56884640e-01 5.75342774e-01 1.47408828e-01
-7.62818381e-02 6.26501441e-01 1.04168124e-01 1.26274524e-03
-6.73458818e-03 -1.14736545e+00 -7.06726074e-01 -8.46100092e-01
3.16089422e-01 6.95322871e-01 5.28030217e-01 1.35640249... | [10.283923149108887, 1.7420681715011597] |
81a95f9b-a74e-45de-92ad-4d781e007259 | few-shot-partial-label-learning | 2106.00984 | null | https://arxiv.org/abs/2106.00984v1 | https://arxiv.org/pdf/2106.00984v1.pdf | Few-Shot Partial-Label Learning | Partial-label learning (PLL) generally focuses on inducing a noise-tolerant multi-class classifier by training on overly-annotated samples, each of which is annotated with a set of labels, but only one is the valid label. A basic promise of existing PLL solutions is that there are sufficient partial-label (PL) samples ... | ['Carlotta Domeniconi', 'Lizhen Cui', 'Zhongmin Yan', 'Lei Liu', 'Guoxian Yu', 'Yunfeng Zhao'] | 2021-06-02 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 4.10704017e-01 -1.72541201e-01 -3.55881453e-01 -5.50062001e-01
-6.97501540e-01 -1.58834651e-01 4.25569683e-01 2.58782119e-01
-5.97795546e-01 7.39056468e-01 -2.61060625e-01 1.70738459e-01
-1.17933862e-01 -6.06689453e-01 -3.27447325e-01 -8.10040236e-01
2.08167747e-01 5.13405502e-01 7.15222895e-01 1.59433559... | [9.999286651611328, 3.1398298740386963] |
f6dc4ab0-1b34-4171-8dd1-b8edb607eb52 | leveraging-multiple-descriptive-features-for | 2307.04317 | null | https://arxiv.org/abs/2307.04317v1 | https://arxiv.org/pdf/2307.04317v1.pdf | Leveraging Multiple Descriptive Features for Robust Few-shot Image Learning | Modern image classification is based upon directly predicting model classes via large discriminative networks, making it difficult to assess the intuitive visual ``features'' that may constitute a classification decision. At the same time, recent works in joint visual language models such as CLIP provide ways to specif... | ['J. Zico Kolter', 'Anna Bair', 'Zhili Feng'] | 2023-07-10 | null | null | null | null | ['image-classification', 'few-shot-learning'] | ['computer-vision', 'methodology'] | [ 3.25211316e-01 -5.74576445e-02 -6.84871435e-01 -5.74395716e-01
-1.20344388e+00 -6.07165217e-01 8.37580383e-01 2.87453115e-01
-2.38844901e-01 4.05747294e-01 1.71095148e-01 -7.47275949e-02
-9.23494101e-02 -6.82407677e-01 -8.09539437e-01 -7.48870671e-01
7.39531964e-02 5.25654495e-01 2.28632674e-01 1.09897286... | [9.977323532104492, 2.306382656097412] |
6e882049-9db0-4ae6-ae41-24bf406c67c1 | improving-generalization-for-multimodal-fake | 2305.18599 | null | https://arxiv.org/abs/2305.18599v1 | https://arxiv.org/pdf/2305.18599v1.pdf | Improving Generalization for Multimodal Fake News Detection | The increasing proliferation of misinformation and its alarming impact have motivated both industry and academia to develop approaches for fake news detection. However, state-of-the-art approaches are usually trained on datasets of smaller size or with a limited set of specific topics. As a consequence, these models la... | ['Eric Müller-Budack', 'Ralph Ewerth', 'Sherzod Hakimov', 'Sahar Tahmasebi'] | 2023-05-29 | null | null | null | null | ['misinformation', 'fake-news-detection'] | ['miscellaneous', 'natural-language-processing'] | [ 8.35048407e-02 1.16898164e-01 -4.97963995e-01 -3.21691692e-01
-6.40975714e-01 -4.35283273e-01 9.28944945e-01 3.72081906e-01
-1.87508538e-01 5.16648650e-01 2.46301845e-01 -3.31580132e-01
5.00592768e-01 -5.71649730e-01 -5.88871956e-01 -8.50973725e-02
6.26336485e-02 1.90386638e-01 4.16553587e-01 -7.57871807... | [8.169490814208984, 10.263863563537598] |
6f3d556e-d732-42c9-b915-2f8f92987fe7 | perceptual-quality-assessment-of-face-video | 2304.07056 | null | https://arxiv.org/abs/2304.07056v2 | https://arxiv.org/pdf/2304.07056v2.pdf | Perceptual Quality Assessment of Face Video Compression: A Benchmark and An Effective Method | Recent years have witnessed an exponential increase in the demand for face video compression, and the success of artificial intelligence has expanded the boundaries beyond traditional hybrid video coding. Generative coding approaches have been identified as promising alternatives with reasonable perceptual rate-distort... | ['Shiqi Wang', 'Meng Wang', 'Baoliang Chen', 'Bolin Chen', 'Yixuan Li'] | 2023-04-14 | null | null | null | null | ['video-quality-assessment', 'video-quality-assessment'] | ['computer-vision', 'time-series'] | [ 1.57249376e-01 -4.06967551e-01 -1.03596203e-01 -5.48574865e-01
-8.17092061e-01 -1.38919711e-01 4.20564532e-01 -6.00224137e-01
1.78016424e-01 3.14034879e-01 4.65512514e-01 3.18609357e-01
-2.88429976e-01 -5.27077794e-01 -3.82488489e-01 -9.52578068e-01
-2.57977247e-01 1.25710338e-01 -2.72205561e-01 -1.48898363... | [12.99276065826416, 0.2762530744075775] |
53d35099-9886-438d-a21a-70f4e462df24 | deep-attention-aware-feature-learning-for | 2003.00517 | null | https://arxiv.org/abs/2003.00517v1 | https://arxiv.org/pdf/2003.00517v1.pdf | Deep Attention Aware Feature Learning for Person Re-Identification | Visual attention has proven to be effective in improving the performance of person re-identification. Most existing methods apply visual attention heuristically by learning an additional attention map to re-weight the feature maps for person re-identification. However, this kind of methods inevitably increase the model... | ['Xiaolu Sun', 'Han Wang', 'Chu Tang', 'Yifan Chen', 'Bin Fan'] | 2020-03-01 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-2.95789629e-01 8.87620822e-03 8.73069540e-02 -4.54097748e-01
3.28381434e-02 -3.82098436e-01 4.92742419e-01 2.20303372e-01
-6.21543288e-01 5.52585185e-01 3.31463158e-01 3.01257789e-01
-5.95111027e-02 -6.38086855e-01 -6.84617341e-01 -7.02402472e-01
1.43468857e-01 4.04340774e-01 2.51939714e-01 -7.31622055... | [14.682653427124023, 0.921638011932373] |
5d18a1d4-6bd9-422b-a91c-4e3b93198db9 | robust-defreg-a-robust-deformable-point-cloud | 2306.04701 | null | https://arxiv.org/abs/2306.04701v1 | https://arxiv.org/pdf/2306.04701v1.pdf | Robust-DefReg: A Robust Deformable Point Cloud Registration Method based on Graph Convolutional Neural Networks | Point cloud registration is a fundamental problem in computer vision that aims to estimate the transformation between corresponding sets of points. Non-rigid registration, in particular, involves addressing challenges including various levels of deformation, noise, outliers, and data incompleteness. This paper introduc... | ['Jürgen Hesser', 'Marvin Kinz', 'Sara Monji-Azad'] | 2023-06-07 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [ 2.04597097e-02 -3.59721422e-01 6.46414831e-02 -3.92157346e-01
-9.99704599e-01 -5.27475953e-01 6.07523203e-01 1.90964222e-01
-3.42189640e-01 1.29760072e-01 -4.94981892e-02 2.68277824e-01
-4.18579668e-01 -7.11911559e-01 -8.78057539e-01 -6.47747397e-01
-1.16327025e-01 7.06322134e-01 1.38493046e-01 -3.56479734... | [7.670616626739502, -3.010953187942505] |
9a06156c-11ea-4cc5-8231-4027e74538b6 | visually-grounded-compound-pcfgs | 2009.12404 | null | https://arxiv.org/abs/2009.12404v1 | https://arxiv.org/pdf/2009.12404v1.pdf | Visually Grounded Compound PCFGs | Exploiting visual groundings for language understanding has recently been drawing much attention. In this work, we study visually grounded grammar induction and learn a constituency parser from both unlabeled text and its visual groundings. Existing work on this task (Shi et al., 2019) optimizes a parser via Reinforce ... | ['Ivan Titov', 'Yanpeng Zhao'] | 2020-09-25 | null | https://aclanthology.org/2020.emnlp-main.354 | https://aclanthology.org/2020.emnlp-main.354.pdf | emnlp-2020-11 | ['constituency-grammar-induction'] | ['natural-language-processing'] | [ 3.73280585e-01 8.03080380e-01 -2.16589585e-01 -2.57347703e-01
-1.58485770e+00 -1.00589263e+00 6.23537719e-01 1.85454220e-01
-4.20490026e-01 6.00846529e-01 3.28986973e-01 -3.85608882e-01
3.30718458e-01 -5.76954842e-01 -1.17108679e+00 -7.78622866e-01
-1.05260201e-01 7.30655432e-01 8.95966813e-02 -2.70578533... | [10.625753402709961, 1.672197937965393] |
c98ab991-4cc6-4995-bfeb-a4659a5178c0 | probabilistic-forecasting-of-sensory-data | 1903.12549 | null | http://arxiv.org/abs/1903.12549v1 | http://arxiv.org/pdf/1903.12549v1.pdf | Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN | Time series forecasting is one of the challenging problems for humankind.
Traditional forecasting methods using mean regression models have severe
shortcomings in reflecting real-world fluctuations. While new probabilistic
methods rush to rescue, they fight with technical difficulties like quantile
crossing or selectin... | ['Andreas Dengel', 'Sheraz Ahmed', 'Alireza Koochali', 'Peter Schichtel'] | 2019-03-29 | null | null | null | null | ['probabilistic-time-series-forecasting', 'univariate-time-series-forecasting'] | ['time-series', 'time-series'] | [-4.25089002e-02 -1.66174337e-01 2.54544646e-01 -7.20394254e-01
-1.04326308e+00 -7.37051666e-01 9.44928885e-01 -5.16703844e-01
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-2.11115137e-01 -9.83800888e-01 -6.76741242e-01 -8.96342099e-01
-2.15961114e-01 7.07047164e-01 -2.42850855e-02 -5.47479451... | [6.947475910186768, 3.292292356491089] |
112c9fe0-8c71-414a-bce6-8e665e95dd64 | a-plug-and-play-approach-to-multiparametric | 2202.05269 | null | https://arxiv.org/abs/2202.05269v1 | https://arxiv.org/pdf/2202.05269v1.pdf | A Plug-and-Play Approach to Multiparametric Quantitative MRI: Image Reconstruction using Pre-Trained Deep Denoisers | Current spatiotemporal deep learning approaches to Magnetic Resonance Fingerprinting (MRF) build artefact-removal models customised to a particular k-space subsampling pattern which is used for fast (compressed) acquisition. This may not be useful when the acquisition process is unknown during training of the deep lear... | ['Mohammad Golbabaee', 'Peter Hall', 'Marion I. Menzel', 'Carolin M. Pirkl', 'Ketan Fatania'] | 2022-02-10 | null | null | null | null | ['de-aliasing', 'magnetic-resonance-fingerprinting'] | ['computer-vision', 'medical'] | [ 5.04506350e-01 -1.36846930e-01 2.40083486e-01 -4.84623015e-01
-7.41319537e-01 -3.18040878e-01 3.77162844e-01 -5.60644530e-02
-5.11084676e-01 7.05190063e-01 3.06455523e-01 -1.57291502e-01
-3.53751123e-01 -4.95345145e-01 -9.72790778e-01 -9.67710257e-01
-2.70711482e-01 4.53549892e-01 1.89549237e-01 -1.74950883... | [13.525620460510254, -2.428417921066284] |
7db8c616-0346-4e78-b586-1876e684b240 | learn-over-past-evolve-for-future-forecasting | 2306.14728 | null | https://arxiv.org/abs/2306.14728v1 | https://arxiv.org/pdf/2306.14728v1.pdf | Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection | Fake news detection has been a critical task for maintaining the health of the online news ecosystem. However, very few existing works consider the temporal shift issue caused by the rapidly-evolving nature of news data in practice, resulting in significant performance degradation when training on past data and testing... | ['Zhiwei Jin', 'Zhengjia Wang', 'Danding Wang', 'Yongchun Zhu', 'Juan Cao', 'Qiang Sheng', 'Beizhe Hu'] | 2023-06-26 | null | null | null | null | ['fake-news-detection'] | ['natural-language-processing'] | [-1.85149908e-01 -1.99842408e-01 -5.09583831e-01 -3.31643939e-01
-1.38951272e-01 -5.71542084e-01 9.32873964e-01 1.90298811e-01
7.16747425e-04 5.83818734e-01 5.04604876e-01 -2.81522781e-01
8.75812694e-02 -8.21807861e-01 -8.09887707e-01 -3.97841483e-01
-3.20205837e-01 1.88236907e-01 7.16554463e-01 -2.22946927... | [8.123244285583496, 10.20563793182373] |
ebe6ead3-348a-4148-98ee-beae09e18ce8 | technology-pipeline-for-large-scale-cross | 2211.01338 | null | https://arxiv.org/abs/2211.01338v1 | https://arxiv.org/pdf/2211.01338v1.pdf | Technology Pipeline for Large Scale Cross-Lingual Dubbing of Lecture Videos into Multiple Indian Languages | Cross-lingual dubbing of lecture videos requires the transcription of the original audio, correction and removal of disfluencies, domain term discovery, text-to-text translation into the target language, chunking of text using target language rhythm, text-to-speech synthesis followed by isochronous lipsyncing to the or... | ['Rajeev Sangal', 'S Umesh', 'Pushpak Bhattacharya', 'Hema Murthy', 'Dipti Sharma', 'Vrunda Sukhadia', 'Kada Sai Venkata Vineeth', 'Vandan Mujadia', 'Vasista Sai Lodagala', 'Sudhanshu Srivastava', 'Pruthwik Mishra', 'Nithya Ravi', 'Nihal John George', 'Navina K', 'Mudit Batra', 'Mohana N', 'Mohammad Wajahat', 'Metilda ... | 2022-11-01 | null | null | null | null | ['text-to-speech-synthesis'] | ['speech'] | [ 7.18705505e-02 9.01187398e-03 4.91057374e-02 -4.09527123e-02
-1.36436915e+00 -8.81573260e-01 1.19399101e-01 1.17674932e-01
-1.76287755e-01 9.45690513e-01 5.05195439e-01 -1.76704749e-01
2.56150275e-01 -5.15850261e-02 -6.21189117e-01 -6.86657548e-01
3.99175018e-01 5.16991019e-02 2.65153795e-01 -8.86660367... | [14.660204887390137, 6.419469356536865] |
6aa82363-b653-4f49-ad9e-c71bb8902bb5 | graph-similarities-and-dual-approach-for | null | null | https://openreview.net/forum?id=CxebB5Psl1 | https://openreview.net/pdf?id=CxebB5Psl1 | Graph Similarities and Dual Approach for Sequential Text-to-Image Retrieval | Sequential text-to-image retrieval, a.k.a. Story-to-images task, requires semantic alignment with a given story and maintaining global coherence in drawn image sequence simultaneously. Most of the previous works have only focused on modeling how to follow the content of a given story faithfully. This kind of overfittin... | ['Seong-Woo Kim', 'Sihyeon Jo', 'Keonwoo Kim'] | 2021-09-29 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [ 6.31226838e-01 -2.50919402e-01 -2.05586225e-01 -3.66679072e-01
-8.18668246e-01 -4.87068415e-01 7.58610189e-01 2.00140819e-01
-1.27881840e-01 5.05271554e-01 5.19662261e-01 9.62342992e-02
-1.85067892e-01 -8.39863479e-01 -1.08941185e+00 -6.21328413e-01
3.70719731e-01 2.55112529e-01 2.96122432e-01 -2.74205536... | [10.897342681884766, 0.8860433101654053] |
3c756a72-d2b9-41ed-b45f-182c365ec6c0 | learning-proximal-operators-using-denoising | 1704.03488 | null | http://arxiv.org/abs/1704.03488v2 | http://arxiv.org/pdf/1704.03488v2.pdf | Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems | While variational methods have been among the most powerful tools for solving
linear inverse problems in imaging, deep (convolutional) neural networks have
recently taken the lead in many challenging benchmarks. A remaining drawback of
deep learning approaches is their requirement for an expensive retraining
whenever t... | ['Daniel Cremers', 'Tim Meinhardt', 'Michael Moeller', 'Caner Hazirbas'] | 2017-04-11 | learning-proximal-operators-using-denoising-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Meinhardt_Learning_Proximal_Operators_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Meinhardt_Learning_Proximal_Operators_ICCV_2017_paper.pdf | iccv-2017-10 | ['image-deconvolution'] | ['computer-vision'] | [ 2.96257406e-01 -1.09119490e-01 2.48113990e-01 -3.36583853e-01
-7.69537151e-01 -2.76516974e-01 5.95314980e-01 -9.74023640e-02
-7.25392401e-01 6.89411819e-01 -1.42742451e-02 -2.69051231e-02
-4.52393353e-01 -5.31487882e-01 -8.25532496e-01 -1.16803479e+00
2.08363339e-01 5.29839516e-01 -2.67367885e-02 -2.61852324... | [11.856534957885742, -2.4172756671905518] |
a42c437b-c2b9-4339-a369-79761fa09fb6 | cross-view-asymmetric-metric-learning-for | 1708.08062 | null | http://arxiv.org/abs/1708.08062v2 | http://arxiv.org/pdf/1708.08062v2.pdf | Cross-view Asymmetric Metric Learning for Unsupervised Person Re-identification | While metric learning is important for Person re-identification (RE-ID), a
significant problem in visual surveillance for cross-view pedestrian matching,
existing metric models for RE-ID are mostly based on supervised learning that
requires quantities of labeled samples in all pairs of camera views for
training. Howeve... | ['An-Cong Wu', 'Wei-Shi Zheng', 'Hong-Xing Yu'] | 2017-08-27 | cross-view-asymmetric-metric-learning-for-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Yu_Cross-View_Asymmetric_Metric_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Yu_Cross-View_Asymmetric_Metric_ICCV_2017_paper.pdf | iccv-2017-10 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-9.23089013e-02 -3.15367311e-01 -1.42964572e-01 -6.45615399e-01
-6.10142469e-01 -4.28649843e-01 7.03097403e-01 -1.90253764e-01
-4.59807634e-01 4.97817397e-01 4.78547424e-01 1.59251481e-01
-1.31964728e-01 -4.99931484e-01 -3.58099669e-01 -6.49993658e-01
1.78825557e-01 6.78232849e-01 3.22217554e-01 1.21200912... | [14.729496002197266, 1.017716884613037] |
2e5f2c95-ee50-47b0-9d29-2f56f5167ff8 | brain-mri-study-for-glioma-segmentation-using | 2207.07622 | null | https://arxiv.org/abs/2207.07622v1 | https://arxiv.org/pdf/2207.07622v1.pdf | Brain MRI study for glioma segmentation using convolutional neural networks and original post-processing techniques with low computational demand | Gliomas are brain tumors composed of different highly heterogeneous histological subregions. Image analysis techniques to identify relevant tumor substructures have high potential for improving patient diagnosis, treatment and prognosis. However, due to the high heterogeneity of gliomas, the segmentation task is curren... | ['Benito de Celis-Alonso', 'Eduardo Moreno-Barbosa', 'José Gerardo Suárez-García Javier Miguel Hernández-López'] | 2022-07-15 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 8.63498524e-02 2.81542659e-01 2.13835642e-01 -2.21689478e-01
-5.85806847e-01 -2.70295680e-01 5.87392151e-01 6.40591085e-01
-7.98995733e-01 5.73455572e-01 4.43169586e-02 -2.08137348e-01
-2.00007409e-01 -6.14570200e-01 -7.49586001e-02 -1.13848948e+00
-1.20053448e-01 6.71362102e-01 2.44311258e-01 -4.09865677... | [14.652329444885254, -2.481720209121704] |
3e19da8b-44ab-4387-aa33-1d9f9d24d853 | realfusion-360deg-reconstruction-of-any-1 | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Melas-Kyriazi_RealFusion_360deg_Reconstruction_of_Any_Object_From_a_Single_Image_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Melas-Kyriazi_RealFusion_360deg_Reconstruction_of_Any_Object_From_a_Single_Image_CVPR_2023_paper.pdf | RealFusion: 360deg Reconstruction of Any Object From a Single Image | We consider the problem of reconstructing a full 360deg photographic model of an object from a single image of it. We do so by fitting a neural radiance field to the image, but find this problem to be severely ill-posed. We thus take an off-the-self conditional image generator based on diffusion and engineer a prom... | ['Andrea Vedaldi', 'Christian Rupprecht', 'Iro Laina', 'Luke Melas-Kyriazi'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-reconstruction'] | ['computer-vision'] | [ 3.76114190e-01 4.34573382e-01 3.75275642e-01 -4.27123755e-01
-6.31556988e-01 -6.72389925e-01 7.55578220e-01 -7.87886739e-01
-8.25620666e-02 6.69419706e-01 4.52719837e-01 7.89137371e-03
3.49342227e-01 -5.89822412e-01 -1.08853936e+00 -8.09703887e-01
7.15007186e-01 4.99683917e-01 -1.63912356e-01 1.59005657... | [9.253439903259277, -3.1232218742370605] |
93b594a5-2008-4f11-9fc1-daf4653dd522 | detecting-photoshopped-faces-by-scripting | 1906.05856 | null | https://arxiv.org/abs/1906.05856v2 | https://arxiv.org/pdf/1906.05856v2.pdf | Detecting Photoshopped Faces by Scripting Photoshop | Most malicious photo manipulations are created using standard image editing tools, such as Adobe Photoshop. We present a method for detecting one very popular Photoshop manipulation -- image warping applied to human faces -- using a model trained entirely using fake images that were automatically generated by scripting... | ['Andrew Owens', 'Sheng-Yu Wang', 'Alexei A. Efros', 'Richard Zhang', 'Oliver Wang'] | 2019-06-13 | detecting-photoshopped-faces-by-scripting-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Detecting_Photoshopped_Faces_by_Scripting_Photoshop_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Detecting_Photoshopped_Faces_by_Scripting_Photoshop_ICCV_2019_paper.pdf | iccv-2019-10 | ['image-manipulation-detection'] | ['computer-vision'] | [ 8.22806954e-01 2.07136303e-01 1.69192031e-01 -5.15091598e-01
-3.05369794e-01 -8.30746233e-01 5.60652137e-01 -3.97172421e-01
-2.74254441e-01 3.23745668e-01 -3.51714849e-01 -1.26678020e-01
6.30910218e-01 -4.18577313e-01 -9.94719267e-01 -2.39554271e-01
-4.07853276e-02 1.45349100e-01 1.26300544e-01 -2.57099539... | [12.52656364440918, 1.0929937362670898] |
b9120446-66e7-4ce6-89ef-069e1c368b47 | cycle-consistency-driven-object-discovery | 2306.02204 | null | https://arxiv.org/abs/2306.02204v1 | https://arxiv.org/pdf/2306.02204v1.pdf | Cycle Consistency Driven Object Discovery | Developing deep learning models that effectively learn object-centric representations, akin to human cognition, remains a challenging task. Existing approaches have explored slot-based methods utilizing architectural priors or auxiliary information such as depth maps or flow maps to facilitate object discovery by repre... | ['Yoshua Bengio', 'Anirudh Goyal', 'Aniket Didolkar'] | 2023-06-03 | null | null | null | null | ['object-discovery'] | ['computer-vision'] | [ 1.60805941e-01 1.10021934e-01 -3.66401434e-01 -3.55938107e-01
-4.89325196e-01 -3.23721975e-01 7.32449114e-01 2.52099663e-01
-3.10038298e-01 8.05408299e-01 4.27408330e-02 -1.42126918e-01
-4.51966077e-01 -8.15103412e-01 -9.06280398e-01 -6.23765767e-01
-1.16985053e-01 3.89625072e-01 4.38599110e-01 -2.72529162... | [9.61950969696045, 0.870410144329071] |
d0840ec1-9227-4e86-8590-90dcfa1761f8 | a-structured-learning-approach-with-neural | 1807.09119 | null | http://arxiv.org/abs/1807.09119v2 | http://arxiv.org/pdf/1807.09119v2.pdf | A Structured Learning Approach with Neural Conditional Random Fields for Sleep Staging | Sleep plays a vital role in human health, both mental and physical. Sleep
disorders like sleep apnea are increasing in prevalence, with the rapid
increase in factors like obesity. Sleep apnea is most commonly treated with
Continuous Positive Air Pressure (CPAP) therapy. Presently, however, there is
no mechanism to moni... | ['Jaideep Srivastava', 'Swaraj Khadanga', 'Louis Kazaglis', 'Shafiq R. Joty', 'Karan Aggarwal'] | 2018-07-23 | null | null | null | null | ['sleep-staging'] | ['medical'] | [ 8.03303048e-02 -1.87092140e-01 -2.24697888e-01 -3.03270340e-01
-5.77819422e-02 -2.72661984e-01 -3.63542698e-02 -5.11870123e-02
-4.90360737e-01 5.59964001e-01 4.68500674e-01 -3.39324176e-01
-5.28367907e-02 -5.39343774e-01 1.40601909e-03 -7.96605468e-01
-1.92862779e-01 1.02901921e-01 2.30678156e-01 -6.10458516... | [13.582651138305664, 3.461106777191162] |
cacaa741-174d-419b-9784-99f3854efd6d | algorithm-unrolling-based-distributed | 2301.02360 | null | https://arxiv.org/abs/2301.02360v1 | https://arxiv.org/pdf/2301.02360v1.pdf | Algorithm Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks | The user-centric cell-free network has emerged as an appealing technology to improve the next-generation wireless network's capacity thanks to its ability to eliminate inter-cell interference effectively. However, the cell-free network inevitably brings in higher hardware cost and backhaul overhead as a larger number o... | ['Chau Yuen', 'Lu Gan', 'Hongbin Li', 'Jiancheng An', 'Wangyang Xu'] | 2023-01-06 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [ 1.09981097e-01 3.06403935e-01 -1.61620200e-01 2.16034025e-01
-4.55362856e-01 -3.03470552e-01 -1.39807776e-01 -3.58676404e-01
-1.57966286e-01 1.08073473e+00 -1.04516245e-01 -6.58794582e-01
-4.95792598e-01 -1.00543058e+00 -5.61438382e-01 -1.10062158e+00
-3.26687306e-01 1.54663965e-01 -4.12383199e-01 -5.50940871... | [6.089115619659424, 1.4783507585525513] |
95027e2e-4164-421c-b06e-7e815e135b27 | panoramic-video-salient-object-detection-with | 2211.14419 | null | https://arxiv.org/abs/2211.14419v1 | https://arxiv.org/pdf/2211.14419v1.pdf | Panoramic Video Salient Object Detection with Ambisonic Audio Guidance | Video salient object detection (VSOD), as a fundamental computer vision problem, has been extensively discussed in the last decade. However, all existing works focus on addressing the VSOD problem in 2D scenarios. With the rapid development of VR devices, panoramic videos have been a promising alternative to 2D videos ... | ['Bhiksha Raj', 'Li Zhang', 'Junlin Li', 'Shijie Zhao', 'Haoyuan Cao', 'Xiang Li'] | 2022-11-26 | null | null | null | null | ['video-salient-object-detection'] | ['computer-vision'] | [ 3.02687377e-01 -5.04076958e-01 1.38732806e-01 2.55704463e-01
-8.15026939e-01 -1.68583170e-01 5.45041680e-01 -1.82925329e-01
-1.57950714e-01 3.28496635e-01 4.59764481e-01 4.06692088e-01
1.79868098e-02 -1.76147163e-01 -6.16342604e-01 -8.96344423e-01
-9.25665647e-02 -3.49574268e-01 5.92679560e-01 -2.08249629... | [9.731104850769043, -0.19467546045780182] |
9dff1118-685e-41f9-b670-e0d8f4369cd9 | contextual-bandits-with-budgeted-information | 2305.18511 | null | https://arxiv.org/abs/2305.18511v1 | https://arxiv.org/pdf/2305.18511v1.pdf | Contextual Bandits with Budgeted Information Reveal | Contextual bandit algorithms are commonly used in digital health to recommend personalized treatments. However, to ensure the effectiveness of the treatments, patients are often requested to take actions that have no immediate benefit to them, which we refer to as pro-treatment actions. In practice, clinicians have a l... | ['Susan Murphy', 'Xueqing Liu', 'Esmaeil Keyvanshokooh', 'Kyra Gan'] | 2023-05-29 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 4.77729231e-01 4.42920178e-01 -1.14166641e+00 -2.94569582e-01
-1.02947187e+00 -4.92907524e-01 -3.28691327e-03 3.55364740e-01
-1.99159503e-01 9.12686110e-01 5.81284165e-01 -5.92221081e-01
-6.87964022e-01 -7.02751160e-01 -6.45499706e-01 -8.23090136e-01
1.44512728e-01 5.67113817e-01 -4.18790251e-01 3.43859732... | [4.5422282218933105, 3.2767324447631836] |
121ae177-b214-4c93-8722-1634a5605a26 | fedhm-efficient-federated-learning-for | 2111.14655 | null | https://arxiv.org/abs/2111.14655v2 | https://arxiv.org/pdf/2111.14655v2.pdf | FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization | One underlying assumption of recent federated learning (FL) paradigms is that all local models usually share the same network architecture and size, which becomes impractical for devices with different hardware resources. A scalable federated learning framework should address the heterogeneity that clients have differe... | ['Lichao Sun', 'Yao Wan', 'Yutong Dai', "Michael J O'Neill", 'Hai Jin', 'Wanning Pan', 'Dezhong Yao'] | 2021-11-29 | null | null | null | null | ['low-rank-compression'] | ['computer-code'] | [-1.70620576e-01 -1.79540947e-01 -6.13304317e-01 -2.98556119e-01
-7.91497707e-01 -3.05460304e-01 1.47604749e-01 -2.92472094e-01
1.26382023e-01 6.90920591e-01 -1.11503355e-01 -3.27744931e-02
-4.76581216e-01 -7.65925705e-01 -7.08441079e-01 -8.34215403e-01
-1.91539656e-02 5.96437395e-01 1.06545217e-01 4.04174328... | [5.891127109527588, 6.159931182861328] |
c66fa5ae-b17e-4484-bba1-11d2dde1730a | uniex-an-effective-and-efficient-framework | 2305.10306 | null | https://arxiv.org/abs/2305.10306v3 | https://arxiv.org/pdf/2305.10306v3.pdf | UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective | We propose a new paradigm for universal information extraction (IE) that is compatible with any schema format and applicable to a list of IE tasks, such as named entity recognition, relation extraction, event extraction and sentiment analysis. Our approach converts the text-based IE tasks as the token-pair problem, whi... | ['Pingjian Zhang', 'Jiaxing Zhang', 'Yuxiang Zhang', 'Junjie Wang', 'Ruyi Gan', 'Ping Yang', 'Junyu Lu'] | 2023-05-17 | null | null | null | null | ['event-extraction', 'relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.99935034e-01 1.10771835e-01 -5.15582383e-01 -3.19942594e-01
-1.25122356e+00 -5.82210958e-01 4.87271786e-01 2.01033100e-01
-4.95902628e-01 8.58901262e-01 4.76477563e-01 -2.38986313e-01
-7.19187334e-02 -1.09889996e+00 -1.00517702e+00 -3.05382967e-01
5.20163625e-02 6.79533005e-01 1.89204421e-03 7.42906611... | [9.357171058654785, 8.852839469909668] |
dd414345-1750-4712-a12e-dcece6476bd7 | lahm-large-annotated-dataset-for-multi-domain | 2304.00913 | null | https://arxiv.org/abs/2304.00913v1 | https://arxiv.org/pdf/2304.00913v1.pdf | LAHM : Large Annotated Dataset for Multi-Domain and Multilingual Hate Speech Identification | Current research on hate speech analysis is typically oriented towards monolingual and single classification tasks. In this paper, we present a new multilingual hate speech analysis dataset for English, Hindi, Arabic, French, German and Spanish languages for multiple domains across hate speech - Abuse, Racism, Sexism, ... | ['Anil Bandhakavi', 'Sushant Chatufale', 'Shubham Chandel', 'Ankit Yadav'] | 2023-04-03 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [-2.67451853e-01 -3.96382064e-01 -3.54637462e-03 2.34533492e-02
-9.95870411e-01 -9.85162020e-01 1.02574348e+00 2.40540937e-01
-3.74640375e-01 8.71009111e-01 3.68386030e-01 -1.53990388e-01
3.47852856e-01 -5.52610457e-02 -2.83769578e-01 -5.62004387e-01
1.74646437e-01 4.95206326e-01 2.22942501e-01 -4.80077267... | [8.80772590637207, 10.574957847595215] |
1a3d7603-cdfe-4daa-9f3a-52688121d126 | spain-net-spatially-informed-stereophonic | 2202.07523 | null | https://arxiv.org/abs/2202.07523v1 | https://arxiv.org/pdf/2202.07523v1.pdf | SpaIn-Net: Spatially-Informed Stereophonic Music Source Separation | With the recent advancements of data driven approaches using deep neural networks, music source separation has been formulated as an instrument-specific supervised problem. While existing deep learning models implicitly absorb the spatial information conveyed by the multi-channel input signals, we argue that a more exp... | ['Minje Kim', 'Darius Petermann'] | 2022-02-15 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 2.80833393e-01 -4.09916639e-01 -1.55683577e-01 -1.98127121e-01
-9.54166949e-01 -8.24282110e-01 5.06567597e-01 -2.04379857e-02
-1.38128489e-01 4.24886376e-01 2.58609205e-01 3.30126435e-02
-7.69766212e-01 -4.98202145e-01 -4.84361380e-01 -8.37291956e-01
1.27336279e-01 2.34070882e-01 -2.15545461e-01 -4.00788069... | [15.470906257629395, 5.44988489151001] |
f57cdec9-b7e5-4b53-b611-ce9ecae32a0e | context-aware-block-net-for-small-object | null | null | https://ieeexplore.ieee.org/abstract/document/9151360 | https://ieeexplore.ieee.org/abstract/document/9151360 | Context-Aware Block Net for Small Object Detection | State-of-the-art object detectors usually progressively downsample the input image until it is represented by small feature maps, which loses the spatial information and compromises the representation of small objects. In this article, we propose a context-aware block net (CAB Net) to improve small object detection by ... | ['Mingliang Xu', 'Ling Shao', 'Luming Zhang', 'Bing Zhou', 'Zhimin Gao', 'Xiaoheng Jiang', 'Pei Lv', 'Lisha Cui'] | 2023-04-01 | null | null | null | ieee-transactions-on-cybernetics-2023-4 | ['traffic-sign-detection', 'small-object-detection'] | ['computer-vision', 'computer-vision'] | [-4.47990978e-03 -2.92406291e-01 2.37412259e-01 -2.64134884e-01
-2.34888718e-01 -5.02873778e-01 4.52231407e-01 2.74063665e-02
-5.96170962e-01 2.35604629e-01 -5.62886223e-02 -2.24948391e-01
4.13652137e-02 -1.06851089e+00 -8.00237715e-01 -6.02517903e-01
1.04826413e-01 -2.12366655e-01 1.10769904e+00 -2.69190967... | [8.797231674194336, -0.4847460389137268] |
ddf2d225-5b6e-4c37-aa01-c5a9ac32edca | activitynet-2019-task-3-exploring-contexts | 1907.05092 | null | https://arxiv.org/abs/1907.05092v1 | https://arxiv.org/pdf/1907.05092v1.pdf | Activitynet 2019 Task 3: Exploring Contexts for Dense Captioning Events in Videos | Contextual reasoning is essential to understand events in long untrimmed videos. In this work, we systematically explore different captioning models with various contexts for the dense-captioning events in video task, which aims to generate captions for different events in the untrimmed video. We propose five types of ... | ['Alexander Hauptmann', 'Jianlong Fu', 'Shizhe Chen', 'Yuqing Song', 'Yida Zhao', 'Zhaoyang Zeng', 'Qin Jin', 'Bei Liu'] | 2019-07-11 | null | null | null | null | ['dense-captioning', 'dense-video-captioning'] | ['computer-vision', 'computer-vision'] | [ 4.30423558e-01 1.54563785e-01 -1.37779284e-02 -5.31179190e-01
-1.17162251e+00 -5.06745994e-01 8.46098602e-01 -1.97076187e-01
-1.37767553e-01 8.38278890e-01 9.70313847e-01 8.31069797e-02
4.74530667e-01 -2.87028939e-01 -1.26535690e+00 -4.42881465e-01
-1.31438911e-01 4.68761474e-01 2.67927468e-01 2.44862214... | [10.482078552246094, 0.7331781387329102] |
778f13d4-fdd7-4562-ab91-edd298a0e03d | robust-pivoting-manipulation-using-contact | 2303.08965 | null | https://arxiv.org/abs/2303.08965v1 | https://arxiv.org/pdf/2303.08965v1.pdf | Robust Pivoting Manipulation using Contact Implicit Bilevel Optimization | Generalizable manipulation requires that robots be able to interact with novel objects and environment. This requirement makes manipulation extremely challenging as a robot has to reason about complex frictional interactions with uncertainty in physical properties of the object and the environment. In this paper, we st... | ['Arvind U. Raghunathan', 'Devesh K. Jha', 'Yuki Shirai'] | 2023-03-15 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [ 3.01728700e-03 3.46078157e-01 -2.55728692e-01 -9.37524438e-03
-2.12628707e-01 -7.66602159e-01 3.97971272e-01 2.69516230e-01
-4.28785026e-01 9.47486877e-01 -3.19262803e-01 6.16241172e-02
-1.10021937e+00 -4.16190237e-01 -1.00833070e+00 -8.03056300e-01
-2.29309425e-01 8.08125913e-01 8.90444778e-03 -4.36670482... | [4.861684799194336, 1.462216854095459] |
f23bfd21-1225-464e-9b49-6a82bde1156d | drlcomplex-reconstruction-of-protein | 2205.13594 | null | https://arxiv.org/abs/2205.13594v1 | https://arxiv.org/pdf/2205.13594v1.pdf | DRLComplex: Reconstruction of protein quaternary structures using deep reinforcement learning | Predicted inter-chain residue-residue contacts can be used to build the quaternary structure of protein complexes from scratch. However, only a small number of methods have been developed to reconstruct protein quaternary structures using predicted inter-chain contacts. Here, we present an agent-based self-learning met... | ['Jianlin Cheng', 'Alex Morehead', 'Nabin Giri', 'Farhan Quadir', 'Raj S. Roy', 'Elham Soltanikazemi'] | 2022-05-26 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [-1.68153882e-01 -1.53854741e-02 -1.47942156e-01 -1.30974829e-01
-7.45943844e-01 -5.69755256e-01 5.17344594e-01 3.49126577e-01
-6.44711077e-01 1.70417988e+00 -2.10375488e-01 -5.73778093e-01
2.02466711e-01 -5.23655355e-01 -1.06136680e+00 -1.02856767e+00
-7.18848258e-02 1.03932285e+00 3.51538002e-01 -4.14616168... | [4.731034278869629, 5.558572292327881] |
9adaa176-ba6a-4cf1-8ab9-074629ca8011 | domain-adaptation-for-deep-entity-resolution | null | null | https://dl.acm.org/doi/10.1145/3514221.3517870 | https://dl.acm.org/doi/pdf/10.1145/3514221.3517870 | Domain Adaptation for Deep Entity Resolution: A Design Space Exploration | Entity resolution (ER) is a core problem of data integration. The state-of-the-art (SOTA) results on ER are achieved by deep learning (DL) based methods, trained with a lot of labeled matching/non-matching entity pairs. This may not be a problem when using well-prepared benchmark datasets. Nevertheless, for many real-w... | ['Xiaoyong Du', 'Ruixue Fan', 'Guoliang Li', 'Chengliang Chai', 'Peng Wang', 'Nan Tang', 'Ju Fan', 'Jianhong Tu'] | 2022-06-01 | null | null | null | sigmod-pods-2022-6 | ['data-integration', 'entity-resolution'] | ['knowledge-base', 'natural-language-processing'] | [-1.06902830e-01 1.27899259e-01 -3.72667402e-01 -4.96265680e-01
-7.78079450e-01 -4.47872400e-01 4.74054426e-01 1.74623758e-01
-5.97426355e-01 8.64990652e-01 1.44526392e-01 -8.93481541e-03
-1.40392780e-01 -9.12155211e-01 -5.84038138e-01 -2.60294735e-01
8.69623795e-02 8.82407308e-01 1.55522794e-01 -5.24087608... | [9.432653427124023, 8.54559326171875] |
ab334a96-fd57-44b9-9470-f3994a5076f2 | investigating-the-translation-performance-of | 2303.01911 | null | https://arxiv.org/abs/2303.01911v2 | https://arxiv.org/pdf/2303.01911v2.pdf | Investigating the Translation Performance of a Large Multilingual Language Model: the Case of BLOOM | The NLP community recently saw the release of a new large open-access multilingual language model, BLOOM (BigScience et al., 2022) covering 46 languages. We focus on BLOOM's multilingual ability by evaluating its machine translation performance across several datasets (WMT, Flores-101 and DiaBLa) and language pairs (hi... | ['François Yvon', 'Rachel Bawden'] | 2023-03-03 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [-6.29842460e-01 -1.59446761e-01 -6.24986410e-01 -9.44942683e-02
-1.49984324e+00 -1.05575144e+00 1.00810981e+00 6.12191036e-02
-8.02788675e-01 1.27093840e+00 7.62896836e-01 -6.55960739e-01
9.66794938e-02 -2.43480757e-01 -6.95352554e-01 3.15217637e-02
1.38844416e-01 9.48410451e-01 -1.33177251e-01 -5.99157810... | [11.507644653320312, 10.261430740356445] |
51334d0e-5424-4b64-82b4-08014fb26ad3 | brain-tumor-segmentation-using-synthetic-mr | 2306.02986 | null | https://arxiv.org/abs/2306.02986v1 | https://arxiv.org/pdf/2306.02986v1.pdf | Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion models | Large annotated datasets are required for training deep learning models, but in medical imaging data sharing is often complicated due to ethics, anonymization and data protection legislation (e.g. the general data protection regulation (GDPR)). Generative AI models, such as generative adversarial networks (GANs) and di... | ['Anders Eklund', 'Måns Larsson', 'Muhammad Usman Akbar'] | 2023-06-05 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation', 'ethics', 'memorization'] | ['computer-vision', 'medical', 'miscellaneous', 'natural-language-processing'] | [ 5.12003481e-01 5.79297662e-01 1.66479379e-01 -3.28172505e-01
-5.72419941e-01 -5.72307169e-01 5.05449533e-01 -1.89574435e-01
-7.29349375e-01 1.08630323e+00 -2.19707210e-02 -4.46283817e-01
1.83756486e-01 -7.33607769e-01 -6.37212813e-01 -6.80485427e-01
2.09042832e-01 5.65571249e-01 -1.25284374e-01 -1.48797911... | [14.19625186920166, -1.9692907333374023] |
5e277c35-1c6e-499f-8131-e7a03689a142 | cate-embedding-mathcal-alc-ontologies-using | 2305.07163 | null | https://arxiv.org/abs/2305.07163v1 | https://arxiv.org/pdf/2305.07163v1.pdf | CatE: Embedding $\mathcal{ALC}$ ontologies using category-theoretical semantics | Machine learning with Semantic Web ontologies follows several strategies, one of which involves projecting ontologies into graph structures and applying graph embeddings or graph-based machine learning methods to the resulting graphs. Several methods have been developed that project ontology axioms into graphs. However... | ['Robert Hoehndorf', 'Fernando Zhapa-Camacho'] | 2023-05-11 | null | null | null | null | ['ontology-embedding'] | ['knowledge-base'] | [ 1.66746840e-01 7.68168569e-01 -2.56592005e-01 -2.62210429e-01
1.93955570e-01 -6.39106214e-01 7.44134724e-01 3.01207691e-01
-2.69437045e-01 3.37046295e-01 3.66122425e-01 -7.44273841e-01
-5.59915423e-01 -1.48296452e+00 -6.51001632e-01 -1.92745432e-01
-2.03670934e-01 8.24465930e-01 2.76624024e-01 -5.61859846... | [8.85603141784668, 7.684133052825928] |
21f202da-8938-499a-9598-366b82611b52 | 6-dof-graspnet-variational-grasp-generation | 1905.10520 | null | https://arxiv.org/abs/1905.10520v2 | https://arxiv.org/pdf/1905.10520v2.pdf | 6-DOF GraspNet: Variational Grasp Generation for Object Manipulation | Generating grasp poses is a crucial component for any robot object manipulation task. In this work, we formulate the problem of grasp generation as sampling a set of grasps using a variational autoencoder and assess and refine the sampled grasps using a grasp evaluator model. Both Grasp Sampler and Grasp Refinement net... | ['Dieter Fox', 'Clemens Eppner', 'Arsalan Mousavian'] | 2019-05-25 | 6-dof-graspnet-variational-grasp-generation-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Mousavian_6-DOF_GraspNet_Variational_Grasp_Generation_for_Object_Manipulation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Mousavian_6-DOF_GraspNet_Variational_Grasp_Generation_for_Object_Manipulation_ICCV_2019_paper.pdf | iccv-2019-10 | ['grasp-generation'] | ['computer-vision'] | [-3.49531770e-01 -2.96376590e-02 3.09716035e-02 -2.26670802e-01
-2.58877397e-01 -4.99757111e-01 1.91233288e-02 -1.67934865e-01
-1.11703701e-01 4.82416809e-01 -2.46477544e-01 9.32358950e-02
1.75828606e-01 -9.67310965e-01 -1.15025532e+00 -8.13610196e-01
-2.10715428e-01 1.09941852e+00 6.83304369e-02 -4.99967374... | [5.694619655609131, -0.7698779702186584] |
7ffa5a0e-08e2-42d5-b11d-d51d8056f8c0 | utopic-uncertainty-aware-overlap-prediction | 2208.02712 | null | https://arxiv.org/abs/2208.02712v6 | https://arxiv.org/pdf/2208.02712v6.pdf | UTOPIC: Uncertainty-aware Overlap Prediction Network for Partial Point Cloud Registration | High-confidence overlap prediction and accurate correspondences are critical for cutting-edge models to align paired point clouds in a partial-to-partial manner. However, there inherently exists uncertainty between the overlapping and non-overlapping regions, which has always been neglected and significantly affects th... | ['Mingqiang Wei', 'Jing Qin', 'Yanwen Guo', 'Jun Wang', 'Xuefeng Yan', 'Lina Gong', 'Honghua Chen', 'Zhilei Chen'] | 2022-08-04 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [ 2.29358226e-02 2.59218037e-01 -1.39853045e-01 -4.43810195e-01
-9.58735526e-01 -4.63931203e-01 5.39035559e-01 4.72473502e-02
7.80141130e-02 3.23120415e-01 7.69057572e-02 1.10347390e-01
-2.93855965e-01 -7.28856146e-01 -8.64090860e-01 -3.04551750e-01
1.73034102e-01 7.26939738e-01 2.69377083e-01 -7.87989944... | [7.750141143798828, -3.1310086250305176] |
7fb6e49a-e691-4a60-9eab-7800224c1030 | linear-convergence-of-natural-policy-gradient | 2210.01400 | null | https://arxiv.org/abs/2210.01400v3 | https://arxiv.org/pdf/2210.01400v3.pdf | Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies | We consider infinite-horizon discounted Markov decision processes and study the convergence rates of the natural policy gradient (NPG) and the Q-NPG methods with the log-linear policy class. Using the compatible function approximation framework, both methods with log-linear policies can be written as inexact versions o... | ['Lin Xiao', 'Alessandro Lazaric', 'Robert M. Gower', 'Simon S. Du', 'Rui Yuan'] | 2022-10-04 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-1.04919598e-01 5.07228434e-01 -2.90260464e-01 -1.08081378e-01
-1.04254186e+00 -6.38914168e-01 4.45312500e-01 2.86491632e-01
-1.00395489e+00 1.27525365e+00 -7.39468634e-02 -7.99326181e-01
-2.14934722e-01 -5.07679343e-01 -6.58503354e-01 -8.63788188e-01
-2.58617550e-01 4.92615908e-01 8.59915540e-02 -4.03268486... | [4.27031135559082, 2.6800436973571777] |
0a1cd9a0-54aa-4ddb-a9c9-6b12215c9f4d | image-to-video-generation-via-3d-facial | 2105.14678 | null | https://arxiv.org/abs/2105.14678v1 | https://arxiv.org/pdf/2105.14678v1.pdf | Image-to-Video Generation via 3D Facial Dynamics | We present a versatile model, FaceAnime, for various video generation tasks from still images. Video generation from a single face image is an interesting problem and usually tackled by utilizing Generative Adversarial Networks (GANs) to integrate information from the input face image and a sequence of sparse facial la... | ['Jiashi Feng', 'Wei Liu', 'Zhifeng Li', 'Guodong Guo', 'Zhikang Wang', 'Yuan YAO', 'Jian Dong', 'Wenjie Ai', 'Jian Zhao', 'Yingtian Zou', 'Xiaoguang Tu'] | 2021-05-31 | null | null | null | null | ['image-to-video'] | ['computer-vision'] | [ 3.52354825e-01 1.56831771e-01 1.27259269e-01 -2.13962734e-01
-4.24131066e-01 -4.48026806e-01 4.69641984e-01 -1.16217959e+00
3.01958412e-01 7.08232045e-01 2.27213815e-01 4.99498695e-01
3.07981044e-01 -6.36070728e-01 -1.01884878e+00 -9.47978854e-01
3.22130114e-01 1.82795629e-01 -5.69482505e-01 -2.66070724... | [12.811972618103027, -0.3486765921115875] |
32057bc3-40fc-483b-8323-a98ec3ad7291 | snakevoxformer-transformer-based-single-image | 2303.16293 | null | https://arxiv.org/abs/2303.16293v1 | https://arxiv.org/pdf/2303.16293v1.pdf | SnakeVoxFormer: Transformer-based Single Image\\Voxel Reconstruction with Run Length Encoding | Deep learning-based 3D object reconstruction has achieved unprecedented results. Among those, the transformer deep neural model showed outstanding performance in many applications of computer vision. We introduce SnakeVoxFormer, a novel, 3D object reconstruction in voxel space from a single image using the transformer.... | ['Bedrich Benes', 'Jae Joong Lee'] | 2023-03-28 | null | null | null | null | ['3d-object-reconstruction', 'object-reconstruction', 'data-compression'] | ['computer-vision', 'computer-vision', 'time-series'] | [ 1.72375575e-01 2.12071031e-01 9.48828682e-02 -2.66423792e-01
-6.16751552e-01 -2.32871458e-01 4.72266912e-01 1.84737936e-01
-4.31118488e-01 1.88081592e-01 7.94079080e-02 -1.98624283e-01
4.13256511e-02 -1.27764595e+00 -1.20509005e+00 -5.60803592e-01
-3.70230615e-01 7.62067616e-01 6.00640416e-01 3.68925929... | [8.547618865966797, -3.654435634613037] |
8fecf19c-849e-41fb-b1bd-ec81afcbd78e | active-sparse-conversations-for-improved | 2306.04047 | null | https://arxiv.org/abs/2306.04047v1 | https://arxiv.org/pdf/2306.04047v1.pdf | Active Sparse Conversations for Improved Audio-Visual Embodied Navigation | Efficient navigation towards an audio-goal necessitates an embodied agent to not only possess the ability to use audio-visual cues effectively, but also be equipped to actively (but occasionally) seek human/oracle assistance without sacrificing autonomy, e.g., when it is uncertain of where to navigate towards locating ... | ['Anoop Cherian', 'Moitreya Chatterjee', 'Sudipta Paul', 'Xiulong Liu'] | 2023-06-06 | null | null | null | null | ['hierarchical-reinforcement-learning', 'navigate', 'visual-navigation'] | ['methodology', 'reasoning', 'robots'] | [ 1.74615130e-01 4.47725773e-01 5.10918140e-01 -4.07716990e-01
-1.62396991e+00 -8.10817242e-01 5.06002188e-01 9.78299901e-02
-7.03801692e-01 4.56978410e-01 5.44902742e-01 -5.53430200e-01
-1.82008013e-01 -7.01188326e-01 -7.64219046e-01 -5.29189289e-01
-1.62731424e-01 7.41209388e-01 -1.46728838e-02 -3.45985293... | [4.426661491394043, 0.7064929604530334] |
c6996ad5-40ad-4f35-af21-dfbf7863c4e2 | chartsumm-a-comprehensive-benchmark-for | 2304.13620 | null | https://arxiv.org/abs/2304.13620v3 | https://arxiv.org/pdf/2304.13620v3.pdf | ChartSumm: A Comprehensive Benchmark for Automatic Chart Summarization of Long and Short Summaries | Automatic chart to text summarization is an effective tool for the visually impaired people along with providing precise insights of tabular data in natural language to the user. A large and well-structured dataset is always a key part for data driven models. In this paper, we propose ChartSumm: a large-scale benchmark... | ['Abu Raihan Mostofa Kamal', 'Md. Hamjajul Ashmafee', 'Md Tahmid Rahman Laskar', 'Abdullah Al Farhad', 'Rizvi Hasan', 'Raian Rahman'] | 2023-04-26 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 6.86431974e-02 1.62368834e-01 -6.04560487e-02 -3.24714005e-01
-1.36284161e+00 -6.39514565e-01 8.08650792e-01 5.44863522e-01
1.94353729e-01 1.13812995e+00 1.47013593e+00 -6.57923445e-02
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2.14835152e-01 4.35301483e-01 -1.01458579e-01 -2.57302940... | [12.402949333190918, 9.401527404785156] |
5ca677c3-fcb9-40f9-b5b5-47dae0f4c86d | grapeqa-graph-augmentation-and-pruning-to | 2303.12320 | null | https://arxiv.org/abs/2303.12320v2 | https://arxiv.org/pdf/2303.12320v2.pdf | GrapeQA: GRaph Augmentation and Pruning to Enhance Question-Answering | Commonsense question-answering (QA) methods combine the power of pre-trained Language Models (LM) with the reasoning provided by Knowledge Graphs (KG). A typical approach collects nodes relevant to the QA pair from a KG to form a Working Graph (WG) followed by reasoning using Graph Neural Networks(GNNs). This faces two... | ['Makarand Tapaswi', 'Charu Sharma', 'Vasudeva Varma', 'Pavan Kandru', 'Lakshya Khanna', 'Dhaval Taunk'] | 2023-03-22 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 1.43320933e-01 1.12482941e+00 5.67877106e-02 -1.37833245e-02
-1.01372015e+00 -5.87601840e-01 4.21370059e-01 7.10566044e-01
-7.12131187e-02 9.49383736e-01 7.18085289e-01 -3.07305187e-01
-2.66677365e-02 -1.26490164e+00 -7.94087708e-01 -2.06077993e-01
8.07482451e-02 1.07208884e+00 5.93363941e-01 -7.97605574... | [10.53759765625, 7.927701950073242] |
4f56ef65-4d15-4b99-8735-4dc328101464 | mintrec-a-new-dataset-for-multimodal-intent | 2209.04355 | null | https://arxiv.org/abs/2209.04355v1 | https://arxiv.org/pdf/2209.04355v1.pdf | MIntRec: A New Dataset for Multimodal Intent Recognition | Multimodal intent recognition is a significant task for understanding human language in real-world multimodal scenes. Most existing intent recognition methods have limitations in leveraging the multimodal information due to the restrictions of the benchmark datasets with only text information. This paper introduces a n... | ['Jiayan Teng', 'Shaojie Zhao', 'Qianrui Zhou', 'Xin Wang', 'Hua Xu', 'Hanlei Zhang'] | 2022-09-09 | null | null | null | null | ['multimodal-intent-recognition', 'intent-recognition'] | ['miscellaneous', 'natural-language-processing'] | [ 3.38200301e-01 -4.88327920e-01 -2.52583057e-01 -6.07694328e-01
-1.33236217e+00 -6.12769186e-01 8.24749112e-01 -2.56564528e-01
-3.76185805e-01 1.71916202e-01 9.78197336e-01 4.79697436e-03
2.38946721e-01 -2.13566213e-03 -3.17893088e-01 -6.24307632e-01
-1.00849688e-01 1.94668636e-01 -3.80980879e-01 -1.84994534... | [13.143182754516602, 5.13377046585083] |
ca610a23-be87-4b3f-a335-14edcd7c94b3 | featurized-density-ratio-estimation | 2107.02212 | null | https://arxiv.org/abs/2107.02212v1 | https://arxiv.org/pdf/2107.02212v1.pdf | Featurized Density Ratio Estimation | Density ratio estimation serves as an important technique in the unsupervised machine learning toolbox. However, such ratios are difficult to estimate for complex, high-dimensional data, particularly when the densities of interest are sufficiently different. In our work, we propose to leverage an invertible generative ... | ['Stefano Ermon', 'Madeline Liao', 'Kristy Choi'] | 2021-07-05 | null | null | null | null | ['density-ratio-estimation', 'mutual-information-estimation'] | ['methodology', 'methodology'] | [ 8.86770412e-02 1.41804993e-01 -5.27609102e-02 -3.01897913e-01
-7.13415861e-01 -6.86561465e-01 7.74709165e-01 -9.84707102e-02
-2.19489485e-01 8.77514839e-01 2.36031517e-01 -2.90509403e-01
-2.87236512e-01 -8.22633803e-01 -6.20011270e-01 -8.76554787e-01
2.55256325e-01 6.95929229e-01 -2.77395278e-01 2.56870121... | [7.442237377166748, 3.928496837615967] |
4b012021-91a2-4c2b-8f05-6db7dc3a7a55 | leveraging-gpt-2-for-classifying-spam-reviews | 2012.13400 | null | https://arxiv.org/abs/2012.13400v1 | https://arxiv.org/pdf/2012.13400v1.pdf | Leveraging GPT-2 for Classifying Spam Reviews with Limited Labeled Data via Adversarial Training | Online reviews are a vital source of information when purchasing a service or a product. Opinion spammers manipulate these reviews, deliberately altering the overall perception of the service. Though there exists a corpus of online reviews, only a few have been labeled as spam or non-spam, making it difficult to train ... | ['Gray Stanton', 'Anubha Agrawal', 'Yankun Shen', 'Hanfei Yu', 'Athirai A. Irissappane'] | 2020-12-24 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 1.85794279e-01 2.84762949e-01 -9.32872146e-02 -5.77040315e-01
-6.33179009e-01 -8.40575278e-01 6.71572030e-01 -8.24113786e-02
-5.90816960e-02 8.00390959e-01 -2.82320619e-01 -7.34358609e-01
6.83014691e-01 -9.78417099e-01 -7.85797834e-01 -5.59268951e-01
3.39457989e-01 6.33868217e-01 3.11354786e-01 -5.27537286... | [7.802196502685547, 9.994695663452148] |
2905536b-2b29-4bfc-af09-98a5caac6aa1 | diffusion-jump-gnns-homophiliation-via | 2306.16976 | null | https://arxiv.org/abs/2306.16976v1 | https://arxiv.org/pdf/2306.16976v1.pdf | Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters | High-order Graph Neural Networks (HO-GNNs) have been developed to infer consistent latent spaces in the heterophilic regime, where the label distribution is not correlated with the graph structure. However, most of the existing HO-GNNs are hop-based, i.e., they rely on the powers of the transition matrix. As a result, ... | ['Edwin R. Hancock', 'Miguel Angel Lozano', 'Francisco Escolano', 'Ahmed Begga'] | 2023-06-29 | null | null | null | null | ['node-classification'] | ['graphs'] | [-5.25837094e-02 2.88238227e-01 -3.63464177e-01 -5.23270592e-02
2.12267593e-01 -4.92032766e-01 8.24067831e-01 1.91320598e-01
-2.42292285e-01 6.41109765e-01 -8.84028599e-02 2.29808185e-02
-4.61450219e-01 -1.37456882e+00 -7.22766459e-01 -1.26680326e+00
-8.62679556e-02 7.75782466e-01 3.28230023e-01 -1.12483375... | [6.947935104370117, 5.976627826690674] |
1804a8d4-8aa2-4f7b-a1f7-b5ab46455cc9 | retrieval-efficiency-trade-off-of | 2208.07262 | null | https://arxiv.org/abs/2208.07262v1 | https://arxiv.org/pdf/2208.07262v1.pdf | Retrieval-efficiency trade-off of Unsupervised Keyword Extraction | Efficiently identifying keyphrases that represent a given document is a challenging task. In the last years, plethora of keyword detection approaches were proposed. These approaches can be based on statistical (frequency-based) properties of e.g., tokens, specialized neural language models, or a graph-based structure d... | ['Senja Pollak', 'Boshko Koloski', 'Blaž Škrlj'] | 2022-08-15 | null | null | null | null | ['keyword-extraction'] | ['natural-language-processing'] | [ 3.07969838e-01 -1.32685844e-02 -3.08537930e-01 1.25322863e-01
-9.08528924e-01 -6.87791407e-01 8.14621270e-01 1.31121325e+00
-6.76965237e-01 6.44575953e-01 2.47357830e-01 -2.78344840e-01
-5.12789369e-01 -8.88167202e-01 -3.23473841e-01 -6.93396926e-01
-5.04632056e-01 4.72217411e-01 6.08261883e-01 6.71591461... | [11.866913795471191, 8.638992309570312] |
f4cca4c7-97a8-479f-89bf-3d96dff7e5e0 | a-persian-benchmark-for-joint-intent | 2303.00408 | null | https://arxiv.org/abs/2303.00408v1 | https://arxiv.org/pdf/2303.00408v1.pdf | A Persian Benchmark for Joint Intent Detection and Slot Filling | Natural Language Understanding (NLU) is important in today's technology as it enables machines to comprehend and process human language, leading to improved human-computer interactions and advancements in fields such as virtual assistants, chatbots, and language-based AI systems. This paper highlights the significance ... | ['Ali Mohades', 'Mohammad Akbari', 'Fatemeh Shamsezat', 'Kiana Ghezelbash', 'Zeinab Saeidi', 'Tayyebeh Saeedi', 'Amir Hossein Karimi', 'Masoud Akbari'] | 2023-03-01 | null | null | null | null | ['intent-detection', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.04666162e-01 2.62586355e-01 -5.82311988e-01 -1.12119965e-01
-4.67454225e-01 -6.31220460e-01 8.03100765e-01 3.99840266e-01
-8.17902327e-01 8.36658061e-01 3.12605232e-01 -6.09655380e-01
2.41258159e-01 -7.49368608e-01 -1.22906808e-02 1.87970236e-01
1.90043733e-01 7.70341277e-01 2.91396499e-01 -3.11246037... | [12.550853729248047, 7.373832702636719] |
27914610-4e4f-4719-8d6b-bc16a4d29d64 | a-physics-informed-machine-learning-for | 2304.00062 | null | https://arxiv.org/abs/2304.00062v1 | https://arxiv.org/pdf/2304.00062v1.pdf | A Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study | This paper addresses the challenge of efficiently solving the optimal power flow problem in real-time electricity markets. The proposed solution, named Physics-Informed Market-Aware Active Set learning OPF (PIMA-AS-OPF), leverages physical constraints and market properties to ensure physical and economic feasibility of... | ['Michael Chertkov', 'Daniel Bienstock', 'Yury Dvorkin', 'Zhirui Liang', 'Robert Mieth', 'Laurent Pagnier', 'Robert Ferrando'] | 2023-03-31 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-2.91220486e-01 -7.14057460e-02 -4.35966790e-01 1.80225223e-01
-2.23531246e-01 -8.05412650e-01 2.53219932e-01 5.59492968e-02
1.18501753e-01 1.43563259e+00 -4.50858206e-01 -7.29619801e-01
-7.94480205e-01 -1.02041829e+00 -2.12171376e-01 -9.91239488e-01
-7.23312974e-01 4.37626392e-01 -4.47775424e-01 -4.31945682... | [5.676564693450928, 2.5621583461761475] |
8e5acf64-fa00-456d-9f7f-4c34a7eb0b6c | hands-on-detection-for-steering-wheels-with | 2306.09044 | null | https://arxiv.org/abs/2306.09044v1 | https://arxiv.org/pdf/2306.09044v1.pdf | Hands-on detection for steering wheels with neural networks | In this paper the concept of a machine learning based hands-on detection algorithm is proposed. The hand detection is implemented on the hardware side using a capacitive method. A sensor mat in the steering wheel detects a change in capacity as soon as the driver's hands come closer. The evaluation and final decision a... | ['Andreas Fischer', 'Michael Hollmer'] | 2023-06-15 | null | null | null | null | ['hand-detection'] | ['computer-vision'] | [-2.50651836e-01 -2.26155907e-01 -4.05155361e-01 -4.51840848e-01
1.22585006e-01 -3.75884026e-01 1.20949805e-01 2.88220942e-01
-7.78157353e-01 3.10061514e-01 -5.76420903e-01 -8.15295637e-01
-1.87743828e-01 -6.87630117e-01 -4.60004061e-03 -6.30780280e-01
3.48919719e-01 4.06253606e-01 5.79993784e-01 -2.82214373... | [6.526397705078125, 0.04350043833255768] |
31a18f46-b355-473e-89cd-700ba412ce96 | selective-clustering-ensemble-based-on-kappa | 2204.11062 | null | https://arxiv.org/abs/2204.11062v1 | https://arxiv.org/pdf/2204.11062v1.pdf | Selective clustering ensemble based on kappa and F-score | Clustering ensemble has an impressive performance in improving the accuracy and robustness of partition results and has received much attention in recent years. Selective clustering ensemble (SCE) can further improve the ensemble performance by selecting base partitions or clusters in according to diversity and stabili... | ['Zhong-Yuan Zhang', 'Tao You', 'Ji Qi', 'Xin Liu', 'Jie Yan'] | 2022-04-23 | null | null | null | null | ['clustering-ensemble'] | ['graphs'] | [-1.63140938e-01 -4.63736951e-01 1.11879461e-01 -3.15444887e-01
-3.25181097e-01 -5.48044980e-01 3.92454684e-01 3.84884238e-01
-2.43258551e-01 8.56410146e-01 1.65147811e-01 2.74661332e-02
-7.14115739e-01 -9.14508402e-01 1.53931230e-01 -1.18187666e+00
-3.28936316e-02 4.12016660e-01 4.08328295e-01 1.14586189... | [7.647529602050781, 4.545526027679443] |
8a82dac6-ac9c-40a7-a4c6-0b8d87e3fd69 | a-tale-of-two-features-stable-diffusion | 2305.15347 | null | https://arxiv.org/abs/2305.15347v1 | https://arxiv.org/pdf/2305.15347v1.pdf | A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence | Text-to-image diffusion models have made significant advances in generating and editing high-quality images. As a result, numerous approaches have explored the ability of diffusion model features to understand and process single images for downstream tasks, e.g., classification, semantic segmentation, and stylization. ... | ['Ming-Hsuan Yang', 'Deqing Sun', 'Varun Jampani', 'Luisa Polania Cabrera', 'Junhwa Hur', 'Charles Herrmann', 'Junyi Zhang'] | 2023-05-24 | null | null | null | null | ['semantic-correspondence'] | ['computer-vision'] | [ 3.70086312e-01 -2.45521128e-01 -2.34266445e-01 -3.33077013e-01
-8.69902372e-01 -7.70635188e-01 1.04618561e+00 3.86471242e-01
-3.88011396e-01 4.19353426e-01 3.59910786e-01 3.44418772e-02
-2.67487347e-01 -7.31654644e-01 -6.40780151e-01 -7.92136550e-01
1.07734911e-01 5.99604607e-01 6.22012794e-01 -2.91181117... | [10.885189056396484, 0.1421458125114441] |
715c7a91-dd2a-4771-ba89-556d2f453acc | deep-residual-correction-network-for-partial | 2004.04914 | null | https://arxiv.org/abs/2004.04914v1 | https://arxiv.org/pdf/2004.04914v1.pdf | Deep Residual Correction Network for Partial Domain Adaptation | Deep domain adaptation methods have achieved appealing performance by learning transferable representations from a well-labeled source domain to a different but related unlabeled target domain. Most existing works assume source and target data share the identical label space, which is often difficult to be satisfied in... | ['Limin Su', 'Shuang Li', 'Qiuxia Lin', 'Gao Huang', 'Qi Wen', 'Zhengming Ding', 'Chi Harold Liu'] | 2020-04-10 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 2.46910542e-01 -1.23668596e-01 -3.61986041e-01 -5.74678719e-01
-4.22362238e-01 -5.34580350e-01 4.29214716e-01 -8.31054747e-02
-3.86914968e-01 7.59346902e-01 1.22526467e-01 1.73750371e-01
-8.51833448e-02 -9.47973549e-01 -6.64727867e-01 -8.59685719e-01
4.59720969e-01 4.17074531e-01 4.38886970e-01 -2.72918910... | [10.335552215576172, 3.035379648208618] |
11c13fc3-84cd-4384-98fc-7193d473b09f | arabic-dialect-identification-using-bert | 2011.06977 | null | https://arxiv.org/abs/2011.06977v1 | https://arxiv.org/pdf/2011.06977v1.pdf | Arabic Dialect Identification Using BERT-Based Domain Adaptation | Arabic is one of the most important and growing languages in the world. With the rise of social media platforms such as Twitter, Arabic spoken dialects have become more in use. In this paper, we describe our approach on the NADI Shared Task 1 that requires us to build a system to differentiate between different 21 Arab... | ['Omar ElSherief', 'Abdelrahman Wael', 'Ahmad Beltagy'] | 2020-11-13 | null | https://aclanthology.org/2020.wanlp-1.26 | https://aclanthology.org/2020.wanlp-1.26.pdf | coling-wanlp-2020-12 | ['dialect-identification'] | ['natural-language-processing'] | [-3.73991966e-01 -2.21499920e-01 2.31475234e-01 -6.51850998e-01
-9.61056411e-01 -8.63902748e-01 9.88989890e-01 3.27863216e-01
-7.41719544e-01 5.42170823e-01 2.41747588e-01 -2.13464886e-01
7.44269788e-02 -8.05660546e-01 -2.87786156e-01 -4.37135786e-01
-3.09385926e-01 1.01856303e+00 -1.95730366e-02 -1.19061399... | [10.159647941589355, 10.765899658203125] |
25d366c6-a751-4594-8355-f4554678c431 | kernel-conditional-moment-constraints-for | 2302.13348 | null | https://arxiv.org/abs/2302.13348v1 | https://arxiv.org/pdf/2302.13348v1.pdf | Kernel Conditional Moment Constraints for Confounding Robust Inference | We study policy evaluation of offline contextual bandits subject to unobserved confounders. Sensitivity analysis methods are commonly used to estimate the policy value under the worst-case confounding over a given uncertainty set. However, existing work often resorts to some coarse relaxation of the uncertainty set for... | ['Niao He', 'Kei Ishikawa'] | 2023-02-26 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 1.01184182e-01 6.15143590e-02 -7.99469769e-01 -1.76191479e-01
-1.02421701e+00 -5.56731820e-01 2.24368006e-01 2.73444444e-01
-4.43407893e-01 1.18590772e+00 1.50227383e-01 -8.07671666e-01
-5.69252789e-01 -7.37421334e-01 -9.89050865e-01 -8.98496211e-01
6.74300492e-02 3.63727175e-02 1.77526437e-02 2.95407772... | [4.596057415008545, 3.2574222087860107] |
39d99060-4e63-4833-84e3-710738a7d39c | spatial-temporal-mitosis-detection-in-phase | 2004.12531 | null | https://arxiv.org/abs/2004.12531v2 | https://arxiv.org/pdf/2004.12531v2.pdf | Spatial-Temporal Mitosis Detection in Phase-Contrast Microscopy via Likelihood Map Estimation by 3DCNN | Automated mitotic detection in time-lapse phasecontrast microscopy provides us much information for cell behavior analysis, and thus several mitosis detection methods have been proposed. However, these methods still have two problems; 1) they cannot detect multiple mitosis events when there are closely placed. 2) they ... | ['Ryoma Bise', 'Kazuya Nishimura'] | 2020-04-27 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 1.71801388e-01 -2.62293518e-01 -6.91335350e-02 5.53486049e-02
-8.86806488e-01 -5.86292386e-01 4.70896959e-01 4.54107970e-01
-8.00823271e-01 1.12942731e+00 -1.15603223e-01 1.20628938e-01
2.24526584e-01 -6.72838032e-01 -5.81128120e-01 -1.18573666e+00
2.75420398e-01 4.81544375e-01 9.57910657e-01 3.92654151... | [14.647644996643066, -3.2374346256256104] |
9d1c688a-6a0c-4c14-8340-f20e0fbe5471 | detecting-recolored-image-by-spatial | 2204.10973 | null | https://arxiv.org/abs/2204.10973v1 | https://arxiv.org/pdf/2204.10973v1.pdf | Detecting Recolored Image by Spatial Correlation | Image forensics, aiming to ensure the authenticity of the image, has made great progress in dealing with common image manipulation such as copy-move, splicing, and inpainting in the past decades. However, only a few researchers pay attention to an emerging editing technique called image recoloring, which can manipulate... | ['Xiaochun Cao', 'Mingfu Xue', 'Shuren Qi', 'Nuo Chen', 'Yushu Zhang'] | 2022-04-23 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 4.81113374e-01 -5.70809603e-01 -9.54509377e-02 -4.44899872e-02
-6.04869187e-01 -4.86039251e-01 4.60605532e-01 -2.84127980e-01
-2.82274067e-01 6.76670134e-01 -2.08053783e-01 -2.91263461e-01
-6.91919401e-02 -7.79803216e-01 -7.78037608e-01 -1.10981476e+00
2.44940087e-01 -4.40013856e-01 -1.77622568e-02 -1.17793595... | [12.363728523254395, 0.921225368976593] |
4b2a3c7f-d355-4825-a787-0c9d58f47b8e | exposure-aware-dynamic-weighted-learning-for | null | null | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136670429.pdf | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136670429.pdf | Exposure-Aware Dynamic Weighted Learning for Single-Shot HDR Imaging | We propose a novel single-shot high dynamic range (HDR) imaging algorithm based on exposure-aware dynamic weighted learning, which reconstructs an HDR image from a spatially varying exposure (SVE) raw image. First, we recover poorly exposed pixels by developing a network that learns local dynamic filters to exploit loc... | ['Chul Lee', 'An Gia Vien'] | 2022-10-23 | null | null | null | european-conference-on-computer-vision-eccv | ['single-shot-hdr-reconstruction', 'hdr-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 6.45367920e-01 -5.21777570e-01 2.25396417e-02 -5.84118128e-01
-9.40472901e-01 -2.30846897e-01 1.41589776e-01 -7.03673303e-01
-5.08921325e-01 8.23358059e-01 6.62797987e-02 1.76845402e-01
-2.96114177e-01 -9.63223159e-01 -7.23981321e-01 -1.10853565e+00
3.50071536e-03 -3.23620826e-01 3.84189814e-01 -2.57938415... | [10.857101440429688, -2.2035467624664307] |
5e521653-19d6-4502-a3e2-d3ac23bad1c0 | 190412732 | 1904.12732 | null | https://arxiv.org/abs/1904.12732v2 | https://arxiv.org/pdf/1904.12732v2.pdf | Multi-scale Microaneurysms Segmentation Using Embedding Triplet Loss | Deep learning techniques are recently being used in fundus image analysis and diabetic retinopathy detection. Microaneurysms are an important indicator of diabetic retinopathy progression. We introduce a two-stage deep learning approach for microaneurysms segmentation using multiple scales of the input with selective s... | ['Nassir Navab', 'Abouzar Eslami', 'Mehmet Yigitsoy', 'Shadi Albarqouni', 'Mhd Hasan Sarhan'] | 2019-04-18 | null | null | null | null | ['diabetic-retinopathy-detection'] | ['medical'] | [ 1.65323257e-01 1.83150172e-01 -1.67521179e-01 -5.59680164e-01
-8.91470015e-01 -2.68893957e-01 2.28233591e-01 6.25922307e-02
-5.57602704e-01 5.50231040e-01 3.37180585e-01 -2.87140489e-01
6.91265240e-02 -6.10852540e-01 -4.85942096e-01 -6.38494015e-01
-2.92827152e-02 2.75714189e-01 2.73282796e-01 1.81418255... | [15.791653633117676, -3.9687132835388184] |
d4385401-4652-47ba-87e5-06e88795b93f | inharmonious-region-localization | 2104.09453 | null | https://arxiv.org/abs/2104.09453v1 | https://arxiv.org/pdf/2104.09453v1.pdf | Inharmonious Region Localization | The advance of image editing techniques allows users to create artistic works, but the manipulated regions may be incompatible with the background. Localizing the inharmonious region is an appealing yet challenging task. Realizing that this task requires effective aggregation of multi-scale contextual information and s... | ['Liqing Zhang', 'Li Niu', 'Jing Liang'] | 2021-04-19 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 3.08743834e-01 -1.49148628e-01 -8.45094994e-02 -2.29496792e-01
-9.52989221e-01 -4.91315126e-01 4.40513104e-01 -3.30827326e-01
-1.48633972e-01 6.10423028e-01 4.59077120e-01 1.12872072e-01
1.68041185e-01 -5.06395161e-01 -6.29642487e-01 -6.47821486e-01
5.23034036e-01 -2.23133340e-01 2.76035696e-01 -3.51454705... | [11.237507820129395, -1.2396513223648071] |
edd6308e-6ada-49f5-80e9-973f9e022db0 | dsmtgcn-a-direction-sensitive-multi-task | 2306.10290 | null | https://arxiv.org/abs/2306.10290v1 | https://arxiv.org/pdf/2306.10290v1.pdf | DsMtGCN: A Direction-sensitive Multi-task framework for Knowledge Graph Completion | To solve the inherent incompleteness of knowledge graphs (KGs), numbers of knowledge graph completion (KGC) models have been proposed to predict missing links from known triples. Among those, several works have achieved more advanced results via exploiting the structure information on KGs with Graph Convolutional Netwo... | ['Yuren Zhou', 'Zibin Zheng', 'Chuan Chen', 'Jining Wang'] | 2023-06-17 | null | null | null | null | ['knowledge-graph-completion', 'knowledge-graphs', 'entity-embeddings'] | ['knowledge-base', 'knowledge-base', 'methodology'] | [-2.75088578e-01 3.03197682e-01 -3.84949297e-01 -4.95219916e-01
-2.09065899e-01 -3.00818354e-01 4.38910425e-01 8.08368102e-02
-2.11462364e-01 9.28899527e-01 3.78731012e-01 -1.08108297e-01
-5.51858246e-01 -9.75887656e-01 -1.08462262e+00 -5.26039302e-01
-1.41319737e-01 3.53692889e-01 3.12075973e-01 -2.07526222... | [8.745194435119629, 7.914479732513428] |
910eab07-79e1-467d-b0fb-4e15bcb2fa93 | maple-masking-words-to-generate-blackout | null | null | https://aclanthology.org/2021.icnlsp-1.6 | https://aclanthology.org/2021.icnlsp-1.6.pdf | MAPLE – MAsking words to generate blackout Poetry using sequence-to-sequence LEarning | null | ['Dr. Mamatha H R', 'Deeksha D', 'Himanshu Jain', 'Aditeya Baral'] | null | null | null | null | icnlsp-2021-11 | ['blackout-poetry-generation'] | ['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... | [-7.329464435577393, 3.7038800716400146] |
754bd8c0-7b5c-448c-a87d-6c13d544768f | night-to-day-image-translation-for-retrieval | 1809.09767 | null | http://arxiv.org/abs/1809.09767v2 | http://arxiv.org/pdf/1809.09767v2.pdf | Night-to-Day Image Translation for Retrieval-based Localization | Visual localization is a key step in many robotics pipelines, allowing the
robot to (approximately) determine its position and orientation in the world.
An efficient and scalable approach to visual localization is to use image
retrieval techniques. These approaches identify the image most similar to a
query photo in a ... | ['Luc van Gool', 'Marc Pollefeys', 'Radu Timofte', 'Torsten Sattler', 'Asha Anoosheh'] | 2018-09-26 | null | null | null | null | ['style-generalization'] | ['computer-vision'] | [-3.17048952e-02 -2.98192084e-01 -1.14500962e-01 -5.44115067e-01
-9.97029483e-01 -1.01161385e+00 8.04822922e-01 7.47948065e-02
-8.25109541e-01 5.12130439e-01 -1.52168304e-01 9.01343152e-02
-1.00155547e-01 -3.89394730e-01 -1.11278892e+00 -5.81903994e-01
1.18354999e-01 7.37802386e-01 3.74856055e-01 -2.50426978... | [7.509369850158691, -2.0612895488739014] |
8e3ca535-a855-41b5-9209-a652feeee3ff | deep-slow-motion-video-reconstruction-with | 2002.12106 | null | https://arxiv.org/abs/2002.12106v2 | https://arxiv.org/pdf/2002.12106v2.pdf | Deep Slow Motion Video Reconstruction with Hybrid Imaging System | Slow motion videos are becoming increasingly popular, but capturing high-resolution videos at extremely high frame rates requires professional high-speed cameras. To mitigate this problem, current techniques increase the frame rate of standard videos through frame interpolation by assuming linear object motion which is... | ['Nima Khademi Kalantari', 'Avinash Paliwal'] | 2020-02-27 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 2.38517001e-01 -3.98465872e-01 -4.14742567e-02 -9.07449275e-02
-6.88660085e-01 -3.61900896e-01 3.36952537e-01 -4.28961575e-01
-5.90062618e-01 6.88691258e-01 4.41568866e-02 1.26618827e-02
4.17325050e-01 -5.12453318e-01 -1.02469933e+00 -7.22163975e-01
1.00368343e-01 3.64672462e-03 5.99724472e-01 2.11228102... | [10.803852081298828, -1.639341950416565] |
36f6ee60-eea3-4a10-81f2-074b2e1924d7 | 2detect-a-large-2d-expandable-trainable | 2306.05907 | null | https://arxiv.org/abs/2306.05907v1 | https://arxiv.org/pdf/2306.05907v1.pdf | 2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning | Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, an... | ['Felix Lucka', 'Tristan van Leeuwen', 'K. Joost Batenburg', 'Sophia B. Coban', 'Maximilian B. Kiss'] | 2023-06-09 | null | null | null | null | ['image-reconstruction', 'image-denoising', 'super-resolution', 'computed-tomography-ct'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology'] | [ 4.17451680e-01 -2.20921546e-01 -4.50358354e-02 -5.95054150e-01
-1.38889611e+00 -1.76754311e-01 2.93103784e-01 8.10895190e-02
-5.55515647e-01 5.04417777e-01 -5.03552631e-02 -3.63763422e-01
-3.57705683e-01 -7.89440393e-01 -4.86747354e-01 -7.75100648e-01
-1.58038855e-01 1.26692235e+00 5.84104240e-01 2.31563598... | [13.39323902130127, -2.637809991836548] |
160971c4-a5b4-4f7b-a5e1-c6c999050b7b | a-dual-level-detection-method-for-video-copy | 2305.12361 | null | https://arxiv.org/abs/2305.12361v1 | https://arxiv.org/pdf/2305.12361v1.pdf | A Dual-level Detection Method for Video Copy Detection | With the development of multimedia technology, Video Copy Detection has been a crucial problem for social media platforms. Meta AI hold Video Similarity Challenge on CVPR 2023 to push the technology forward. In this paper, we share our winner solutions on both tracks to help progress in this area. For Descriptor Track,... | ['Fengyun Rao', 'Zhenhua Liu', 'Feipeng Ma', 'Tianyi Wang'] | 2023-05-21 | null | null | null | null | ['partial-video-copy-detection', 'video-similarity'] | ['computer-vision', 'computer-vision'] | [ 1.26105800e-01 -5.11588693e-01 -3.85701448e-01 1.32206485e-01
-8.90056372e-01 -4.62889582e-01 5.55477560e-01 -1.87608808e-01
-8.25906098e-02 2.33850494e-01 3.05287212e-01 -3.22002429e-03
3.22985619e-01 -3.75742048e-01 -6.16638303e-01 -1.14048690e-01
-3.02363098e-01 -2.65105754e-01 8.15095723e-01 -1.37933046... | [10.269224166870117, 0.6523919105529785] |
474bbc6a-fd53-4a69-87a7-f100cc619f69 | neural-extractive-text-summarization-with | 1902.00863 | null | https://arxiv.org/abs/1902.00863v2 | https://arxiv.org/pdf/1902.00863v2.pdf | Neural Extractive Text Summarization with Syntactic Compression | Recent neural network approaches to summarization are largely either selection-based extraction or generation-based abstraction. In this work, we present a neural model for single-document summarization based on joint extraction and syntactic compression. Our model chooses sentences from the document, identifies possib... | ['Jiacheng Xu', 'Greg Durrett'] | 2019-02-03 | neural-extractive-text-summarization-with-1 | https://aclanthology.org/D19-1324 | https://aclanthology.org/D19-1324.pdf | ijcnlp-2019-11 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 4.35869426e-01 6.55401945e-01 -5.42586148e-01 -4.78131294e-01
-1.33702826e+00 -2.99778998e-01 6.19850993e-01 4.37358797e-01
-5.47524929e-01 8.58641863e-01 1.05925953e+00 -2.03206226e-01
1.43627882e-01 -6.81012690e-01 -9.17692125e-01 -1.99981079e-01
2.80667357e-02 8.00864518e-01 -1.90933481e-01 -8.79632607... | [12.506385803222656, 9.485590934753418] |
9def26e3-965b-4b5a-9822-48fe66c8b610 | camlpad-cybersecurity-autonomous-machine | 1907.10442 | null | https://arxiv.org/abs/1907.10442v1 | https://arxiv.org/pdf/1907.10442v1.pdf | CAMLPAD: Cybersecurity Autonomous Machine Learning Platform for Anomaly Detection | As machine learning and cybersecurity continue to explode in the context of the digital ecosystem, the complexity of cybersecurity data combined with complicated and evasive machine learning algorithms leads to vast difficulties in designing an end to end system for intelligent, automatic anomaly classification. On the... | ['Ayush Hariharan', 'Trisha Pal', 'Ankit Gupta'] | 2019-07-23 | null | null | null | null | ['anomaly-classification'] | ['computer-vision'] | [-3.48028511e-01 -3.65495026e-01 -6.43575389e-04 3.30175221e-01
-6.38229251e-02 -9.14021790e-01 6.48950279e-01 9.41739857e-01
-8.41443911e-02 1.95671976e-01 -3.80576611e-01 -8.88890028e-01
-5.95728695e-01 -9.39148724e-01 -2.67788440e-01 -7.48259902e-01
-6.61946595e-01 3.14489901e-01 3.16684574e-01 -1.55208679... | [5.309225559234619, 7.1090087890625] |
06fa8543-d3df-4996-bfc5-a4438f01669b | mplug-effective-and-efficient-vision-language | 2205.12005 | null | https://arxiv.org/abs/2205.12005v2 | https://arxiv.org/pdf/2205.12005v2.pdf | mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections | Large-scale pretrained foundation models have been an emerging paradigm for building artificial intelligence (AI) systems, which can be quickly adapted to a wide range of downstream tasks. This paper presents mPLUG, a new vision-language foundation model for both cross-modal understanding and generation. Most existing ... | ['Luo Si', 'Jingren Zhou', 'Fei Huang', 'Songfang Huang', 'Ji Zhang', 'Zheng Cao', 'Guohai Xu', 'Hehong Chen', 'Jiabo Ye', 'Bin Bi', 'Ming Yan', 'Wei Wang', 'Junfeng Tian', 'Haiyang Xu', 'Chenliang Li'] | 2022-05-24 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [ 2.73745298e-01 -1.64074488e-02 -2.18894139e-01 -4.22576576e-01
-1.05731583e+00 -4.16592419e-01 1.00091684e+00 -1.97363123e-01
-4.48800355e-01 2.18224794e-01 2.75703132e-01 -3.77834976e-01
3.17949742e-01 -5.45230746e-01 -1.06250834e+00 -3.36983442e-01
4.97085094e-01 5.26586175e-01 2.37975225e-01 -3.23882461... | [10.878488540649414, 1.5883601903915405] |
8ccc19d6-c1bc-4137-bfec-567b0e19b014 | neural-sentence-ordering | 1607.06952 | null | http://arxiv.org/abs/1607.06952v1 | http://arxiv.org/pdf/1607.06952v1.pdf | Neural Sentence Ordering | Sentence ordering is a general and critical task for natural language
generation applications. Previous works have focused on improving its
performance in an external, downstream task, such as multi-document
summarization. Given its importance, we propose to study it as an isolated
task. We collect a large corpus of ac... | ['Xinchi Chen', 'Xuanjing Huang', 'Xipeng Qiu'] | 2016-07-23 | null | null | null | null | ['sentence-ordering'] | ['natural-language-processing'] | [ 5.37014246e-01 2.68085808e-01 -2.73955345e-01 -5.08005738e-01
-1.25405777e+00 -8.17817986e-01 8.38755786e-01 2.51707405e-01
-2.61541456e-01 1.28640902e+00 9.18497086e-01 -3.88491184e-01
4.58269902e-02 -3.47530216e-01 -4.74562138e-01 -2.98814446e-01
-1.53567031e-01 6.22752607e-01 2.21052110e-01 -5.47860265... | [12.335261344909668, 9.476688385009766] |
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