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7785893c-e43f-47e1-80eb-91d1fc3e6a0f | stefann-scene-text-editor-using-font-adaptive | 1903.01192 | null | https://arxiv.org/abs/1903.01192v3 | https://arxiv.org/pdf/1903.01192v3.pdf | STEFANN: Scene Text Editor using Font Adaptive Neural Network | Textual information in a captured scene plays an important role in scene interpretation and decision making. Though there exist methods that can successfully detect and interpret complex text regions present in a scene, to the best of our knowledge, there is no significant prior work that aims to modify the textual inf... | ['Prasun Roy', 'Umapada Pal', 'Subhankar Ghosh', 'Saumik Bhattacharya'] | 2019-03-04 | stefann-scene-text-editor-using-font-adaptive-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Roy_STEFANN_Scene_Text_Editor_Using_Font_Adaptive_Neural_Network_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Roy_STEFANN_Scene_Text_Editor_Using_Font_Adaptive_Neural_Network_CVPR_2020_paper.pdf | cvpr-2020-6 | ['scene-text-editing'] | ['computer-vision'] | [ 1.00891924e+00 -1.62184000e-01 2.85656661e-01 -4.32715148e-01
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7.78041422e-01 1.69984967e-01 6.20160460e-01 -1.19660936... | [11.785948753356934, 1.8597841262817383] |
729ce678-3bd6-4539-9dc9-e4d034c77a66 | tome-a-two-stage-approach-for-model-based | 2305.11161 | null | https://arxiv.org/abs/2305.11161v1 | https://arxiv.org/pdf/2305.11161v1.pdf | TOME: A Two-stage Approach for Model-based Retrieval | Recently, model-based retrieval has emerged as a new paradigm in text retrieval that discards the index in the traditional retrieval model and instead memorizes the candidate corpora using model parameters. This design employs a sequence-to-sequence paradigm to generate document identifiers, which enables the complete ... | ['Haifeng Wang', 'Ji-Rong Wen', 'Hua Wu', 'Jing Liu', 'Wayne Xin Zhao', 'Ruiyang Ren'] | 2023-05-18 | null | null | null | null | ['natural-questions'] | ['miscellaneous'] | [ 1.22956291e-01 -4.25043851e-01 -3.43879312e-01 -3.27123404e-02
-1.30516410e+00 -6.85071886e-01 9.93199944e-01 1.11588247e-01
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2.15569064e-01 7.47675598e-01 5.55979788e-01 -4.35298949... | [11.461852073669434, 7.636738300323486] |
622d6fc9-e91b-4be5-b25d-1f854bdc93de | implicit-autoencoder-for-point-cloud-self | 2201.00785 | null | https://arxiv.org/abs/2201.00785v4 | https://arxiv.org/pdf/2201.00785v4.pdf | Implicit Autoencoder for Point Cloud Self-supervised Representation Learning | This paper advocates the use of implicit surface representation in autoencoder-based self-supervised 3D representation learning. The most popular and accessible 3D representation, i.e., point clouds, involves discrete samples of the underlying continuous 3D surface. This discretization process introduces sampling varia... | ['Gang Hua', 'Hao Kang', 'Chen Song', 'QiXing Huang', 'Li Guan', 'Haoxiang Li', 'Zhenpei Yang', 'Siming Yan'] | 2022-01-03 | null | null | null | null | ['3d-point-cloud-linear-classification'] | ['computer-vision'] | [-3.08941193e-02 3.07092994e-01 8.35236385e-02 -3.30941468e-01
-6.52828097e-01 -4.09144431e-01 5.89247286e-01 -2.18635835e-02
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1.39223918e-01 9.74456966e-01 -1.68880165e-01 -3.89932096... | [8.750897407531738, -3.6044998168945312] |
d5b19f41-eefd-453d-8ce4-bd2a0dbe27cc | a-new-lda-formulation-with-covariates | 2202.11527 | null | https://arxiv.org/abs/2202.11527v1 | https://arxiv.org/pdf/2202.11527v1.pdf | A new LDA formulation with covariates | The Latent Dirichlet Allocation (LDA) model is a popular method for creating mixed-membership clusters. Despite having been originally developed for text analysis, LDA has been used for a wide range of other applications. We propose a new formulation for the LDA model which incorporates covariates. In this model, a neg... | ['Denis Valle', 'Rafael Izbicki', 'Gilson Shimizu'] | 2022-02-18 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [ 4.32641059e-02 -8.03508535e-02 -1.91572860e-01 -3.30676109e-01
-3.55903894e-01 -3.52039784e-01 7.63579547e-01 5.10522902e-01
-2.45091230e-01 4.49026078e-01 2.95863867e-01 -3.64581078e-01
-1.71754852e-01 -1.02240229e+00 -4.15606350e-01 -9.87537622e-01
-5.64014196e-01 9.15420890e-01 8.59654844e-02 3.42591763... | [10.311253547668457, 6.890551567077637] |
acf6e12d-1728-41bc-b2e3-ff5e2a5f1e35 | multi-script-handwritten-digit-recognition | 2106.08267 | null | https://arxiv.org/abs/2106.08267v1 | https://arxiv.org/pdf/2106.08267v1.pdf | Multi-script Handwritten Digit Recognition Using Multi-task Learning | Handwritten digit recognition is one of the extensively studied area in machine learning. Apart from the wider research on handwritten digit recognition on MNIST dataset, there are many other research works on various script recognition. However, it is not very common for multi-script digit recognition which encourage ... | ['Randolf Scholz', 'Durga Prasad Sharma', 'Lars Schmidt-Thieme', 'Mesay Samuel Gondere'] | 2021-06-15 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 1.59386545e-01 -4.90253747e-01 -3.63258086e-02 -6.70056045e-01
-6.76334321e-01 -5.64586759e-01 7.54387915e-01 -2.89771497e-01
-6.05901599e-01 7.81825542e-01 -1.42494649e-01 -1.91758603e-01
-3.68176728e-01 -4.51060712e-01 -5.39154291e-01 -8.91931057e-01
3.68868679e-01 5.37756324e-01 1.94842547e-01 -3.80291305... | [11.822907447814941, 2.606790781021118] |
4f3f4435-c15a-4c15-98d3-e4a438b42036 | a-deep-cascade-model-for-multi-document | 1811.11374 | null | http://arxiv.org/abs/1811.11374v1 | http://arxiv.org/pdf/1811.11374v1.pdf | A Deep Cascade Model for Multi-Document Reading Comprehension | A fundamental trade-off between effectiveness and efficiency needs to be
balanced when designing an online question answering system. Effectiveness
comes from sophisticated functions such as extractive machine reading
comprehension (MRC), while efficiency is obtained from improvements in
preliminary retrieval component... | ['Rui Wang', 'Haiqing Chen', 'Ming Yan', 'Jiangnan Xia', 'Bin Bi', 'Zhongzhou Zhao', 'Luo Si', 'Chen Wu', 'Wei Wang', 'Ji Zhang'] | 2018-11-28 | null | null | null | null | ['triviaqa'] | ['miscellaneous'] | [-1.80808250e-02 -2.20878869e-01 8.22809618e-03 -3.67024362e-01
-1.25803339e+00 -6.43534005e-01 5.41606665e-01 5.65618932e-01
-7.79958963e-01 5.36783159e-01 3.00367445e-01 -5.63681245e-01
-3.65389556e-01 -8.09407830e-01 -5.78152716e-01 -2.87222207e-01
4.37815189e-01 7.02253461e-01 5.27729750e-01 -4.05290693... | [11.162005424499512, 7.856837749481201] |
85785d55-1324-4a27-9a73-11f6d8730f93 | auxiliary-task-based-deep-reinforcement-1 | 2302.14312 | null | https://arxiv.org/abs/2302.14312v1 | https://arxiv.org/pdf/2302.14312v1.pdf | Auxiliary Task-based Deep Reinforcement Learning for Quantum Control | Due to its property of not requiring prior knowledge of the environment, reinforcement learning has significant potential for quantum control problems. In this work, we investigate the effectiveness of continuous control policies based on deep deterministic policy gradient. To solve the sparse reward signal in quantum ... | ['Daoyi Dong', 'Sen Kuang', 'Hailan Ma', 'Shumin Zhou'] | 2023-02-28 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 7.97831491e-02 -1.04812821e-02 -8.74207541e-02 -5.09163481e-04
-5.70082784e-01 -9.95425358e-02 4.92107093e-01 -7.28311092e-02
-7.49502420e-01 1.05340946e+00 3.04563344e-02 -2.69691616e-01
-1.74028650e-01 -1.00680363e+00 -7.22685575e-01 -1.36195290e+00
8.79905969e-02 8.09698254e-02 -9.65191647e-02 -6.48994982... | [4.143901824951172, 1.987600326538086] |
c930978c-945c-4a70-b0ef-00ef4d8556b5 | hallucinating-pose-compatible-scenes | 2112.06909 | null | https://arxiv.org/abs/2112.06909v2 | https://arxiv.org/pdf/2112.06909v2.pdf | Hallucinating Pose-Compatible Scenes | What does human pose tell us about a scene? We propose a task to answer this question: given human pose as input, hallucinate a compatible scene. Subtle cues captured by human pose -- action semantics, environment affordances, object interactions -- provide surprising insight into which scenes are compatible. We presen... | ['Alexei A. Efros', 'Tim Brooks'] | 2021-12-13 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 4.15632844e-01 1.16066858e-01 5.17866254e-01 -3.52094293e-01
-6.42610669e-01 -8.05312991e-01 8.68050933e-01 -4.40836608e-01
-2.97131509e-01 4.86751646e-01 6.80696130e-01 2.37813760e-02
4.27814931e-01 -6.73208237e-01 -1.14808381e+00 -2.80273974e-01
1.61076486e-01 5.79213858e-01 2.34148018e-02 -3.21294695... | [11.83080768585205, -0.6829266548156738] |
91770b92-ac0b-46c4-ae08-faf61048867b | leveraging-native-language-speech-for-accent | 1712.08992 | null | http://arxiv.org/abs/1712.08992v2 | http://arxiv.org/pdf/1712.08992v2.pdf | Leveraging Native Language Speech for Accent Identification using Deep Siamese Networks | The problem of automatic accent identification is important for several
applications like speaker profiling and recognition as well as for improving
speech recognition systems. The accented nature of speech can be primarily
attributed to the influence of the speaker's native language on the given
speech recording. In t... | ['Sriram Ganapathy', 'Aditya Siddhant', 'Preethi Jyothi'] | 2017-12-25 | null | null | null | null | ['speaker-profiling'] | ['speech'] | [ 2.58592423e-02 -6.31005615e-02 -5.34374118e-02 -7.88466036e-01
-7.66388535e-01 -6.04087353e-01 4.05833632e-01 -2.12649047e-01
-6.89056218e-01 5.20702660e-01 4.94950563e-01 -5.06560445e-01
2.79397547e-01 -2.86533684e-01 -5.53522646e-01 -7.10256517e-01
7.05639226e-03 6.94725871e-01 -4.73903745e-01 -2.82249749... | [14.3590669631958, 6.700863361358643] |
f2a8c440-eff9-4078-b673-1e29b9a0afa5 | multi-task-item-attribute-graph-pre-training | 2306.14462 | null | https://arxiv.org/abs/2306.14462v1 | https://arxiv.org/pdf/2306.14462v1.pdf | Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation | Recommendation systems suffer in the strict cold-start (SCS) scenario, where the user-item interactions are entirely unavailable. The ID-based approaches completely fail to work. Cold-start recommenders, on the other hand, leverage item contents to map the new items to the existing ones. However, the existing SCS recom... | ['Philip S. Yu', 'Chenyu You', 'Hao Peng', 'Zhiwei Liu', 'Chen Wang', 'Liangwei Yang', 'Yuwei Cao'] | 2023-06-26 | null | null | null | null | ['multi-task-learning'] | ['methodology'] | [-3.51168513e-01 -5.27022302e-01 -7.34159410e-01 -4.89905596e-01
-7.24970222e-01 -7.71866024e-01 3.60539764e-01 -1.34755373e-01
-4.67623264e-01 5.64366400e-01 5.24736702e-01 -2.32650135e-02
-3.00181836e-01 -6.96426988e-01 -7.58956611e-01 -7.05345929e-01
1.33669019e-01 5.95372200e-01 -1.04511298e-01 -3.23054880... | [10.161707878112793, 5.566991806030273] |
10b0c246-cdb0-4032-b5f3-65e10df3350b | self-supervised-3d-mesh-reconstruction-from | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Hu_Self-Supervised_3D_Mesh_Reconstruction_From_Single_Images_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Hu_Self-Supervised_3D_Mesh_Reconstruction_From_Single_Images_CVPR_2021_paper.pdf | Self-Supervised 3D Mesh Reconstruction From Single Images | Recent single-view 3D reconstruction methods reconstruct object's shape and texture from a single image with only 2D image-level annotation. However, without explicit 3D attribute-level supervision, it is still difficult to achieve satisfying reconstruction accuracy. In this paper, we propose a Self-supervised Mesh... | ['Jiaya Jia', 'Shu Liu', 'Xiaogang Xu', 'LiWei Wang', 'Tao Hu'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 1.99213564e-01 2.47259423e-01 -1.93190277e-01 -6.13233507e-01
-8.86574268e-01 -4.25737530e-01 3.98401320e-01 -3.52524109e-02
3.11422080e-01 5.67758203e-01 1.09124467e-01 1.28684729e-01
2.10476011e-01 -8.06834698e-01 -1.22603118e+00 -3.85396063e-01
4.52160418e-01 8.66932273e-01 2.44431198e-01 -4.26994264... | [8.546640396118164, -3.053508758544922] |
7346992c-1305-4009-82a9-15827bb731c8 | team-dadefrni-at-case-2021-task-1-document | null | null | https://aclanthology.org/2021.case-1.22 | https://aclanthology.org/2021.case-1.22.pdf | Team “DaDeFrNi” at CASE 2021 Task 1: Document and Sentence Classification for Protest Event Detection | This paper accompanies our top-performing submission to the CASE 2021 shared task, which is hosted at the workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text. Subtasks 1 and 2 of Task 1 concern the classification of newspaper articles and sentences into “conflict” versus ... | ['Niklas Stoehr', 'Dennis Atzenhofer', 'Daniel Vegh', 'Francesco Re'] | null | null | null | null | acl-case-2021-8 | ['sentence-classification'] | ['natural-language-processing'] | [ 2.14633822e-01 1.80027723e-01 -3.07488978e-01 -5.80906510e-01
-1.52303004e+00 -8.01822603e-01 1.52655888e+00 5.36480427e-01
-8.47500741e-01 1.11154032e+00 9.11501527e-01 -7.97835827e-01
-1.29052490e-01 -7.45705545e-01 -7.12777972e-01 -3.05889547e-01
-1.56070188e-01 5.54380000e-01 6.80416971e-02 -3.85256290... | [9.011157035827637, 9.809412002563477] |
4aa4d49d-7fb7-4c45-8131-15b13cea894a | sketch-based-medical-image-retrieval | 2303.03633 | null | https://arxiv.org/abs/2303.03633v1 | https://arxiv.org/pdf/2303.03633v1.pdf | Sketch-based Medical Image Retrieval | The amount of medical images stored in hospitals is increasing faster than ever; however, utilizing the accumulated medical images has been limited. This is because existing content-based medical image retrieval (CBMIR) systems usually require example images to construct query vectors; nevertheless, example images cann... | ['Ryuji Hamamoto', 'Tatsuya Harada', 'Yusuke Kurose', 'Amina Bolatkan', 'Nobuji Kouno', 'Satoshi Nakamura', 'Yukihiro Yoshida', 'Yasuyuki Takamizawa', 'Masamichi Takahashi', 'Hirokazu Watanabe', 'Mototaka Miyake', 'Takaaki Mizuno', 'Ryuichiro Hataya', 'Lin Gu', 'Kazuma Kobayashi'] | 2023-03-07 | null | null | null | null | ['medical-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'medical'] | [ 2.54673541e-01 -4.04708624e-01 -2.64620423e-01 -2.08412826e-01
-7.93987989e-01 -4.73520607e-01 3.38267386e-01 3.78522128e-01
-3.37252975e-01 8.14577863e-02 -4.12212349e-02 -2.54147440e-01
-4.42347556e-01 -9.09568250e-01 1.58647809e-03 -7.64608920e-01
3.61076087e-01 4.13310349e-01 3.47782195e-01 -1.43180668... | [14.374837875366211, -1.5511887073516846] |
8be67383-72d6-49b4-b051-1e1c18867891 | fp-age-leveraging-face-parsing-attention-for | 2106.11145 | null | https://arxiv.org/abs/2106.11145v2 | https://arxiv.org/pdf/2106.11145v2.pdf | FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wild | Image-based age estimation aims to predict a person's age from facial images. It is used in a variety of real-world applications. Although end-to-end deep models have achieved impressive results for age estimation on benchmark datasets, their performance in-the-wild still leaves much room for improvement due to the cha... | ['Maja Pantic', 'Yujiang Wang', 'Jie Shen', 'Yiming Lin'] | 2021-06-21 | null | null | null | null | ['age-estimation', 'face-parsing', 'age-estimation'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [-1.84243336e-01 3.41185361e-01 -7.99914822e-02 -1.14058614e+00
-7.65235484e-01 -1.61407813e-01 4.00802970e-01 -3.06413829e-01
-3.18233162e-01 2.46217921e-01 3.56042594e-01 5.00420749e-01
3.02195936e-01 -4.85825032e-01 -5.75494349e-01 -6.04925394e-01
-1.02071822e-01 5.80086768e-01 -3.59383821e-01 2.37809405... | [13.48222541809082, 0.8208431005477905] |
4350acc5-ddd9-4445-a6f6-f307f8e4b2c1 | sophia-a-scalable-stochastic-second-order | 2305.14342 | null | https://arxiv.org/abs/2305.14342v1 | https://arxiv.org/pdf/2305.14342v1.pdf | Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training | Given the massive cost of language model pre-training, a non-trivial improvement of the optimization algorithm would lead to a material reduction on the time and cost of training. Adam and its variants have been state-of-the-art for years, and more sophisticated second-order (Hessian-based) optimizers often incur too m... | ['Tengyu Ma', 'Percy Liang', 'David Hall', 'Zhiyuan Li', 'Hong Liu'] | 2023-05-23 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-2.93434292e-01 -8.39237198e-02 -1.20146021e-01 -2.99291790e-01
-1.03796160e+00 -5.00682354e-01 3.08268577e-01 2.80965120e-01
-8.91837716e-01 3.22606951e-01 -2.20637918e-01 -7.30207324e-01
1.63883969e-01 -4.19995755e-01 -9.76194918e-01 -5.40527880e-01
-3.27629298e-01 7.06011415e-01 2.33004361e-01 -1.27103493... | [8.27196979522705, 3.5317296981811523] |
4e66a2c6-a464-47b6-bcbe-d3b1dd2eb6e0 | cluda-contrastive-learning-in-unsupervised | 2208.14227 | null | https://arxiv.org/abs/2208.14227v2 | https://arxiv.org/pdf/2208.14227v2.pdf | CLUDA : Contrastive Learning in Unsupervised Domain Adaptation for Semantic Segmentation | In this work, we propose CLUDA, a simple, yet novel method for performing unsupervised domain adaptation (UDA) for semantic segmentation by incorporating contrastive losses into a student-teacher learning paradigm, that makes use of pseudo-labels generated from the target domain by the teacher network. More specificall... | ['Rahul Tallamraju', 'Shuaib Ahmed', 'Anuraag Bhattacharya', 'Jaswin Kasi', 'Midhun Vayyat'] | 2022-08-27 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 2.57225424e-01 1.86804563e-01 -1.06509201e-01 -5.86038172e-01
-1.28668106e+00 -6.56661034e-01 5.49803019e-01 -1.90772012e-01
-7.25941241e-01 5.75526059e-01 -1.07947931e-01 6.42635226e-02
-3.93859576e-03 -9.55991924e-01 -1.03660488e+00 -5.39070785e-01
2.24950820e-01 8.08624506e-01 4.83330369e-01 -1.94627732... | [9.733423233032227, 1.386281967163086] |
26546ad5-bd88-4ddd-80c7-aa6f53b8d9bf | harmonious-semantic-line-detection-via | 2104.06903 | null | https://arxiv.org/abs/2104.06903v1 | https://arxiv.org/pdf/2104.06903v1.pdf | Harmonious Semantic Line Detection via Maximal Weight Clique Selection | A novel algorithm to detect an optimal set of semantic lines is proposed in this work. We develop two networks: selection network (S-Net) and harmonization network (H-Net). First, S-Net computes the probabilities and offsets of line candidates. Second, we filter out irrelevant lines through a selection-and-removal proc... | ['Chang-Su Kim', 'Seong-Gyun Jeong', 'Wonhui Park', 'Dongkwon Jin'] | 2021-04-14 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Jin_Harmonious_Semantic_Line_Detection_via_Maximal_Weight_Clique_Selection_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Jin_Harmonious_Semantic_Line_Detection_via_Maximal_Weight_Clique_Selection_CVPR_2021_paper.pdf | cvpr-2021-1 | ['line-detection'] | ['computer-vision'] | [ 2.64180191e-02 -1.81162767e-02 -2.55808830e-01 -1.96500033e-01
-4.50976819e-01 -4.94555771e-01 1.37818307e-01 1.98378846e-01
-6.12168713e-03 5.42170107e-01 5.39970361e-02 1.21367641e-01
-3.62517208e-01 -1.29883754e+00 -4.12819654e-01 -3.86761904e-01
5.30815795e-02 2.23203853e-01 5.39165914e-01 -2.96636969... | [7.582265377044678, 4.667274475097656] |
5dd2facb-9fe4-437b-a4f8-db71bb020478 | fourier-domain-optimization-for-image | 1809.04187 | null | http://arxiv.org/abs/1809.04187v1 | http://arxiv.org/pdf/1809.04187v1.pdf | Fourier-Domain Optimization for Image Processing | Image optimization problems encompass many applications such as spectral
fusion, deblurring, deconvolution, dehazing, matting, reflection removal and
image interpolation, among others. With current image sizes in the order of
megabytes, it is extremely expensive to run conventional algorithms such as
gradient descent, ... | ['Sabine Süsstrunk', 'Frederike Dümbgen', 'Majed El Helou', 'Radhakrishna Achanta'] | 2018-09-11 | null | null | null | null | ['reflection-removal'] | ['computer-vision'] | [ 5.79100847e-01 -1.73637554e-01 3.77183408e-01 -1.61695749e-01
-7.58037329e-01 -4.89965349e-01 1.49848640e-01 -3.42236489e-01
-6.25168562e-01 7.14128971e-01 -5.28907217e-02 -3.93855751e-01
-2.24019825e-01 -1.54621065e-01 -4.28256035e-01 -8.80515277e-01
-1.74792185e-01 -1.95521593e-01 -4.01139081e-01 -1.62347276... | [11.651957511901855, -2.6571545600891113] |
f7b3a4b3-31c1-4ac3-b8c7-6d869e84d59f | feta-a-benchmark-for-few-sample-task-transfer | 2205.06262 | null | https://arxiv.org/abs/2205.06262v2 | https://arxiv.org/pdf/2205.06262v2.pdf | FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue | Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models. Dialogue understanding encompasses many diverse tasks, yet task transfer has not been thoroughly studied in conversational AI. This work explores conversati... | ['William Yang Wang', 'Jay Pujara', 'Lise Getoor', 'Deepak Ramachandran', 'Luke Yoffe', 'Connor Pryor', 'Pegah Jandaghi', 'Yi-Lin Tuan', 'Alon Albalak'] | 2022-05-12 | null | null | null | null | ['dialogue-understanding'] | ['natural-language-processing'] | [ 4.31786686e-01 2.69134253e-01 -1.70285016e-01 -6.99210405e-01
-1.10274923e+00 -9.91086364e-01 9.36218143e-01 -1.75431103e-01
-5.79199076e-01 1.19639158e+00 6.16112888e-01 -3.65204751e-01
1.73557654e-01 -3.37868363e-01 -3.74545068e-01 -3.98348361e-01
-5.45827709e-02 1.05934989e+00 6.09068833e-02 -6.41579151... | [12.592072486877441, 8.165547370910645] |
62c2e5a9-fcb8-4e1a-8aa8-50a8da1aa9c5 | exploring-smoothness-and-class-separation-for | 2203.01324 | null | https://arxiv.org/abs/2203.01324v3 | https://arxiv.org/pdf/2203.01324v3.pdf | Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation | Semi-supervised segmentation remains challenging in medical imaging since the amount of annotated medical data is often scarce and there are many blurred pixels near the adhesive edges or in the low-contrast regions. To address the issues, we advocate to firstly constrain the consistency of pixels with and without stro... | ['Jianfei Cai', 'ZongYuan Ge', 'Qianyi Wu', 'Zhonghua Wu', 'Yicheng Wu'] | 2022-03-02 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 5.02422273e-01 4.45462435e-01 -4.73561525e-01 -5.26532769e-01
-7.73828030e-01 -1.85782522e-01 1.45357028e-01 -1.59235820e-02
-4.60016429e-01 6.38505399e-01 3.40742208e-02 -5.83734587e-02
4.39376980e-02 -4.56920147e-01 -5.29526114e-01 -1.16458392e+00
1.54502690e-01 9.08974037e-02 5.10873616e-01 1.83437049... | [14.613163948059082, -2.05411434173584] |
c0d2a9a8-b23a-4ee8-b0c1-2090899ea265 | rethinking-spatial-invariance-of-1 | 2206.05253 | null | https://arxiv.org/abs/2206.05253v2 | https://arxiv.org/pdf/2206.05253v2.pdf | Rethinking Spatial Invariance of Convolutional Networks for Object Counting | Previous work generally believes that improving the spatial invariance of convolutional networks is the key to object counting. However, after verifying several mainstream counting networks, we surprisingly found too strict pixel-level spatial invariance would cause overfit noise in the density map generation. In this ... | ['Alexander G. Hauptmann', 'Xiao Wu', 'Jingkuan Song', 'Hong Li', 'Qi Dai', 'Zhi-Qi Cheng'] | 2022-06-10 | rethinking-spatial-invariance-of | http://openaccess.thecvf.com//content/CVPR2022/html/Cheng_Rethinking_Spatial_Invariance_of_Convolutional_Networks_for_Object_Counting_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Cheng_Rethinking_Spatial_Invariance_of_Convolutional_Networks_for_Object_Counting_CVPR_2022_paper.pdf | cvpr-2022-1 | ['object-counting'] | ['computer-vision'] | [ 5.33137843e-02 -3.04121524e-01 1.68094020e-02 -4.79832351e-01
-1.63922787e-01 -4.22181278e-01 7.19173253e-01 -6.47468194e-02
-8.26858222e-01 7.27309525e-01 2.65055932e-02 -4.48882401e-01
1.53848127e-01 -1.26552212e+00 -7.74021626e-01 -5.34197569e-01
2.34041303e-01 3.31889689e-01 6.86033845e-01 1.41255930... | [8.72249698638916, -0.03189729526638985] |
47545e04-652c-41d8-84ca-709294026dba | adaptive-direction-guided-structure-tensor | 2001.05717 | null | https://arxiv.org/abs/2001.05717v1 | https://arxiv.org/pdf/2001.05717v1.pdf | Adaptive Direction-Guided Structure Tensor Total Variation | Direction-guided structure tensor total variation (DSTV) is a recently proposed regularization term that aims at increasing the sensitivity of the structure tensor total variation (STV) to the changes towards a predetermined direction. Despite of the plausible results obtained on the uni-directional images, the DSTV mo... | ['Mustafa E. Kamasak', 'Ezgi Demircan-Tureyen'] | 2020-01-16 | null | null | null | null | ['art-analysis'] | ['computer-vision'] | [ 1.08062215e-01 -4.36071217e-01 3.38514954e-01 -2.75671899e-01
-4.46060181e-01 -3.21808815e-01 4.68424886e-01 -7.07754046e-02
-2.01789036e-01 4.09943938e-01 4.93286639e-01 1.12997182e-01
-7.31803834e-01 -8.61550391e-01 -4.83951181e-01 -1.27341986e+00
-1.37440145e-01 -1.41470045e-01 3.16433787e-01 -3.76585603... | [11.41195297241211, -2.438284158706665] |
1975f26b-e6f0-4dd8-ab68-28cb910996ce | how-should-agents-ask-questions-for-situated | 2106.06504 | null | https://arxiv.org/abs/2106.06504v1 | https://arxiv.org/pdf/2106.06504v1.pdf | How Should Agents Ask Questions For Situated Learning? An Annotated Dialogue Corpus | Intelligent agents that are confronted with novel concepts in situated environments will need to ask their human teammates questions to learn about the physical world. To better understand this problem, we need data about asking questions in situated task-based interactions. To this end, we present the Human-Robot Dial... | ['Matthew Marge', 'Matthias Scheutz', 'Gordon Briggs', 'Antonio Roque', 'Felix Gervits'] | 2021-06-11 | null | https://aclanthology.org/2021.sigdial-1.37 | https://aclanthology.org/2021.sigdial-1.37.pdf | sigdial-acl-2021-7 | ['novel-concepts'] | ['reasoning'] | [ 3.30982506e-01 1.01325643e+00 4.86710638e-01 -4.66480166e-01
-6.36827826e-01 -9.36613977e-01 9.97799695e-01 1.92144111e-01
-4.27968770e-01 9.91391659e-01 5.85654318e-01 -5.14393210e-01
-1.15535609e-01 -5.07898629e-01 -1.83571696e-01 -1.84479684e-01
-1.59210384e-01 1.02721167e+00 1.91763252e-01 -8.38035524... | [4.426083564758301, 0.7429066896438599] |
e7ee855c-0b8a-4c6d-97ea-b68689c40741 | learning-sequence-encoders-for-temporal | 1809.03202 | null | http://arxiv.org/abs/1809.03202v1 | http://arxiv.org/pdf/1809.03202v1.pdf | Learning Sequence Encoders for Temporal Knowledge Graph Completion | Research on link prediction in knowledge graphs has mainly focused on static
multi-relational data. In this work we consider temporal knowledge graphs where
relations between entities may only hold for a time interval or a specific
point in time. In line with previous work on static knowledge graphs, we
propose to addr... | ['Sebastijan Dumančić', 'Alberto García-Durán', 'Mathias Niepert'] | 2018-09-10 | learning-sequence-encoders-for-temporal-1 | https://aclanthology.org/D18-1516 | https://aclanthology.org/D18-1516.pdf | emnlp-2018-10 | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-9.95042250e-02 3.06056082e-01 -1.01110256e+00 -2.19580501e-01
-1.85750321e-01 -5.44063210e-01 5.56069493e-01 4.88718927e-01
2.57142987e-02 8.04629564e-01 1.89902991e-01 -5.10194004e-01
-6.46849871e-01 -1.20287466e+00 -6.25257194e-01 -2.39399105e-01
-8.05431783e-01 5.15033603e-01 2.65838712e-01 -2.07790673... | [8.55846118927002, 7.89893102645874] |
1b7784c7-61f8-4dd4-8027-34600277a8a9 | persuasive-dialogue-understanding-the | 2011.09954 | null | https://arxiv.org/abs/2011.09954v2 | https://arxiv.org/pdf/2011.09954v2.pdf | Persuasive Dialogue Understanding: the Baselines and Negative Results | Persuasion aims at forming one's opinion and action via a series of persuasive messages containing persuader's strategies. Due to its potential application in persuasive dialogue systems, the task of persuasive strategy recognition has gained much attention lately. Previous methods on user intent recognition in dialogu... | ['Soujanya Poria', 'Amir Hussain', 'Navonil Majumder', 'Deepanway Ghosal', 'Hui Chen'] | 2020-11-19 | null | null | null | null | ['intent-recognition', 'dialogue-understanding'] | ['natural-language-processing', 'natural-language-processing'] | [ 8.85776699e-01 6.34585321e-01 -2.45207846e-01 -7.08136857e-01
-6.87571704e-01 -4.83693957e-01 1.24500310e+00 1.69021841e-02
-7.58946836e-01 9.72435832e-01 8.76704335e-01 -8.90079558e-01
4.88859303e-02 -6.06936812e-01 -3.22094113e-01 -3.46967399e-01
3.31412524e-01 3.01615328e-01 -1.43660277e-01 -5.72468877... | [12.759254455566406, 7.78432559967041] |
594a203c-6a9b-4753-a057-7253c328df61 | unsupervised-domain-adaptation-for-clinical | null | null | https://aclanthology.org/W17-2320 | https://aclanthology.org/W17-2320.pdf | Unsupervised Domain Adaptation for Clinical Negation Detection | Detecting negated concepts in clinical texts is an important part of NLP information extraction systems. However, generalizability of negation systems is lacking, as cross-domain experiments suffer dramatic performance losses. We examine the performance of multiple unsupervised domain adaptation algorithms on clinical ... | ['Hadi Amiri', 'Guergana Savova', 'Timothy Miller', 'Steven Bethard'] | 2017-08-01 | null | null | null | ws-2017-8 | ['negation-detection'] | ['natural-language-processing'] | [ 3.38607162e-01 4.26318944e-01 -6.46702945e-01 -6.18327498e-01
-9.66190696e-01 -6.42635465e-01 1.26636431e-01 1.03558278e+00
-7.15852022e-01 1.23154795e+00 4.73333538e-01 -5.51794946e-01
-3.80341351e-01 -4.24897015e-01 -2.76377738e-01 -2.46056840e-01
-2.74453551e-01 8.64392936e-01 2.16471404e-01 -4.64720309... | [8.464381217956543, 8.794323921203613] |
638ed437-025c-4ce6-834f-e83e7c83b5f5 | neighborhood-aware-scalable-temporal-network | 2209.01084 | null | https://arxiv.org/abs/2209.01084v3 | https://arxiv.org/pdf/2209.01084v3.pdf | Neighborhood-aware Scalable Temporal Network Representation Learning | Temporal networks have been widely used to model real-world complex systems such as financial systems and e-commerce systems. In a temporal network, the joint neighborhood of a set of nodes often provides crucial structural information useful for predicting whether they may interact at a certain time. However, recent r... | ['Pan Li', 'Yuhong Luo'] | 2022-09-02 | null | null | null | null | ['inductive-link-prediction'] | ['graphs'] | [-1.86345652e-01 -2.77565539e-01 -6.92137361e-01 -2.54539937e-01
-3.89352739e-02 -5.19791782e-01 6.97507977e-01 6.22990251e-01
-2.92974383e-01 5.86420596e-01 6.07548356e-02 -6.01236403e-01
-3.27597111e-01 -1.29612935e+00 -5.02764285e-01 -7.18316436e-01
-6.41275525e-01 5.65773726e-01 6.38651013e-01 -3.61498445... | [7.22967529296875, 6.021739482879639] |
e911c154-8ab8-45d7-86dd-abc95731bdc8 | diverse-multi-answer-retrieval-with-1 | 2211.16029 | null | https://arxiv.org/abs/2211.16029v1 | https://arxiv.org/pdf/2211.16029v1.pdf | Diverse Multi-Answer Retrieval with Determinantal Point Processes | Often questions provided to open-domain question answering systems are ambiguous. Traditional QA systems that provide a single answer are incapable of answering ambiguous questions since the question may be interpreted in several ways and may have multiple distinct answers. In this paper, we address multi-answer retrie... | ['Manish Shrivastava', 'Nikhil Rayaprolu', 'Poojitha Nandigam'] | 2022-11-29 | diverse-multi-answer-retrieval-with | https://aclanthology.org/2022.coling-1.194 | https://aclanthology.org/2022.coling-1.194.pdf | coling-2022-10 | ['point-processes', 'open-domain-question-answering'] | ['methodology', 'natural-language-processing'] | [-2.83719242e-01 -1.12223633e-01 1.92234024e-01 -2.95096189e-01
-2.24864054e+00 -1.27912307e+00 5.86035430e-01 4.21134442e-01
-3.78261507e-01 1.07537901e+00 3.70546579e-01 -5.44916630e-01
-7.21845269e-01 -8.11533988e-01 -4.81995225e-01 -1.59970984e-01
3.51558715e-01 1.23186934e+00 9.56304550e-01 -8.76786888... | [11.364776611328125, 7.8683905601501465] |
f737e1d5-6d72-4c3f-8d0d-2f46e073cdec | effective-modeling-of-encoder-decoder | 1911.09886 | null | https://arxiv.org/abs/1911.09886v1 | https://arxiv.org/pdf/1911.09886v1.pdf | Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction | A relation tuple consists of two entities and the relation between them, and often such tuples are found in unstructured text. There may be multiple relation tuples present in a text and they may share one or both entities among them. Extracting such relation tuples from a sentence is a difficult task and sharing of en... | ['Hwee Tou Ng', 'Tapas Nayak'] | 2019-11-22 | null | null | null | null | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 0.2119807 0.6159588 -0.19698708 -0.45471698 -0.76540697 -0.65523124
0.32015967 0.8602835 -0.38539782 1.0919371 0.48316276 -0.34265032
-0.02811869 -1.1613059 -0.893169 -0.09423227 -0.14599106 0.9645997
0.4483424 -0.24546425 -0.09353817 0.16337223 -1.2865055 0.66716677
0.9191947 0.6916563 0.2... | [9.24360179901123, 8.704204559326172] |
6a3bbc22-73b1-446b-85b9-84439c0cd140 | deep-graph-reprogramming | 2304.14593 | null | https://arxiv.org/abs/2304.14593v1 | https://arxiv.org/pdf/2304.14593v1.pdf | Deep Graph Reprogramming | In this paper, we explore a novel model reusing task tailored for graph neural networks (GNNs), termed as "deep graph reprogramming". We strive to reprogram a pre-trained GNN, without amending raw node features nor model parameters, to handle a bunch of cross-level downstream tasks in various domains. To this end, we p... | ['DaCheng Tao', 'Xinchao Wang', 'Yiding Yang', 'Li Ju', 'Chongbin Yuan', 'Yongcheng Jing'] | 2023-04-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jing_Deep_Graph_Reprogramming_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jing_Deep_Graph_Reprogramming_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-object-recognition', 'object-recognition', 'action-recognition-in-videos', 'graph-classification'] | ['computer-vision', 'computer-vision', 'computer-vision', 'graphs'] | [ 5.55663168e-01 3.53829265e-01 -7.86394104e-02 -1.35736927e-01
-3.59364659e-01 -5.71895301e-01 6.03381276e-01 -2.71569759e-01
-2.45333731e-01 7.08283663e-01 -1.39686704e-01 -1.74125955e-01
-3.98863047e-01 -7.38055110e-01 -8.03317368e-01 -8.52557778e-01
2.80875444e-01 5.67783356e-01 3.47012207e-02 -4.08767760... | [7.302810192108154, 6.272518157958984] |
94f68bbb-f6f6-49e2-a804-14983bac95e2 | near-infrared-depth-independent-image | 2203.14085 | null | https://arxiv.org/abs/2203.14085v1 | https://arxiv.org/pdf/2203.14085v1.pdf | Near-Infrared Depth-Independent Image Dehazing using Haar Wavelets | We propose a fusion algorithm for haze removal that combines color information from an RGB image and edge information extracted from its corresponding NIR image using Haar wavelets. The proposed algorithm is based on the key observation that NIR edge features are more prominent in the hazy regions of the image than the... | ['Hassan Foroosh', 'Shengnan Hu', 'Ankit Sharma', 'Sumit Laha'] | 2022-03-26 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 4.63118702e-02 -4.64360982e-01 4.53214407e-01 -7.19282478e-02
-3.74371350e-01 -1.31872073e-01 1.60646185e-01 -1.74930423e-01
-2.63650358e-01 4.32259947e-01 1.06668867e-01 -5.97226899e-04
-1.34088844e-01 -1.05244982e+00 -3.02018702e-01 -1.29597092e+00
2.74662584e-01 -2.68440813e-01 4.30966467e-01 -5.09898245... | [10.849465370178223, -3.1669416427612305] |
9a3e476c-739b-4eb2-aa92-b33d6bcd1385 | a-novel-policy-for-pre-trained-deep | 2101.00738 | null | https://arxiv.org/abs/2101.00738v2 | https://arxiv.org/pdf/2101.00738v2.pdf | A novel policy for pre-trained Deep Reinforcement Learning for Speech Emotion Recognition | Reinforcement Learning (RL) is a semi-supervised learning paradigm which an agent learns by interacting with an environment. Deep learning in combination with RL provides an efficient method to learn how to interact with the environment is called Deep Reinforcement Learning (deep RL). Deep RL has gained tremendous succ... | ['Jiajun Liu', 'Björn W. Schuller', 'Sara Khalifa', 'Rajib Rana', 'Thejan Rajapakshe'] | 2021-01-04 | null | null | null | null | ['cross-corpus'] | ['computer-vision'] | [-2.28519782e-01 1.68143496e-01 2.63426095e-01 -4.86993313e-01
-4.65841055e-01 -5.42701840e-01 4.64011371e-01 -1.32265836e-01
-6.75744116e-01 9.07630920e-01 1.02917232e-01 -9.37908515e-02
1.07217714e-01 -5.35157621e-01 -4.76426303e-01 -7.42032886e-01
-2.10897744e-01 5.98057747e-01 -1.49938762e-01 -7.90972233... | [13.32573127746582, 6.04916524887085] |
e420cd3f-2427-45c3-b8ad-30b4608d8cef | v2ifi-in-vehicle-vital-sign-monitoring-via | 2110.14848 | null | https://arxiv.org/abs/2110.14848v1 | https://arxiv.org/pdf/2110.14848v1.pdf | V2iFi: in-Vehicle Vital Sign Monitoring via Compact RF Sensing | Given the significant amount of time people spend in vehicles, health issues under driving condition have become a major concern. Such issues may vary from fatigue, asthma, stroke, to even heart attack, yet they can be adequately indicated by vital signs and abnormal activities. Therefore, in-vehicle vital sign monitor... | ['Xu Zhang', 'Jun Luo', 'Chao Cai', 'Zhe Chen', 'Tianyue Zheng'] | 2021-10-28 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 1.63467273e-01 -4.17338870e-02 -2.43735716e-01 -2.54486531e-01
-4.05639052e-01 -2.18674049e-01 1.04011282e-01 -6.59660175e-02
-3.66657704e-01 7.97498763e-01 -1.28401443e-01 -4.27468300e-01
-1.01968810e-01 -5.76146483e-01 -9.03141424e-02 -7.07101941e-01
3.49878967e-02 -2.60056138e-01 1.36166811e-01 1.36397004... | [13.384800910949707, 2.8136484622955322] |
cb9818b3-7724-4386-93fc-9e7f2153fa7b | low-dimensional-denoising-embedding | 2103.17099 | null | https://arxiv.org/abs/2103.17099v1 | https://arxiv.org/pdf/2103.17099v1.pdf | Low-dimensional Denoising Embedding Transformer for ECG Classification | The transformer based model (e.g., FusingTF) has been employed recently for Electrocardiogram (ECG) signal classification. However, the high-dimensional embedding obtained via 1-D convolution and positional encoding can lead to the loss of the signal's own temporal information and a large amount of training parameters.... | ['Wenwu Wang', 'Xinxin Wang', 'Pengming Feng', 'Wenbo Wang', 'Jian Guan'] | 2021-03-31 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 2.29690656e-01 -3.01274657e-01 2.36081824e-01 -2.68491417e-01
-5.01830578e-01 -1.84335560e-01 -3.75358872e-02 -5.57509474e-02
-3.08677614e-01 5.99944472e-01 2.35483631e-01 -4.45183329e-02
-3.23744178e-01 -6.08883917e-01 -1.23159565e-01 -1.09961760e+00
-2.63883442e-01 -2.40171254e-01 1.01642638e-01 9.74582694... | [14.150919914245605, 3.258960247039795] |
152e5508-c048-496a-972d-73d398f3ee27 | 190513313 | 1905.13313 | null | https://arxiv.org/abs/1905.13313v5 | https://arxiv.org/pdf/1905.13313v5.pdf | Technical Report of the Video Event Reconstruction and Analysis (VERA) System -- Shooter Localization, Models, Interface, and Beyond | Every minute, hundreds of hours of video are uploaded to social media sites and the Internet from around the world. This material creates a visual record of the experiences of a significant percentage of humanity and can help illuminate how we live in the present moment. When properly analyzed, this video can also help... | ['Alexander Hauptmann', 'Junwei Liang', 'Jay D. Aronson'] | 2019-05-26 | null | null | null | null | ['shooter-localization', 'gunshot-detection', 'video-synchronization'] | ['audio', 'audio', 'computer-vision'] | [-5.23081347e-02 -3.71125817e-01 -1.29346903e-02 -3.18095274e-02
-8.73615742e-01 -8.15477729e-01 4.97506797e-01 3.74329239e-01
-3.83347660e-01 2.31936723e-01 4.50986624e-01 -3.57986271e-01
1.57605298e-02 -7.09413469e-01 -3.80909711e-01 -3.11285138e-01
-2.92164296e-01 6.58254921e-02 4.77435231e-01 -1.83177106... | [8.305780410766602, 0.3097838759422302] |
31a97517-8093-4c2b-af1b-58c1105ae6b0 | redirtrans-latent-to-latent-translation-for-1 | 2305.11452 | null | https://arxiv.org/abs/2305.11452v1 | https://arxiv.org/pdf/2305.11452v1.pdf | ReDirTrans: Latent-to-Latent Translation for Gaze and Head Redirection | Learning-based gaze estimation methods require large amounts of training data with accurate gaze annotations. Facing such demanding requirements of gaze data collection and annotation, several image synthesis methods were proposed, which successfully redirected gaze directions precisely given the assigned conditions. H... | ['Truong Nguyen', 'Ning Bi', 'Lei Wang', 'Zhen Wang', 'Shiwei Jin'] | 2023-05-19 | redirtrans-latent-to-latent-translation-for | http://openaccess.thecvf.com//content/CVPR2023/html/Jin_ReDirTrans_Latent-to-Latent_Translation_for_Gaze_and_Head_Redirection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jin_ReDirTrans_Latent-to-Latent_Translation_for_Gaze_and_Head_Redirection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['gaze-estimation'] | ['computer-vision'] | [ 4.80757535e-01 3.34565729e-01 -1.78296074e-01 -7.40205109e-01
-1.67971924e-01 -5.39220572e-01 2.92365819e-01 -7.73753226e-01
-3.09315026e-01 5.33983231e-01 2.52148211e-01 -1.37665616e-02
1.37915716e-01 -4.37702239e-01 -7.46186137e-01 -7.99502254e-01
3.26547891e-01 -2.30699569e-01 -3.08992684e-01 -8.63782912... | [14.06370735168457, 0.0138698760420084] |
97e7ad1f-3adc-4c4b-b5b8-72e186995676 | spacecraft-depth-completion-based-on-the-gray | 2208.14030 | null | https://arxiv.org/abs/2208.14030v1 | https://arxiv.org/pdf/2208.14030v1.pdf | Spacecraft depth completion based on the gray image and the sparse depth map | Perceiving the three-dimensional (3D) structure of the spacecraft is a prerequisite for successfully executing many on-orbit space missions, and it can provide critical input for many downstream vision algorithms. In this paper, we propose to sense the 3D structure of spacecraft using light detection and ranging sensor... | ['WeiChun Chen', 'Xinlong Chen', 'Yu Chen', 'Zhiqiang Yan', 'Hongyuan Wang', 'Xiang Liu'] | 2022-08-30 | null | null | null | null | ['depth-completion', 'foreground-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.54699574e-03 -2.80504227e-01 1.04781061e-01 -3.75915468e-01
-3.74905348e-01 -5.52735209e-01 5.91839254e-01 -4.58403915e-01
-3.71238112e-01 4.11818177e-01 -2.78792698e-02 -5.65087870e-02
-2.35096425e-01 -6.05088532e-01 -3.70826274e-01 -8.56744468e-01
1.76030044e-02 5.30387342e-01 2.39132613e-01 9.64085683... | [7.990692138671875, -2.4145913124084473] |
63cd7359-e145-4848-b647-004d3ac90711 | composing-structure-aware-batches-for-1 | null | null | https://aclanthology.org/2022.findings-acl.239 | https://aclanthology.org/2022.findings-acl.239.pdf | Composing Structure-Aware Batches for Pairwise Sentence Classification | Identifying the relation between two sentences requires datasets with pairwise annotations. In many cases, these datasets contain instances that are annotated multiple times as part of different pairs. They constitute a structure that contains additional helpful information about the inter-relatedness of the text insta... | ['Iryna Gurevych', 'Tilman Beck', 'Andreas Waldis'] | null | null | null | null | findings-acl-2022-5 | ['sentence-classification'] | ['natural-language-processing'] | [ 3.26423734e-01 3.49932700e-01 -6.83325380e-02 -8.94271076e-01
-1.18205142e+00 -9.40177381e-01 7.27786839e-01 8.85462821e-01
-6.54971361e-01 8.28508019e-01 7.72288978e-01 -1.75825655e-01
5.99609576e-02 -3.02681893e-01 -7.14008272e-01 -6.83912396e-01
-1.32011101e-01 5.99632382e-01 1.93536937e-01 -3.87894034... | [9.594117164611816, 8.897502899169922] |
ec22d3bc-bbd4-41d1-b2d0-2ac6707be6db | incorporating-knowledge-into-document | 2301.11719 | null | https://arxiv.org/abs/2301.11719v4 | https://arxiv.org/pdf/2301.11719v4.pdf | The Exploration of Knowledge-Preserving Prompts for Document Summarisation | Despite the great development of document summarisation techniques nowadays, factual inconsistencies between the generated summaries and the original texts still occur from time to time. This study explores the possibility of adopting prompts to incorporate factual knowledge into generated summaries. We specifically st... | ['Makhmoor Fiza', 'Alireza Seyed Shakeri', 'Wei Emma Zhang', 'Chen Chen'] | 2023-01-27 | null | null | null | null | ['document-summarization'] | ['natural-language-processing'] | [ 5.70094287e-01 6.85225546e-01 -3.25641662e-01 -3.97499233e-01
-1.34430516e+00 -8.81156027e-01 1.09913027e+00 7.22738087e-01
-2.64124751e-01 1.15048301e+00 1.15449786e+00 -2.90807337e-01
-2.15870127e-01 -6.76638663e-01 -6.03626907e-01 -3.81835997e-02
6.25237748e-02 6.17965877e-01 1.02063827e-01 -3.76269192... | [12.400884628295898, 9.393078804016113] |
1daf6f89-6da1-41b0-93dc-77dadc0a39d1 | all-in-1-at-ijcnlp-2017-task-4-short-text | null | null | https://aclanthology.org/I17-4024 | https://aclanthology.org/I17-4024.pdf | All-In-1 at IJCNLP-2017 Task 4: Short Text Classification with One Model for All Languages | We present All-In-1, a simple model for multilingual text classification that does not require any parallel data. It is based on a traditional Support Vector Machine classifier exploiting multilingual word embeddings and character n-grams. Our model is simple, easily extendable yet very effective, overall ranking 1st (... | ['Barbara Plank'] | 2017-12-01 | all-in-1-at-ijcnlp-2017-task-4-short-text-1 | https://aclanthology.org/I17-4024 | https://aclanthology.org/I17-4024.pdf | ijcnlp-2017-12 | ['multilingual-word-embeddings', 'multilingual-text-classification'] | ['methodology', 'miscellaneous'] | [-6.46601260e-01 -3.33499402e-01 -7.01109052e-01 -4.22266245e-01
-9.76532161e-01 -9.08014357e-01 7.56743252e-01 9.43792522e-01
-1.04385781e+00 8.53858888e-01 5.71303606e-01 -1.05537117e+00
1.65602267e-01 -1.39712244e-01 -3.00354809e-01 -1.36485711e-01
1.84118718e-01 9.39291239e-01 -7.09674880e-02 -8.56588364... | [10.852616310119629, 9.780913352966309] |
5d92187d-eb78-43a3-9ff7-341ea206329a | multi-modal-unsupervised-pre-training-for | 2207.07894 | null | https://arxiv.org/abs/2207.07894v1 | https://arxiv.org/pdf/2207.07894v1.pdf | Multi-Modal Unsupervised Pre-Training for Surgical Operating Room Workflow Analysis | Data-driven approaches to assist operating room (OR) workflow analysis depend on large curated datasets that are time consuming and expensive to collect. On the other hand, we see a recent paradigm shift from supervised learning to self-supervised and/or unsupervised learning approaches that can learn representations f... | ['Omid Mohareri', 'Muhammad Abdullah Jamal'] | 2022-07-16 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 5.62941611e-01 3.96729022e-01 -5.08798122e-01 -5.83260894e-01
-7.56920099e-01 -6.21541381e-01 2.79003888e-01 3.03927600e-01
-5.77221811e-01 3.96379381e-01 4.43870038e-01 -1.71280086e-01
-2.08496317e-01 -3.93498629e-01 -6.71897352e-01 -8.89623106e-01
3.34350854e-01 4.78182793e-01 7.91320875e-02 1.98764279... | [14.18350887298584, -3.0535244941711426] |
67c71575-08fa-454c-b9a4-9f6c6e7a1426 | towards-summarizing-multiple-documents-with | 2305.01498 | null | https://arxiv.org/abs/2305.01498v1 | https://arxiv.org/pdf/2305.01498v1.pdf | Towards Summarizing Multiple Documents with Hierarchical Relationships | Most existing multi-document summarization (MDS) datasets lack human-generated and genuine (i.e., not synthetic) summaries or source documents with explicit inter-document relationships that a summary must capture. To enhance the capabilities of MDS systems we present PeerSum, a novel dataset for generating meta-review... | ['Jey Han Lau', 'Eduard Hovy', 'Miao Li'] | 2023-05-02 | null | null | null | null | ['review-generation', 'multi-document-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.36572748e-01 5.91177821e-01 -4.68619436e-01 -2.67988145e-01
-1.52041280e+00 -4.83385265e-01 1.30375195e+00 4.04818684e-01
1.59330338e-01 1.20312715e+00 1.12244499e+00 -2.84840554e-01
6.59966618e-02 -5.92476130e-01 -8.64830673e-01 -2.57915795e-01
3.27495992e-01 6.13989532e-01 -1.72041640e-01 -8.09102654... | [12.461299896240234, 9.537919044494629] |
c7429cac-e95a-424e-a16e-c34f287a8cc3 | rethinking-dimensionality-reduction-in-grid | 2209.09464 | null | https://arxiv.org/abs/2209.09464v4 | https://arxiv.org/pdf/2209.09464v4.pdf | Rethinking Dimensionality Reduction in Grid-based 3D Object Detection | Bird's eye view (BEV) is widely adopted by most of the current point cloud detectors due to the applicability of well-explored 2D detection techniques. However, existing methods obtain BEV features by simply collapsing voxel or point features along the height dimension, which causes the heavy loss of 3D spatial informa... | ['Zhiheng Li', 'Chengjie Wang', 'Yong liu', 'Qiang Nie', 'Kai Wu', 'Jianlin Liu', 'Jinli Liao', 'Yikang Ding', 'Ying Chen', 'Dihe Huang'] | 2022-09-20 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [-3.01092751e-02 -6.08698368e-01 -3.92012149e-02 -1.84791982e-01
-2.26349562e-01 -4.47072059e-01 5.05179226e-01 1.45957604e-01
-2.38668591e-01 6.31247833e-02 -1.61308706e-01 -1.21439636e-01
-3.25245917e-01 -1.11014366e+00 -2.53445983e-01 -5.86348057e-01
2.09296778e-01 1.55346185e-01 9.03769195e-01 -2.75694937... | [7.8930983543396, -2.9453837871551514] |
32bb49ed-9526-4e37-b0f1-6773bd105131 | thematic-cohesion-measuring-terms | null | null | https://aclanthology.org/L14-1742 | https://aclanthology.org/L14-1742.pdf | Thematic Cohesion: measuring terms discriminatory power toward themes | We present a new measure of thematic cohesion. This measure associates each term with a weight representing its discriminatory power toward a theme, this theme being itself expressed by a list of terms (a thematic lexicon). This thematic cohesion criterion can be used in many applications, such as query expansion, comp... | ['Claude de Loupy', "Cl{\\'e}ment de Groc", 'Xavier Tannier'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['text-clustering'] | ['natural-language-processing'] | [ 2.79866815e-01 1.61529317e-01 -4.16424990e-01 -1.46533445e-01
-4.53612536e-01 -6.62784338e-01 9.27454472e-01 9.47770834e-01
-4.09084052e-01 3.61243188e-01 5.74048281e-01 -2.03847051e-01
-7.88059771e-01 -1.06691408e+00 -2.26736054e-01 -7.18809545e-01
-2.43663222e-01 7.09921718e-01 3.28437328e-01 -3.70212108... | [10.160544395446777, 8.384496688842773] |
56efaebd-5eed-4706-97c4-8480800581a0 | distilling-style-from-image-pairs-for-global | 2209.15165 | null | https://arxiv.org/abs/2209.15165v2 | https://arxiv.org/pdf/2209.15165v2.pdf | Distilling Style from Image Pairs for Global Forward and Inverse Tone Mapping | Many image enhancement or editing operations, such as forward and inverse tone mapping or color grading, do not have a unique solution, but instead a range of solutions, each representing a different style. Despite this, existing learning-based methods attempt to learn a unique mapping, disregarding this style. In this... | ['Rafal K. Mantiuk', 'Param Hanji', 'Aamir Mustafa'] | 2022-09-30 | null | null | null | null | ['tone-mapping', 'inverse-tone-mapping'] | ['computer-vision', 'computer-vision'] | [ 5.74396610e-01 -1.62484705e-01 8.67254974e-04 -5.38714468e-01
-4.97754604e-01 -7.16207504e-01 5.74131191e-01 -4.06485319e-01
-2.79077470e-01 6.58908784e-01 1.89079598e-01 -5.05770259e-02
4.51306626e-02 -7.67348945e-01 -6.46649957e-01 -7.82348156e-01
2.66055793e-01 2.24320009e-01 -1.39117718e-01 -2.57174134... | [11.645468711853027, -0.49930983781814575] |
5a67d5c5-751f-455b-b28a-04ef082d38ab | abstractive-and-extractive-text-summarization | 1807.08000 | null | http://arxiv.org/abs/1807.08000v2 | http://arxiv.org/pdf/1807.08000v2.pdf | Abstractive and Extractive Text Summarization using Document Context Vector and Recurrent Neural Networks | Sequence to sequence (Seq2Seq) learning has recently been used for
abstractive and extractive summarization. In current study, Seq2Seq models have
been used for eBay product description summarization. We propose a novel
Document-Context based Seq2Seq models using RNNs for abstractive and extractive
summarizations. Intu... | ['Nish Parikh', 'Gyanit Singh', 'Chandra Khatri'] | 2018-07-20 | null | null | null | null | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 5.95926642e-01 2.90670693e-01 -3.38609397e-01 -3.68322164e-01
-1.03157043e+00 -7.71267533e-01 7.89740920e-01 4.63259697e-01
-4.11941200e-01 9.98236477e-01 1.05154502e+00 5.72835915e-02
8.03943798e-02 -5.63728213e-01 -6.57738745e-01 -3.30479562e-01
1.85967952e-01 4.90370721e-01 -6.67147413e-02 -2.81034499... | [12.44977855682373, 9.415557861328125] |
15b4a4d5-efff-4e4c-a33f-08ddb3018235 | large-scale-pre-trained-models-are | 2303.15975 | null | https://arxiv.org/abs/2303.15975v2 | https://arxiv.org/pdf/2303.15975v2.pdf | Large-scale Pre-trained Models are Surprisingly Strong in Incremental Novel Class Discovery | Discovering novel concepts from unlabelled data and in a continuous manner is an important desideratum of lifelong learners. In the literature such problems have been partially addressed under very restricted settings, where either access to labelled data is provided for discovering novel concepts (e.g., NCD) or learni... | ['Elisa Ricci', 'Nicu Sebe', 'Zhun Zhong', 'Subhankar Roy', 'Mingxuan Liu'] | 2023-03-28 | null | null | null | null | ['novel-class-discovery', 'novel-class-discovery', 'novel-concepts'] | ['computer-vision', 'methodology', 'reasoning'] | [ 5.07031024e-01 2.58775800e-01 -4.85850424e-01 -3.79412830e-01
-7.50881791e-01 -6.72969162e-01 9.57470298e-01 4.71241862e-01
-8.12873304e-01 1.02224910e+00 4.17387411e-02 -2.92623043e-01
-1.98684379e-01 -4.37111646e-01 -9.59268153e-01 -5.55091321e-01
-1.44261628e-01 5.52555799e-01 3.46299976e-01 -5.54963062... | [9.729674339294434, 3.2416365146636963] |
7b02d3ee-61d0-455f-839e-50bfc430c576 | copula-based-sensitivity-analysis-for-multi | 2102.09412 | null | https://arxiv.org/abs/2102.09412v3 | https://arxiv.org/pdf/2102.09412v3.pdf | Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding | Recent work has focused on the potential and pitfalls of causal identification in observational studies with multiple simultaneous treatments. Building on previous work, we show that even if the conditional distribution of unmeasured confounders given treatments were known exactly, the causal effects would not in gener... | ['Alexander Franks', "Alexander D'Amour", 'Jiajing Zheng'] | 2021-02-18 | null | null | null | null | ['causal-identification'] | ['reasoning'] | [ 3.97775769e-01 1.61474332e-01 -7.70700872e-01 -3.30809474e-01
-7.72181749e-01 -7.75625408e-01 4.40185517e-01 4.58464921e-01
-1.88990235e-01 1.01336932e+00 6.68085754e-01 -6.01438403e-01
-8.08308005e-01 -7.99741745e-01 -8.79016995e-01 -5.69040835e-01
-2.84012258e-01 5.80540240e-01 -1.79627031e-01 4.85904455... | [7.93298864364624, 5.266634941101074] |
a61c6cb1-5115-4b13-9fd6-d130ea1f009d | inferring-methodological-meta-knowledge-from | null | null | https://aclanthology.org/Y16-1002 | https://aclanthology.org/Y16-1002.pdf | Inferring Methodological Meta-knowledge from Large Biomedical Corpora | null | ['Goran Nenadic'] | 2016-10-01 | inferring-methodological-meta-knowledge-from-1 | https://aclanthology.org/Y16-1002 | https://aclanthology.org/Y16-1002.pdf | paclic-2016-10 | ['temporal-information-extraction'] | ['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... | [-7.3386149406433105, 3.8228201866149902] |
c2f57446-191d-49cc-80cc-bb38c636249f | low-compute-and-fully-parallel-computer | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Fanello_Low_Compute_and_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Fanello_Low_Compute_and_ICCV_2017_paper.pdf | Low Compute and Fully Parallel Computer Vision With HashMatch | Numerous computer vision problems such as stereo depth estimation, object-class segmentation and foreground/background segmentation can be formulated as per-pixel image labeling tasks. Given one or many images as input, the desired output of these methods is usually a spatially smooth assignment of labels. The large am... | ['Sean Ryan Fanello', 'Julien Valentin', 'Carlo Ciliberto', 'Philip Davidson', 'Vladimir Tankovich', 'Shahram Izadi', 'Christoph Rhemann', 'Adarsh Kowdle'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['stereo-depth-estimation'] | ['computer-vision'] | [ 5.49823999e-01 -5.62820286e-02 -1.09700322e-01 -4.75401849e-01
-9.94581461e-01 -5.56086600e-01 5.91801167e-01 2.84624875e-01
-5.60262501e-01 5.32481194e-01 -3.82133126e-01 -4.10179675e-01
4.18708891e-01 -7.72228181e-01 -5.71747184e-01 -6.67653143e-01
4.71683294e-01 6.24604762e-01 7.63150036e-01 1.94339320... | [9.484844207763672, 0.2207546979188919] |
3d150ad0-64e5-4690-8754-296a440e8c24 | 3d-regression-neural-network-for-the | 1802.05914 | null | http://arxiv.org/abs/1802.05914v2 | http://arxiv.org/pdf/1802.05914v2.pdf | 3D Regression Neural Network for the Quantification of Enlarged Perivascular Spaces in Brain MRI | Enlarged perivascular spaces (EPVS) in the brain are an emerging imaging
marker for cerebral small vessel disease, and have been shown to be related to
increased risk of various neurological diseases, including stroke and dementia.
Automatic quantification of EPVS would greatly help to advance research into
its etiolog... | ['Wiro Niessen', 'Gerda Bortsova', 'Florian Dubost', 'Meike Vernooij', 'Marleen de Bruijne', 'M. Arfan Ikram', 'Hieab Adams'] | 2018-02-16 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [-1.13287330e-01 1.05001613e-01 -5.09443134e-02 -6.68432117e-01
-7.52536535e-01 -4.02193218e-01 4.79721069e-01 1.49408638e-01
-1.09835410e+00 6.27862453e-01 1.86390817e-01 -2.86198944e-01
1.44502625e-01 -8.38759065e-01 -3.60648781e-01 -5.48997819e-01
-7.05700278e-01 4.49578673e-01 6.80216789e-01 2.72255749... | [14.152261734008789, -2.0642173290252686] |
3137ecd5-4eab-4ed6-a54c-272580ef33fd | gtnet-generative-transfer-network-for-zero | 2001.06812 | null | https://arxiv.org/abs/2001.06812v2 | https://arxiv.org/pdf/2001.06812v2.pdf | GTNet: Generative Transfer Network for Zero-Shot Object Detection | We propose a Generative Transfer Network (GTNet) for zero shot object detection (ZSD). GTNet consists of an Object Detection Module and a Knowledge Transfer Module. The Object Detection Module can learn large-scale seen domain knowledge. The Knowledge Transfer Module leverages a feature synthesizer to generate unseen c... | ['Shizhen Zhao', 'Lerenhan Li', 'Changqian Yu', 'Zhong Ji', 'Nong Sang', 'Yuanjie Shao', 'Changxin Gao'] | 2020-01-19 | null | null | null | null | ['zero-shot-object-detection'] | ['computer-vision'] | [ 2.83377975e-01 2.75593102e-01 1.98110074e-01 -2.86655128e-01
-6.58935368e-01 -3.60166848e-01 8.40451062e-01 -3.66763592e-01
-7.76933208e-02 4.64108944e-01 -5.70244938e-02 -6.12907857e-02
4.37669992e-01 -1.40077007e+00 -1.10829628e+00 -7.19022512e-01
1.69161722e-01 3.01069438e-01 6.36739612e-01 7.49199688... | [9.789335250854492, 2.162303924560547] |
abf02605-a97a-4972-8ee6-c5ad1cf024d5 | feddebug-systematic-debugging-for-federated | 2301.03553 | null | https://arxiv.org/abs/2301.03553v1 | https://arxiv.org/pdf/2301.03553v1.pdf | FedDebug: Systematic Debugging for Federated Learning Applications | In Federated Learning (FL), clients train a model locally and share it with a central aggregator to build a global model. Impermissibility to access client's data and collaborative training makes FL appealing for applications with data-privacy concerns such as medical imaging. However, these FL characteristics pose unp... | ['Muhammad Ali Gulzar', 'Ali Anwar', 'Waris Gill'] | 2023-01-09 | null | null | null | null | ['fault-localization'] | ['computer-code'] | [-3.57881367e-01 6.89318404e-02 -3.56681138e-01 -5.68566740e-01
-1.17213082e+00 -7.44832933e-01 -1.98283553e-01 9.14266482e-02
-7.76490197e-02 5.51850677e-01 -4.58675623e-01 -6.91224873e-01
-8.73944387e-02 -5.81360936e-01 -8.04256380e-01 -6.40004992e-01
-4.36470568e-01 2.74629921e-01 2.06221089e-01 3.99926841... | [6.057352542877197, 6.4258880615234375] |
177a7b69-0bb9-4ee0-8436-475aaa0e1ac0 | neural-probabilistic-logic-programming-in | 2303.04660 | null | https://arxiv.org/abs/2303.04660v2 | https://arxiv.org/pdf/2303.04660v2.pdf | Neural Probabilistic Logic Programming in Discrete-Continuous Domains | Neural-symbolic AI (NeSy) allows neural networks to exploit symbolic background knowledge in the form of logic. It has been shown to aid learning in the limited data regime and to facilitate inference on out-of-distribution data. Probabilistic NeSy focuses on integrating neural networks with both logic and probability ... | ['Luc De Raedt', 'Angelika Kimmig', 'Giuseppe Marra', 'Robin Manhaeve', 'Pedro Zuidberg Dos Martires', 'Lennert De Smet'] | 2023-03-08 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [-4.21934351e-02 5.53592086e-01 -5.30560315e-01 -5.75097024e-01
-7.56129324e-01 -5.82666457e-01 8.36347401e-01 -3.65559310e-02
-2.32860535e-01 1.05772972e+00 -9.31856111e-02 -6.27745628e-01
-4.58672613e-01 -1.35142577e+00 -1.08992147e+00 -4.33824807e-01
-3.52753758e-01 9.44317877e-01 2.32253924e-01 2.63417393... | [8.674386978149414, 6.923358917236328] |
460870af-1390-44d1-92af-46a21a4d6bb7 | towards-argument-aware-abstractive | 2306.00672 | null | https://arxiv.org/abs/2306.00672v1 | https://arxiv.org/pdf/2306.00672v1.pdf | Towards Argument-Aware Abstractive Summarization of Long Legal Opinions with Summary Reranking | We propose a simple approach for the abstractive summarization of long legal opinions that considers the argument structure of the document. Legal opinions often contain complex and nuanced argumentation, making it challenging to generate a concise summary that accurately captures the main points of the legal opinion. ... | ['Diane Litman', 'Yang Zhong', 'Mohamed Elaraby'] | 2023-06-01 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 3.20969403e-01 8.18790197e-01 -8.33052933e-01 -4.54430580e-01
-1.46457040e+00 -1.14104402e+00 8.84754479e-01 1.00489628e+00
-2.48274133e-01 1.18145919e+00 1.44531417e+00 -6.95276916e-01
-1.59514278e-01 -4.44875807e-01 -4.64861095e-01 -1.24565512e-01
4.66598392e-01 6.17369473e-01 1.59320906e-01 -6.28513992... | [12.16167163848877, 9.594408988952637] |
6ca88a44-bac6-4eec-bf10-dffc5d36f152 | temporal-wasserstein-non-negative-matrix | 1912.03463 | null | https://arxiv.org/abs/1912.03463v1 | https://arxiv.org/pdf/1912.03463v1.pdf | Temporal Wasserstein non-negative matrix factorization for non-rigid motion segmentation and spatiotemporal deconvolution | Motion segmentation for natural images commonly relies on dense optic flow to yield point trajectories which can be grouped into clusters through various means including spectral clustering or minimum cost multicuts. However, in biological imaging scenarios, such as fluorescence microscopy or calcium imaging, where the... | ['Erdem Varol', 'Amin Nejatbakhsh', 'Conor McGrory'] | 2019-12-07 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 2.45836511e-01 -4.08101320e-01 2.28039801e-01 -1.55992597e-01
-1.94043249e-01 -9.03794706e-01 1.16912372e-01 -8.70879889e-02
-1.00411177e+00 9.39071655e-01 -5.99601269e-01 1.50171652e-01
-2.16323540e-01 -8.29325467e-02 -8.30822349e-01 -1.24488318e+00
-2.50045151e-01 1.79093778e-01 1.34244129e-01 5.02028465... | [8.7245512008667, -1.1259485483169556] |
c231061b-1a29-4d50-8e20-c454cca7c86c | attention-based-natural-language-person | 1705.08923 | null | http://arxiv.org/abs/1705.08923v1 | http://arxiv.org/pdf/1705.08923v1.pdf | Attention-based Natural Language Person Retrieval | Following the recent progress in image classification and captioning using
deep learning, we develop a novel natural language person retrieval system
based on an attention mechanism. More specifically, given the description of a
person, the goal is to localize the person in an image. To this end, we first
construct a b... | ['Jie Yu', 'Demetri Terzopoulos', 'Tao Zhou', 'Muhao Chen'] | 2017-05-24 | null | null | null | null | ['person-retrieval'] | ['computer-vision'] | [ 7.65164942e-02 -3.60952020e-01 -2.02551320e-01 -5.31105578e-01
-8.99903774e-01 -5.90889931e-01 6.63947940e-01 -5.48895560e-02
-8.54700744e-01 5.23177981e-01 2.92519927e-01 3.28363389e-01
2.78805822e-01 -8.35964084e-01 -6.77753508e-01 -5.52937090e-01
1.87678784e-01 6.59115970e-01 1.54606164e-01 -5.88531829... | [14.643486976623535, 0.8472605347633362] |
d1bdc0cf-a824-4b3d-91e4-c1c050473f08 | one-shot-domain-adaptation-in-video-based | 2301.00812 | null | https://arxiv.org/abs/2301.00812v3 | https://arxiv.org/pdf/2301.00812v3.pdf | One-shot domain adaptation in video-based assessment of surgical skills | Deep Learning (DL) has achieved automatic and objective assessment of surgical skills. However, DL models are data-hungry and restricted to their training domain. This prevents them from transitioning to new tasks where data is limited. Hence, domain adaptation is crucial to implement DL in real life. Here, we propose ... | ['Suvranu De', 'Xavier Intes', 'Gene Yang', 'Steven Schwaitzberg', 'Erim Yanik'] | 2022-12-16 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [-4.13172394e-02 1.48089081e-01 -3.63186240e-01 -3.89496386e-01
-7.87664711e-01 -6.44069016e-01 1.52433336e-01 2.80428410e-01
-8.47440541e-01 4.14237857e-01 5.04165739e-02 -4.71628606e-01
-4.51356560e-01 -4.90858614e-01 -6.80844128e-01 -2.50581235e-01
-2.39969790e-01 5.55589736e-01 8.50846469e-02 -3.33676040... | [14.071737289428711, -3.3710811138153076] |
bbf73999-f8e9-4b0b-95cf-00758fd1e2ab | fairness-in-visual-clustering-a-novel | 2304.07408 | null | https://arxiv.org/abs/2304.07408v1 | https://arxiv.org/pdf/2304.07408v1.pdf | Fairness in Visual Clustering: A Novel Transformer Clustering Approach | Promoting fairness for deep clustering models in unsupervised clustering settings to reduce demographic bias is a challenging goal. This is because of the limitation of large-scale balanced data with well-annotated labels for sensitive or protected attributes. In this paper, we first evaluate demographic bias in deep c... | ['Khoa Luu', 'Kaushik Roy', 'Marios Savvides', 'Chi Nhan Duong', 'Xuan-Bac Nguyen'] | 2023-04-14 | null | null | null | null | ['deep-clustering', 'deep-clustering'] | ['miscellaneous', 'natural-language-processing'] | [-3.93636316e-01 -9.14302468e-02 -1.54249594e-01 -8.44635844e-01
-4.09242928e-01 -1.96026891e-01 3.06419075e-01 5.58298528e-01
-5.98321199e-01 5.40028632e-01 3.15660477e-01 3.36019993e-01
-2.54675031e-01 -8.82429957e-01 -2.18857512e-01 -1.08815527e+00
1.04333743e-01 4.42191720e-01 -4.34064746e-01 1.81435734... | [9.2808198928833, 3.84321928024292] |
46bf5510-4f16-44a1-b085-0578f2e1d7ad | instance-segmentation-of-fallen-trees-in | 2105.01998 | null | https://arxiv.org/abs/2105.01998v1 | https://arxiv.org/pdf/2105.01998v1.pdf | Instance segmentation of fallen trees in aerial color infrared imagery using active multi-contour evolution with fully convolutional network-based intensity priors | In this paper, we introduce a framework for segmenting instances of a common object class by multiple active contour evolution over semantic segmentation maps of images obtained through fully convolutional networks. The contour evolution is cast as an energy minimization problem, where the aggregate energy functional i... | ['Marco Heurich', 'Wei Yao', 'Jacquelyn Shelton', 'Przemyslaw Polewski'] | 2021-05-05 | null | null | null | null | ['line-segment-detection'] | ['computer-vision'] | [ 5.89333236e-01 2.51047432e-01 3.28005366e-02 -1.90212473e-01
-5.42834044e-01 -6.53298140e-01 4.88571107e-01 6.38952434e-01
-7.93353856e-01 7.10908771e-01 -5.71985185e-01 -3.20976883e-01
-5.50994992e-01 -1.20439517e+00 -5.68118572e-01 -7.35866249e-01
-3.34549636e-01 7.30783761e-01 4.38850224e-01 -1.39472470... | [9.312124252319336, -1.6124305725097656] |
22bfc8af-835c-4555-a3ca-e8d70a8e03b3 | query-expansion-with-locally-trained-word | 1605.07891 | null | http://arxiv.org/abs/1605.07891v2 | http://arxiv.org/pdf/1605.07891v2.pdf | Query Expansion with Locally-Trained Word Embeddings | Continuous space word embeddings have received a great deal of attention in
the natural language processing and machine learning communities for their
ability to model term similarity and other relationships. We study the use of
term relatedness in the context of query expansion for ad hoc information
retrieval. We dem... | ['Nick Craswell', 'Bhaskar Mitra', 'Fernando Diaz'] | 2016-05-25 | query-expansion-with-locally-trained-word-1 | https://aclanthology.org/P16-1035 | https://aclanthology.org/P16-1035.pdf | acl-2016-8 | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [-9.50230211e-02 -2.75106907e-01 -4.92881685e-01 -2.64914125e-01
-7.59721935e-01 -5.68384290e-01 1.12883878e+00 8.54079485e-01
-9.98658061e-01 2.09378138e-01 8.30424428e-01 -4.92424935e-01
-5.27907491e-01 -6.79432631e-01 1.47772238e-01 -1.56938672e-01
-3.18776757e-01 4.42296654e-01 1.67688355e-01 -6.19604230... | [10.78756332397461, 8.414078712463379] |
d91a530a-14d4-4176-b331-bfdb0096814a | the-greedy-dirichlet-process-filter-an-online | 1811.05911 | null | http://arxiv.org/abs/1811.05911v2 | http://arxiv.org/pdf/1811.05911v2.pdf | The Greedy Dirichlet Process Filter - An Online Clustering Multi-Target Tracker | Reliable collision avoidance is one of the main requirements for autonomous
driving. Hence, it is important to correctly estimate the states of an unknown
number of static and dynamic objects in real-time. Here, data association is a
major challenge for every multi-target tracker. We propose a novel multi-target
tracke... | ['Hans-Joachim Wuensche', 'Benjamin Naujoks', 'Patrick Burger'] | 2018-11-14 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [-4.89752293e-01 -4.22011703e-01 -1.47358431e-02 -2.14529052e-01
-5.21200716e-01 -4.30557877e-01 8.06813180e-01 1.61077008e-01
-5.78636348e-01 6.85810685e-01 -4.71759140e-01 -1.31829649e-01
-3.47600907e-01 -6.93927944e-01 -5.34169257e-01 -9.02027190e-01
-1.05322890e-01 1.09976685e+00 1.15474272e+00 1.54230362... | [6.558304786682129, -2.0658016204833984] |
fdbf352f-34b1-4aab-96b4-240e7512db31 | a-denoised-mean-teacher-for-domain-adaptive | 2306.14749 | null | https://arxiv.org/abs/2306.14749v2 | https://arxiv.org/pdf/2306.14749v2.pdf | A denoised Mean Teacher for domain adaptive point cloud registration | Point cloud-based medical registration promises increased computational efficiency, robustness to intensity shifts, and anonymity preservation but is limited by the inefficacy of unsupervised learning with similarity metrics. Supervised training on synthetic deformations is an alternative but, in turn, suffers from the... | ['Mattias P. Heinrich', 'Alexander Bigalke'] | 2023-06-26 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [ 2.64194101e-01 4.03300464e-01 -4.96370718e-02 -3.28884363e-01
-1.54604626e+00 -6.56503439e-01 5.42012691e-01 2.29048342e-01
-4.74779934e-01 6.42962515e-01 6.33359775e-02 8.63225982e-02
-3.00853521e-01 -6.12141609e-01 -6.45948470e-01 -1.27454615e+00
2.87038833e-01 9.28027332e-01 3.32729399e-01 -1.72004372... | [14.59825611114502, -2.139113426208496] |
35d5bf95-e386-4130-a5c6-6479aa101e76 | a-hybrid-approach-to-vietnamese-word | null | null | https://ieeexplore.ieee.org/document/7800279 | https://ieeexplore.ieee.org/document/7800279 | A hybrid approach to Vietnamese word segmentation | Word segmentation is the very first task for Vietnamese language processing. Word-segmented text is the input of almost other NLP tasks. This task faces some challenges due to specific characteristics of the language. As in many other Asian languages such as Japanese, Korean and Chinese, white spaces in Vietnamese are ... | ['Anh-Cuong Le', 'Tuan-Phong Nguyen'] | 2016-12-29 | null | null | null | null | ['vietnamese-word-segmentation'] | ['natural-language-processing'] | [-3.82222161e-02 -3.12272102e-01 -3.29664439e-01 -2.15261579e-01
-5.76835573e-01 -5.52318215e-01 -1.28515795e-01 3.51969212e-01
-9.94725168e-01 7.36179948e-01 -1.87282190e-01 -7.21932471e-01
5.78372359e-01 -8.44688594e-01 -1.01812534e-01 -6.63027287e-01
4.25541490e-01 6.08551860e-01 5.87628543e-01 -2.08811909... | [10.158830642700195, 10.147780418395996] |
8600deb0-f216-40a4-8a5e-77ccded78b08 | optical-flow-and-scene-flow-estimation-a | null | null | https://www.sciencedirect.com/science/article/abs/pii/S0031320321000480 | https://www.sciencedirect.com/science/article/abs/pii/S0031320321000480 | Optical flow and scene flow estimation: A survey | Motion analysis is one of the most fundamental and challenging problems in the field of computer vision, which can be widely applied in many areas, such as autonomous driving, action recognition, scene understanding, and robotics. In general, the displacement field between subsequent frames can be divided into two type... | ['XiangdongKong', 'NingLv', 'XuezhiXiang', 'MingliangZhai'] | 2021-02-01 | null | null | null | pattern-recognition-2021-2 | ['scene-flow-estimation'] | ['computer-vision'] | [ 3.30421887e-02 -6.37894809e-01 -4.44030315e-01 -1.32381946e-01
-2.16584489e-01 -3.81619155e-01 4.91548479e-01 -2.14311451e-01
-4.45381552e-01 6.80562198e-01 9.87655371e-02 -1.52027637e-01
-6.31512627e-02 -5.80358803e-01 -4.59740192e-01 -7.92292118e-01
-1.43107295e-01 -2.53827065e-01 3.56210530e-01 -2.19889414... | [8.766536712646484, -1.7167549133300781] |
96825d15-554b-4673-97df-e312096fde04 | gaining-insights-into-unrecognized-user | 2204.05158 | null | https://arxiv.org/abs/2204.05158v2 | https://arxiv.org/pdf/2204.05158v2.pdf | Gaining Insights into Unrecognized User Utterances in Task-Oriented Dialog Systems | The rapidly growing market demand for automatic dialogue agents capable of goal-oriented behavior has caused many tech-industry leaders to invest considerable efforts into task-oriented dialog systems. The success of these systems is highly dependent on the accuracy of their intent identification -- the process of dedu... | ['Ateret Anaby-Tavor', 'Gaurav Pandey', 'Vineet Kumar', 'David Boaz', 'Matan Vetzler', 'Ella Rabinovich'] | 2022-04-11 | null | null | null | null | ['goal-oriented-dialog'] | ['natural-language-processing'] | [ 1.16257720e-01 2.09665313e-01 1.63395613e-01 -8.53899717e-01
-8.18640292e-01 -8.42051744e-01 7.40919292e-01 3.16430807e-01
-2.57255793e-01 4.26935017e-01 6.84990525e-01 -4.57348794e-01
-3.07239387e-02 -2.20252037e-01 4.64381605e-01 -3.13646406e-01
-1.96715832e-01 1.34523046e+00 1.23697728e-01 -7.03588903... | [12.70824909210205, 7.814077854156494] |
d9fae795-fc52-4486-a374-33633e9d77aa | optimized-directed-roadmap-graph-for-multi | 2003.12924 | null | https://arxiv.org/abs/2003.12924v1 | https://arxiv.org/pdf/2003.12924v1.pdf | Optimized Directed Roadmap Graph for Multi-Agent Path Finding Using Stochastic Gradient Descent | We present a novel approach called Optimized Directed Roadmap Graph (ODRM). It is a method to build a directed roadmap graph that allows for collision avoidance in multi-robot navigation. This is a highly relevant problem, for example for industrial autonomous guided vehicles. The core idea of ODRM is, that a directed ... | ['Marc Toussaint', 'Christian Henkel'] | 2020-03-29 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-1.57584339e-01 7.49028623e-01 3.29443961e-01 1.29284948e-01
-3.70674551e-01 -5.52544057e-01 8.13903391e-01 3.42500865e-01
-5.18539488e-01 1.16662061e+00 -1.14150919e-01 -2.85661519e-01
-5.03482640e-01 -1.35792160e+00 -7.64772415e-01 -7.60152161e-01
-8.87562096e-01 1.48138523e+00 6.73885942e-01 -7.77041733... | [4.932121753692627, 1.5520479679107666] |
9c57e292-1f8a-4ca2-861c-0fe42f7bfed0 | synthetic-human-model-dataset-for-skeleton | 1903.02679 | null | http://arxiv.org/abs/1903.02679v1 | http://arxiv.org/pdf/1903.02679v1.pdf | Synthetic Human Model Dataset for Skeleton Driven Non-rigid Motion Tracking and 3D Reconstruction | We introduce a synthetic dataset for evaluating non-rigid 3D human
reconstruction based on conventional RGB-D cameras. The dataset consist of
seven motion sequences of a single human model. For each motion sequence
per-frame ground truth geometry and ground truth skeleton are given. The
dataset also contains skinning w... | ['Peyman Moghadam', 'Shafeeq Elanattil'] | 2019-03-07 | null | null | null | null | ['3d-human-reconstruction'] | ['computer-vision'] | [-6.18558116e-02 1.12575695e-01 -2.49314949e-01 -2.74492025e-01
-4.70371574e-01 -3.08822691e-01 3.19359928e-01 -4.42625105e-01
-4.31823879e-01 2.75263876e-01 3.26643497e-01 2.24115476e-01
4.51717973e-01 -3.91671151e-01 -6.89204991e-01 -4.30635482e-01
1.99341834e-01 6.83847666e-01 3.98275644e-01 -3.63791846... | [7.114962100982666, -0.9234035611152649] |
76e461e8-d04f-4d07-8874-69eeff161afc | two-steps-to-risk-sensitivity | 2111.06803 | null | https://arxiv.org/abs/2111.06803v1 | https://arxiv.org/pdf/2111.06803v1.pdf | Two steps to risk sensitivity | Distributional reinforcement learning (RL) -- in which agents learn about all the possible long-term consequences of their actions, and not just the expected value -- is of great recent interest. One of the most important affordances of a distributional view is facilitating a modern, measured, approach to risk when out... | ['Peter Dayan', 'Chris Gagne'] | 2021-11-12 | null | http://proceedings.neurips.cc/paper/2021/hash/ba530cdf0a884348613f2aaa3a5ba5e8-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/ba530cdf0a884348613f2aaa3a5ba5e8-Paper.pdf | neurips-2021-12 | ['distributional-reinforcement-learning'] | ['methodology'] | [ 2.38409676e-02 3.19751889e-01 -2.51742452e-01 -3.80283326e-01
-4.33592707e-01 -5.63482881e-01 7.50175178e-01 5.65798759e-01
-1.21855521e+00 7.67830789e-01 2.43146703e-01 -6.29582226e-01
-6.57012105e-01 -7.16158807e-01 -4.63046461e-01 -7.66708672e-01
-5.94652772e-01 2.93303668e-01 -1.22670144e-01 -1.73155859... | [4.250586986541748, 2.6534652709960938] |
3a020e01-8aaa-476a-bf06-232c1bf345e8 | cenet-toward-concise-and-efficient-lidar | 2207.12691 | null | https://arxiv.org/abs/2207.12691v1 | https://arxiv.org/pdf/2207.12691v1.pdf | CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving | Accurate and fast scene understanding is one of the challenging task for autonomous driving, which requires to take full advantage of LiDAR point clouds for semantic segmentation. In this paper, we present a \textbf{concise} and \textbf{efficient} image-based semantic segmentation network, named \textbf{CENet}. In orde... | ['Guo-Qiang Xiao', 'Xian-Feng Han', 'Hui-Xian Cheng'] | 2022-07-26 | null | null | null | null | ['robust-3d-semantic-segmentation', 'lidar-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.89866608e-02 7.45330080e-02 -1.82254672e-01 -9.24754024e-01
-6.39693320e-01 -2.98788697e-01 2.90426642e-01 -1.80009022e-01
-7.79172838e-01 6.05199933e-01 -3.72019053e-01 -4.42934036e-01
-8.66284892e-02 -9.61956620e-01 -1.02426851e+00 -5.21541476e-01
3.67675006e-01 5.67297101e-01 5.85092068e-01 1.52485529... | [8.23227596282959, -2.6287498474121094] |
9e037aed-b882-4b0b-be4f-3c2602b0a9e4 | expanding-scope-adapting-english-adversarial | 2306.04874 | null | https://arxiv.org/abs/2306.04874v1 | https://arxiv.org/pdf/2306.04874v1.pdf | Expanding Scope: Adapting English Adversarial Attacks to Chinese | Recent studies have revealed that NLP predictive models are vulnerable to adversarial attacks. Most existing studies focused on designing attacks to evaluate the robustness of NLP models in the English language alone. Literature has seen an increasing need for NLP solutions for other languages. We, therefore, ask one n... | ['Yanjun Qi', 'Chengyuan Cai', 'Hanyu Liu'] | 2023-06-08 | null | null | null | null | ['adversarial-attack', 'adversarial-robustness'] | ['adversarial', 'adversarial'] | [ 3.03086549e-01 1.02383837e-01 -5.36724105e-02 -1.62849709e-01
-1.02537704e+00 -1.28267241e+00 6.67974174e-01 -4.11289841e-01
-3.27551156e-01 6.03490174e-01 8.84113833e-02 -6.89666927e-01
4.18957591e-01 -7.96876073e-01 -7.72092342e-01 -3.85282904e-01
3.79820257e-01 5.82802474e-01 1.48393035e-01 -3.87538731... | [6.024596691131592, 8.156030654907227] |
e1ce86b3-4b38-41cb-a197-fc7271a49337 | detailed-2d-3d-joint-representation-for-human | 2004.08154 | null | https://arxiv.org/abs/2004.08154v2 | https://arxiv.org/pdf/2004.08154v2.pdf | Detailed 2D-3D Joint Representation for Human-Object Interaction | Human-Object Interaction (HOI) detection lies at the core of action understanding. Besides 2D information such as human/object appearance and locations, 3D pose is also usually utilized in HOI learning since its view-independence. However, rough 3D body joints just carry sparse body information and are not sufficient t... | ['Junqi Liu', 'Yong-Lu Li', 'Shiyi Wang', 'Jiefeng Li', 'Xinpeng Liu', 'Han Lu', 'Cewu Lu'] | 2020-04-17 | detailed-2d-3d-joint-representation-for-human-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Li_Detailed_2D-3D_Joint_Representation_for_Human-Object_Interaction_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Detailed_2D-3D_Joint_Representation_for_Human-Object_Interaction_CVPR_2020_paper.pdf | cvpr-2020-6 | ['action-understanding'] | ['computer-vision'] | [-1.18087396e-01 -9.63016078e-02 -2.27970466e-01 -2.47733071e-01
-5.07975876e-01 -2.47331470e-01 2.57088631e-01 -4.61124986e-01
-3.11590880e-02 4.20423150e-01 3.56786937e-01 3.65507990e-01
-1.12546481e-01 -2.87127644e-01 -8.37745428e-01 -6.95820570e-01
1.73523337e-01 9.78561759e-01 2.52353877e-01 -3.88088301... | [7.122542381286621, -0.8576562404632568] |
8376273e-d7a4-47c9-81e2-f47e4acc9111 | wmt-2016-multimodal-translation-system | null | null | https://aclanthology.org/W16-2362 | https://aclanthology.org/W16-2362.pdf | WMT 2016 Multimodal Translation System Description based on Bidirectional Recurrent Neural Networks with Double-Embeddings | null | ['Marta R. Costa-juss{\\`a}', "Sergio Rodr{\\'\\i}guez Guasch"] | 2016-08-01 | null | null | null | ws-2016-8 | ['multimodal-machine-translation'] | ['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... | [-7.43734073638916, 3.630056381225586] |
0fc55e09-7c80-4c6c-b718-7b60b7c66256 | adversarial-attacks-and-defenses-in-machine | 2303.06302 | null | https://arxiv.org/abs/2303.06302v1 | https://arxiv.org/pdf/2303.06302v1.pdf | Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey | Adversarial attacks and defenses in machine learning and deep neural network have been gaining significant attention due to the rapidly growing applications of deep learning in the Internet and relevant scenarios. This survey provides a comprehensive overview of the recent advancements in the field of adversarial attac... | ['H. Vincent Poor', 'Ekram Hossain', 'Wei Ni', 'Xin Yuan', 'Shenghong Li', 'Tong Sun', 'Yulong Wang'] | 2023-03-11 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 1.27533808e-01 -3.56223553e-01 -6.37217909e-02 -8.11405405e-02
-5.87932765e-01 -8.88734043e-01 5.17149866e-01 -1.87870488e-01
-3.41388941e-01 4.97291148e-01 -6.57248311e-03 -7.15659082e-01
-2.99011618e-01 -7.21675336e-01 -5.91179371e-01 -9.58373189e-01
-6.31677508e-01 -4.74400938e-01 4.79199663e-02 -5.50801873... | [5.589046955108643, 7.8387556076049805] |
257901c5-2f17-4bee-9e27-0faa398c02e3 | inference-post-selection-of-group-sparse | 2012.15664 | null | https://arxiv.org/abs/2012.15664v4 | https://arxiv.org/pdf/2012.15664v4.pdf | Approximate Post-Selective Inference for Regression with the Group LASSO | After selection with the Group LASSO (or generalized variants such as the overlapping, sparse, or standardized Group LASSO), inference for the selected parameters is unreliable in the absence of adjustments for selection bias. In the penalized Gaussian regression setup, existing approaches provide adjustments for selec... | ['Daniel Kessler', 'Peter W. MacDonald', 'Snigdha Panigrahi'] | 2020-12-31 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 4.84890968e-01 2.70589530e-01 -7.64180660e-01 -7.96011269e-01
-7.67109096e-01 -3.75057817e-01 1.17264375e-01 2.90994853e-01
-3.96669596e-01 1.23344362e+00 4.31952715e-01 -3.60220373e-01
-5.11425376e-01 -6.10584378e-01 -6.95132554e-01 -6.34033263e-01
-3.27743143e-01 3.29176158e-01 -3.45404685e-01 4.23516065... | [7.852373123168945, 5.1396613121032715] |
ee08b86e-71a8-4d45-a6a0-a7d0882d0ac7 | unsupervised-clinical-language-translation | 1902.01177 | null | https://arxiv.org/abs/1902.01177v2 | https://arxiv.org/pdf/1902.01177v2.pdf | Unsupervised Clinical Language Translation | As patients' access to their doctors' clinical notes becomes common, translating professional, clinical jargon to layperson-understandable language is essential to improve patient-clinician communication. Such translation yields better clinical outcomes by enhancing patients' understanding of their own health condition... | ['Yu-An Chung', 'Wei-Hung Weng', 'Peter Szolovits'] | 2019-02-04 | null | null | null | null | ['clinical-language-translation'] | ['natural-language-processing'] | [ 3.61064732e-01 3.75072956e-01 -5.97196698e-01 -2.43941680e-01
-1.43098783e+00 -6.10313177e-01 -7.38890693e-02 7.84282446e-01
-5.94942629e-01 9.26109672e-01 7.73356199e-01 -7.86955476e-01
-1.50141641e-01 -3.52407753e-01 -3.04081827e-01 -4.57051188e-01
8.16680253e-01 8.17951024e-01 -5.78288138e-01 -1.86781093... | [8.606769561767578, 8.49281120300293] |
fa814a33-e018-4871-8bdf-59ab2729226a | dream-a-challenge-dataset-and-models-for | 1902.00164 | null | http://arxiv.org/abs/1902.00164v1 | http://arxiv.org/pdf/1902.00164v1.pdf | DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension | We present DREAM, the first dialogue-based multiple-choice reading
comprehension dataset. Collected from English-as-a-foreign-language
examinations designed by human experts to evaluate the comprehension level of
Chinese learners of English, our dataset contains 10,197 multiple-choice
questions for 6,444 dialogues. In ... | ['Jianshu Chen', 'Yejin Choi', 'Kai Sun', 'Dian Yu', 'Dong Yu', 'Claire Cardie'] | 2019-02-01 | null | null | null | null | ['dialogue-understanding'] | ['natural-language-processing'] | [ 1.75158530e-01 8.12444508e-01 1.08106337e-01 -4.41724807e-01
-1.06001294e+00 -8.81226122e-01 7.41331220e-01 5.23425460e-01
-5.04671454e-01 7.73525596e-01 8.10599685e-01 -9.13336337e-01
-1.75864473e-01 -1.03240812e+00 -5.81521988e-01 1.79579347e-01
4.58219290e-01 6.13090038e-01 3.22343320e-01 -1.05092096... | [11.313668251037598, 8.10371208190918] |
d4d354b2-9f67-459f-b3e8-1ebb37d70418 | plas-latent-action-space-for-offline | 2011.07213 | null | https://arxiv.org/abs/2011.07213v1 | https://arxiv.org/pdf/2011.07213v1.pdf | PLAS: Latent Action Space for Offline Reinforcement Learning | The goal of offline reinforcement learning is to learn a policy from a fixed dataset, without further interactions with the environment. This setting will be an increasingly more important paradigm for real-world applications of reinforcement learning such as robotics, in which data collection is slow and potentially d... | ['David Held', 'Sujay Bajracharya', 'Wenxuan Zhou'] | 2020-11-14 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [ 1.32654145e-01 6.55482290e-03 -7.06061423e-01 -1.17115460e-01
-6.72097683e-01 -7.38950193e-01 6.70364738e-01 7.65579846e-03
-6.30186439e-01 1.02345002e+00 -3.22948545e-02 -3.43914241e-01
-3.14690083e-01 -5.49421966e-01 -9.50240374e-01 -6.92804337e-01
-3.65821719e-01 8.14967453e-01 1.83800533e-01 -7.95387775... | [4.29074764251709, 1.5809839963912964] |
c0fbf2b6-611d-4624-9bba-a1ea47dffa74 | spatiotemporal-and-semantic-zero-inflated | 2304.01569 | null | https://arxiv.org/abs/2304.01569v1 | https://arxiv.org/pdf/2304.01569v1.pdf | Spatiotemporal and Semantic Zero-inflated Urban Anomaly Prediction | Urban anomaly predictions, such as traffic accident prediction and crime prediction, are of vital importance to smart city security and maintenance. Existing methods typically use deep learning to capture the intra-dependencies in spatial and temporal dimensions. However, numerous key challenges remain unsolved, for in... | ['Haiyong Xie', 'Yong Liao', 'Pengyuan Zhou', 'Yao Lu'] | 2023-04-04 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [-2.68200964e-01 -4.12842989e-01 4.80390191e-02 -3.03350031e-01
-5.91361344e-01 1.74134389e-01 4.65264291e-01 2.37766176e-01
-2.79759258e-01 5.43832719e-01 2.18241334e-01 -4.05371606e-01
-4.51789796e-01 -9.69464958e-01 -4.81472045e-01 -6.82059288e-01
-3.38507295e-01 3.06664407e-01 4.49004769e-01 -3.28217566... | [6.5339131355285645, 2.078439950942993] |
5ff1f282-be78-45ff-bd6f-fb59139603f9 | learning-from-high-dimensional-cyber-physical | 2303.08300 | null | https://arxiv.org/abs/2303.08300v1 | https://arxiv.org/pdf/2303.08300v1.pdf | Learning From High-Dimensional Cyber-Physical Data Streams for Diagnosing Faults in Smart Grids | The performance of fault diagnosis systems is highly affected by data quality in cyber-physical power systems. These systems generate massive amounts of data that overburden the system with excessive computational costs. Another issue is the presence of noise in recorded measurements, which prevents building a precise ... | ['Mehrdad Saif', 'Roozbeh Razavi-Far', 'Ehsan Hallaji', 'Hossein Hassani'] | 2023-03-15 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-2.57382765e-02 -4.36654538e-01 3.34557891e-02 -1.91788614e-01
-4.62265104e-01 -4.02399272e-01 2.42266223e-01 1.12006016e-01
4.46330100e-01 9.92254972e-01 -3.72103810e-01 -2.63485074e-01
-9.73011374e-01 -7.28295863e-01 -2.82576736e-02 -1.00049984e+00
-5.62284231e-01 5.45469224e-01 -3.17234725e-01 3.68976314... | [6.197067737579346, 2.5535120964050293] |
856e3658-898f-4760-ba62-e59c15a76d6d | differential-equation-and-probability | 2202.13800 | null | https://arxiv.org/abs/2202.13800v2 | https://arxiv.org/pdf/2202.13800v2.pdf | Differential equation and probability inspired graph neural networks for latent variable learning | Probabilistic theory and differential equation are powerful tools for the interpretability and guidance of the design of machine learning models, especially for illuminating the mathematical motivation of learning latent variable from observation. Subspace learning maps high-dimensional features on low-dimensional subs... | ['Zhuangwei Shi'] | 2022-02-28 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-2.76588827e-01 2.73949534e-01 -8.03762913e-01 -1.65504485e-01
-2.67032087e-01 -5.23772597e-01 5.46214163e-01 -6.21490359e-01
2.98854560e-01 5.49569905e-01 3.66942793e-01 -3.29356492e-01
-3.08666021e-01 -7.43124068e-01 -3.90688270e-01 -1.07220840e+00
-4.36903536e-02 5.45757294e-01 -7.11508393e-01 4.05624211... | [7.928440570831299, 4.72159481048584] |
bde5281e-3634-411b-80de-03e29862262b | sega-structural-entropy-guided-anchor-view | 2305.04501 | null | https://arxiv.org/abs/2305.04501v2 | https://arxiv.org/pdf/2305.04501v2.pdf | SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning | In contrastive learning, the choice of ``view'' controls the information that the representation captures and influences the performance of the model. However, leading graph contrastive learning methods generally produce views via random corruption or learning, which could lead to the loss of essential information and ... | ['Ke Xu', 'Shangzhe Li', 'Bowen Shi', 'Xueyuan Chen', 'Junran Wu'] | 2023-05-08 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 4.28847820e-01 7.73968816e-01 -3.85095179e-01 -1.39511928e-01
-2.77554482e-01 -5.91308415e-01 6.70271814e-01 5.56015790e-01
5.24469167e-02 5.91328740e-01 6.20282814e-02 -1.19259723e-01
-4.24375176e-01 -8.84134114e-01 -8.01599860e-01 -9.99278307e-01
-1.91861942e-01 2.49808148e-01 4.91013601e-02 -2.54236553... | [7.363410949707031, 6.2198686599731445] |
2d581471-772a-41fa-bbe8-534b36889c80 | gpv-pose-category-level-object-pose | 2203.07918 | null | https://arxiv.org/abs/2203.07918v2 | https://arxiv.org/pdf/2203.07918v2.pdf | GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting | While 6D object pose estimation has recently made a huge leap forward, most methods can still only handle a single or a handful of different objects, which limits their applications. To circumvent this problem, category-level object pose estimation has recently been revamped, which aims at predicting the 6D pose as wel... | ['Federico Tombari', 'Nassir Navab', 'Xiangyang Ji', 'Fabian Manhardt', 'Zhiqiang Lou', 'Ruida Zhang', 'Yan Di'] | 2022-03-15 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Di_GPV-Pose_Category-Level_Object_Pose_Estimation_via_Geometry-Guided_Point-Wise_Voting_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Di_GPV-Pose_Category-Level_Object_Pose_Estimation_via_Geometry-Guided_Point-Wise_Voting_CVPR_2022_paper.pdf | cvpr-2022-1 | ['6d-pose-estimation-using-rgbd', '6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 9.77787841e-03 -3.88928145e-01 -2.38091871e-01 -3.69382828e-01
-1.11043942e+00 -8.90621901e-01 5.90186179e-01 3.33797455e-01
-2.58863419e-01 1.12073950e-01 -4.42281775e-02 1.69105101e-02
-1.85940146e-01 -5.02767265e-01 -8.22893023e-01 -7.57507205e-01
8.74564573e-02 8.01527143e-01 4.07837391e-01 9.42881107... | [7.480472564697266, -2.638451337814331] |
a78ecc65-1d78-40d1-a83b-406d4adb7544 | k-rater-reliability-the-correct-unit-of | null | null | https://openreview.net/forum?id=35V2c0WUkuZ | https://openreview.net/pdf?id=35V2c0WUkuZ | k-Rater Reliability: The Correct Unit of Reliability for Aggregated Human Annotations | Since the inception of crowdsourcing, aggregation has been a common strategy for dealing with unreliable data. Aggregate ratings are more reliable than individual ones. However, many NLP datasets that rely on aggregate ratings only report the reliability of individual ones, which is the incorrect unit of analysis. In t... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['word-similarity'] | ['natural-language-processing'] | [-4.42314744e-01 1.32293984e-01 -1.84943795e-01 -3.51513118e-01
-1.14082134e+00 -8.52172613e-01 4.68579888e-01 6.73672557e-01
-6.91619039e-01 9.76226807e-01 4.28044051e-01 -1.19326681e-01
-2.77073625e-02 -5.77598810e-01 -2.83187240e-01 -2.77590960e-01
2.06657015e-02 2.75224715e-01 1.15977779e-01 -2.20897928... | [11.767109870910645, 8.8574800491333] |
250480df-dd2a-4f6c-8e14-cc4d9193aa5e | focalmix-semi-supervised-learning-for-3d | 2003.09108 | null | https://arxiv.org/abs/2003.09108v1 | https://arxiv.org/pdf/2003.09108v1.pdf | FocalMix: Semi-Supervised Learning for 3D Medical Image Detection | Applying artificial intelligence techniques in medical imaging is one of the most promising areas in medicine. However, most of the recent success in this area highly relies on large amounts of carefully annotated data, whereas annotating medical images is a costly process. In this paper, we propose a novel method, cal... | ['Li-Wei Wang', 'Yuan Zhang', 'Kexin Zhang', 'Dong Wang'] | 2020-03-20 | focalmix-semi-supervised-learning-for-3d-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Wang_FocalMix_Semi-Supervised_Learning_for_3D_Medical_Image_Detection_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_FocalMix_Semi-Supervised_Learning_for_3D_Medical_Image_Detection_CVPR_2020_paper.pdf | cvpr-2020-6 | ['medical-image-detection', 'lung-nodule-detection'] | ['computer-vision', 'medical'] | [ 2.90284783e-01 3.33702683e-01 -5.75333953e-01 -2.37795010e-01
-1.18060684e+00 -3.38868380e-01 3.20256233e-01 3.45056891e-01
-3.55520010e-01 3.38171035e-01 7.74388760e-02 -6.87592924e-01
-1.75404660e-02 -3.82607341e-01 -4.76863891e-01 -6.78888977e-01
-1.20508805e-01 8.40881646e-01 6.35871649e-01 3.49626124... | [15.04142951965332, -2.2441916465759277] |
403a6e53-9a66-4e54-b950-1813f08fbe43 | parallelized-acquisition-for-active-learning | 2305.19267 | null | https://arxiv.org/abs/2305.19267v1 | https://arxiv.org/pdf/2305.19267v1.pdf | Parallelized Acquisition for Active Learning using Monte Carlo Sampling | Bayesian inference remains one of the most important tool-kits for any scientist, but increasingly expensive likelihood functions are required for ever-more complex experiments, raising the cost of generating a Monte Carlo sample of the posterior. Recent attention has been directed towards the use of emulators of the p... | ['Jonas El Gammal', 'Nils Schöneberg', 'Jesús Torrado'] | 2023-05-30 | null | null | null | null | ['bayesian-inference', 'active-learning', 'active-learning'] | ['methodology', 'methodology', 'natural-language-processing'] | [ 1.86069623e-01 -4.47261706e-02 3.88333768e-01 -4.26776916e-01
-1.23725188e+00 -4.45555568e-01 8.56520176e-01 2.44115308e-01
-8.00179541e-01 1.02508450e+00 -1.70351475e-01 -2.96267807e-01
-3.44812907e-02 -9.41281736e-01 -5.15025020e-01 -1.20354092e+00
1.81762338e-01 1.33703589e+00 5.94733953e-01 2.74296016... | [6.725743770599365, 3.915452480316162] |
48e4e999-5b74-41f5-b0d2-a65228022fec | centralized-cooperative-exploration-policy | 2301.02375 | null | https://arxiv.org/abs/2301.02375v1 | https://arxiv.org/pdf/2301.02375v1.pdf | Centralized Cooperative Exploration Policy for Continuous Control Tasks | The deep reinforcement learning (DRL) algorithm works brilliantly on solving various complex control tasks. This phenomenal success can be partly attributed to DRL encouraging intelligent agents to sufficiently explore the environment and collect diverse experiences during the agent training process. Therefore, explora... | ['Yu Liu', 'Xinwen Hou', 'Qiang He', 'Chen Gong', 'Chao Li'] | 2023-01-06 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-5.08499146e-01 7.39075989e-02 -6.07530057e-01 1.78753749e-01
-4.03644234e-01 -3.79778147e-01 6.17491782e-01 -2.31675580e-02
-6.42776012e-01 1.28739500e+00 7.95268789e-02 -3.75149071e-01
-3.12161595e-01 -7.61399329e-01 -5.96145689e-01 -1.15784347e+00
-7.83201456e-01 5.51640034e-01 6.72900528e-02 -4.72119242... | [3.90847110748291, 2.0250210762023926] |
2be972da-8586-4e8f-a7e6-f0a153483ddf | self-supervised-face-presentation-attack | 2208.13070 | null | https://arxiv.org/abs/2208.13070v4 | https://arxiv.org/pdf/2208.13070v4.pdf | Self-Supervised Face Presentation Attack Detection with Dynamic Grayscale Snippets | Face presentation attack detection (PAD) plays an important role in defending face recognition systems against presentation attacks. The success of PAD largely relies on supervised learning that requires a huge number of labeled data, which is especially challenging for videos and often requires expert knowledge. To av... | ['Mourad Oussalah', 'Usman Muhammad'] | 2022-08-27 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 2.04924807e-01 -5.76798797e-01 -1.89663321e-01 -2.67189443e-01
-4.50432569e-01 -7.58618057e-01 3.90136689e-01 -8.24221000e-02
-8.44776481e-02 5.73795676e-01 -2.67130248e-02 -4.97167021e-01
-8.31907764e-02 -4.76684004e-01 -6.24637961e-01 -1.00699043e+00
-4.24206346e-01 -3.07314694e-01 3.96936089e-02 -1.83490783... | [13.03078842163086, 1.1780937910079956] |
eec3d538-c054-4c66-a1e8-74230a73ef69 | temporally-coherent-completion-of-dynamic | null | null | https://dl.acm.org/doi/10.1145/2980179.2982398 | https://dl.acm.org/doi/pdf/10.1145/2980179.2982398 | Temporally coherent completion of dynamic video | We present an automatic video completion algorithm that synthesizes missing regions in videos in a temporally coherent fashion. Our algorithm can handle dynamic scenes captured using a moving camera. State-of-the-art approaches have difficulties handling such videos because viewpoint changes cause image-space motion ve... | ['J. Kopf', 'N. Ahuja', 'S. B. Kang', 'J.-B. Huang'] | 2016-11-01 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [ 1.33820415e-01 -6.97728038e-01 3.82439718e-02 -9.42776650e-02
-7.28933632e-01 -8.38971734e-01 2.44446367e-01 -7.58452773e-01
-2.40245759e-01 8.38285387e-01 2.81592328e-02 1.13114685e-01
2.74810672e-01 -1.97217748e-01 -9.29405153e-01 -6.16104364e-01
-9.24310386e-02 -1.92499515e-02 2.61529833e-01 1.24318503... | [10.67921257019043, -1.5223337411880493] |
1421d382-832e-4265-8cff-840a0ddfc910 | dissecting-self-supervised-learning-methods | 2207.00449 | null | https://arxiv.org/abs/2207.00449v3 | https://arxiv.org/pdf/2207.00449v3.pdf | Dissecting Self-Supervised Learning Methods for Surgical Computer Vision | The field of surgical computer vision has undergone considerable breakthroughs in recent years with the rising popularity of deep neural network-based methods. However, standard fully-supervised approaches for training such models require vast amounts of annotated data, imposing a prohibitively high cost; especially in... | ['Antoine Fleurentin', 'Saurav Sharma', 'Nicolas Padoy', 'Alexandros Karargyris', 'Georgios Exarchakis', 'Idris Hamoud', 'Chinedu Innocent Nwoye', 'Luca Sestini', 'Aditya Murali', 'Tong Yu', 'Deepak Alapatt', 'Vinkle Srivastav', 'Sanat Ramesh'] | 2022-07-01 | null | null | null | null | ['action-triplet-recognition', 'surgical-tool-detection', 'surgical-phase-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.63874578e-01 3.89397353e-01 -7.85234094e-01 -1.44120827e-01
-1.07489824e+00 -5.16788125e-01 4.41958785e-01 4.61829901e-01
-6.99400246e-01 5.14627695e-01 2.19159320e-01 -4.13412571e-01
-2.60575473e-01 -2.59234756e-01 -4.98747468e-01 -7.50937581e-01
-2.59539455e-01 5.35242558e-01 1.62764281e-01 -1.04748346... | [14.193161964416504, -3.167637586593628] |
975a6f74-f453-4a04-919e-0a48db535626 | large-scale-adversarial-representation | 1907.02544 | null | https://arxiv.org/abs/1907.02544v2 | https://arxiv.org/pdf/1907.02544v2.pdf | Large Scale Adversarial Representation Learning | Adversarially trained generative models (GANs) have recently achieved compelling image synthesis results. But despite early successes in using GANs for unsupervised representation learning, they have since been superseded by approaches based on self-supervision. In this work we show that progress in image generation qu... | ['Jeff Donahue', 'Karen Simonyan'] | 2019-07-04 | large-scale-adversarial-representation-1 | http://papers.nips.cc/paper/9240-large-scale-adversarial-representation-learning | http://papers.nips.cc/paper/9240-large-scale-adversarial-representation-learning.pdf | neurips-2019-12 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 4.42190558e-01 6.82467222e-01 -9.06317979e-02 -3.97846192e-01
-8.49506795e-01 -5.86747825e-01 1.25972462e+00 -6.26952291e-01
6.13302961e-02 7.88677752e-01 6.43873155e-01 -3.53568494e-01
4.14347589e-01 -1.10777152e+00 -9.15561438e-01 -6.67979777e-01
1.90419912e-01 5.49601734e-01 -3.45758229e-01 -3.60418558... | [11.549504280090332, -0.20997492969036102] |
0fec7b75-d5fc-4268-b09b-b91880264337 | high-fidelity-synthetic-face-generation-for | 2303.04839 | null | https://arxiv.org/abs/2303.04839v1 | https://arxiv.org/pdf/2303.04839v1.pdf | High Fidelity Synthetic Face Generation for Rosacea Skin Condition from Limited Data | Similar to the majority of deep learning applications, diagnosing skin diseases using computer vision and deep learning often requires a large volume of data. However, obtaining sufficient data for particular types of facial skin conditions can be difficult due to privacy concerns. As a result, conditions like Rosacea ... | ['Hossein Javidnia', 'Marija Bezbradica', 'Alistair Sutherland', 'Anwesha Mohanty'] | 2023-03-08 | null | null | null | null | ['face-generation'] | ['computer-vision'] | [ 3.04883808e-01 4.57579672e-01 1.03870228e-01 -4.07772243e-01
-7.72015393e-01 -3.59656125e-01 3.46654773e-01 -4.09796298e-01
-1.82946883e-02 7.51570702e-01 -3.79058011e-02 4.97753881e-02
1.21820763e-01 -6.19362235e-01 -3.96421194e-01 -8.90861869e-01
1.49984568e-01 -1.34431282e-02 -5.26930511e-01 -1.79352686... | [12.910597801208496, 0.30817967653274536] |
96669b71-3bef-4f86-bc2b-c5e06f3ed9f4 | an-investigation-of-interpretability | 2002.09192 | null | https://arxiv.org/abs/2002.09192v1 | https://arxiv.org/pdf/2002.09192v1.pdf | An Investigation of Interpretability Techniques for Deep Learning in Predictive Process Analytics | This paper explores interpretability techniques for two of the most successful learning algorithms in medical decision-making literature: deep neural networks and random forests. We applied these algorithms in a real-world medical dataset containing information about patients with cancer, where we learn models that try... | ['Peter Bruza', 'Chun Ouyang', 'Catarina Moreira', 'Renuka Sindhgatta', 'Andreas Wichert'] | 2020-02-21 | null | null | null | null | ['interpretability-techniques-for-deep-learning'] | ['miscellaneous'] | [ 2.42609978e-01 8.16467404e-01 -3.51066947e-01 -6.81519508e-01
9.63644311e-02 -1.14780970e-01 4.55771089e-01 3.67150396e-01
1.84716024e-02 6.83293581e-01 6.32707655e-01 -7.46275008e-01
-6.99424088e-01 -1.04724026e+00 -5.83883405e-01 -7.73830175e-01
-2.17177927e-01 8.91942322e-01 -2.98516303e-01 -1.17738709... | [8.608857154846191, 5.743265151977539] |
40f3f5be-2b9f-4c07-a2a1-bb87e67eb563 | siamese-networks-with-location-prior-for | 1901.08109 | null | http://arxiv.org/abs/1901.08109v1 | http://arxiv.org/pdf/1901.08109v1.pdf | Siamese Networks with Location Prior for Landmark Tracking in Liver Ultrasound Sequences | Image-guided radiation therapy can benefit from accurate motion tracking by
ultrasound imaging, in order to minimize treatment margins and radiate moving
anatomical targets, e.g., due to breathing. One way to formulate this tracking
problem is the automatic localization of given tracked anatomical landmarks
throughout ... | ['Weiye Li', 'Orcun Goksel', 'Christine Tanner', 'Ece Ozkan', 'Alvaro Gomariz'] | 2019-01-23 | null | null | null | null | ['landmark-tracking'] | ['computer-vision'] | [ 1.94993570e-01 2.22025737e-01 -2.30846852e-01 -1.03989609e-01
-1.01191318e+00 -9.87224460e-01 4.52993453e-01 1.12115443e-01
-4.78016019e-01 2.16743633e-01 4.43182230e-01 -2.34518662e-01
-4.62185204e-01 -3.74630183e-01 -7.74898648e-01 -9.39561307e-01
-5.15150547e-01 3.87972206e-01 1.77124396e-01 2.18430594... | [14.391539573669434, -2.6482324600219727] |
cd2549d6-be89-48de-ae0f-b51924b032b3 | physics-informed-deep-diffusion-mri | 2210.11388 | null | https://arxiv.org/abs/2210.11388v1 | https://arxiv.org/pdf/2210.11388v1.pdf | Physics-informed deep diffusion MRI reconstruction: break the bottleneck of training data in artificial intelligence | In this work, we propose a Physics-Informed Deep Diffusion magnetic resonance imaging (DWI) reconstruction method (PIDD). PIDD contains two main components: The multi-shot DWI data synthesis and a deep learning reconstruction network. For data synthesis, we first mathematically analyze the motion during the multi-shot ... | ['Xiaobo Qu', 'Di Guo', 'Zhigang Wu', 'Ran Tao', 'Boyu Jiang', 'Taishan Kang', 'Qingrui Cai', 'Xinlin Zhang', 'Zi Wang', 'Chen Qian'] | 2022-10-20 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 1.54818445e-01 -3.01318467e-01 6.92197606e-02 -3.22512239e-01
-5.85269332e-01 1.03244983e-01 4.66595918e-01 -4.17624116e-01
-3.77332926e-01 7.38586605e-01 3.49856317e-01 1.00331753e-01
-4.47114468e-01 -6.28323615e-01 -5.11180222e-01 -1.05839038e+00
-1.12121284e-01 3.75460327e-01 4.84527946e-01 4.78754053... | [13.544145584106445, -2.3991639614105225] |
0adcaefe-32de-4175-80a0-46a92fa9696b | d-2-nerf-self-supervised-decoupling-of | 2205.15838 | null | https://arxiv.org/abs/2205.15838v4 | https://arxiv.org/pdf/2205.15838v4.pdf | D$^2$NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Video | Given a monocular video, segmenting and decoupling dynamic objects while recovering the static environment is a widely studied problem in machine intelligence. Existing solutions usually approach this problem in the image domain, limiting their performance and understanding of the environment. We introduce Decoupled Dy... | ['Cengiz Oztireli', 'Forrester Cole', 'Andrea Tagliasacchi', 'Fangcheng Zhong', 'Tianhao Wu'] | 2022-05-31 | null | null | null | null | ['shadow-removal'] | ['computer-vision'] | [ 6.17495060e-01 -3.41541439e-01 1.34764493e-01 -3.77383471e-01
-2.33690500e-01 -7.15938687e-01 4.67483342e-01 -5.03827691e-01
-3.80878299e-01 6.29608631e-01 -1.85456634e-01 -2.51845449e-01
2.57832527e-01 -6.64054155e-01 -9.35137510e-01 -1.26701713e+00
-2.55378690e-02 1.67261109e-01 9.72811818e-01 -4.48221080... | [10.824285507202148, -4.069308757781982] |
2e633fd4-28ab-41a8-8ec8-7bcdf0e73509 | lmsim-computing-domain-specific-semantic-word | null | null | https://aclanthology.org/W14-5116 | https://aclanthology.org/W14-5116.pdf | LMSim : Computing Domain-specific Semantic Word Similarities Using a Language Modeling Approach | null | ['Sachin Pawar', 'Swapnil Hingmire', 'Girish K. Palshikar'] | 2014-12-01 | null | null | null | ws-2014-12 | ['text-clustering'] | ['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... | [-7.258103370666504, 3.826150417327881] |
dcfba564-18a1-410a-815e-5ee9d60afc11 | large-scale-genealogical-information | 2304.14044 | null | https://arxiv.org/abs/2304.14044v1 | https://arxiv.org/pdf/2304.14044v1.pdf | Large Scale Genealogical Information Extraction From Handwritten Quebec Parish Records | This paper presents a complete workflow designed for extracting information from Quebec handwritten parish registers. The acts in these documents contain individual and family information highly valuable for genetic, demographic and social studies of the Quebec population. From an image of parish records, our workflow ... | ['Christopher Kermorvant', 'Hélène Vézina', 'Eugénie Capel', 'James McGrath', 'Mélodie Boillet', 'Martin Maarand', 'Solène Tarride'] | 2023-04-27 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 2.00557083e-01 3.01623017e-01 -1.34784788e-01 -4.11825180e-01
-6.70572400e-01 -5.75323641e-01 9.07209694e-01 8.04782808e-01
-7.20897853e-01 1.14582062e+00 5.15031636e-01 1.35165825e-01
-1.56194568e-01 -1.06951165e+00 -2.39030793e-01 -3.11165422e-01
1.37654409e-01 1.17523098e+00 9.90790278e-02 -1.35729715... | [10.122241020202637, 10.282012939453125] |
f6f984c0-fae2-4a8a-9038-02125ec4b838 | compressing-deep-neural-networks-on-fpgas-to | 2003.06308 | null | https://arxiv.org/abs/2003.06308v2 | https://arxiv.org/pdf/2003.06308v2.pdf | Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML | We present the implementation of binary and ternary neural networks in the hls4ml library, designed to automatically convert deep neural network models to digital circuits with FPGA firmware. Starting from benchmark models trained with floating point precision, we investigate different strategies to reduce the network'... | ['Nhan Tran', 'Sheila Sagear', 'Sergo Jindariani', 'Philip Harris', 'Kevin Pedro', 'Jennifer Ngadiuba', 'Giuseppe Di Guglielmo', 'Edward Kreinar', 'Zhenbin Wu', 'Vladimir Loncar', 'Mia Liu', 'Maurizio Pierini', 'Javier Duarte', 'Sioni Summers', 'Dylan Rankin', 'Duc Hoang'] | 2020-03-11 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 2.15677932e-01 1.54434457e-01 -2.40663409e-01 -7.33629465e-01
-9.11110118e-02 -4.12818015e-01 4.24126208e-01 4.14618589e-02
-8.64090979e-01 7.88157761e-01 -6.99524581e-01 -6.19881690e-01
-2.80095458e-01 -9.70723748e-01 -8.75306785e-01 -4.98051643e-01
2.82452822e-01 7.13258922e-01 2.89164960e-01 1.25521556... | [8.355791091918945, 2.9334046840667725] |
cfbf28b6-a663-4255-9de5-853329d83c95 | ridiculously-fast-shot-boundary-detection | 1705.08214 | null | http://arxiv.org/abs/1705.08214v1 | http://arxiv.org/pdf/1705.08214v1.pdf | Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks | Shot boundary detection (SBD) is an important component of many video
analysis tasks, such as action recognition, video indexing, summarization and
editing. Previous work typically used a combination of low-level features like
color histograms, in conjunction with simple models such as SVMs. Instead, we
propose to lear... | ['Michael Gygli'] | 2017-05-23 | null | null | null | null | ['camera-shot-boundary-detection'] | ['computer-vision'] | [ 3.47511053e-01 -2.82218367e-01 -1.39994472e-01 -4.51797694e-02
-4.52423662e-01 -4.79420960e-01 7.19215870e-01 3.86472374e-01
-5.69335461e-01 4.02809620e-01 1.66219547e-01 -1.31619141e-01
3.79478365e-01 -7.07750976e-01 -8.24952483e-01 -2.97921687e-01
-3.78037900e-01 1.56152859e-01 6.95302248e-01 7.97472671... | [8.51487922668457, 0.23042486608028412] |
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