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46df8c80-a48f-462d-8276-359c25e159f0 | capsulevos-semi-supervised-video-object | 1910.00132 | null | https://arxiv.org/abs/1910.00132v1 | https://arxiv.org/pdf/1910.00132v1.pdf | CapsuleVOS: Semi-Supervised Video Object Segmentation Using Capsule Routing | In this work we propose a capsule-based approach for semi-supervised video object segmentation. Current video object segmentation methods are frame-based and often require optical flow to capture temporal consistency across frames which can be difficult to compute. To this end, we propose a video based capsule network,... | ['Yogesh S Rawat', 'Mubarak Shah', 'Kevin Duarte'] | 2019-09-30 | capsulevos-semi-supervised-video-object-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Duarte_CapsuleVOS_Semi-Supervised_Video_Object_Segmentation_Using_Capsule_Routing_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Duarte_CapsuleVOS_Semi-Supervised_Video_Object_Segmentation_Using_Capsule_Routing_ICCV_2019_paper.pdf | iccv-2019-10 | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 2.91629676e-02 -2.34066606e-01 -4.43308711e-01 -7.77366087e-02
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-5.37830651e-01 3.92413050e-01 -1.54215470e-01 -7.57643059e-02
1.90948009e-01 -3.30660164e-01 -8.82576406e-01 -5.95920622e-01
-2.65285552e-01 2.82538354e-01 7.27174640e-01 3.62141043... | [9.17383098602295, -0.08460091054439545] |
b6f2fbe4-53c9-4d57-904d-4949617dd9ed | real-time-hyperspectral-imaging-in-hardware | 2204.02084 | null | https://arxiv.org/abs/2204.02084v1 | https://arxiv.org/pdf/2204.02084v1.pdf | Real-time Hyperspectral Imaging in Hardware via Trained Metasurface Encoders | Hyperspectral imaging has attracted significant attention to identify spectral signatures for image classification and automated pattern recognition in computer vision. State-of-the-art implementations of snapshot hyperspectral imaging rely on bulky, non-integrated, and expensive optical elements, including lenses, spe... | ['Andrea Fratalocchi', 'Bernard Ghanem', 'Silvio Giancola', 'Fedor Getman', 'Qizhou Wang', 'Arturo Burguete-Lopez', 'Maksim Makarenko'] | 2022-04-05 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Makarenko_Real-Time_Hyperspectral_Imaging_in_Hardware_via_Trained_Metasurface_Encoders_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Makarenko_Real-Time_Hyperspectral_Imaging_in_Hardware_via_Trained_Metasurface_Encoders_CVPR_2022_paper.pdf | cvpr-2022-1 | ['spectral-reconstruction'] | ['computer-vision'] | [ 1.05742371e+00 -3.14796776e-01 1.14999905e-01 -5.95888793e-02
-4.31116313e-01 -7.77803302e-01 1.54545859e-01 -9.95048732e-02
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2.71691710e-01 5.22555232e-01 2.73945600e-01 7.63600543... | [10.20314884185791, -2.434542417526245] |
baf486a7-d016-4692-92ad-dd4c39cbd2eb | end-to-end-video-instance-segmentation-via-1 | 2203.03145 | null | https://arxiv.org/abs/2203.03145v1 | https://arxiv.org/pdf/2203.03145v1.pdf | End-to-end video instance segmentation via spatial-temporal graph neural networks | Video instance segmentation is a challenging task that extends image instance segmentation to the video domain. Existing methods either rely only on single-frame information for the detection and segmentation subproblems or handle tracking as a separate post-processing step, which limit their capability to fully levera... | ['Weiyao Lin', 'Kean Chen', 'Ning Xu', 'Tao Wang'] | 2022-03-07 | end-to-end-video-instance-segmentation-via | http://openaccess.thecvf.com//content/ICCV2021/html/Wang_End-to-End_Video_Instance_Segmentation_via_Spatial-Temporal_Graph_Neural_Networks_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wang_End-to-End_Video_Instance_Segmentation_via_Spatial-Temporal_Graph_Neural_Networks_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-instance-segmentation'] | ['computer-vision'] | [-8.55025277e-03 -3.12457010e-02 -6.64464355e-01 -1.48277298e-01
-5.78841448e-01 -5.92927754e-01 1.05821416e-01 -8.15142225e-03
-4.17887598e-01 4.87011552e-01 -1.99245855e-01 -2.86749572e-01
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-2.08618030e-01 2.60305852e-01 9.25816536e-01 1.36357903... | [9.153003692626953, -0.08170034736394882] |
f191aeff-e3d9-47c3-8d99-53a6f01aa8a9 | sparse-adversarial-video-attacks-with-spatial | 2111.05468 | null | https://arxiv.org/abs/2111.05468v1 | https://arxiv.org/pdf/2111.05468v1.pdf | Sparse Adversarial Video Attacks with Spatial Transformations | In recent years, a significant amount of research efforts concentrated on adversarial attacks on images, while adversarial video attacks have seldom been explored. We propose an adversarial attack strategy on videos, called DeepSAVA. Our model includes both additive perturbation and spatial transformation by a unified ... | ['Qiang Ni', 'Leandro Soriano Marcolino', 'Wenjie Ruan', 'Ronghui Mu'] | 2021-11-10 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 2.91254401e-01 -1.83647543e-01 3.19026589e-01 4.48371656e-02
-5.92893541e-01 -7.01931357e-01 7.03038275e-01 -3.59653026e-01
-4.20921981e-01 3.92902821e-01 2.01547563e-01 -3.84889424e-01
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-3.13730538e-01 -3.24215323e-01 4.77438718e-01 -4.26970035... | [5.4308271408081055, 7.943722248077393] |
073d1313-339a-4248-b8b3-b55b46281d7f | disturbing-target-values-for-neural-network | 2110.05003 | null | https://arxiv.org/abs/2110.05003v1 | https://arxiv.org/pdf/2110.05003v1.pdf | Disturbing Target Values for Neural Network Regularization | Diverse regularization techniques have been developed such as L2 regularization, Dropout, DisturbLabel (DL) to prevent overfitting. DL, a newcomer on the scene, regularizes the loss layer by flipping a small share of the target labels at random and training the neural network on this distorted data so as to not learn t... | ['Mofassir ul Islam Arif', 'Klavdia Zavalich', 'Paweena Tarepakdee', 'Hanna Lukashonak', 'Yongho Kim'] | 2021-10-11 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 1.13501102e-01 3.61413419e-01 -3.80506426e-01 -6.92269385e-01
-7.93851376e-01 -4.25028503e-01 3.07671517e-01 3.59701850e-02
-6.97567523e-01 1.05395985e+00 1.14055984e-01 1.46139842e-02
5.61466534e-03 -5.47850490e-01 -1.07462394e+00 -9.09586132e-01
1.87745079e-01 1.39884517e-01 3.28126550e-01 2.85438269... | [9.260207176208496, 3.817368268966675] |
87aea591-f201-49d6-8124-1c84a6c49ab1 | collaboratively-learning-preferences-from | 1506.07947 | null | http://arxiv.org/abs/1506.07947v1 | http://arxiv.org/pdf/1506.07947v1.pdf | Collaboratively Learning Preferences from Ordinal Data | In applications such as recommendation systems and revenue management, it is
important to predict preferences on items that have not been seen by a user or
predict outcomes of comparisons among those that have never been compared. A
popular discrete choice model of multinomial logit model captures the structure
of the ... | ['Sewoong Oh', 'Jiaming Xu', 'Kiran K. Thekumparampil'] | 2015-06-26 | collaboratively-learning-preferences-from-1 | http://papers.nips.cc/paper/5818-collaboratively-learning-preferences-from-ordinal-data | http://papers.nips.cc/paper/5818-collaboratively-learning-preferences-from-ordinal-data.pdf | neurips-2015-12 | ['collaborative-ranking'] | ['graphs'] | [ 2.53059745e-01 6.03289455e-02 -7.10524678e-01 -7.53420532e-01
-9.15758789e-01 -6.72724664e-01 2.25188255e-01 3.04265499e-01
-6.71928644e-01 6.28854394e-01 7.52999544e-01 -2.81609088e-01
-9.22157407e-01 -6.39699399e-01 -8.32409501e-01 -5.30293226e-01
-3.59906614e-01 9.04176176e-01 -5.34397304e-01 -9.62290019... | [9.384124755859375, 5.450090408325195] |
c44bd776-6604-450e-b514-5584eeca1f2f | leveraging-vision-language-models-for | 2301.10166 | null | https://arxiv.org/abs/2301.10166v1 | https://arxiv.org/pdf/2301.10166v1.pdf | Leveraging Vision-Language Models for Granular Market Change Prediction | Predicting future direction of stock markets using the historical data has been a fundamental component in financial forecasting. This historical data contains the information of a stock in each specific time span, such as the opening, closing, lowest, and highest price. Leveraging this data, the future direction of th... | ['Navid Rekabsaz', 'Christopher Wimmer'] | 2023-01-17 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-5.77607810e-01 -9.43645298e-01 -3.07945490e-01 -3.72615635e-01
-5.11175573e-01 -9.28987622e-01 1.15618038e+00 1.38400704e-01
-5.86398602e-01 4.98234361e-01 5.54317594e-01 -5.28990388e-01
4.20411110e-01 -1.43352902e+00 -8.88723493e-01 -1.91307023e-01
-3.57714981e-01 3.22892278e-01 8.20435882e-02 -4.83726978... | [4.41765022277832, 4.257970809936523] |
c70ed3d3-cb62-427c-898b-d9ceccde2aa0 | multiple-human-pose-estimation-with | null | null | http://campar.in.tum.de/pub/belagiannis2014eccvChalearn/belagiannis2014eccvChalearn.pdf | http://campar.in.tum.de/pub/belagiannis2014eccvChalearn/belagiannis2014eccvChalearn.pdf | Multiple human pose estimation with temporally consistent 3d pictorial structures | Multiple human 3D pose estimation from multiple camera views is a challenging task in unconstrained environments. Each individual has to be matched across each view and then the body pose has to be estimated. Additionally, the body pose of every individual changes in a consistent manner over time. To address these chal... | ['Vasileios Belagiannis', 'Pascal Fua', 'Xinchao Wang', 'Slobodan Ilic', 'Nassir Navab', 'Bernt Schiele'] | 2014-09-06 | null | null | null | null | ['3d-multi-person-pose-estimation'] | ['computer-vision'] | [-1.02311306e-01 -1.90407515e-01 -5.59023283e-02 -1.82775602e-01
-2.68100619e-01 -5.80187798e-01 4.84516740e-01 -1.75935686e-01
-3.46524209e-01 5.22710085e-01 1.28185317e-01 4.03282762e-01
2.01713517e-01 -3.35926265e-01 -7.18866050e-01 -2.69991785e-01
-4.07368727e-02 7.60364771e-01 6.14892006e-01 3.54937352... | [7.042168140411377, -0.9822030663490295] |
53dd2bc6-0087-4640-a29b-1385cc2746e6 | hierarchical-supervision-and-shuffle-data | 2304.01464 | null | https://arxiv.org/abs/2304.01464v1 | https://arxiv.org/pdf/2304.01464v1.pdf | Hierarchical Supervision and Shuffle Data Augmentation for 3D Semi-Supervised Object Detection | State-of-the-art 3D object detectors are usually trained on large-scale datasets with high-quality 3D annotations. However, such 3D annotations are often expensive and time-consuming, which may not be practical for real applications. A natural remedy is to adopt semi-supervised learning (SSL) by leveraging a limited am... | ['Xinbo Gao', 'Deyu Meng', 'Pengcheng Li', 'Fangcen Liu', 'Chenqiang Gao', 'Chuandong Liu'] | 2023-04-04 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_Hierarchical_Supervision_and_Shuffle_Data_Augmentation_for_3D_Semi-Supervised_Object_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_Hierarchical_Supervision_and_Shuffle_Data_Augmentation_for_3D_Semi-Supervised_Object_CVPR_2023_paper.pdf | cvpr-2023-1 | ['semi-supervised-object-detection'] | ['computer-vision'] | [-1.09328076e-01 2.31268667e-02 -3.75750929e-01 -4.07385528e-01
-4.64095265e-01 -3.01295787e-01 6.22222900e-01 -8.21197685e-03
-2.16690019e-01 2.35617116e-01 -1.84165165e-01 -3.29905272e-01
2.04115376e-01 -6.84836805e-01 -6.29062593e-01 -1.00234616e+00
4.28769827e-01 3.70887101e-01 7.67535269e-01 -1.33719072... | [7.758639335632324, -2.7805960178375244] |
fb0b53f5-f222-4042-90a9-e78a79dec0bb | mrgan-multi-rooted-3d-shape-generation-with | 2007.12944 | null | https://arxiv.org/abs/2007.12944v1 | https://arxiv.org/pdf/2007.12944v1.pdf | MRGAN: Multi-Rooted 3D Shape Generation with Unsupervised Part Disentanglement | We present MRGAN, a multi-rooted adversarial network which generates part-disentangled 3D point-cloud shapes without part-based shape supervision. The network fuses multiple branches of tree-structured graph convolution layers which produce point clouds, with learnable constant inputs at the tree roots. Each branch lea... | ['Daniel Cohen-Or', 'Hao Zhang', 'Rinon Gal', 'Amit Bermano'] | 2020-07-25 | null | null | null | null | ['3d-shape-generation'] | ['computer-vision'] | [ 3.04072827e-01 9.69075501e-01 -3.54275629e-02 7.63342232e-02
-5.27860403e-01 -1.20798814e+00 6.35865033e-01 -1.25086337e-01
4.45245504e-01 4.77463156e-01 8.41508955e-02 -1.93519443e-01
2.09846318e-01 -1.22848797e+00 -1.10730946e+00 -9.84698236e-01
-7.32044280e-02 8.33414376e-01 -1.32065400e-01 -3.39053333... | [8.9763765335083, -3.6267504692077637] |
4a10e263-bb9a-481b-90ed-59e43129be2c | a-sequence-modelling-approach-to-question | null | null | https://aclanthology.org/2022.wordplay-1.4 | https://aclanthology.org/2022.wordplay-1.4.pdf | A Sequence Modelling Approach to Question Answering in Text-Based Games | Interactive Question Answering (IQA) requires an intelligent agent to interact with a dynamic environment in order to gather information necessary to answer a question. IQA tasks have been proposed as means of training systems to develop language or visual comprehension abilities. To this end, the Question Answering wi... | ['Jan Buys', 'Jonathan Shock', 'Edan Toledo', 'Gregory Furman'] | null | null | null | null | naacl-wordplay-2022-7 | ['text-based-games'] | ['playing-games'] | [ 4.49264824e-01 4.99501199e-01 4.86963093e-01 -3.82888049e-01
-1.10554528e+00 -7.91705251e-01 1.06503201e+00 1.41859353e-01
-3.32797676e-01 4.55522060e-01 1.55401275e-01 -8.98929298e-01
-4.69895899e-02 -7.96310842e-01 -6.95590436e-01 -2.41180003e-01
-1.37340084e-01 1.10560513e+00 5.17329931e-01 -5.12355268... | [11.47983169555664, 7.899476051330566] |
56d0163c-b7dd-413e-8f9c-bf42ad8385ce | from-saliency-to-dino-saliency-guided-vision | 2304.03140 | null | https://arxiv.org/abs/2304.03140v1 | https://arxiv.org/pdf/2304.03140v1.pdf | From Saliency to DINO: Saliency-guided Vision Transformer for Few-shot Keypoint Detection | Unlike current deep keypoint detectors that are trained to recognize limited number of body parts, few-shot keypoint detection (FSKD) attempts to localize any keypoints, including novel or base keypoints, depending on the reference samples. FSKD requires the semantically meaningful relations for keypoint similarity lea... | ['Piotr Koniusz', 'Hao Zhu', 'Changsheng Lu'] | 2023-04-06 | null | null | null | null | ['keypoint-detection'] | ['computer-vision'] | [ 2.90891498e-01 2.06158787e-01 -2.98934847e-01 -1.25640899e-01
-7.02431560e-01 -3.14929664e-01 5.93985498e-01 1.47387415e-01
-3.38395029e-01 2.67191768e-01 3.31426084e-01 3.25372398e-01
-6.57505989e-02 -5.36078453e-01 -1.00989389e+00 -5.17115533e-01
6.09517805e-02 7.13958368e-02 9.72894430e-01 -3.91851276... | [9.64699649810791, -0.4776627719402313] |
ad6e746c-efa1-47ce-8dfd-7186fe4075a3 | unsupervised-structure-consistent-image-to | 2208.11546 | null | https://arxiv.org/abs/2208.11546v1 | https://arxiv.org/pdf/2208.11546v1.pdf | Unsupervised Structure-Consistent Image-to-Image Translation | The Swapping Autoencoder achieved state-of-the-art performance in deep image manipulation and image-to-image translation. We improve this work by introducing a simple yet effective auxiliary module based on gradient reversal layers. The auxiliary module's loss forces the generator to learn to reconstruct an image with ... | ['Charalambos Poullis', 'Shima Shahfar'] | 2022-08-24 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 4.75904942e-01 1.74663976e-01 1.63607910e-01 -2.97067851e-01
-6.07927740e-01 -6.26493573e-01 8.29044223e-01 -3.47786248e-01
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-3.13803740e-02 -1.00307393e+00 -1.04106987e+00 -1.10492957e+00
2.81881213e-01 3.35693568e-01 -3.25998254e-02 -4.03976470... | [11.489838600158691, -0.6151317954063416] |
ecf3fe35-dd70-4ce7-869b-4c6e7902da22 | understanding-and-mitigating-overfitting-in | 2211.02219 | null | https://arxiv.org/abs/2211.02219v3 | https://arxiv.org/pdf/2211.02219v3.pdf | Understanding and Mitigating Overfitting in Prompt Tuning for Vision-Language Models | Pretrained vision-language models (VLMs) such as CLIP have shown impressive generalization capability in downstream vision tasks with appropriate text prompts. Instead of designing prompts manually, Context Optimization (CoOp) has been recently proposed to learn continuous prompts using taskspecific training data. Desp... | ['Changsheng Xu', 'WeiMing Dong', 'Lingxi Xie', 'Jiankang Deng', 'Yang Liu', 'Chengcheng Ma'] | 2022-11-04 | null | null | null | null | ['open-vocabulary-object-detection'] | ['computer-vision'] | [ 2.28188634e-01 -3.24482620e-01 -1.34271309e-01 -3.89492363e-01
-4.91359055e-01 -5.17410398e-01 5.94086528e-01 -1.17388614e-01
-6.30226493e-01 3.35148901e-01 2.71130770e-01 -2.18235657e-01
-1.07062973e-01 -3.26054633e-01 -6.02872789e-01 -7.54557848e-01
4.42326605e-01 -1.90038085e-02 2.72705436e-01 -6.53513446... | [10.146772384643555, 1.9059910774230957] |
63abc144-ecff-49f0-b1db-5179ff4301bd | jointly-fine-tuning-bert-like-self-supervised-1 | null | null | https://arxiv.org/abs/2008.06682 | https://arxiv.org/abs/2008.06682 | Jointly Fine-Tuning “BERT-like” Self Supervised Models to Improve Multimodal Speech Emotion Recognition | Multimodal emotion recognition from speech is an important area in affective computing. Fusing multiple data modalities and learning representations with limited amounts of labeled data is a challenging task. In this paper, we explore the use of modality-specific "BERT-like" pretrained Self Supervised Learning (SSL) ar... | ['Suranga Nanayakkara', 'Shamane Siriwardhana', 'Rivindu Weerasekera', 'Andrew Reis'] | 2020-08-15 | null | null | null | interspeech-2020-8 | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 5.79253249e-02 6.91073611e-02 -4.43638442e-03 -8.92959118e-01
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-5.28410316e-01 5.96917689e-01 4.86938894e-01 7.72934556e-02
2.87987620e-01 -5.25498092e-02 -5.53713083e-01 -4.65333909e-01
1.20276429e-01 2.73707390e-01 -3.21228534e-01 -4.96566772... | [13.297468185424805, 5.344238758087158] |
72566a6d-1a23-4345-8da2-5c24e92ae462 | a-deep-representation-for-depth-images-from | 1609.09713 | null | http://arxiv.org/abs/1609.09713v1 | http://arxiv.org/pdf/1609.09713v1.pdf | A deep representation for depth images from synthetic data | Convolutional Neural Networks (CNNs) trained on large scale RGB databases
have become the secret sauce in the majority of recent approaches for object
categorization from RGB-D data. Thanks to colorization techniques, these
methods exploit the filters learned from 2D images to extract meaningful
representations in 2.5D... | ['Barbara Caputo', 'Paolo Russo', 'Fabio Maria Carlucci'] | 2016-09-30 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 1.15880489e-01 3.35303515e-01 2.52701938e-01 -3.05520087e-01
-3.27578127e-01 -6.76076531e-01 8.38902593e-01 2.71321416e-01
-5.40180326e-01 5.37483037e-01 -7.24620894e-02 -6.17246814e-02
-2.11206917e-03 -1.25638294e+00 -6.83459699e-01 -8.95016313e-01
-5.81167452e-02 5.62943637e-01 3.87710094e-01 -3.27298164... | [8.426189422607422, -2.769665002822876] |
aa509eac-4bd0-4845-8544-549252c2ae56 | da-bev-depth-aware-bev-transformer-for-3d | 2302.13002 | null | https://arxiv.org/abs/2302.13002v2 | https://arxiv.org/pdf/2302.13002v2.pdf | Introducing Depth into Transformer-based 3D Object Detection | In this paper, we present DAT, a Depth-Aware Transformer framework designed for camera-based 3D detection. Our model is based on observing two major issues in existing methods: large depth translation errors and duplicate predictions along depth axes. To mitigate these issues, we propose two key solutions within DAT. T... | ['Xingyu Liao', 'Ailing Zeng', 'Lei Zhang', 'Shilong Liu', 'Feng Li', 'Hongyang Li', 'Hao Zhang'] | 2023-02-25 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [-3.96433985e-03 -2.08259463e-01 -4.22026545e-01 -2.47968823e-01
-9.22186196e-01 -6.07286990e-01 6.47636294e-01 -3.75781834e-01
-2.88825780e-01 2.54426181e-01 2.50420898e-01 -2.69304395e-01
1.74147353e-01 -8.22970986e-01 -7.35739291e-01 -6.08226061e-01
2.74306566e-01 1.27458185e-01 7.56720603e-01 -2.13696241... | [8.105399131774902, -2.5303804874420166] |
f414e603-db76-4127-886c-75c0cc8133d7 | contour-detection-from-deep-patch-level | 1705.03159 | null | http://arxiv.org/abs/1705.03159v1 | http://arxiv.org/pdf/1705.03159v1.pdf | Contour Detection from Deep Patch-level Boundary Prediction | In this paper, we present a novel approach for contour detection with
Convolutional Neural Networks. A multi-scale CNN learning framework is designed
to automatically learn the most relevant features for contour patch detection.
Our method uses patch-level measurements to create contour maps with
overlapping patches. W... | ['Li Shen', 'Teck Wee Chua'] | 2017-05-09 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 1.84343025e-01 -8.01987574e-03 7.29939193e-02 -1.66880503e-01
-9.60084081e-01 -6.84627593e-01 1.51555657e-01 4.26896006e-01
-2.54379064e-01 1.72355354e-01 -2.44207382e-01 -9.70354378e-02
2.06670299e-01 -1.27615845e+00 -6.69986665e-01 -4.65984941e-01
-2.71132141e-01 -6.80350587e-02 9.57038760e-01 -3.28280896... | [9.497518539428711, 0.217572420835495] |
96baa127-f1db-4b24-84ca-6e42d9597695 | 4d-panoptic-segmentation-as-invariant-and | 2303.15651 | null | https://arxiv.org/abs/2303.15651v1 | https://arxiv.org/pdf/2303.15651v1.pdf | 4D Panoptic Segmentation as Invariant and Equivariant Field Prediction | In this paper, we develop rotation-equivariant neural networks for 4D panoptic segmentation. 4D panoptic segmentation is a recently established benchmark task for autonomous driving, which requires recognizing semantic classes and object instances on the road based on LiDAR scans, as well as assigning temporally consis... | ['Fatih Porikli', 'Maani Ghaffari Jadidi', 'Shubhankar Borse', 'Hong Cai', 'Shizong Han', 'Minghan Zhu'] | 2023-03-28 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [-1.03812359e-01 -4.67785120e-01 -3.27668488e-01 -7.34223604e-01
-2.83732653e-01 -7.82940745e-01 8.55815291e-01 -2.28027776e-01
-3.18724573e-01 -6.37328774e-02 -4.53520238e-01 -4.10389811e-01
-3.85507494e-01 -8.61875355e-01 -6.98160648e-01 -8.05599511e-01
-2.61925578e-01 9.85743940e-01 4.12929088e-01 -3.07484508... | [8.103056907653809, -2.7090184688568115] |
68db2228-d8db-4824-aaf8-4f82896b891a | co-saliency-detection-with-co-attention-fully | 2008.08909 | null | https://arxiv.org/abs/2008.08909v1 | https://arxiv.org/pdf/2008.08909v1.pdf | Co-Saliency Detection with Co-Attention Fully Convolutional Network | Co-saliency detection aims to detect common salient objects from a group of relevant images. Some attempts have been made with the Fully Convolutional Network (FCN) framework and achieve satisfactory detection results. However, due to stacking convolution layers and pooling operation, the boundary details tend to be lo... | ['Guangshuai Gao', 'Qingjie Liu', 'Yunhong Wang', 'Wenting Zhao'] | 2020-08-20 | null | null | null | null | ['co-saliency-detection'] | ['computer-vision'] | [ 1.58067599e-01 -5.72028756e-02 2.23499741e-02 2.12610010e-02
-2.76658326e-01 2.16647372e-01 5.19276977e-01 6.13250136e-02
-3.05954784e-01 4.84466344e-01 3.99835646e-01 2.58649886e-01
1.79673299e-01 -5.21747530e-01 -6.63288653e-01 -5.73466003e-01
1.09763779e-01 -4.78035212e-01 8.90675068e-01 -1.67403862... | [9.715469360351562, -0.400240957736969] |
bde31ea4-d20a-4b32-9b97-d79edd4cd9fe | stochastic-online-convex-optimization | 2102.00729 | null | https://arxiv.org/abs/2102.00729v3 | https://arxiv.org/pdf/2102.00729v3.pdf | Stochastic Online Convex Optimization. Application to probabilistic time series forecasting | We introduce a general framework of stochastic online convex optimization to obtain fast-rate stochastic regret bounds. We prove that algorithms such as online newton steps and a scale-free 10 version of Bernstein online aggregation achieve best-known rates in unbounded stochastic settings. We apply our approach to cal... | ['Olivier Wintenberger'] | 2021-02-01 | null | null | null | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [-5.59472919e-01 7.90104344e-02 -4.91117388e-02 -6.31641567e-01
-1.49849200e+00 -1.01927626e+00 -2.15734705e-01 -2.73737404e-02
-5.16660810e-01 1.20238864e+00 6.99719489e-02 -6.95560396e-01
-4.70809668e-01 -6.74791753e-01 -1.12211096e+00 -7.52402842e-01
-6.21305048e-01 8.30473065e-01 -1.63854614e-01 1.32009625... | [4.781369686126709, 3.429440498352051] |
b95e3c74-5e74-4a05-8b58-267a3bae4502 | unihd-at-tsar-2022-shared-task-is-compute-all | 2301.01764 | null | https://arxiv.org/abs/2301.01764v2 | https://arxiv.org/pdf/2301.01764v2.pdf | UniHD at TSAR-2022 Shared Task: Is Compute All We Need for Lexical Simplification? | Previous state-of-the-art models for lexical simplification consist of complex pipelines with several components, each of which requires deep technical knowledge and fine-tuned interaction to achieve its full potential. As an alternative, we describe a frustratingly simple pipeline based on prompted GPT-3 responses, be... | ['Michael Gertz', 'Dennis Aumiller'] | 2023-01-04 | null | null | null | null | ['lexical-simplification'] | ['natural-language-processing'] | [ 1.13958173e-01 2.02076674e-01 -3.58548202e-03 -5.88485599e-01
-1.44521523e+00 -8.73294294e-01 6.46324277e-01 1.95954442e-01
-7.76966631e-01 6.81761324e-01 4.80817497e-01 -4.72975194e-01
-1.79038737e-02 -2.39122704e-01 -5.48293293e-01 -1.12548910e-01
3.53401214e-01 8.38938236e-01 2.03326866e-01 -8.61864209... | [10.943750381469727, 10.269387245178223] |
7e227572-0943-453f-9850-5f5516a1c343 | cear-cross-entity-aware-reranker-for | 2104.08741 | null | https://arxiv.org/abs/2104.08741v2 | https://arxiv.org/pdf/2104.08741v2.pdf | CEAR: Cross-Entity Aware Reranker for Knowledge Base Completion | Pre-trained language models (LMs) like BERT have shown to store factual knowledge about the world. This knowledge can be used to augment the information present in Knowledge Bases, which tend to be incomplete. However, prior attempts at using BERT for task of Knowledge Base Completion (KBC) resulted in performance wors... | ['Mausam', 'Parag Singla', 'Yatin Nandwani', 'Mayank Singh Chauhan', 'Keshav Kolluru'] | 2021-04-18 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [-5.19281805e-01 4.67157632e-01 -5.38901269e-01 -3.26663740e-02
-7.65081346e-01 -5.45775771e-01 8.34717751e-01 6.43228710e-01
-8.44301999e-01 9.10379708e-01 9.26861882e-01 -2.34796464e-01
-2.41424158e-01 -9.38579321e-01 -8.34612727e-01 8.67338851e-02
-3.18499297e-01 8.11655939e-01 5.10619938e-01 -4.80504185... | [9.1321439743042, 8.116639137268066] |
8260a34b-973d-4493-a7ee-bfe985e23e54 | video-based-camera-localization-using-anchor | 2107.03068 | null | https://arxiv.org/abs/2107.03068v1 | https://arxiv.org/pdf/2107.03068v1.pdf | Video-Based Camera Localization Using Anchor View Detection and Recursive 3D Reconstruction | In this paper we introduce a new camera localization strategy designed for image sequences captured in challenging industrial situations such as industrial parts inspection. To deal with peculiar appearances that hurt standard 3D reconstruction pipeline, we exploit pre-knowledge of the scene by selecting key frames in ... | ['Masatoshi Okutomi', 'Naoyuki Miyashita', 'Koki Onbe', 'Hajime Taira'] | 2021-07-07 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [ 2.73854017e-01 -9.29284543e-02 -5.59736937e-02 -8.44858140e-02
-6.74359322e-01 -9.25741553e-01 2.50168592e-01 1.71410456e-01
-5.84498420e-02 2.83260018e-01 -2.76118636e-01 -1.14644542e-01
8.86019319e-02 -2.87428886e-01 -8.16243172e-01 -5.15325129e-01
-1.39821991e-02 4.86215383e-01 6.08499467e-01 1.88496992... | [7.827647686004639, -2.151437282562256] |
43644347-1dd6-4b1f-975c-1e29febd90c8 | moso-decomposing-motion-scene-and-object-for | 2303.03684 | null | https://arxiv.org/abs/2303.03684v2 | https://arxiv.org/pdf/2303.03684v2.pdf | MOSO: Decomposing MOtion, Scene and Object for Video Prediction | Motion, scene and object are three primary visual components of a video. In particular, objects represent the foreground, scenes represent the background, and motion traces their dynamics. Based on this insight, we propose a two-stage MOtion, Scene and Object decomposition framework (MOSO) for video prediction, consist... | ['Jing Liu', 'Xinxin Zhu', 'Weining Wang', 'Mingzhen Sun'] | 2023-03-07 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sun_MOSO_Decomposing_MOtion_Scene_and_Object_for_Video_Prediction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sun_MOSO_Decomposing_MOtion_Scene_and_Object_for_Video_Prediction_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-generation', 'video-prediction', 'unconditional-video-generation', 'video-frame-interpolation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.37113476e-01 -3.98563147e-01 -1.78829491e-01 1.67578608e-02
-4.07516539e-01 -2.77667493e-01 5.84054768e-01 -3.44012767e-01
1.37903377e-01 4.77359653e-01 2.15342090e-01 7.15938285e-02
2.55891353e-01 -5.03847718e-01 -1.07079208e+00 -6.92463279e-01
1.07667316e-02 2.46195689e-01 8.55590165e-01 1.56147346... | [9.583178520202637, -0.033877067267894745] |
b226f553-1c83-46ce-998d-4c84565f0209 | online-video-streaming-super-resolution-with | 2303.00334 | null | https://arxiv.org/abs/2303.00334v3 | https://arxiv.org/pdf/2303.00334v3.pdf | Online Streaming Video Super-Resolution with Convolutional Look-Up Table | Online video streaming has fundamental limitations on the transmission bandwidth and computational capacity and super-resolution is a promising potential solution. However, applying existing video super-resolution methods to online streaming is non-trivial. Existing video codecs and streaming protocols (\eg, WebRTC) dy... | ['Lili Qiu', 'Dongsheng Li', 'Yuqing Yang', 'Huan Yang', 'Ningxin Zheng', 'Zhenhua Han', 'Shan Jiang', 'Xinyang Jiang', 'Zefan Qu', 'Guanghao Yin'] | 2023-03-01 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 1.95318058e-01 -7.55967796e-01 -3.07061136e-01 -3.45894605e-01
-6.85409486e-01 -3.13180447e-01 5.65270474e-03 -2.15673611e-01
-2.23289698e-01 5.90686500e-01 1.98791444e-01 -9.00928117e-03
-6.62986888e-03 -7.96440303e-01 -7.20398903e-01 -5.89731276e-01
-4.20641243e-01 -3.34511250e-01 9.15736079e-01 -4.02469516... | [11.153438568115234, -1.7062418460845947] |
51e216b5-ca23-4981-ac3d-40ba653cc624 | light-coreference-resolution-for-russian-with | 2306.01465 | null | https://arxiv.org/abs/2306.01465v1 | https://arxiv.org/pdf/2306.01465v1.pdf | Light Coreference Resolution for Russian with Hierarchical Discourse Features | Coreference resolution is the task of identifying and grouping mentions referring to the same real-world entity. Previous neural models have mainly focused on learning span representations and pairwise scores for coreference decisions. However, current methods do not explicitly capture the referential choice in the hie... | ['Ivan Smirnov', 'Elena Chistova'] | 2023-06-02 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 1.56118974e-01 7.45280921e-01 -3.61421794e-01 -2.39334345e-01
-1.02128148e+00 -6.92910910e-01 7.71270096e-01 2.63055801e-01
-7.69393146e-01 1.02373171e+00 1.04307044e+00 -2.53003299e-01
-2.45394170e-01 -5.95103085e-01 -6.93074286e-01 -2.61968464e-01
1.41666792e-02 1.05045271e+00 1.56205133e-01 -7.95907438... | [9.343486785888672, 9.539780616760254] |
f997da2d-b1ca-4ebb-bfa1-96e54409d32e | translation-of-algorithmic-descriptions-of | 1805.07239 | null | https://arxiv.org/abs/1805.07239v5 | https://arxiv.org/pdf/1805.07239v5.pdf | Translation of Algorithmic Descriptions of Discrete Functions to SAT with Applications to Cryptanalysis Problems | In the present paper, we propose a technology for translating algorithmic descriptions of discrete functions to SAT. The proposed technology is aimed at applications in algebraic cryptanalysis. We describe how cryptanalysis problems are reduced to SAT in such a way that it should be perceived as natural by the cryptogr... | ['Oleg Zaikin', 'Ilya Otpuschennikov', 'Alexander Semenov', 'Stepan Kochemazov', 'Irina Gribanova'] | 2018-05-17 | null | null | null | null | ['cryptanalysis'] | ['miscellaneous'] | [ 2.21779376e-01 1.67117000e-01 4.68941629e-01 -5.60794115e-01
-5.11461318e-01 -9.22082663e-01 5.92939734e-01 -1.33056894e-01
5.78793744e-03 8.91593397e-01 -3.46959472e-01 -8.76513124e-01
-3.85384291e-01 -1.11066258e+00 -5.60845971e-01 -5.01640618e-01
-1.92026213e-01 9.19234812e-01 2.14906499e-01 -1.01973379... | [5.783853054046631, 4.727225303649902] |
35354b98-b355-4d0d-a9b9-d22d284cb289 | learning-trajectory-word-alignments-for-video | 2301.01953 | null | https://arxiv.org/abs/2301.01953v3 | https://arxiv.org/pdf/2301.01953v3.pdf | Learning Trajectory-Word Alignments for Video-Language Tasks | In a video, an object usually appears as the trajectory, i.e., it spans over a few spatial but longer temporal patches, that contains abundant spatiotemporal contexts. However, modern Video-Language BERTs (VDL-BERTs) neglect this trajectory characteristic that they usually follow image-language BERTs (IL-BERTs) to depl... | ['Songfang Huang', 'Fei Huang', 'Yu Zhang', 'Ming Yan', 'Chenliang Li', 'Qinghao Ye', 'Hanwang Zhang', 'Haiyang Xu', 'Zhangzikang Li', 'Xu Yang'] | 2023-01-05 | null | null | null | null | ['video-question-answering', 'video-retrieval', 'word-alignment'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [-3.65088023e-02 -3.79403144e-01 -4.92568314e-01 -3.40644330e-01
-8.80988121e-01 -4.47454184e-01 4.85051602e-01 -2.31569245e-01
-5.83758295e-01 3.96852851e-01 2.44316012e-01 -3.13553005e-01
-5.65276481e-02 -4.86678153e-01 -8.75747323e-01 -6.72979891e-01
9.13501680e-02 1.51980102e-01 7.25780487e-01 -1.52455196... | [9.99427604675293, 0.6183263063430786] |
cdc9810c-9cae-4eee-978f-b0ccb3bc8be2 | on-estimating-total-time-to-solve-sat-in | 1308.0761 | null | http://arxiv.org/abs/1308.0761v1 | http://arxiv.org/pdf/1308.0761v1.pdf | On estimating total time to solve SAT in distributed computing environments: Application to the SAT@home project | This paper proposes a method to estimate the total time required to solve SAT
in distributed environments via partitioning approach. It is based on the
observation that for some simple forms of problem partitioning one can use the
Monte Carlo approach to estimate the time required to solve an original
problem. The meth... | ['Oleg Zaikin', 'Alexander Semenov'] | 2013-08-04 | null | null | null | null | ['cryptanalysis'] | ['miscellaneous'] | [ 4.72180843e-02 2.64905274e-01 5.32433450e-01 -3.21511626e-01
-1.06087959e+00 -9.68839049e-01 1.99357033e-01 5.02121866e-01
-4.20097202e-01 1.10784602e+00 -5.30846059e-01 -7.13025212e-01
-5.06630480e-01 -1.14444363e+00 -5.41379571e-01 -6.89508915e-01
-1.15955524e-01 1.22318268e+00 2.52244294e-01 -2.34869376... | [5.751817226409912, 4.699889183044434] |
a6fba741-f7c2-4ce5-9261-bb5f4ea86b32 | an-analytical-framework-for-downlink-leo | 2212.03549 | null | https://arxiv.org/abs/2212.03549v2 | https://arxiv.org/pdf/2212.03549v2.pdf | Modeling and Analysis of LEO Satellite Networks Leveraging Cox Point Processes | This work develops an analytical framework for downlink low earth orbit (LEO) satellite communications, leveraging tools from stochastic geometry. We propose a tractable approach to the analysis of such satellite communication systems accounting for the fact that satellites are located on circular orbits. We accurately... | ['François Baccelli', 'Chang-Sik Choi'] | 2022-12-07 | null | null | null | null | ['point-processes'] | ['methodology'] | [-3.47286522e-01 1.92845181e-01 -2.22066134e-01 1.92903593e-01
-9.86719057e-02 -7.73724616e-01 5.61754823e-01 -3.16187888e-01
2.70994250e-02 1.04518151e+00 8.98026377e-02 -8.50183010e-01
-5.52321076e-01 -7.80899048e-01 -3.70433003e-01 -1.12826967e+00
-1.10473025e+00 6.78574324e-01 -1.42321229e-01 -2.95312643... | [6.136445045471191, 1.5562694072723389] |
6af0502a-469b-413f-8856-f730e4174e06 | optimal-learning-rates-for-kernel-conjugate | null | null | http://papers.nips.cc/paper/4077-optimal-learning-rates-for-kernel-conjugate-gradient-regression | http://papers.nips.cc/paper/4077-optimal-learning-rates-for-kernel-conjugate-gradient-regression.pdf | Optimal learning rates for Kernel Conjugate Gradient regression | We prove rates of convergence in the statistical sense for kernel-based least squares regression using a conjugate gradient algorithm, where regularization against overfitting is obtained by early stopping. This method is directly related to Kernel Partial Least Squares, a regression method that combines supervised dim... | ['Nicole Krämer', 'Gilles Blanchard'] | 2010-12-01 | null | null | null | neurips-2010-12 | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 1.92427143e-01 2.22978741e-01 -2.44219214e-01 -2.17762411e-01
-7.69400537e-01 -3.19028646e-01 4.21238661e-01 2.80643851e-01
-8.58597517e-01 9.03482974e-01 -2.55377203e-01 -1.36172920e-01
-3.25662106e-01 -3.40856105e-01 -7.06519008e-01 -1.01897633e+00
-2.58549359e-02 3.91475230e-01 1.74743086e-01 -2.17321813... | [7.622591495513916, 4.153568267822266] |
ab7e874f-bd51-4853-a904-d36d2300c233 | transsionadd-a-multi-frame-reinforcement | 2306.15212 | null | https://arxiv.org/abs/2306.15212v1 | https://arxiv.org/pdf/2306.15212v1.pdf | TranssionADD: A multi-frame reinforcement based sequence tagging model for audio deepfake detection | Thanks to recent advancements in end-to-end speech modeling technology, it has become increasingly feasible to imitate and clone a user`s voice. This leads to a significant challenge in differentiating between authentic and fabricated audio segments. To address the issue of user voice abuse and misuse, the second Audio... | ['Fengjie Zhu', 'Benlai Tang', 'Jiangli Hong', 'Quanxiu Wang', 'Caiyan Wan', 'Hui Huang', 'Zhiba Su', 'Jie Liu'] | 2023-06-27 | null | null | null | null | ['deepfake-detection', 'face-swapping'] | ['computer-vision', 'computer-vision'] | [ 1.04431927e-01 -1.57075047e-01 9.88768190e-02 -5.67585789e-02
-1.52343953e+00 -6.38144195e-01 2.21963391e-01 1.75785616e-01
-2.18336523e-01 2.79711097e-01 4.00464326e-01 -2.17910066e-01
1.66832343e-01 1.23266332e-01 -8.26166511e-01 -1.68736473e-01
-5.63305337e-03 -2.01285351e-02 3.19565862e-01 7.37601295... | [14.472946166992188, 5.9093918800354] |
b005ab84-4687-4039-9a60-e113c26944f5 | fmodetect-robust-detection-and-trajectory | 2012.08216 | null | https://arxiv.org/abs/2012.08216v2 | https://arxiv.org/pdf/2012.08216v2.pdf | FMODetect: Robust Detection of Fast Moving Objects | We propose the first learning-based approach for fast moving objects detection. Such objects are highly blurred and move over large distances within one video frame. Fast moving objects are associated with a deblurring and matting problem, also called deblatting. We show that the separation of deblatting into consecuti... | ['Martin R. Oswald', 'Marc Pollefeys', 'Filip Sroubek', 'Jiri Matas', 'Denys Rozumnyi'] | 2020-12-15 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Rozumnyi_FMODetect_Robust_Detection_of_Fast_Moving_Objects_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Rozumnyi_FMODetect_Robust_Detection_of_Fast_Moving_Objects_ICCV_2021_paper.pdf | iccv-2021-1 | ['moving-object-detection'] | ['computer-vision'] | [ 2.18784437e-01 -5.96435070e-01 1.02282517e-01 9.43651889e-03
-7.23856866e-01 -5.48148334e-01 5.41483939e-01 -2.50796080e-01
-4.27849472e-01 5.91700435e-01 -5.70926405e-02 5.49402051e-02
-1.03736751e-01 -2.82497853e-01 -1.06381929e+00 -8.44816744e-01
-3.10441554e-01 3.59600276e-01 6.44543767e-01 5.03065407... | [11.463141441345215, -2.54290509223938] |
6d23317c-db07-4477-bbae-b6bd56e4b22f | embeddings-evaluation-using-a-novel-measure | null | null | https://link.springer.com/article/10.1007/s12559-021-09987-7 | https://link.springer.com/epdf/10.1007/s12559-021-09987-7?sharing_token=k8n-ML0c5GR2MNCx1njYEPe4RwlQNchNByi7wbcMAY6OYcAV1IB_c4V9xMrd-RK6fL_MvX93E8KnZ0fIlJ9W-PFNTzF5q4CKJ1aaVhvbV71JZEvoWEbRsnSKq-q51QeowZBeBcJBoAcMLoXGlGG6GYKm5hc9TXk8vMyPSA4-pD0%3D | Embeddings Evaluation Using a Novel Measure of Semantic Similarity | Lexical taxonomies and distributional representations are largely used to support a wide range of NLP applications, including semantic similarity measurements. Recently, several scholars have proposed new approaches to combine those resources into unified representation preserving distributional and knowledge-based lex... | ['Navid Nobani', 'Mario Mezzanzanica', 'Fabio Mercorio', 'Lorenzo Malandri', 'Anna Giabelli'] | 2022-01-08 | null | null | null | cognitive-computation-2022-1 | ['embeddings-evaluation'] | ['natural-language-processing'] | [-8.29221010e-02 -3.77384812e-01 -3.60219628e-01 -3.37274849e-01
-5.65870106e-01 -6.67808115e-01 7.79254258e-01 7.56526113e-01
-8.77207100e-01 3.55371922e-01 5.37843227e-01 -2.54917424e-02
-5.75681150e-01 -8.84355724e-01 9.48605835e-02 -5.59343338e-01
1.61408305e-01 4.74385411e-01 2.60202616e-01 -1.62504926... | [10.371586799621582, 8.825360298156738] |
3ab35518-ec8b-4b9d-b743-dcf519b5ecdd | fine-tuning-bert-with-focus-words-for | null | null | https://aclanthology.org/2020.starsem-1.13 | https://aclanthology.org/2020.starsem-1.13.pdf | Fine-tuning BERT with Focus Words for Explanation Regeneration | Explanation generation introduced as the world tree corpus (Jansen et al., 2018) is an emerging NLP task involving multi-hop inference for explaining the correct answer in multiple-choice QA. It is a challenging task evidenced by low state-of-the-art performances(below 60{\%} in F-score) demonstrated on the task. Of th... | ['S{\\"o}ren Auer', "Jennifer D{'}Souza", "Isaiah Onando Mulang{'}"] | 2020-12-01 | null | null | null | joint-conference-on-lexical-and-computational | ['multiple-choice-qa'] | ['natural-language-processing'] | [ 3.75168949e-01 8.49425316e-01 -7.17006698e-02 -3.82250428e-01
-1.75647438e+00 -5.62548339e-01 9.62187767e-01 2.38683477e-01
-2.50478923e-01 1.01236892e+00 5.80920279e-01 -7.06401587e-01
-2.52913952e-01 -2.38231868e-01 -7.27308512e-01 -2.36741871e-01
1.35622442e-01 8.81505430e-01 2.38300219e-01 -7.00784683... | [11.008758544921875, 8.331466674804688] |
164febca-7b5d-4622-86aa-637073d8c667 | retrieving-supporting-evidence-for-llms | 2306.13781 | null | https://arxiv.org/abs/2306.13781v1 | https://arxiv.org/pdf/2306.13781v1.pdf | Retrieving Supporting Evidence for LLMs Generated Answers | Current large language models (LLMs) can exhibit near-human levels of performance on many natural language tasks, including open-domain question answering. Unfortunately, they also convincingly hallucinate incorrect answers, so that responses to questions must be verified against external sources before they can be acc... | ['Charles L. A. Clarke', 'Negar Arabzadeh', 'Siqing Huo'] | 2023-06-23 | null | null | null | null | ['retrieval', 'question-answering', 'open-domain-question-answering'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 1.26567528e-01 4.59716678e-01 2.02221215e-01 -8.19481835e-02
-1.80055785e+00 -9.66174364e-01 7.15690792e-01 6.50254786e-01
-5.07992089e-01 7.71160424e-01 3.09903711e-01 -6.93395257e-01
-1.27777800e-01 -5.25717378e-01 -3.84905607e-01 3.35494839e-02
5.41140735e-01 6.46403551e-01 7.76023686e-01 -4.25129175... | [11.433693885803223, 8.069167137145996] |
20529bfb-e492-402b-8da2-806adb6664cf | learning-universal-shape-dictionary-for | 2012.01050 | null | https://arxiv.org/abs/2012.01050v1 | https://arxiv.org/pdf/2012.01050v1.pdf | Learning Universal Shape Dictionary for Realtime Instance Segmentation | We present a novel explicit shape representation for instance segmentation. Based on how to model the object shape, current instance segmentation systems can be divided into two categories, implicit and explicit models. The implicit methods, which represent the object mask/contour by intractable network parameters, and... | ['Cewu Lu', 'Lixin Yang', 'Ruolin Ye', 'Wenqiang Xu', 'Tutian Tang'] | 2020-12-02 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 1.55096158e-01 1.82783097e-01 -2.80654393e-02 -2.55127192e-01
-5.81480265e-01 -5.90173185e-01 2.69447148e-01 -7.01283738e-02
-4.19867426e-01 3.34247023e-01 -5.71376026e-01 -9.60308835e-02
1.15807898e-01 -8.35034430e-01 -7.96054959e-01 -7.77532995e-01
2.30885729e-01 6.37309253e-01 4.86524642e-01 4.43040580... | [9.452642440795898, -0.058503374457359314] |
cb1a10ca-d476-42c7-b7cb-d11dc8ea8c6a | multi-aspect-explainable-inductive-relation | 2301.01664 | null | https://arxiv.org/abs/2301.01664v2 | https://arxiv.org/pdf/2301.01664v2.pdf | Multi-Aspect Explainable Inductive Relation Prediction by Sentence Transformer | Recent studies on knowledge graphs (KGs) show that path-based methods empowered by pre-trained language models perform well in the provision of inductive and explainable relation predictions. In this paper, we introduce the concepts of relation path coverage and relation path confidence to filter out unreliable paths p... | ['Lizhen Cui', 'Chunyan Miao', 'Di Wang', 'Zhixiang Su'] | 2023-01-04 | null | null | null | null | ['inductive-relation-prediction'] | ['graphs'] | [-1.53564485e-02 9.44804311e-01 -8.24501157e-01 -3.43536794e-01
-5.57275414e-01 -1.59716025e-01 4.89216566e-01 2.97186464e-01
4.21651334e-01 1.22221828e+00 2.89294243e-01 -6.68328941e-01
-5.73110759e-01 -1.32121944e+00 -8.69124293e-01 -1.04023822e-01
-3.20285201e-01 7.76191413e-01 5.40692031e-01 -2.06594720... | [9.013265609741211, 8.067075729370117] |
40becb42-0b8d-4cf7-8af5-5910f46cdc95 | kernel-e-greedy-for-contextual-bandits | 2306.17329 | null | https://arxiv.org/abs/2306.17329v1 | https://arxiv.org/pdf/2306.17329v1.pdf | Kernel $ε$-Greedy for Contextual Bandits | We consider a kernelized version of the $\epsilon$-greedy strategy for contextual bandits. More precisely, in a setting with finitely many arms, we consider that the mean reward functions lie in a reproducing kernel Hilbert space (RKHS). We propose an online weighted kernel ridge regression estimator for the reward fun... | ['Bharath K. Sriperumbudur', 'Sakshi Arya'] | 2023-06-29 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [-2.49576584e-01 3.26635629e-01 -4.72331583e-01 -2.23022103e-01
-1.15631402e+00 -5.88860989e-01 -1.35910749e-01 5.68708368e-02
-7.64584720e-01 8.91614497e-01 -1.55591905e-01 -6.30763531e-01
-6.36590421e-01 -6.50090754e-01 -9.56839025e-01 -7.93648541e-01
-6.07322752e-01 1.39833108e-01 -3.68967652e-01 1.84024975... | [4.7273783683776855, 3.4760966300964355] |
06f28e73-79cb-48cc-9aea-05a97a63f9a1 | asymmetric-feature-maps-with-application-to | 1704.03946 | null | http://arxiv.org/abs/1704.03946v1 | http://arxiv.org/pdf/1704.03946v1.pdf | Asymmetric Feature Maps with Application to Sketch Based Retrieval | We propose a novel concept of asymmetric feature maps (AFM), which allows to
evaluate multiple kernels between a query and database entries without
increasing the memory requirements. To demonstrate the advantages of the AFM
method, we derive a short vector image representation that, due to asymmetric
feature maps, sup... | ['Ondřej Chum', 'Giorgos Tolias'] | 2017-04-12 | asymmetric-feature-maps-with-application-to-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Tolias_Asymmetric_Feature_Maps_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Tolias_Asymmetric_Feature_Maps_CVPR_2017_paper.pdf | cvpr-2017-7 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 1.10088885e-01 -7.23142922e-01 -2.34050214e-01 1.36822537e-01
-8.80592346e-01 -9.87194300e-01 8.91857028e-01 2.83477366e-01
-4.60078120e-01 1.79784387e-01 -1.61506698e-01 -2.55940884e-01
-4.82604265e-01 -6.57912254e-01 -6.89070046e-01 -5.11745274e-01
3.22461650e-02 5.82420468e-01 5.19294441e-01 -3.21251154... | [10.684209823608398, 0.3617570698261261] |
2b8f3b84-6a17-4b26-815b-ca14ed3d24f4 | bci-breast-cancer-immunohistochemical-image | 2204.11425 | null | https://arxiv.org/abs/2204.11425v2 | https://arxiv.org/pdf/2204.11425v2.pdf | BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix | The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer. The routine evaluation of HER2 is conducted with immunohistochemical techniques (IHC), which is very expensive. Therefore, for the first time, we propose a breast cancer immunohi... | ['Mulan Jin', 'Zhongyue Shi', 'Xinyu Jia', 'Feng Xu', 'Chuang Zhu', 'ShengJie Liu'] | 2022-04-25 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection', 'breast-cancer-histology-image-classification', 'medical-image-generation', 'classification-of-breast-cancer-histology'] | ['knowledge-base', 'medical', 'medical', 'medical', 'medical'] | [ 2.83171326e-01 5.40460497e-02 -5.45526028e-01 -3.64973426e-01
-1.11782157e+00 -3.89454782e-01 1.24892503e-01 2.42681310e-01
-2.48630315e-01 7.59093523e-01 -1.14259213e-01 -4.74660695e-01
3.78039777e-01 -9.41043794e-01 -5.77912688e-01 -9.67934489e-01
3.57958853e-01 5.30976355e-01 -8.10298324e-03 -2.79900819... | [15.040985107421875, -3.0660312175750732] |
89ac9376-3696-40fd-9632-32ee825d5eac | convolutional-proteinunetlm-competitive-with | null | null | https://doi.org/10.1002/prot.26452 | https://onlinelibrary.wiley.com/doi/epdf/10.1002/prot.26452 | Convolutional ProteinUnetLM competitive with long short-term memory-based protein secondary structure predictors | The protein secondary structure (SS) prediction plays an important role in the characterization of general protein structure and function. In recent years, a new generation of algorithms for SS prediction based on embeddings from protein language models (pLMs) is emerging. These algorithms reach state-of-the-art accura... | ['Katarzyna Stapor', 'Irena Roterman', 'Piotr Fabian', 'Krzysztof Kotowski'] | 2022-11-30 | null | null | null | proteins-2022-11 | ['unet-segmentation', 'protein-secondary-structure-prediction', 'multiple-sequence-alignment'] | ['computer-vision', 'medical', 'medical'] | [ 3.08761269e-01 -3.76349911e-02 -3.09400141e-01 -3.39367390e-01
-7.43862092e-01 -3.00565690e-01 1.52071163e-01 7.55251884e-01
-5.24419785e-01 1.11446500e+00 -1.40370384e-01 -8.11840296e-01
1.21037915e-01 -2.88474828e-01 -1.30718887e+00 -1.00816512e+00
-2.09190205e-01 6.18259251e-01 9.12846848e-02 -2.96860278... | [4.706903457641602, 5.617234230041504] |
cdfd860d-1585-462c-8bae-39d9df652bf9 | semantic-data-set-construction-from-human | null | null | https://aclanthology.org/2021.cl-1.4 | https://aclanthology.org/2021.cl-1.4.pdf | Semantic Data Set Construction from Human Clustering and Spatial Arrangement | Abstract Research into representation learning models of lexical semantics usually utilizes some form of intrinsic evaluation to ensure that the learned representations reflect human semantic judgments. Lexical semantic similarity estimation is a widely used evaluation method, but efforts have typically focused on pair... | ['Anna Korhonen', 'Ivan Vulić', 'Nikolaus Kriegeskorte', 'Jasper J. F. van den Bosch', 'Diana McCarthy', 'Olga Majewska'] | null | null | null | null | cl-acl-2021-3 | ['word-similarity'] | ['natural-language-processing'] | [ 3.06545883e-01 -1.58353671e-01 -6.53799772e-02 -4.74748433e-01
-6.95951521e-01 -7.81570792e-01 9.32746291e-01 7.05094278e-01
-9.00075436e-01 3.44763041e-01 5.61265230e-01 -2.09007174e-01
-3.64045948e-01 -8.87343764e-01 -2.36752003e-01 -6.40227318e-01
2.33076215e-01 5.25133014e-01 3.79954189e-01 -4.59688425... | [10.388326644897461, 8.988271713256836] |
e065b908-40ac-4745-84e0-3334730e44c1 | cdec-net-composite-deformable-cascade-network | 2008.10831 | null | https://arxiv.org/abs/2008.10831v1 | https://arxiv.org/pdf/2008.10831v1.pdf | CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Images | Localizing page elements/objects such as tables, figures, equations, etc. is the primary step in extracting information from document images. We propose a novel end-to-end trainable deep network, (CDeC-Net) for detecting tables present in the documents. The proposed network consists of a multistage extension of Mask R-... | ['Madhav Agarwal', 'C. V. Jawahar', 'Ajoy Mondal'] | 2020-08-25 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [-2.80950189e-01 -4.07384522e-02 2.29190998e-02 -2.17565522e-01
-1.29203749e+00 -9.65505838e-01 6.49474561e-01 1.58472672e-01
-2.56240904e-01 6.75940633e-01 4.09327865e-01 -2.74652511e-01
-3.49368304e-02 -7.36378133e-01 -1.09451544e+00 -2.76745230e-01
-1.48398414e-01 5.23092091e-01 4.20593768e-01 -1.56033248... | [11.683302879333496, 2.994267702102661] |
fa8ff8ed-cced-4b02-87f2-60dea67f957b | from-universal-humanoid-control-to-automatic | 2206.09286 | null | https://arxiv.org/abs/2206.09286v1 | https://arxiv.org/pdf/2206.09286v1.pdf | From Universal Humanoid Control to Automatic Physically Valid Character Creation | Automatically designing virtual humans and humanoids holds great potential in aiding the character creation process in games, movies, and robots. In some cases, a character creator may wish to design a humanoid body customized for certain motions such as karate kicks and parkour jumps. In this work, we propose a humano... | ['Kris M. Kitani', 'Ye Yuan', 'Zhengyi Luo'] | 2022-06-18 | null | null | null | null | ['humanoid-control'] | ['robots'] | [-9.03928503e-02 1.91053599e-01 -4.16438729e-02 1.14954002e-01
1.87026665e-01 -6.43278182e-01 2.97033429e-01 -3.47395241e-01
-1.86835706e-01 4.01911378e-01 1.80286705e-01 9.80797783e-02
-1.10925227e-01 -8.72928262e-01 -6.50931299e-01 -2.22285718e-01
7.41321892e-02 8.46041977e-01 4.02714700e-01 -7.35232830... | [5.146846294403076, 0.6731878519058228] |
81f891d0-f027-4d62-9b45-7eac6dc62e6d | high-dimensional-bayesian-optimisation-with | 2106.03609 | null | https://arxiv.org/abs/2106.03609v3 | https://arxiv.org/pdf/2106.03609v3.pdf | High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning | We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces. By adapting ideas from deep metric learning, we use label guidance from the blackbox function to structure the VAE latent space, facilitating ... | ['Haitham Bou-Ammar', 'Jan Peters', 'Jun Wang', 'Zhitang Chen', 'Wenlong Lyu', 'Lin Zhu', 'Lin Yang', 'Alexander I. Cowen-Rivers', 'Ryan-Rhys Griffiths', 'Alexandre Max Maraval', 'Rasul Tutunov', 'Antoine Grosnit'] | 2021-06-07 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 2.80485660e-01 3.10769916e-01 6.56459108e-02 -2.97957808e-01
-9.99845982e-01 -6.89203978e-01 9.20929551e-01 7.58746490e-02
-7.46717632e-01 1.05014479e+00 1.56147584e-01 -3.42137367e-01
-4.08025533e-01 -5.57047367e-01 -8.47825527e-01 -9.60386574e-01
2.97119431e-02 7.61141300e-01 -2.13682979e-01 6.28563836... | [6.75409460067749, 4.031822681427002] |
5590f7a0-519d-4ce7-8d4a-75ca994d99b2 | ovenet-offset-vector-network-for-semantic | 2303.14516 | null | https://arxiv.org/abs/2303.14516v1 | https://arxiv.org/pdf/2303.14516v1.pdf | OVeNet: Offset Vector Network for Semantic Segmentation | Semantic segmentation is a fundamental task in visual scene understanding. We focus on the supervised setting, where ground-truth semantic annotations are available. Based on knowledge about the high regularity of real-world scenes, we propose a method for improving class predictions by learning to selectively exploit ... | ['Petros Maragos', 'Christos Sakaridis', 'Stamatis Alexandropoulos'] | 2023-03-25 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 5.14363170e-01 4.69222397e-01 -3.50972295e-01 -7.72054732e-01
-6.10366404e-01 -3.32030743e-01 4.88224119e-01 2.73069352e-01
-6.10958755e-01 4.75112915e-01 6.43885054e-04 -5.79598993e-02
2.60365635e-01 -8.28319013e-01 -1.01659369e+00 -7.01899946e-01
3.16338032e-01 4.07696784e-01 6.45450592e-01 -1.33957639... | [9.54838752746582, 0.43084728717803955] |
29a67c63-0083-4660-ae75-60b0d4280246 | encoder-decoder-approach-to-automated-essay | null | null | https://aclanthology.org/2021.icon-main.48 | https://aclanthology.org/2021.icon-main.48.pdf | Encoder Decoder Approach to Automated Essay Scoring For Deeper Semantic Analysis | Descriptive or essay type of answers have always played a major role in education. They clearly capture the student’s grasp on knowledge and presentation skills. Manual essay scoring can be a daunting process to human evaluators; assessing descriptive answers can present a huge overhead owing to limited numbers of eval... | ['Srujana Inturi', 'Sreedeep Rayavarapu', 'Priyatam Naravajhula'] | null | null | null | null | icon-2021-12 | ['automated-essay-scoring'] | ['natural-language-processing'] | [ 4.05702740e-02 3.05071026e-01 1.68703288e-01 -4.79043990e-01
-9.23029304e-01 -7.04259098e-01 4.54096317e-01 6.95807755e-01
-6.09516621e-01 6.62797868e-01 2.71765351e-01 -6.22342646e-01
-4.72855657e-01 -8.76001716e-01 -1.96400076e-01 -1.62528828e-01
6.21237040e-01 5.41126728e-01 2.92800218e-01 -4.20699030... | [11.290714263916016, 9.286169052124023] |
ce1175ed-6ac3-4811-abfa-9d2afe10ccd1 | findings-of-the-wmt-2020-shared-task-on-1 | null | null | https://aclanthology.org/2020.wmt-1.75 | https://aclanthology.org/2020.wmt-1.75.pdf | Findings of the WMT 2020 Shared Task on Automatic Post-Editing | We present the results of the 6th round of the WMT task on MT Automatic Post-Editing. The task consists in automatically correcting the output of a “black-box” machine translation system by learning from existing human corrections of different sentences. This year, the challenge consisted of fixing the errors present i... | ['Marco Turchi', 'Matteo Negri', 'Markus Freitag', 'Rajen Chatterjee'] | null | null | null | null | wmt-emnlp-2020-11 | ['automatic-post-editing', 'automatic-post-editing'] | ['computer-vision', 'natural-language-processing'] | [ 2.33373940e-01 2.55538434e-01 3.88661996e-02 -1.63737565e-01
-1.33182847e+00 -6.05217755e-01 8.57468247e-01 1.81533530e-01
-9.40899909e-01 1.34995854e+00 5.31314075e-01 -4.32681799e-01
3.41683090e-01 -2.25828275e-01 -1.06233215e+00 -1.04731910e-01
4.98682916e-01 7.17306256e-01 -5.26116155e-02 -6.85068846... | [11.628674507141113, 10.312995910644531] |
ae4e9415-7f57-4468-ace5-e1b9544f9f8e | deeparchitect-automatically-designing-and | 1704.08792 | null | http://arxiv.org/abs/1704.08792v1 | http://arxiv.org/pdf/1704.08792v1.pdf | DeepArchitect: Automatically Designing and Training Deep Architectures | In deep learning, performance is strongly affected by the choice of
architecture and hyperparameters. While there has been extensive work on
automatic hyperparameter optimization for simple spaces, complex spaces such as
the space of deep architectures remain largely unexplored. As a result, the
choice of architecture ... | ['Renato Negrinho', 'Geoff Gordon'] | 2017-04-28 | deeparchitect-automatically-designing-and-1 | https://openreview.net/forum?id=rkTBjG-AZ | https://openreview.net/pdf?id=rkTBjG-AZ | iclr-2018-1 | ['model-discovery'] | ['miscellaneous'] | [-2.89212346e-01 -5.43207936e-02 -2.95352876e-01 -4.70102608e-01
-7.27036476e-01 -7.36193895e-01 5.52492797e-01 1.05387624e-02
-6.41347826e-01 5.94379067e-01 -1.12390667e-01 -7.20943868e-01
-2.54304588e-01 -6.22799575e-01 -5.05514681e-01 -6.52355671e-01
-2.77821580e-03 9.75016952e-01 2.49331996e-01 -7.20822588... | [8.462992668151855, 3.604593276977539] |
ceb44ad3-3154-4a4a-85c7-04dc5ceb609f | on-the-use-of-modality-specific-large-scale | 2210.15937 | null | https://arxiv.org/abs/2210.15937v1 | https://arxiv.org/pdf/2210.15937v1.pdf | On the Use of Modality-Specific Large-Scale Pre-Trained Encoders for Multimodal Sentiment Analysis | This paper investigates the effectiveness and implementation of modality-specific large-scale pre-trained encoders for multimodal sentiment analysis~(MSA). Although the effectiveness of pre-trained encoders in various fields has been reported, conventional MSA methods employ them for only linguistic modality, and their... | ['Hiroshi Sato', 'Takanori Ashihara', 'Takafumi Moriya', 'Keita Suzuki', 'Naoki Makishima', 'Satoshi Suzuki', 'Akihiko Takashima', 'Ryo Masumura', 'Atsushi Ando'] | 2022-10-28 | null | null | null | null | ['multimodal-sentiment-analysis', 'multimodal-sentiment-analysis'] | ['computer-vision', 'natural-language-processing'] | [ 1.98851675e-01 3.47025208e-02 -7.48304725e-02 -7.58717239e-01
-1.13932455e+00 -6.17443740e-01 5.64008296e-01 1.23493504e-02
-6.73028409e-01 6.28790736e-01 3.74927044e-01 -2.83739120e-01
4.47359651e-01 -3.82026911e-01 -9.16855812e-01 -6.17181718e-01
1.28671691e-01 2.69066185e-01 -1.61493167e-01 -3.76786321... | [13.210585594177246, 5.09957218170166] |
07eccf41-3150-496b-aba7-4dfa97b74a77 | automatic-segmentation-of-the-placenta-in | 2208.02895 | null | https://arxiv.org/abs/2208.02895v1 | https://arxiv.org/pdf/2208.02895v1.pdf | Automatic Segmentation of the Placenta in BOLD MRI Time Series | Blood oxygen level dependent (BOLD) MRI with maternal hyperoxia can assess oxygen transport within the placenta and has emerged as a promising tool to study placental function. Measuring signal changes over time requires segmenting the placenta in each volume of the time series. Due to the large number of volumes in th... | ['Polina Golland', 'Esra Abaci Turk', 'P. Ellen Grant', 'Clinton J. Wang', 'Eileen Pan', 'Katherine Hobgood', 'Sean I. Young', 'S. Mazdak Abulnaga'] | 2022-08-04 | null | null | null | null | ['placenta-segmentation'] | ['medical'] | [ 2.19051227e-01 2.63791859e-01 1.42283991e-01 -7.85101593e-01
-3.57856125e-01 -4.95259285e-01 9.85054020e-03 3.00091833e-01
-2.95417607e-01 4.29100901e-01 -1.11985557e-01 -5.92847914e-02
-2.19494067e-02 -6.95476651e-01 -6.77284658e-01 -6.95864379e-01
-6.90943003e-01 4.86906171e-01 1.92299083e-01 3.15196723... | [14.054388999938965, -2.393808126449585] |
b179926d-f0d4-4b40-922a-b2bcb216a9b7 | semi-supervised-and-long-tailed-object | 2305.14813 | null | https://arxiv.org/abs/2305.14813v1 | https://arxiv.org/pdf/2305.14813v1.pdf | Semi-Supervised and Long-Tailed Object Detection with CascadeMatch | This paper focuses on long-tailed object detection in the semi-supervised learning setting, which poses realistic challenges, but has rarely been studied in the literature. We propose a novel pseudo-labeling-based detector called CascadeMatch. Our detector features a cascade network architecture, which has multi-stage ... | ['Chen Change Loy', 'Chen Huang', 'Kaiyang Zhou', 'Yuhang Zang'] | 2023-05-24 | null | null | null | null | ['pseudo-label'] | ['miscellaneous'] | [ 2.04430401e-01 3.89403522e-01 -3.46794695e-01 -3.77491742e-01
-9.32541788e-01 -5.32136142e-01 3.73121738e-01 -1.21523459e-02
-6.74261570e-01 4.76616085e-01 -3.28009695e-01 -2.33852714e-01
3.02663088e-01 -3.73633772e-01 -1.00218606e+00 -6.28410876e-01
1.48280933e-01 6.67363524e-01 7.53648818e-01 1.78091168... | [9.220699310302734, 1.3178293704986572] |
b1f2ff78-5902-43f1-aec7-5d01ccaa4bd7 | modeling-personalized-item-frequency | 2006.00556 | null | https://arxiv.org/abs/2006.00556v1 | https://arxiv.org/pdf/2006.00556v1.pdf | Modeling Personalized Item Frequency Information for Next-basket Recommendation | Next-basket recommendation (NBR) is prevalent in e-commerce and retail industry. In this scenario, a user purchases a set of items (a basket) at a time. NBR performs sequential modeling and recommendation based on a sequence of baskets. NBR is in general more complex than the widely studied sequential (session-based) r... | ['Zhi-Li Zhang', 'Jinyang Gao', 'Haoji Hu', 'Xiangnan He'] | 2020-05-31 | null | null | null | null | ['next-basket-recommendation'] | ['miscellaneous'] | [ 5.96677698e-02 -7.08421528e-01 -7.88273454e-01 -3.48263562e-01
-3.45543176e-01 -5.29865026e-01 1.71959385e-01 -1.46949530e-01
-2.75203705e-01 3.71846229e-01 7.15737104e-01 -5.09605944e-01
-4.53777909e-01 -1.02078450e+00 -8.47939014e-01 -4.42181468e-01
-2.06992373e-01 2.65944362e-01 -3.19625199e-01 -6.38075709... | [10.118081092834473, 5.632514476776123] |
ade098fb-8571-4034-9c5e-e45dc399fdd1 | classification-of-ecg-based-on-hybrid | 2206.07648 | null | https://arxiv.org/abs/2206.07648v1 | https://arxiv.org/pdf/2206.07648v1.pdf | Classification of ECG based on Hybrid Features using CNNs for Wearable Applications | Sudden cardiac death and arrhythmia account for a large percentage of all deaths worldwide. Electrocardiography (ECG) is the most widely used screening tool for cardiovascular diseases. Traditionally, ECG signals are classified manually, requiring experience and great skill, while being time-consuming and prone to erro... | ['Deepu John', 'Barry Cardiff', 'Rajesh C. Panicker', 'Fang Xiang', 'Li Xiaolin'] | 2022-06-14 | null | null | null | null | ['arrhythmia-detection', 'ecg-classification', 'electrocardiography-ecg'] | ['medical', 'medical', 'methodology'] | [ 1.05258152e-01 -3.50406855e-01 9.53474566e-02 -1.90683812e-01
-7.62541533e-01 -2.33312994e-01 -1.18582338e-01 5.00918210e-01
-5.30230165e-01 8.01544070e-01 -2.37900957e-01 -1.52883962e-01
-1.81130588e-01 -6.66749775e-01 1.20693957e-02 -6.28992558e-01
-5.07687926e-01 -8.99512768e-02 -6.96523562e-02 -5.32678291... | [14.248613357543945, 3.26104998588562] |
2ed50e99-0645-42d7-905c-5fe3b191f4ef | scalable-multiagent-driving-policies-for | 2103.00058 | null | https://arxiv.org/abs/2103.00058v2 | https://arxiv.org/pdf/2103.00058v2.pdf | Scalable Multiagent Driving Policies For Reducing Traffic Congestion | Traffic congestion is a major challenge in modern urban settings. The industry-wide development of autonomous and automated vehicles (AVs) motivates the question of how can AVs contribute to congestion reduction. Past research has shown that in small scale mixed traffic scenarios with both AVs and human-driven vehicles... | ['Daniel Urieli', 'Peter Stone', 'Harel Yedidsion', 'William Macke', 'Jiaxun Cui'] | 2021-02-26 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [-2.72436917e-01 4.22924340e-01 -3.86015475e-01 -1.52030904e-02
-2.91042089e-01 -5.74595213e-01 5.70846379e-01 -1.62739724e-01
-5.60980201e-01 1.22542143e+00 -2.94195592e-01 -8.29995453e-01
-1.98545560e-01 -1.31091130e+00 -7.24303246e-01 -3.58507454e-01
-4.26848590e-01 6.70703173e-01 9.21972394e-01 -9.26286817... | [5.270834445953369, 1.4127401113510132] |
a01bca1f-21ed-4e6a-84c6-e636d4a8a425 | a-multi-task-deep-learning-approach-for | 2303.11100 | null | https://arxiv.org/abs/2303.11100v1 | https://arxiv.org/pdf/2303.11100v1.pdf | A Multi-Task Deep Learning Approach for Sensor-based Human Activity Recognition and Segmentation | Sensor-based human activity segmentation and recognition are two important and challenging problems in many real-world applications and they have drawn increasing attention from the deep learning community in recent years. Most of the existing deep learning works were designed based on pre-segmented sensor streams and ... | ['Yaping Wan', 'Huansheng Ning', 'Liming Chen', 'Jinqiang Wang', 'Tao Zhu', 'Furong Duan'] | 2023-03-20 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 6.63083375e-01 -4.78060573e-01 -3.73599291e-01 -3.23711574e-01
-5.22990406e-01 -1.51246414e-01 4.13722217e-01 2.02210382e-01
-6.49577200e-01 6.63546503e-01 2.74076223e-01 1.22146174e-01
1.16820103e-02 -5.71588695e-01 -4.95717436e-01 -9.50152218e-01
-1.90448910e-01 -4.41078618e-02 5.48542261e-01 4.23702359... | [7.910459041595459, 0.7207064032554626] |
35fa5bbe-4ddb-4eb3-ad54-5126acde84bd | texture-relative-superpixel-generation-with | null | null | https://ieeexplore.ieee.org/document/8626535 | https://ieeexplore.ieee.org/document/8626535 | Texture Relative Superpixel Generation With Adaptive Parameters | Abstract—Superpixel generation, which is an essential step
in many image processing applications, has attracted increasing
attention from researchers. In this paper, we present an efficient
flooding-based superpixel generation algorithm that generates
compact and highly boundary-adherent superpixels. In particular,... | ['Caiming Zhang', 'Zhonggui Chen', 'Yuanfeng Zhou', 'Xiao Pan'] | 2019-01-25 | null | null | null | ieee-2019-1 | ['superpixels'] | ['computer-vision'] | [ 3.12273204e-01 -1.29885033e-01 -1.11653887e-01 -2.93823749e-01
-2.85161972e-01 -2.76948839e-01 3.02017421e-01 2.67724812e-01
-4.96050239e-01 8.34582746e-01 4.61402461e-02 1.69250891e-01
-1.62565801e-02 -9.74220812e-01 -5.49248755e-01 -1.03475189e+00
5.76991960e-02 -9.69244316e-02 7.81209409e-01 1.12153560... | [9.636926651000977, -0.5314019322395325] |
d44966d5-0a1f-4bbe-966e-9adb79e2540c | semantic-composition-in-visually-grounded | 2305.16328 | null | https://arxiv.org/abs/2305.16328v1 | https://arxiv.org/pdf/2305.16328v1.pdf | Semantic Composition in Visually Grounded Language Models | What is sentence meaning and its ideal representation? Much of the expressive power of human language derives from semantic composition, the mind's ability to represent meaning hierarchically & relationally over constituents. At the same time, much sentential meaning is outside the text and requires grounding in sensor... | ['Rohan Pandey'] | 2023-05-15 | null | null | null | null | ['image-captioning', 'sentence-embedding', 'sentence-embeddings', 'philosophy', 'sentence-embedding', 'sentence-embeddings', 'semantic-composition'] | ['computer-vision', 'methodology', 'methodology', 'miscellaneous', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 3.44973832e-01 4.35480028e-01 -4.66079712e-02 -4.98130918e-01
-2.13872820e-01 -5.00872552e-01 9.22209740e-01 2.07765862e-01
-2.80400008e-01 2.35089317e-01 1.25723863e+00 -4.28188413e-01
-6.52032578e-03 -6.30145609e-01 -6.06174707e-01 -4.59445238e-01
1.65051416e-01 1.18910894e-01 -1.48627415e-01 -3.80231291... | [10.728156089782715, 1.9423229694366455] |
9f790380-14c4-4fdb-8f54-d58c2975af15 | mask-selection-and-propagation-for | null | null | https://openaccess.thecvf.com/content/WACV2021/html/Garg_Mask_Selection_and_Propagation_for_Unsupervised_Video_Object_Segmentation_WACV_2021_paper.html | https://openaccess.thecvf.com/content/WACV2021/papers/Garg_Mask_Selection_and_Propagation_for_Unsupervised_Video_Object_Segmentation_WACV_2021_paper.pdf | Mask Selection and Propagation for Unsupervised Video Object Segmentation | In this work we present a novel approach for Unsupervised Video Object Segmentation, that is automatically generating instance level segmentation masks for salient objects and tracking them in a video. We efficiently handle problems present in existing methods such as drift while temporal propagation, tracking and addi... | ['Vidit Goel', 'Shubhika Garg'] | 2021-01-05 | null | null | null | null | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 5.00840247e-01 6.89188614e-02 6.81673288e-02 -4.17192280e-01
-5.48914135e-01 -6.02960467e-01 4.72145379e-01 8.75062570e-02
-8.82915020e-01 6.69883549e-01 -6.92492276e-02 2.50576109e-01
-1.19674392e-01 -4.11139995e-01 -8.18460524e-01 -5.13040841e-01
-2.00351268e-01 5.44868290e-01 1.02653837e+00 -7.84562156... | [9.070505142211914, -0.2355431318283081] |
371cdef6-3a82-4ed2-9fe6-88c82abd475f | a-multilingual-modeling-method-for-span | 2105.14880 | null | https://arxiv.org/abs/2105.14880v1 | https://arxiv.org/pdf/2105.14880v1.pdf | A Multilingual Modeling Method for Span-Extraction Reading Comprehension | Span-extraction reading comprehension models have made tremendous advances enabled by the availability of large-scale, high-quality training datasets. Despite such rapid progress and widespread application, extractive reading comprehension datasets in languages other than English remain scarce, and creating such a suff... | ['Bangchang Liu', 'Dejie Chang', 'Bin Xu', 'Gaochen Wu'] | 2021-05-31 | null | null | null | null | ['multilingual-nlp'] | ['natural-language-processing'] | [ 3.65307927e-01 6.84571266e-02 -1.39431298e-01 -3.63684505e-01
-1.21123254e+00 -6.65987849e-01 3.43393326e-01 9.33919623e-02
-6.97008550e-01 7.05520451e-01 5.38161457e-01 -8.32050085e-01
1.30925089e-01 -8.66870821e-01 -1.12557936e+00 -1.49927676e-01
6.06304169e-01 5.67837656e-01 -2.74328232e-01 -5.53246796... | [11.007255554199219, 9.190930366516113] |
1c9b8bac-8b56-43c6-b99a-1d1aa15581e3 | punctuation-restoration-using-transformer | null | null | https://aclanthology.org/2020.wnut-1.18 | https://aclanthology.org/2020.wnut-1.18.pdf | Punctuation Restoration using Transformer Models for High-and Low-Resource Languages | Punctuation restoration is a common post-processing problem for Automatic Speech Recognition (ASR) systems. It is important to improve the readability of the transcribed text for the human reader and facilitate NLP tasks. Current state-of-art address this problem using different deep learning models. Recently, transfor... | ['Firoj Alam', 'Akib Khan', 'Tanvirul Alam'] | null | null | null | null | emnlp-wnut-2020-11 | ['punctuation-restoration'] | ['natural-language-processing'] | [ 1.61385298e-01 8.50641280e-02 6.37188042e-03 -3.54956120e-01
-1.16911507e+00 -4.53582346e-01 6.12714291e-01 6.41011745e-02
-6.72157645e-01 7.22373724e-01 6.49423003e-01 -7.11613774e-01
3.73365849e-01 -3.44185859e-01 -5.08958220e-01 -4.74999845e-01
3.04350555e-01 4.59771603e-01 7.08416989e-03 -3.01325023... | [14.284995079040527, 7.126537799835205] |
ccbe1b10-5ee3-4348-bfd6-78f4d53bc565 | leveraging-triplet-loss-for-unsupervised | 2304.06403 | null | https://arxiv.org/abs/2304.06403v1 | https://arxiv.org/pdf/2304.06403v1.pdf | Leveraging triplet loss for unsupervised action segmentation | In this paper, we propose a novel fully unsupervised framework that learns action representations suitable for the action segmentation task from the single input video itself, without requiring any training data. Our method is a deep metric learning approach rooted in a shallow network with a triplet loss operating on ... | ['M. Dimiccoli', 'B. Tura', 'E. Bueno-Benito'] | 2023-04-13 | null | null | null | null | ['metric-learning', 'video-understanding', 'action-segmentation', 'metric-learning'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology'] | [ 8.46807837e-01 -2.79790815e-02 -4.94564265e-01 -5.15624106e-01
-8.86236668e-01 -2.83048838e-01 6.11349761e-01 -2.96503287e-02
-6.88319206e-01 5.46989083e-01 1.56494528e-01 3.27358544e-01
-5.36283195e-01 -3.92800182e-01 -5.15096486e-01 -7.90577352e-01
4.62302975e-02 6.41259134e-01 5.47006488e-01 2.68511802... | [8.528399467468262, 0.7484104037284851] |
2d59d51d-c885-4ab3-9b8e-e1c0d0eacd40 | shadowdiffusion-when-degradation-prior-meets | 2212.04711 | null | https://arxiv.org/abs/2212.04711v2 | https://arxiv.org/pdf/2212.04711v2.pdf | ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal | Recent deep learning methods have achieved promising results in image shadow removal. However, their restored images still suffer from unsatisfactory boundary artifacts, due to the lack of degradation prior embedding and the deficiency in modeling capacity. Our work addresses these issues by proposing a unified diffusi... | ['Bihan Wen', 'Hanspeter Pfister', 'YuFei Wang', 'Siyu Huang', 'Wenhan Yang', 'Chong Wang', 'Lanqing Guo'] | 2022-12-09 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Guo_ShadowDiffusion_When_Degradation_Prior_Meets_Diffusion_Model_for_Shadow_Removal_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Guo_ShadowDiffusion_When_Degradation_Prior_Meets_Diffusion_Model_for_Shadow_Removal_CVPR_2023_paper.pdf | cvpr-2023-1 | ['shadow-removal', 'image-shadow-removal'] | ['computer-vision', 'computer-vision'] | [ 3.81932944e-01 -2.69473851e-01 1.82352766e-01 -4.24626470e-02
-4.54481691e-01 -9.35942680e-02 4.79304045e-01 -5.11738479e-01
3.70152364e-03 8.26497257e-01 6.27043903e-01 -4.59389359e-01
3.33033264e-01 -6.28255606e-01 -4.61474150e-01 -1.18294787e+00
2.82730162e-01 -3.30807000e-01 5.51963747e-01 -2.14291826... | [10.830586433410645, -3.978990077972412] |
ee363ec1-74a9-45d5-8619-46efa291a76a | understanding-and-generalizing-monotonic | 2107.13052 | null | https://arxiv.org/abs/2107.13052v1 | https://arxiv.org/pdf/2107.13052v1.pdf | Understanding and Generalizing Monotonic Proximity Graphs for Approximate Nearest Neighbor Search | Graph-based algorithms have shown great empirical potential for the approximate nearest neighbor (ANN) search problem. Currently, graph-based ANN search algorithms are designed mainly using heuristics, whereas theoretical analysis of such algorithms is quite lacking. In this paper, we study a fundamental model of proxi... | ['Minjia Zhang', 'Dantong Zhu'] | 2021-07-27 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [-2.05576211e-01 2.23771200e-01 -6.19792163e-01 -3.15024137e-01
-3.27641457e-01 -6.38635218e-01 2.48697370e-01 4.32488024e-01
3.87827978e-02 6.75207615e-01 2.30113968e-01 -6.21250272e-01
-9.80107546e-01 -1.57499754e+00 -6.31159544e-01 -3.17465514e-01
-4.46630418e-01 7.62416244e-01 2.95752168e-01 -4.18045819... | [7.213820457458496, 5.977259159088135] |
d2fc70cd-9e7e-46e2-a13d-e5b37f6db6d9 | maximum-correntropy-value-decomposition-for | 2208.03663 | null | https://arxiv.org/abs/2208.03663v1 | https://arxiv.org/pdf/2208.03663v1.pdf | Maximum Correntropy Value Decomposition for Multi-agent Deep Reinforcemen Learning | We explore value decomposition solutions for multi-agent deep reinforcement learning in the popular paradigm of centralized training with decentralized execution(CTDE). As the recognized best solution to CTDE, Weighted QMIX is cutting-edge on StarCraft Multi-agent Challenge (SMAC), with a weighting scheme implemented o... | ['Lingjiang Kong', 'Tianxian Zhang', 'Kai Liu'] | 2022-08-07 | null | null | null | null | ['smac-1', 'starcraft', 'smac'] | ['playing-games', 'playing-games', 'playing-games'] | [-2.48617560e-01 -5.35745472e-02 -7.96019733e-02 2.47048423e-01
-7.35082388e-01 -3.97734523e-01 2.31406093e-01 -6.11156635e-02
-8.90841246e-01 1.10139823e+00 -1.06221057e-01 -3.31309557e-01
-9.37320292e-01 -5.39502859e-01 -5.49726009e-01 -9.08344805e-01
-5.32356083e-01 3.42613935e-01 1.52477533e-01 -6.10787094... | [3.8630733489990234, 2.118299961090088] |
850c247a-eb08-4bef-a8ff-9e130fa331dd | matching-adversarial-networks | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Mattyus_Matching_Adversarial_Networks_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Mattyus_Matching_Adversarial_Networks_CVPR_2018_paper.pdf | Matching Adversarial Networks | Generative Adversarial Nets (GANs) and Conditonal GANs (CGANs) show that using a trained network as loss function (discriminator) enables to synthesize highly structured outputs (e.g. natural images). However, applying a discriminator network as a universal loss function for common supervised tasks (e.g. semantic segme... | ['Gellért Máttyus', 'Raquel Urtasun'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['line-detection'] | ['computer-vision'] | [ 5.74604213e-01 4.69906092e-01 1.57575071e-01 -3.07327062e-01
-9.07408416e-01 -6.95279062e-01 7.95888901e-01 -5.33678681e-02
-4.17191505e-01 9.30942595e-01 -2.89530933e-01 -7.54913017e-02
2.34329298e-01 -9.86919582e-01 -9.26278949e-01 -8.59760284e-01
5.34525871e-01 6.79715097e-01 2.64186472e-01 -9.22066867... | [11.597972869873047, -0.38413962721824646] |
9c2dab79-f1f9-45c2-ad18-b898a175e7ff | a-principled-design-of-image-representation | 2203.00913 | null | https://arxiv.org/abs/2203.00913v4 | https://arxiv.org/pdf/2203.00913v4.pdf | A Principled Design of Image Representation: Towards Forensic Tasks | Image forensics is a rising topic as the trustworthy multimedia content is critical for modern society. Like other vision-related applications, forensic analysis relies heavily on the proper image representation. Despite the importance, current theoretical understanding for such representation remains limited, with var... | ['Xiaochun Cao', 'Jiantao Zhou', 'Chao Wang', 'Yushu Zhang', 'Shuren Qi'] | 2022-03-02 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 3.33190441e-01 -3.23886424e-01 1.87556110e-02 1.49501666e-01
-7.83803582e-01 -4.86420363e-01 3.77445519e-01 1.51582167e-01
-3.24467510e-01 3.95041227e-01 -1.86410055e-01 -1.71850860e-01
-5.35395324e-01 -6.45177007e-01 -3.03282171e-01 -1.04567063e+00
-1.01242535e-01 6.61360100e-02 2.99971282e-01 -1.14130482... | [12.387300491333008, 0.9560402035713196] |
689ee461-fba4-4cdf-8e1a-4497d4c18338 | cramer-rao-bound-informed-training-of-neural | 2109.10535 | null | https://arxiv.org/abs/2109.10535v2 | https://arxiv.org/pdf/2109.10535v2.pdf | Cramér-Rao bound-informed training of neural networks for quantitative MRI | Neural networks are increasingly used to estimate parameters in quantitative MRI, in particular in magnetic resonance fingerprinting. Their advantages over the gold standard non-linear least square fitting are their superior speed and their immunity to the non-convexity of many fitting problems. We find, however, that ... | ['Jakob Assländer', 'Carlos Fernandez-Granda', 'Cem Gultekin', 'Sebastian Flassbeck', 'Kangning Liu', 'Quentin Duchemin', 'Xiaoxia Zhang'] | 2021-09-22 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 3.26184362e-01 1.68137595e-01 -1.27310961e-01 -5.08043110e-01
-9.78214145e-01 -6.42902106e-02 -2.33644973e-02 2.63833731e-01
-9.98299181e-01 9.90429461e-01 -1.74002007e-01 -2.13028416e-01
-5.62393367e-01 -3.99599820e-01 -8.18353415e-01 -1.08690023e+00
-1.17387488e-01 2.96171010e-01 9.11094993e-02 1.39843047... | [7.102792263031006, 3.9549379348754883] |
069336bf-0937-4c92-9a09-a36d9726c5f5 | fine-grained-image-captioning-with-clip | 2205.13115 | null | https://arxiv.org/abs/2205.13115v2 | https://arxiv.org/pdf/2205.13115v2.pdf | Fine-grained Image Captioning with CLIP Reward | Modern image captioning models are usually trained with text similarity objectives. However, since reference captions in public datasets often describe the most salient common objects, models trained with text similarity objectives tend to ignore specific and detailed aspects of an image that distinguish it from others... | ['Mohit Bansal', 'Trung Bui', 'Franck Dernoncourt', 'Ajinkya Kale', 'Seunghyun Yoon', 'Jaemin Cho'] | 2022-05-26 | null | https://aclanthology.org/2022.findings-naacl.39 | https://aclanthology.org/2022.findings-naacl.39.pdf | findings-naacl-2022-7 | ['text-annotation'] | ['natural-language-processing'] | [ 3.05347890e-01 3.83578688e-02 -8.66859108e-02 -6.53537810e-01
-1.40555441e+00 -8.58427227e-01 6.87349677e-01 1.58468127e-01
-3.93895715e-01 5.62541425e-01 3.50073218e-01 1.14467934e-01
2.00043812e-01 -2.39127576e-01 -1.03535616e+00 -5.61039388e-01
4.48287696e-01 6.25865519e-01 8.76631774e-03 -1.05530873... | [11.031025886535645, 1.051996111869812] |
a486fdae-ea70-46a5-ad92-e7131098560a | contrastive-regression-for-domain-adaptation | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Contrastive_Regression_for_Domain_Adaptation_on_Gaze_Estimation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Contrastive_Regression_for_Domain_Adaptation_on_Gaze_Estimation_CVPR_2022_paper.pdf | Contrastive Regression for Domain Adaptation on Gaze Estimation | Appearance-based Gaze Estimation leverages deep neural networks to regress the gaze direction from monocular images and achieve impressive performance. However, its success depends on expensive and cumbersome annotation capture. When lacking precise annotation, the large domain gap hinders the performance of traine... | ['Teng Li', 'Hongkai Xiong', 'Chenglin Li', 'Wenrui Dai', 'Bingbing Ni', 'Jin Li', 'Yangzhou Jiang', 'Yaoming Wang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['gaze-estimation'] | ['computer-vision'] | [ 2.34898385e-02 -1.08491443e-01 -2.16526106e-01 -6.82295263e-01
-1.62252977e-01 -2.96369523e-01 3.74419779e-01 -5.82776904e-01
-3.71362507e-01 8.74160111e-01 -7.58244768e-02 2.56594401e-02
6.42598420e-02 2.67871153e-02 -8.43163252e-01 -8.99695277e-01
2.94156820e-01 -6.37531653e-03 1.63041726e-01 -2.43658423... | [14.152359008789062, 0.010211425833404064] |
3482a6c3-df45-408a-ad24-22180baed117 | visualizing-color-wise-saliency-of-black-box | 2010.02468 | null | https://arxiv.org/abs/2010.02468v1 | https://arxiv.org/pdf/2010.02468v1.pdf | Visualizing Color-wise Saliency of Black-Box Image Classification Models | Image classification based on machine learning is being commonly used. However, a classification result given by an advanced method, including deep learning, is often hard to interpret. This problem of interpretability is one of the major obstacles in deploying a trained model in safety-critical systems. Several techni... | ['Kohei Suenaga', 'Yoshinori Konishi', 'Hiroki Sakuma', 'Yuhki Hatakeyama'] | 2020-10-06 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 4.82768744e-01 6.88402429e-02 -2.44824722e-01 -6.79258883e-01
-1.21474840e-01 -1.46315873e-01 3.82347941e-01 1.37541100e-01
-1.07867874e-01 6.55934513e-01 6.53722137e-02 -5.28740227e-01
-6.11363165e-02 -6.26022756e-01 -8.09679508e-01 -6.23936057e-01
2.97034979e-01 9.05491505e-03 4.38024163e-01 -4.12180543... | [9.975016593933105, 2.0305206775665283] |
417adad0-69a2-49a3-b5ea-793ac107c678 | geoglue-a-geographic-language-understanding | 2305.06545 | null | https://arxiv.org/abs/2305.06545v1 | https://arxiv.org/pdf/2305.06545v1.pdf | GeoGLUE: A GeoGraphic Language Understanding Evaluation Benchmark | With a fast developing pace of geographic applications, automatable and intelligent models are essential to be designed to handle the large volume of information. However, few researchers focus on geographic natural language processing, and there has never been a benchmark to build a unified standard. In this work, we ... | ['Xiaofeng He', 'Fei Huang', 'Ning Guo', 'Xin Li', 'Yao Xu', 'Pengjun Xie', 'Boli Chen', 'Zheng Li', 'Qiang Zhang', 'Ruixue Ding', 'Dongyang Li'] | 2023-05-11 | null | null | null | null | ['entity-alignment', 'entity-alignment'] | ['knowledge-base', 'natural-language-processing'] | [-5.74598610e-01 -2.23682821e-01 -5.47434211e-01 -6.84936345e-01
-8.61207426e-01 -8.54610503e-01 1.06665254e+00 6.80828869e-01
-6.17689550e-01 6.14825845e-01 9.02132630e-01 -4.04213250e-01
-2.40332797e-01 -8.47940505e-01 -2.61108011e-01 -4.79738712e-02
-1.35636136e-01 6.49114251e-01 8.65588114e-02 -2.13867307... | [9.318225860595703, 9.077142715454102] |
f25d3364-9050-4b5a-8342-5e9585395761 | is-automated-topic-model-evaluation-broken-1 | null | null | https://openreview.net/forum?id=tjdHCnPqoo | https://openreview.net/pdf?id=tjdHCnPqoo | Is Automated Topic Model Evaluation Broken? The Incoherence of Coherence | Topic model evaluation, like evaluation of other unsupervised methods, can be contentious. However, the field has coalesced around automated estimates of topic coherence, which rely on the frequency of word co-occurrences in a reference corpus. Contemporary neural topic models surpass classical ones according to these ... | ['Philip Resnik', 'Jordan Lee Boyd-Graber', 'Denis Peskov', 'Andrew Hian-Cheong', 'Pranav Goel', 'Alexander Hoyle'] | 2021-05-21 | null | null | null | neurips-2021-12 | ['topic-models'] | ['natural-language-processing'] | [ 7.76749849e-02 5.41195989e-01 -2.49946520e-01 -3.61219347e-01
-8.90577614e-01 -4.50600296e-01 1.04297936e+00 7.11372733e-01
-5.64805984e-01 4.93312567e-01 4.12513644e-01 -5.41102998e-02
-3.65960389e-01 -7.92051971e-01 -2.00261697e-01 -4.70587283e-01
1.77978754e-01 9.15626764e-01 2.99416244e-01 -5.78117138... | [10.382389068603516, 7.1030354499816895] |
18637b52-4c8f-4f32-9e48-678bcc7ec4d3 | mostgan-v-video-generation-with-temporal | 2304.02777 | null | https://arxiv.org/abs/2304.02777v1 | https://arxiv.org/pdf/2304.02777v1.pdf | MoStGAN-V: Video Generation with Temporal Motion Styles | Video generation remains a challenging task due to spatiotemporal complexity and the requirement of synthesizing diverse motions with temporal consistency. Previous works attempt to generate videos in arbitrary lengths either in an autoregressive manner or regarding time as a continuous signal. However, they struggle t... | ['Mohamed Elhoseiny', 'Xiang Li', 'Xiaoqian Shen'] | 2023-04-05 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Shen_MoStGAN-V_Video_Generation_With_Temporal_Motion_Styles_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shen_MoStGAN-V_Video_Generation_With_Temporal_Motion_Styles_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-generation'] | ['computer-vision'] | [ 1.64203539e-01 -2.67228812e-01 -2.09566668e-01 -1.65890425e-01
-5.48567891e-01 -5.42295635e-01 7.16613710e-01 -8.49537373e-01
-2.51353290e-02 6.60052001e-01 5.53723752e-01 8.86614993e-03
3.30985665e-01 -7.19074249e-01 -7.73297846e-01 -8.44768167e-01
1.14008822e-01 -1.85708866e-01 1.27807468e-01 -2.80910164... | [10.858342170715332, -0.6657297015190125] |
0542f1e5-5237-4243-8ca8-2d6d0bc4d5f5 | a-probabilistic-formulation-of-unsupervised-1 | 2002.03912 | null | https://arxiv.org/abs/2002.03912v3 | https://arxiv.org/pdf/2002.03912v3.pdf | A Probabilistic Formulation of Unsupervised Text Style Transfer | We present a deep generative model for unsupervised text style transfer that unifies previously proposed non-generative techniques. Our probabilistic approach models non-parallel data from two domains as a partially observed parallel corpus. By hypothesizing a parallel latent sequence that generates each observed seque... | ['Taylor Berg-Kirkpatrick', 'Xinyi Wang', 'Junxian He', 'Graham Neubig'] | 2020-02-10 | null | https://openreview.net/forum?id=HJlA0C4tPS | https://openreview.net/pdf?id=HJlA0C4tPS | iclr-2020-1 | ['decipherment', 'unsupervised-machine-translation'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.67806888e-01 4.38904017e-01 -2.43621573e-01 -2.83220500e-01
-1.26116383e+00 -9.63697791e-01 1.09635460e+00 -6.07900262e-01
-6.93406239e-02 1.04225528e+00 4.12919551e-01 -4.56300795e-01
4.90735292e-01 -7.47365713e-01 -1.21898937e+00 -6.60350442e-01
6.96843505e-01 1.16077101e+00 -2.47152701e-01 -3.78776014... | [11.667075157165527, 9.588464736938477] |
32f90a39-6abb-4da6-ae4c-0c022beeeb71 | detecting-hands-in-egocentric-videos-towards | 1709.02780 | null | http://arxiv.org/abs/1709.02780v1 | http://arxiv.org/pdf/1709.02780v1.pdf | Detecting Hands in Egocentric Videos: Towards Action Recognition | Recently, there has been a growing interest in analyzing human daily
activities from data collected by wearable cameras. Since the hands are
involved in a vast set of daily tasks, detecting hands in egocentric images is
an important step towards the recognition of a variety of egocentric actions.
However, besides extre... | ['Petia Radeva', 'Mariella Dimiccoli', 'Alejandro Cartas'] | 2017-09-08 | null | null | null | null | ['hand-detection'] | ['computer-vision'] | [-3.29114497e-03 -4.82179582e-01 -1.37343913e-01 -1.65772021e-01
-8.03687871e-02 -7.34339416e-01 4.31253076e-01 -5.81285715e-01
-4.75904226e-01 5.67780018e-01 4.24800456e-01 4.26152200e-01
9.66945589e-02 -3.19530398e-01 -3.60480577e-01 -7.95983255e-01
5.29413186e-02 1.25140503e-01 2.30782166e-01 2.70321399... | [6.628370761871338, -0.5933261513710022] |
61ac820e-67e1-4500-95b1-81d1ad645f03 | implicit-discourse-relation-classification-we | null | null | https://aclanthology.org/2020.acl-main.480 | https://aclanthology.org/2020.acl-main.480.pdf | Implicit Discourse Relation Classification: We Need to Talk about Evaluation | Implicit relation classification on Penn Discourse TreeBank (PDTB) 2.0 is a common benchmark task for evaluating the understanding of discourse relations. However, the lack of consistency in preprocessing and evaluation poses challenges to fair comparison of results in the literature. In this work, we highlight these i... | ['Luis Lastras', 'Song Feng', 'Chulaka Gunasekara', 'Najoung Kim'] | 2020-07-01 | null | null | null | acl-2020-6 | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [ 1.75684929e-01 1.08789527e+00 -4.35009509e-01 -6.90568745e-01
-1.01833236e+00 -6.66643858e-01 9.27998304e-01 5.76688647e-01
-4.41901475e-01 1.11696661e+00 8.52226794e-01 -8.66314888e-01
-6.42805770e-02 -7.27003872e-01 -5.70528209e-01 -1.01892196e-01
-2.75284111e-01 7.09058940e-01 2.70925552e-01 -7.64321089... | [10.851173400878906, 9.353347778320312] |
96a724e2-839a-4cf0-bf4a-c1349795a137 | high-order-recurrent-neural-networks-for | 1802.08314 | null | http://arxiv.org/abs/1802.08314v1 | http://arxiv.org/pdf/1802.08314v1.pdf | High Order Recurrent Neural Networks for Acoustic Modelling | Vanishing long-term gradients are a major issue in training standard
recurrent neural networks (RNNs), which can be alleviated by long short-term
memory (LSTM) models with memory cells. However, the extra parameters
associated with the memory cells mean an LSTM layer has four times as many
parameters as an RNN with the... | ['Philip Woodland', 'Chao Zhang'] | 2018-02-22 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [ 2.49497369e-01 5.15289128e-01 1.35042280e-01 -2.73084849e-01
-5.33144295e-01 -5.71155921e-02 4.53234851e-01 -5.33684611e-01
-8.75092566e-01 1.01260245e+00 4.20832217e-01 -7.44098723e-01
4.31194812e-01 -5.89891076e-01 -7.49370337e-01 -7.96889007e-01
2.05704883e-01 9.65710953e-02 2.06636295e-01 -5.28320730... | [10.865675926208496, 6.345398426055908] |
02bbc0b7-61b5-4724-9a3f-b05aaf01c837 | word-alignment-in-the-era-of-deep-learning-a | 2212.00138 | null | https://arxiv.org/abs/2212.00138v1 | https://arxiv.org/pdf/2212.00138v1.pdf | Word Alignment in the Era of Deep Learning: A Tutorial | The word alignment task, despite its prominence in the era of statistical machine translation (SMT), is niche and under-explored today. In this two-part tutorial, we argue for the continued relevance for word alignment. The first part provides a historical background to word alignment as a core component of the traditi... | ['Bryan Li'] | 2022-11-30 | null | null | null | null | ['nmt', 'word-alignment'] | ['computer-code', 'natural-language-processing'] | [ 7.16499567e-01 2.76788712e-01 -3.40117484e-01 -3.16072375e-01
-1.24105024e+00 -5.73566496e-01 6.41322792e-01 -1.20019667e-01
-5.62409163e-01 8.06922436e-01 4.36907440e-01 -1.06911898e+00
2.44549915e-01 -3.04830492e-01 -6.61419034e-01 -5.03507733e-01
1.41528487e-01 9.16639805e-01 -7.22143769e-01 -7.63902783... | [11.576485633850098, 10.29174518585205] |
1704dd60-55b3-4838-8a8c-69559a14d176 | deeplsd-line-segment-detection-and-refinement | 2212.07766 | null | https://arxiv.org/abs/2212.07766v3 | https://arxiv.org/pdf/2212.07766v3.pdf | DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients | Line segments are ubiquitous in our human-made world and are increasingly used in vision tasks. They are complementary to feature points thanks to their spatial extent and the structural information they provide. Traditional line detectors based on the image gradient are extremely fast and accurate, but lack robustness... | ['Marc Pollefeys', 'Martin R. Oswald', 'Viktor Larsson', 'Daniel Barath', 'Rémi Pautrat'] | 2022-12-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Pautrat_DeepLSD_Line_Segment_Detection_and_Refinement_With_Deep_Image_Gradients_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Pautrat_DeepLSD_Line_Segment_Detection_and_Refinement_With_Deep_Image_Gradients_CVPR_2023_paper.pdf | cvpr-2023-1 | ['line-segment-detection', 'line-detection'] | ['computer-vision', 'computer-vision'] | [-1.02483705e-01 -3.34815562e-01 -2.01412112e-01 -4.45923179e-01
-6.65081918e-01 -7.37490714e-01 5.77962577e-01 2.79552191e-01
-4.96466547e-01 3.51866454e-01 -1.14971288e-01 -1.39550522e-01
3.50814134e-01 -6.81602299e-01 -9.01701808e-01 -2.71480501e-01
1.81804910e-01 2.71780044e-01 6.75567746e-01 -2.44732082... | [8.234997749328613, -1.8226003646850586] |
1afe8a97-6882-4763-a594-40045214162d | a-multi-scale-video-denoising-algorithm-for | 2209.01740 | null | https://arxiv.org/abs/2209.01740v1 | https://arxiv.org/pdf/2209.01740v1.pdf | A Multi-scale Video Denoising Algorithm for Raw Image | Video denoising for raw image has always been the difficulty of camera image processing. On the one hand, image denoising performance largely determines the image quality, moreover denoising effect in raw image will affect the accuracy of the following operations of ISP processing flow. On the other hand, compared with... | ['Kai Li', 'Xianxian Lv', 'Yueli Hu', 'Bin Ma'] | 2022-09-05 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 2.18253940e-01 -7.68227458e-01 2.70518929e-01 -2.03154594e-01
8.64532441e-02 -1.13909692e-01 4.62779924e-02 -3.61872166e-01
-9.95213449e-01 4.33845431e-01 6.03701174e-02 -1.29091293e-01
-1.42899733e-02 -8.42056572e-01 -2.62641519e-01 -1.06851220e+00
9.74752307e-02 -5.31059563e-01 3.12959611e-01 -2.26300418... | [11.358030319213867, -2.2590293884277344] |
46ed51f9-7198-4da7-a911-92b83eda8b00 | generative-and-pseudo-relevant-feedback-for | 2305.07477 | null | https://arxiv.org/abs/2305.07477v1 | https://arxiv.org/pdf/2305.07477v1.pdf | Generative and Pseudo-Relevant Feedback for Sparse, Dense and Learned Sparse Retrieval | Pseudo-relevance feedback (PRF) is a classical approach to address lexical mismatch by enriching the query using first-pass retrieval. Moreover, recent work on generative-relevance feedback (GRF) shows that query expansion models using text generated from large language models can improve sparse retrieval without depen... | ['Jeffrey Dalton', 'Shubham Chatterjee', 'Iain Mackie'] | 2023-05-12 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [ 2.78232008e-01 -8.20939913e-02 -7.17796326e-01 -1.08592369e-01
-1.58735049e+00 -7.13753879e-01 1.09661233e+00 5.19611597e-01
-5.86378157e-01 6.87226951e-01 8.15459788e-01 -1.61493286e-01
-4.55457360e-01 -6.82201326e-01 -5.07229388e-01 1.08979391e-02
4.11906280e-02 7.17644989e-01 5.37272215e-01 -8.51094961... | [11.477747917175293, 7.613698482513428] |
df6d4193-666b-4c99-9bb9-2773dd2b7061 | self-supervised-deep-learning-to-enhance | 2203.08812 | null | https://arxiv.org/abs/2203.08812v1 | https://arxiv.org/pdf/2203.08812v1.pdf | Self-Supervised Deep Learning to Enhance Breast Cancer Detection on Screening Mammography | A major limitation in applying deep learning to artificial intelligence (AI) systems is the scarcity of high-quality curated datasets. We investigate strong augmentation based self-supervised learning (SSL) techniques to address this problem. Using breast cancer detection as an example, we first identify a mammogram-sp... | ['Li Shen', 'Weiva Sieh', 'Laurie R. Margolies', 'Albert X. Pu', 'Vignesh A. Arasu', 'John D. Miller'] | 2022-03-16 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 8.92835259e-01 6.31099761e-01 -4.08803046e-01 -5.98108172e-01
-1.14908242e+00 -1.72691971e-01 6.74786866e-01 1.55285522e-02
-4.89908248e-01 6.71703815e-01 1.85814649e-01 -2.17752740e-01
-8.94760992e-03 -6.35193408e-01 -9.20974433e-01 -8.40601563e-01
1.89615488e-01 5.18151104e-01 1.99176371e-01 8.89140368... | [14.950340270996094, -2.4859132766723633] |
55adb12f-d969-4e30-9dd1-d57a9cdf6d2c | sim-to-real-segmentation-in-robot-assisted | 2305.11686 | null | https://arxiv.org/abs/2305.11686v2 | https://arxiv.org/pdf/2305.11686v2.pdf | Domain Adaptive Sim-to-Real Segmentation of Oropharyngeal Organs Towards Robot-assisted Intubation | Robotic-assisted tracheal intubation requires the robot to distinguish anatomical features like an experienced physician using deep-learning techniques. However, real datasets of oropharyngeal organs are limited due to patient privacy issues, making it challenging to train deep-learning models for accurate image segmen... | ['Hongliang Ren', 'Long Bai', 'JIEWEN LAI', 'Tian-Ao Ren', 'Guankun Wang'] | 2023-05-19 | null | null | null | null | ['style-transfer'] | ['computer-vision'] | [-1.48899823e-01 6.05320871e-01 -2.31517747e-01 -2.64270484e-01
-8.78091693e-01 -8.15106571e-01 2.46121988e-01 -1.06493466e-01
-6.32921338e-01 4.17367607e-01 -9.54354480e-02 -8.04964185e-01
-9.61719230e-02 -4.22114730e-01 -7.34595060e-01 -6.25998616e-01
2.55025089e-01 5.88561118e-01 7.69451186e-02 -1.61092520... | [14.484713554382324, -2.517747163772583] |
88b2bd7c-8cf3-486a-99bc-8b65b2251a2d | cpnet-a-context-preserver-convolutional | 1810.05778 | null | http://arxiv.org/abs/1810.05778v1 | http://arxiv.org/pdf/1810.05778v1.pdf | CPNet: A Context Preserver Convolutional Neural Network for Detecting Shadows in Single RGB Images | Automatic detection of shadow regions in an image is a difficult task due to
the lack of prior information about the illumination source and the dynamic of
the scene objects. To address this problem, in this paper, a deep-learning
based segmentation method is proposed that identifies shadow regions at the
pixel-level i... | ['Parvaneh Saeedi', 'Sorour Mohajerani'] | 2018-10-13 | null | null | null | null | ['detecting-shadows'] | ['computer-vision'] | [ 7.60092676e-01 -1.13569871e-01 1.17893338e-01 -6.58054888e-01
-6.81429267e-01 -3.78001571e-01 1.71901226e-01 -7.71851763e-02
-4.23358649e-01 6.63699687e-01 -2.24602550e-01 -2.67329663e-01
3.19223672e-01 -5.80376387e-01 -8.16729903e-01 -9.07195270e-01
1.35258496e-01 -2.34282643e-01 6.49674714e-01 5.07166758... | [10.851176261901855, -4.113837718963623] |
9898cc34-707c-479b-a8e7-369f87e1d2d3 | speaking-the-language-of-your-listener | 2305.19933 | null | https://arxiv.org/abs/2305.19933v1 | https://arxiv.org/pdf/2305.19933v1.pdf | Speaking the Language of Your Listener: Audience-Aware Adaptation via Plug-and-Play Theory of Mind | Dialogue participants may have varying levels of knowledge about the topic under discussion. In such cases, it is essential for speakers to adapt their utterances by taking their audience into account. Yet, it is an open question how such adaptation can be modelled in computational agents. In this paper, we model a vis... | ['Raquel Fernández', 'Sandro Pezzelle', 'Mario Giulianelli', "Nicolo' Brandizzi", 'Ece Takmaz'] | 2023-05-31 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [-2.28703216e-01 1.11125624e+00 4.16961133e-01 -2.25626528e-01
-2.88822591e-01 -7.66988039e-01 8.50339472e-01 3.87217253e-01
-4.73120749e-01 5.06301939e-01 4.83137369e-01 -2.90973485e-01
2.53841937e-01 -8.85048509e-01 -4.29971308e-01 -2.32942656e-01
1.66168973e-01 6.13628924e-01 3.25176984e-01 -6.62373364... | [12.94544506072998, 7.893821716308594] |
526d7a6b-cfcd-46f1-99c8-aa43f250999b | a-teacher-student-framework-for-semi | 2010.12219 | null | https://arxiv.org/abs/2010.12219v1 | https://arxiv.org/pdf/2010.12219v1.pdf | A Teacher-Student Framework for Semi-supervised Medical Image Segmentation From Mixed Supervision | Standard segmentation of medical images based on full-supervised convolutional networks demands accurate dense annotations. Such learning framework is built on laborious manual annotation with restrict demands for expertise, leading to insufficient high-quality labels. To overcome such limitation and exploit massive we... | ['Yizhou Yu', 'Guisheng Wang', 'Yue Huang', 'Xinghao Ding', 'Jianxiong Wu', 'Liyan Sun'] | 2020-10-23 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 2.21652940e-01 9.24966097e-01 -3.43926281e-01 -6.22260034e-01
-1.06763315e+00 -5.90462089e-01 3.90046805e-01 2.30963781e-01
-4.31969613e-01 5.57653487e-01 -9.85612497e-02 -2.81409949e-01
1.54955566e-01 -4.36971843e-01 -9.61600184e-01 -7.14592040e-01
1.60154596e-01 7.32936442e-01 6.31410420e-01 1.47283077... | [14.710126876831055, -2.412985324859619] |
6863ed7f-e723-4c5f-90c1-143aa9b56f58 | facing-off-world-model-backbones-rnns | 2307.02064 | null | https://arxiv.org/abs/2307.02064v1 | https://arxiv.org/pdf/2307.02064v1.pdf | Facing off World Model Backbones: RNNs, Transformers, and S4 | World models are a fundamental component in model-based reinforcement learning (MBRL) agents. To perform temporally extended and consistent simulations of the future in partially observable environments, world models need to possess long-term memory. However, state-of-the-art MBRL agents, such as Dreamer, predominantly... | ['Sungjin Ahn', 'Junyeong Park', 'Fei Deng'] | 2023-07-05 | null | null | null | null | ['model-based-reinforcement-learning'] | ['reasoning'] | [-3.39182556e-01 4.79285903e-02 -2.62234598e-01 1.88301906e-01
-1.82469040e-01 -3.59760314e-01 1.04377604e+00 -3.65770340e-01
-4.46881086e-01 1.04377496e+00 4.17685211e-01 -1.54187292e-01
-4.61364865e-01 -1.17459214e+00 -6.88811898e-01 -4.82242078e-01
-3.21065724e-01 7.95867562e-01 1.64367676e-01 -7.44619131... | [4.15914249420166, 1.5507582426071167] |
15407c91-7286-4c08-82a4-ec92673ac5bc | unsupervised-meta-learning-through-latent | 2006.10236 | null | https://arxiv.org/abs/2006.10236v1 | https://arxiv.org/pdf/2006.10236v1.pdf | Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models | Unsupervised meta-learning approaches rely on synthetic meta-tasks that are created using techniques such as random selection, clustering and/or augmentation. Unfortunately, clustering and augmentation are domain-dependent, and thus they require either manual tweaking or expensive learning. In this work, we describe an... | ['Ladislau Bölöni', 'Sharare Zehtabian', 'Saeed Vahidian', 'Weijia Wang', 'Siavash Khodadadeh', 'Bill Lin'] | 2020-06-18 | null | https://openreview.net/forum?id=XOjv2HxIF6i | https://openreview.net/pdf?id=XOjv2HxIF6i | iclr-2021-1 | ['unsupervised-few-shot-image-classification'] | ['computer-vision'] | [ 6.76207185e-01 2.80542284e-01 -4.36496496e-01 -3.63327980e-01
-1.24571860e+00 -3.63347828e-01 1.33436620e+00 1.93047464e-01
-4.38739717e-01 1.01434863e+00 3.55124980e-01 8.31409916e-02
-2.48092432e-02 -7.63458073e-01 -6.92903101e-01 -7.06720531e-01
4.98671979e-01 9.55797613e-01 1.61269590e-01 1.20311461... | [9.958028793334961, 3.08443021774292] |
f754dc45-4a3f-41f9-b45b-15833e10fc73 | dont-search-for-a-search-method-simple | null | null | https://aclanthology.org/2021.emnlp-main.647 | https://aclanthology.org/2021.emnlp-main.647.pdf | Don’t Search for a Search Method — Simple Heuristics Suffice for Adversarial Text Attacks | Recently more attention has been given to adversarial attacks on neural networks for natural language processing (NLP). A central research topic has been the investigation of search algorithms and search constraints, accompanied by benchmark algorithms and tasks. We implement an algorithm inspired by zeroth order optim... | ['Artem Sokolov', 'Sebastian Ebert', 'Stefan Riezler', 'Nathaniel Berger'] | null | null | null | null | emnlp-2021-11 | ['adversarial-text'] | ['adversarial'] | [ 3.23758513e-01 -2.86113820e-03 -3.34503055e-01 -3.55665594e-01
-1.06989610e+00 -1.18973219e+00 7.45294452e-01 1.27661675e-01
-1.19931757e+00 7.94779301e-01 6.31415769e-02 -6.13543153e-01
-8.09914693e-02 -5.95742047e-01 -8.68941784e-01 -6.82527125e-01
-2.23669708e-02 6.29720688e-01 1.92149758e-01 -2.15125382... | [5.99072790145874, 8.103569984436035] |
b2c8262d-d41a-4728-915b-db15be538537 | differentiable-tree-operations-promote | 2306.00751 | null | https://arxiv.org/abs/2306.00751v1 | https://arxiv.org/pdf/2306.00751v1.pdf | Differentiable Tree Operations Promote Compositional Generalization | In the context of structure-to-structure transformation tasks, learning sequences of discrete symbolic operations poses significant challenges due to their non-differentiability. To facilitate the learning of these symbolic sequences, we introduce a differentiable tree interpreter that compiles high-level symbolic tree... | ['Jianfeng Gao', 'Paul Smolensky', 'Roland Fernandez', 'Yunmo Chen', 'Kate McCurdy', 'Edward Hu', 'Paul Soulos'] | 2023-06-01 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 8.12257648e-01 7.56800413e-01 -7.55354390e-02 -6.96219802e-01
-1.07349503e+00 -7.92256236e-01 7.35416591e-01 -2.51857996e-01
-2.60292124e-02 5.22260606e-01 2.37862408e-01 -9.14824188e-01
3.46637338e-01 -1.03678644e+00 -1.20325232e+00 -2.87282795e-01
-2.13756412e-01 8.89566362e-01 -1.06619811e-02 -2.24108338... | [10.467679023742676, 8.685667991638184] |
3bb0b21a-4c66-44ae-ad6e-68886f079173 | unpaired-image-to-image-translation-via-1 | 2305.15086 | null | https://arxiv.org/abs/2305.15086v1 | https://arxiv.org/pdf/2305.15086v1.pdf | Unpaired Image-to-Image Translation via Neural Schrödinger Bridge | Diffusion models are a powerful class of generative models which simulate stochastic differential equations (SDEs) to generate data from noise. Although diffusion models have achieved remarkable progress in recent years, they have limitations in the unpaired image-to-image translation tasks due to the Gaussian prior as... | ['Jong Chul Ye', 'Kwanyoung Kim', 'Gihyun Kwon', 'Beomsu Kim'] | 2023-05-24 | null | null | null | null | ['image-to-image-translation', 'image-to-image-translation'] | ['computer-vision', 'miscellaneous'] | [ 4.02458906e-01 -4.66260426e-02 2.56769419e-01 -2.85551786e-01
-1.16430163e+00 -5.80619276e-01 7.57377088e-01 -7.72504389e-01
-2.17265785e-01 1.04666090e+00 -9.37586054e-02 -3.27892989e-01
1.99990600e-01 -7.54696012e-01 -9.42576766e-01 -1.07720208e+00
4.94968742e-01 5.63041210e-01 -1.87142678e-02 -2.32453763... | [11.603638648986816, -0.38879454135894775] |
9d7cd246-d932-4951-82ad-f0e511f4a299 | a-bayesian-topic-model-for-human-evaluated | null | null | https://aclanthology.org/2022.lrec-1.674 | https://aclanthology.org/2022.lrec-1.674.pdf | A Bayesian Topic Model for Human-Evaluated Interpretability | One desiderata of topic modeling is to produce interpretable topics. Given a cluster of document-tokens comprising a topic, we can order the topic by counting each word. It is natural to think that each topic could easily be labeled by looking at the words with the highest word count. However, this is not always the ca... | ['Wei Wang', 'Corey Arnold', 'Justin Wood'] | null | null | null | null | lrec-2022-6 | ['topic-models'] | ['natural-language-processing'] | [ 6.94615915e-02 8.20195973e-01 -3.38063776e-01 -6.21339858e-01
-8.53274822e-01 -8.32579255e-01 8.61444533e-01 4.18267578e-01
1.12226129e-01 6.66657865e-01 4.50633079e-01 -4.22145605e-01
-1.67062715e-01 -7.47108638e-01 -6.12461567e-01 -5.71623445e-01
-1.57220438e-02 1.27576542e+00 6.52109534e-02 -2.42766812... | [10.404953002929688, 6.964382648468018] |
95166af7-faa1-487c-9e06-61da20ea2d9a | filmy-cloud-removal-on-satellite-imagery-with | 1710.04835 | null | http://arxiv.org/abs/1710.04835v1 | http://arxiv.org/pdf/1710.04835v1.pdf | Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets | In this paper, we propose a method for cloud removal from visible light RGB
satellite images by extending the conditional Generative Adversarial Networks
(cGANs) from RGB images to multispectral images. Satellite images have been
widely utilized for various purposes, such as natural environment monitoring
(pollution, f... | ['Nobuo Kawaguchi', 'Weimin WANG', 'Hiroshi Fukui', 'Masashi Matsuoka', 'Ken Sakurada', 'Kenji Enomoto', 'Ryosuke Nakamura'] | 2017-10-13 | null | null | null | null | ['cloud-removal'] | ['computer-vision'] | [ 5.62068582e-01 -3.99399906e-01 3.06798518e-01 -1.68148935e-01
-4.01115805e-01 -7.60918081e-01 3.98202866e-01 -7.30220020e-01
-4.91522253e-01 1.03696644e+00 -2.09309697e-01 -2.66125947e-01
9.76548418e-02 -1.36364365e+00 -7.49152243e-01 -1.39801097e+00
3.20227236e-01 -2.90945888e-01 -8.34881067e-02 -2.83995539... | [10.01423454284668, -1.9199985265731812] |
1dcff945-b52f-4723-8e68-369b084ba55c | training-data-set-assessment-for-decision | 2004.05380 | null | https://arxiv.org/abs/2004.05380v1 | https://arxiv.org/pdf/2004.05380v1.pdf | Training Data Set Assessment for Decision-Making in a Multiagent Landmine Detection Platform | Real-world problems such as landmine detection require multiple sources of information to reduce the uncertainty of decision-making. A novel approach to solve these problems includes distributed systems, as presented in this work based on hardware and software multi-agent systems. To achieve a high rate of landmine det... | ['Johana Florez-Lozano', 'Fabio Caraffini', 'Mario Gongora', 'Carlos Parra'] | 2020-04-11 | null | null | null | null | ['landmine'] | ['computer-vision'] | [ 2.11227074e-01 3.46145868e-01 -8.76692757e-02 -2.87580937e-01
-5.70246816e-01 -1.53029695e-01 4.37793344e-01 3.20670128e-01
-6.94897294e-01 1.07935417e+00 -3.88581812e-01 -2.73916095e-01
-5.39831996e-01 -1.39716768e+00 -4.56897616e-01 -8.91672194e-01
-2.80443639e-01 9.65417027e-01 3.92617226e-01 -4.41154867... | [4.5448784828186035, 2.0691747665405273] |
a12668cf-60b0-4eaa-9cf1-80ce27ca8a40 | contextual-text-block-detection-towards-scene | 2207.12955 | null | https://arxiv.org/abs/2207.12955v1 | https://arxiv.org/pdf/2207.12955v1.pdf | Contextual Text Block Detection towards Scene Text Understanding | Most existing scene text detectors focus on detecting characters or words that only capture partial text messages due to missing contextual information. For a better understanding of text in scenes, it is more desired to detect contextual text blocks (CTBs) which consist of one or multiple integral text units (e.g., ch... | ['Song Bai', 'Changhu Wang', 'Shijian Lu', 'Jiaxing Huang', 'Chuhui Xue'] | 2022-07-26 | null | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [ 5.52319229e-01 -5.04704118e-01 -1.41493440e-01 -2.77576566e-01
-6.44852102e-01 -5.97707510e-01 8.65973353e-01 4.55407202e-01
-2.25703046e-01 1.71570331e-01 4.91590917e-01 -5.06780624e-01
4.01396155e-01 -6.76872671e-01 -5.74959993e-01 -7.34864295e-01
4.72631007e-01 2.46141076e-01 5.81753731e-01 -1.20341117... | [11.961259841918945, 2.236856460571289] |
ba2d0c0d-733b-429a-b57a-6080fa67f79c | autoregressive-denoising-diffusion-models-for | 2101.12072 | null | https://arxiv.org/abs/2101.12072v2 | https://arxiv.org/pdf/2101.12072v2.pdf | Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting | In this work, we propose \texttt{TimeGrad}, an autoregressive model for multivariate probabilistic time series forecasting which samples from the data distribution at each time step by estimating its gradient. To this end, we use diffusion probabilistic models, a class of latent variable models closely connected to sco... | ['Roland Vollgraf', 'Ingmar Schuster', 'Calvin Seward', 'Kashif Rasul'] | 2021-01-28 | null | null | null | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [-7.69395456e-02 -7.89562315e-02 -4.69496883e-02 -4.50135946e-01
-9.67550397e-01 -3.99449944e-01 8.09802592e-01 -2.21687600e-01
-1.96601868e-01 5.21287143e-01 4.38226491e-01 3.26763093e-02
-1.14755780e-01 -8.81564975e-01 -6.83200002e-01 -1.15355957e+00
-2.31253162e-01 9.78299797e-01 7.62589648e-02 2.72609860... | [6.9056925773620605, 3.7397594451904297] |
823b0f42-02b4-4a99-b569-f2a025da783a | rotation-constrained-cross-view-feature | 2305.12704 | null | https://arxiv.org/abs/2305.12704v1 | https://arxiv.org/pdf/2305.12704v1.pdf | Rotation-Constrained Cross-View Feature Fusion for Multi-View Appearance-based Gaze Estimation | Appearance-based gaze estimation has been actively studied in recent years. However, its generalization performance for unseen head poses is still a significant limitation for existing methods. This work proposes a generalizable multi-view gaze estimation task and a cross-view feature fusion method to address this issu... | ['Yusuke Sugano', 'Jiawei Qin', 'Tianyi Wu', 'Yoichiro Hisadome'] | 2023-05-22 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [-8.04735254e-03 -2.78009791e-02 -1.87162399e-01 -5.98075092e-01
-6.04814589e-01 -4.47265714e-01 3.16899776e-01 -5.53800523e-01
-5.45368016e-01 5.79625905e-01 -5.40788956e-02 1.82323948e-01
6.82050511e-02 -1.09371714e-01 -8.68677199e-01 -8.42492163e-01
4.20411140e-01 -3.78248170e-02 1.45287737e-01 -3.44013348... | [14.11101245880127, 0.039550408720970154] |
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