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a0049d49-be99-4d08-9862-7819ef9de820 | cross-label-suppression-a-discriminative-and | 1705.02928 | null | http://arxiv.org/abs/1705.02928v1 | http://arxiv.org/pdf/1705.02928v1.pdf | Cross-label Suppression: A Discriminative and Fast Dictionary Learning with Group Regularization | This paper addresses image classification through learning a compact and
discriminative dictionary efficiently. Given a structured dictionary with each
atom (columns in the dictionary matrix) related to some label, we propose
cross-label suppression constraint to enlarge the difference among
representations for differe... | ['Yuantao Gu', 'Xiudong Wang'] | 2017-05-08 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 3.98546487e-01 -2.12872982e-01 -4.55893964e-01 -5.27606726e-01
-2.72673637e-01 -1.53058350e-01 2.37398803e-01 2.89783895e-01
-3.73050392e-01 6.19133830e-01 1.49953932e-01 1.26457468e-01
-1.84314206e-01 -7.29511380e-01 -1.80183560e-01 -9.82635677e-01
6.72350675e-02 2.49535721e-02 -1.70147613e-01 2.98635103... | [12.387375831604004, 0.42809247970581055] |
64832fb9-9154-4871-a668-d69d1e55285f | mrfusion-a-deep-learning-architecture-to-fuse | 1806.11452 | null | http://arxiv.org/abs/1806.11452v1 | http://arxiv.org/pdf/1806.11452v1.pdf | MRFusion: A Deep Learning architecture to fuse PAN and MS imagery for land cover mapping | Nowadays, Earth Observation systems provide a multitude of heterogeneous
remote sensing data. How to manage such richness leveraging its complementarity
is a crucial chal- lenge in modern remote sensing analysis. Data Fusion
techniques deal with this point proposing method to combine and exploit
complementarity among t... | ['Kenji Ose', 'Remi Cresson', 'Raffaele Gaetano', 'Dino Ienco'] | 2018-06-29 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 6.33592188e-01 -2.02373460e-01 -6.13655187e-02 -3.25401783e-01
-6.69233739e-01 -4.54266816e-01 7.76191890e-01 1.63725689e-01
-5.57706177e-01 1.00981343e+00 8.02515373e-02 -3.51133317e-01
-5.09849429e-01 -1.52022874e+00 -5.90359509e-01 -1.01813102e+00
-2.29827508e-01 3.60006690e-02 -3.72928649e-01 -6.39140606... | [9.772173881530762, -1.7349746227264404] |
976c6a3a-a89d-46bc-8ba7-9c7dc830c43d | nestfuse-an-infrared-and-visible-image-fusion | 2007.00328 | null | https://arxiv.org/abs/2007.00328v2 | https://arxiv.org/pdf/2007.00328v2.pdf | NestFuse: An Infrared and Visible Image Fusion Architecture based on Nest Connection and Spatial/Channel Attention Models | In this paper we propose a novel method for infrared and visible image fusion where we develop nest connection-based network and spatial/channel attention models. The nest connection-based network can preserve significant amounts of information from input data in a multi-scale perspective. The approach comprises three ... | ['Xiao-Jun Wu', 'Tariq Durrani', 'Hui Li'] | 2020-07-01 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 1.52015135e-01 -3.71169239e-01 1.22395800e-02 -1.99190587e-01
-8.63191307e-01 -2.08945096e-01 4.33757842e-01 1.72611419e-02
-2.81865358e-01 6.98949039e-01 4.47109193e-01 -8.35942402e-02
-1.96046960e-02 -8.13338697e-01 -8.23108852e-01 -7.46418774e-01
2.82201201e-01 -5.31893730e-01 -2.59447191e-02 -1.41957089... | [10.497836112976074, -1.8619657754898071] |
8992483f-3389-4b06-843b-136a5e42e5bf | inductive-linear-probing-for-few-shot-node | 2306.08192 | null | https://arxiv.org/abs/2306.08192v1 | https://arxiv.org/pdf/2306.08192v1.pdf | Inductive Linear Probing for Few-shot Node Classification | Meta-learning has emerged as a powerful training strategy for few-shot node classification, demonstrating its effectiveness in the transductive setting. However, the existing literature predominantly focuses on transductive few-shot node classification, neglecting the widely studied inductive setting in the broader few... | ['Huan Liu', 'Nivedh Mudiam', 'Zhen Tan', 'Hirthik Mathavan'] | 2023-06-14 | null | null | null | null | ['node-classification', 'meta-learning', 'classification-1'] | ['graphs', 'methodology', 'methodology'] | [ 3.13145697e-01 4.13944095e-01 -1.00533962e+00 -1.28531262e-01
-6.30187273e-01 2.98371189e-03 8.37625206e-01 3.36673707e-01
-1.93804815e-01 4.25947994e-01 2.47850433e-01 -3.20199877e-01
-2.34146923e-01 -1.23962283e+00 -2.82143682e-01 -6.30201101e-01
-1.35338649e-01 3.35675210e-01 8.95010680e-02 -5.74045956... | [9.924932479858398, 3.0778756141662598] |
a803864d-2d7d-4dee-962d-9485424e24a3 | rethinking-interactive-image-segmentation | 2101.04378 | null | https://arxiv.org/abs/2101.04378v3 | https://arxiv.org/pdf/2101.04378v3.pdf | Rethinking Interactive Image Segmentation: Feature Space Annotation | Despite the progress of interactive image segmentation methods, high-quality pixel-level annotation is still time-consuming and laborious - a bottleneck for several deep learning applications. We take a step back to propose interactive and simultaneous segment annotation from multiple images guided by feature space pro... | ['Alexandre X Falc{ã}o', 'Jord{ã}o Bragantini', 'Laurent Najman'] | 2021-01-12 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 4.28877145e-01 4.18356508e-01 -2.07340255e-01 -3.78953427e-01
-1.01608181e+00 -9.80133832e-01 2.37495705e-01 -1.63127035e-02
-3.76349986e-01 5.60182452e-01 -2.66747296e-01 -4.24445778e-01
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2.05982074e-01 5.72893262e-01 7.06494033e-01 2.06819981... | [9.489652633666992, 0.2057841569185257] |
8e0d4233-21cd-4216-ad15-5dfd50e1359e | feature-fusion-for-robust-patch-matching-with | 1901.03547 | null | http://arxiv.org/abs/1901.03547v1 | http://arxiv.org/pdf/1901.03547v1.pdf | Feature Fusion for Robust Patch Matching With Compact Binary Descriptors | This work addresses the problem of learning compact yet discriminative patch
descriptors within a deep learning framework. We observe that features
extracted by convolutional layers in the pixel domain are largely complementary
to features extracted in a transformed domain. We propose a convolutional
network framework ... | ['Skjalg Lepsoy', 'Gianluca Francini', 'Andrea Migliorati', 'Riccardo Leonardi', 'Attilio Fiandrotti'] | 2019-01-11 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [ 5.77960730e-01 -1.93978563e-01 -3.24785143e-01 -4.51399803e-01
-1.11845219e+00 -5.17680168e-01 6.94585264e-01 3.04136276e-01
-5.44568777e-01 4.45344746e-01 4.21985164e-02 2.95581609e-01
-2.77061790e-01 -1.09555864e+00 -8.31743121e-01 -7.43750274e-01
-1.22522421e-01 -1.92491636e-01 3.23710889e-01 8.43640044... | [10.245447158813477, 0.11777088791131973] |
2ab448c9-884d-47bd-a4bf-dc4d5c8ffc56 | edge-aware-graph-representation-learning-and | 2007.11240 | null | https://arxiv.org/abs/2007.11240v1 | https://arxiv.org/pdf/2007.11240v1.pdf | Edge-aware Graph Representation Learning and Reasoning for Face Parsing | Face parsing infers a pixel-wise label to each facial component, which has drawn much attention recently. Previous methods have shown their efficiency in face parsing, which however overlook the correlation among different face regions. The correlation is a critical clue about the facial appearance, pose, expression et... | ['Hailin Shi', 'Gusi Te', 'Yinglu Liu', 'Wei Hu', 'Tao Mei'] | 2020-07-22 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1543_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123570256.pdf | eccv-2020-8 | ['face-parsing'] | ['computer-vision'] | [-1.49958609e-02 3.77958924e-01 -2.46235684e-01 -8.86823833e-01
-5.87270975e-01 -3.47420663e-01 3.13411653e-01 -8.55102837e-02
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1.13145910e-01 -9.63930547e-01 -7.40390778e-01 -6.39297247e-01
-1.03242680e-01 2.78232187e-01 -3.58540341e-02 -5.36673218... | [13.429743766784668, 0.6541008353233337] |
eceabe2e-c250-4c53-afaf-9365629ef701 | point-cloud-classification-using-content | 2303.04599 | null | https://arxiv.org/abs/2303.04599v1 | https://arxiv.org/pdf/2303.04599v1.pdf | Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space | Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention, but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial ... | ['FeiYue Wang', 'Lingxi Li', 'Yisheng Lv', 'Bin Tian', 'Yahui Liu'] | 2023-03-08 | null | null | null | null | ['3d-point-cloud-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [-4.04962271e-01 -5.08496284e-01 -2.14554101e-01 -4.26859587e-01
-9.40692782e-01 -4.19410110e-01 4.45286036e-01 4.82814699e-01
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-4.27125424e-01 -1.16885293e+00 -9.13011074e-01 -6.78234637e-01
-4.84346375e-02 6.26857996e-01 5.21572888e-01 -9.86818597... | [7.8443145751953125, -3.4241747856140137] |
9808f6ca-9ae1-4927-8fd7-9da3e4becbf7 | gnn-encoder-learning-a-dual-encoder | 2204.08241 | null | https://arxiv.org/abs/2204.08241v2 | https://arxiv.org/pdf/2204.08241v2.pdf | GNN-encoder: Learning a Dual-encoder Architecture via Graph Neural Networks for Dense Passage Retrieval | Recently, retrieval models based on dense representations are dominant in passage retrieval tasks, due to their outstanding ability in terms of capturing semantics of input text compared to the traditional sparse vector space models. A common practice of dense retrieval models is to exploit a dual-encoder architecture ... | ['Rui Yan', 'Dongyan Zhao', 'Wei Wu', 'Jingang Wang', 'Yang Yang', 'Jiahao Liu', 'Jiduan Liu'] | 2022-04-18 | null | null | null | null | ['natural-questions', 'triviaqa', 'passage-retrieval'] | ['miscellaneous', 'miscellaneous', 'natural-language-processing'] | [-2.61692077e-01 -3.51682514e-01 -4.23712611e-01 2.34553944e-02
-9.92680252e-01 -4.66223001e-01 9.24773395e-01 4.47435915e-01
-3.12773228e-01 5.08795559e-01 8.17175806e-01 -1.83477059e-01
-4.05209094e-01 -1.19752026e+00 -6.28786683e-01 -3.12395841e-01
-8.37194398e-02 5.62576890e-01 2.93291628e-01 -7.68692017... | [11.32787799835205, 7.807013511657715] |
0df8496b-18c4-4116-afd3-db37408a29d8 | generalization-bounds-and-algorithms-for-1 | 2205.14692 | null | https://arxiv.org/abs/2205.14692v1 | https://arxiv.org/pdf/2205.14692v1.pdf | Generalization bounds and algorithms for estimating conditional average treatment effect of dosage | We investigate the task of estimating the conditional average causal effect of treatment-dosage pairs from a combination of observational data and assumptions on the causal relationships in the underlying system. This has been a longstanding challenge for fields of study such as epidemiology or economics that require a... | ['Giulia Prando', 'Anish Dhir', 'Alexis Bellot'] | 2022-05-29 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 6.52213573e-01 1.50966629e-01 -1.04845524e+00 -3.84196252e-01
-1.01796699e+00 -4.84112680e-01 8.24498832e-01 4.12179768e-01
-4.76411760e-01 1.26041186e+00 5.96681356e-01 -6.93859100e-01
-7.04172373e-01 -5.32936335e-01 -9.38047349e-01 -6.68223739e-01
-4.80008066e-01 6.70226276e-01 -5.73356688e-01 2.11066023... | [8.06107234954834, 5.374167442321777] |
57e3a7b1-5f73-4cd5-99f7-1f3351933e03 | task-independent-capsule-based-agents-for | null | null | https://www.researchgate.net/publication/357764898_Task_Independent_Capsule-Based_Agents_for_Deep_Q-Learning | https://link.springer.com/chapter/10.1007/978-3-030-93842-0_4 | Task Independent Capsule-Based Agents for Deep Q-Learning | In recent years, Capsule Networks (CapsNets) have achieved promising results in tasks such as object recognition thanks to their invariance characteristics towards pose and lighting. They have been proposed as an alternative to relational insensitive and translation invariant Convolutional Neural Networks (CNN). It has... | ['Steven Latre ́', 'Jose ́ Oramas', 'Peter Hellinckx', 'Kevin Mets', 'Tom De Schepper', 'Akash Singh'] | 2022-01-11 | null | null | null | benelux-conference-on-artificial-intelligence | ['object-recognition'] | ['computer-vision'] | [-2.45335922e-01 -1.40978366e-01 -1.45155266e-01 -8.70978087e-02
-5.16229033e-01 -6.30557954e-01 7.40580261e-01 -2.84356147e-01
-7.59344339e-01 8.00093055e-01 -3.82424481e-02 -1.28882334e-01
-3.58689815e-01 -6.24177873e-01 -1.04803658e+00 -7.97015011e-01
-4.98152107e-01 5.25036156e-01 2.13965371e-01 -5.87363362... | [4.077277183532715, 1.607572078704834] |
1a6ddd6d-7af4-41d2-8545-1d07f973bd71 | scene-graph-as-pivoting-inference-time-image | 2305.12256 | null | https://arxiv.org/abs/2305.12256v2 | https://arxiv.org/pdf/2305.12256v2.pdf | Scene Graph as Pivoting: Inference-time Image-free Unsupervised Multimodal Machine Translation with Visual Scene Hallucination | In this work, we investigate a more realistic unsupervised multimodal machine translation (UMMT) setup, inference-time image-free UMMT, where the model is trained with source-text image pairs, and tested with only source-text inputs. First, we represent the input images and texts with the visual and language scene grap... | ['Tat-Seng Chua', 'Min Zhang', 'Meishan Zhang', 'Qian Liu', 'Hao Fei'] | 2023-05-20 | null | null | null | null | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 4.98496503e-01 2.02640533e-01 -9.14392844e-02 -2.56642491e-01
-9.83165085e-01 -7.53925979e-01 1.21972895e+00 -3.42020839e-01
-2.57491797e-01 5.15708745e-01 3.99211735e-01 -4.70300019e-01
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7.00345576e-01 7.83287764e-01 -1.39828309e-01 -1.49900302... | [11.339550018310547, 1.4018806219100952] |
ae391e9e-b5d8-4542-86bc-217051238991 | pure-exploration-in-multi-armed-bandits-with-1 | 2306.15856 | null | https://arxiv.org/abs/2306.15856v1 | https://arxiv.org/pdf/2306.15856v1.pdf | Pure exploration in multi-armed bandits with low rank structure using oblivious sampler | In this paper, we consider the low rank structure of the reward sequence of the pure exploration problems. Firstly, we propose the separated setting in pure exploration problem, where the exploration strategy cannot receive the feedback of its explorations. Due to this separation, it requires that the exploration strat... | ['Eiji Takimoto', 'Kohei Hatano', 'Atsuyoshi Nakamura', 'Yaxiong Liu'] | 2023-06-28 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 7.96061307e-02 3.58701259e-01 -7.16940463e-01 1.17415050e-02
-9.43209469e-01 -1.16481137e+00 -1.51743710e-01 -1.17998511e-01
-7.61953115e-01 9.34007883e-01 7.57849123e-03 -8.48409891e-01
-8.85948539e-01 -8.93694520e-01 -9.51767921e-01 -9.92650867e-01
-5.96407533e-01 7.78270304e-01 9.64103173e-03 -1.06562495... | [4.577956199645996, 3.3597092628479004] |
95361383-a7dc-466a-8fe6-419018f87d68 | generating-safe-diversity-in-nlg-via | 2004.14364 | null | https://arxiv.org/abs/2004.14364v2 | https://arxiv.org/pdf/2004.14364v2.pdf | Informed Sampling for Diversity in Concept-to-Text NLG | Deep-learning models for language generation tasks tend to produce repetitive output. Various methods have been proposed to encourage lexical diversity during decoding, but this often comes at a cost to the perceived fluency and adequacy of the output. In this work, we propose to ameliorate this cost by using an Imitat... | ['Giulio Zhou', 'Gerasimos Lampouras'] | 2020-04-29 | null | https://aclanthology.org/2021.findings-emnlp.213 | https://aclanthology.org/2021.findings-emnlp.213.pdf | findings-emnlp-2021-11 | ['concept-to-text-generation'] | ['natural-language-processing'] | [ 1.55971721e-01 4.17495936e-01 -1.13016121e-01 -1.12029940e-01
-6.11531854e-01 -4.85068768e-01 1.01956344e+00 1.45028383e-01
-2.28545755e-01 8.49930644e-01 5.85438013e-01 -1.93597600e-01
3.37631732e-01 -7.36840010e-01 -5.01487255e-01 -3.49796861e-01
2.50591248e-01 3.96592647e-01 -2.67543674e-01 -3.53251159... | [11.720465660095215, 9.148380279541016] |
6b112117-e6da-4f0a-b015-30d45aadf00c | learning-multi-attention-convolutional-neural | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Zheng_Learning_Multi-Attention_Convolutional_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Zheng_Learning_Multi-Attention_Convolutional_ICCV_2017_paper.pdf | Learning Multi-Attention Convolutional Neural Network for Fine-Grained Image Recognition | Recognizing fine-grained categories (e.g., bird species) highly relies on discriminative part localization and part-based fine-grained feature learning. Existing approaches predominantly solve these challenges independently, while neglecting the fact that part localization (e.g., head of a bird) and fine-grained featur... | ['Jiebo Luo', 'Jianlong Fu', 'Heliang Zheng', 'Tao Mei'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['fine-grained-image-recognition'] | ['computer-vision'] | [-5.11884764e-02 -2.29128689e-01 -1.92652941e-02 -7.43321002e-01
-5.98024368e-01 -6.32924020e-01 4.23785597e-01 -1.49398282e-01
-2.42229432e-01 6.02323651e-01 2.35015944e-01 3.75676006e-01
-8.37463364e-02 -9.27919984e-01 -1.15343976e+00 -6.60283685e-01
-6.03167526e-02 2.04274520e-01 2.60426044e-01 4.48828787... | [9.582928657531738, 1.986059308052063] |
94fd0e8c-1ed7-426e-b109-511d4a62bf16 | intrinsic-decomposition-of-document-images-in | 2011.14447 | null | https://arxiv.org/abs/2011.14447v1 | https://arxiv.org/pdf/2011.14447v1.pdf | Intrinsic Decomposition of Document Images In-the-Wild | Automatic document content processing is affected by artifacts caused by the shape of the paper, non-uniform and diverse color of lighting conditions. Fully-supervised methods on real data are impossible due to the large amount of data needed. Hence, the current state of the art deep learning models are trained on full... | ['Dimitris Samaras', 'Maria Vanrell', 'Ramon Baldrich', 'Ke Ma', 'Hassan Ahmed Sial', 'Sagnik Das'] | 2020-11-29 | null | null | null | null | ['shadow-removal', 'intrinsic-image-decomposition'] | ['computer-vision', 'computer-vision'] | [ 7.33546674e-01 -2.96572804e-01 5.89963496e-01 -1.62815839e-01
-5.78626633e-01 -7.03598976e-01 8.97962987e-01 -6.90247267e-02
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-6.22159429e-02 -6.72874391e-01 -7.13434279e-01 -9.65296328e-01
3.04719836e-01 6.29849434e-01 3.74006368e-02 -2.37236112... | [10.062957763671875, -2.809771776199341] |
e4d6be64-d349-4728-b0b3-6d4037dc3dab | offline-policy-evaluation-for-reinforcement | 2306.14063 | null | https://arxiv.org/abs/2306.14063v1 | https://arxiv.org/pdf/2306.14063v1.pdf | Offline Policy Evaluation for Reinforcement Learning with Adaptively Collected Data | Developing theoretical guarantees on the sample complexity of offline RL methods is an important step towards making data-hungry RL algorithms practically viable. Currently, most results hinge on unrealistic assumptions about the data distribution -- namely that it comprises a set of i.i.d. trajectories collected by a ... | ['Yu-Xiang Wang', 'Ming Yin', 'Dan Xiao', 'Sunil Madhow'] | 2023-06-24 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-2.02242389e-01 1.18155353e-01 -9.44169700e-01 -1.85049295e-01
-1.31586933e+00 -1.01025164e+00 3.37062091e-01 1.75838277e-01
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-6.02332711e-01 8.98604453e-01 -9.62321162e-02 3.34284753... | [4.295804500579834, 2.7747855186462402] |
714d36d3-79ca-4643-bb26-174fbf316ee6 | frame-interpolation-transformer-and | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Plack_Frame_Interpolation_Transformer_and_Uncertainty_Guidance_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Plack_Frame_Interpolation_Transformer_and_Uncertainty_Guidance_CVPR_2023_paper.pdf | Frame Interpolation Transformer and Uncertainty Guidance | Video frame interpolation has seen important progress in recent years, thanks to developments in several directions. Some works leverage better optical flow methods with improved splatting strategies or additional cues from depth, while others have investigated alternative approaches through direct predictions or t... | ['Christopher Schroers', 'Markus Gross', 'Matthias B. Hullin', 'Abdelaziz Djelouah', 'Karlis Martins Briedis', 'Markus Plack'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-frame-interpolation'] | ['computer-vision'] | [ 2.68983722e-01 -3.91559377e-02 -1.81192048e-02 -2.29729697e-01
-4.36208040e-01 -3.22252423e-01 4.63592201e-01 1.20753676e-01
-2.66309440e-01 9.60693955e-01 2.30248004e-01 -2.92710904e-02
1.24656238e-01 -7.81867862e-01 -7.46811628e-01 -5.50361276e-01
-1.79803491e-01 1.29078664e-02 5.39238751e-01 -2.51285821... | [10.765586853027344, -1.413009524345398] |
86ab0393-5693-4bfd-8dcc-036014b551cd | brent-bidirectional-retrieval-enhanced | 2304.09649 | null | https://arxiv.org/abs/2304.09649v1 | https://arxiv.org/pdf/2304.09649v1.pdf | BRENT: Bidirectional Retrieval Enhanced Norwegian Transformer | Retrieval-based language models are increasingly employed in question-answering tasks. These models search in a corpus of documents for relevant information instead of having all factual knowledge stored in its parameters, thereby enhancing efficiency, transparency, and adaptability. We develop the first Norwegian retr... | ['Egil Rønningstad', 'David Samuel', 'Sondre Wold', 'Lucas Georges Gabriel Charpentier'] | 2023-04-19 | null | null | null | null | ['lemmatization', 'dependency-parsing', 'part-of-speech-tagging'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-9.42651927e-02 4.15007710e-01 -6.41588941e-02 -3.19376230e-01
-1.45351136e+00 -9.86498594e-01 6.19416535e-01 5.02989709e-01
-9.12834883e-01 4.88474190e-01 6.30895734e-01 -7.35997498e-01
-2.35875487e-01 -5.02565920e-01 -4.16964591e-01 -1.03673013e-02
3.01458418e-01 5.80366313e-01 5.15178978e-01 -4.88424212... | [11.20077133178711, 8.039229393005371] |
b5651fcb-c090-4aad-b361-a03748e34d77 | task-oriented-hand-motion-retargeting-for | 1810.01845 | null | http://arxiv.org/abs/1810.01845v1 | http://arxiv.org/pdf/1810.01845v1.pdf | Task-Oriented Hand Motion Retargeting for Dexterous Manipulation Imitation | Human hand actions are quite complex, especially when they involve object
manipulation, mainly due to the high dimensionality of the hand and the vast
action space that entails. Imitating those actions with dexterous hand models
involves different important and challenging steps: acquiring human hand
information, retar... | ['Tae-Kyun Kim', 'Guillermo Garcia-Hernando', 'Dafni Antotsiou'] | 2018-10-03 | null | null | null | null | ['motion-retargeting'] | ['computer-vision'] | [ 1.05617821e-01 2.25157589e-01 -1.66963506e-02 3.01571518e-01
-3.91686440e-01 -7.80895174e-01 5.50304174e-01 -8.30610693e-01
-6.23655260e-01 8.06445658e-01 2.05560066e-02 6.59894720e-02
-2.51526117e-01 -2.41033062e-01 -1.01894641e+00 -7.31650889e-01
-2.38485299e-02 1.03043151e+00 4.41280678e-02 -2.81933039... | [4.743877410888672, 0.5872628688812256] |
4d19def0-0e2b-418b-9942-ae3952229b1b | learning-attraction-field-representation-for | 1812.02122 | null | http://arxiv.org/abs/1812.02122v2 | http://arxiv.org/pdf/1812.02122v2.pdf | Learning Attraction Field Representation for Robust Line Segment Detection | This paper presents a region-partition based attraction field dual
representation for line segment maps, and thus poses the problem of line
segment detection (LSD) as the region coloring problem. The latter is then
addressed by learning deep convolutional neural networks (ConvNets) for
accuracy, robustness and efficien... | ['Fu-Dong Wang', 'Gui-Song Xia', 'Song Bai', 'Nan Xue', 'Liangpei Zhang', 'Tianfu Wu'] | 2018-12-05 | learning-attraction-field-representation-for-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Xue_Learning_Attraction_Field_Representation_for_Robust_Line_Segment_Detection_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Xue_Learning_Attraction_Field_Representation_for_Robust_Line_Segment_Detection_CVPR_2019_paper.pdf | cvpr-2019-6 | ['line-segment-detection'] | ['computer-vision'] | [ 4.04738933e-01 8.10367987e-02 -5.23950875e-01 -3.15687537e-01
-7.69251585e-01 -7.20780373e-01 1.14818394e-01 1.81792572e-01
-4.50240999e-01 4.07859534e-01 -5.70558250e-01 -4.89968002e-01
2.54255146e-01 -9.77697790e-01 -1.13072658e+00 -4.20021981e-01
7.70287355e-03 2.19045311e-01 5.21765113e-01 -1.92317396... | [8.313459396362305, -1.5467902421951294] |
dbef2cd9-ef65-420b-b096-77e7c95b81ae | over-the-air-membership-inference-attacks-as | 2006.14576 | null | https://arxiv.org/abs/2006.14576v1 | https://arxiv.org/pdf/2006.14576v1.pdf | Over-the-Air Membership Inference Attacks as Privacy Threats for Deep Learning-based Wireless Signal Classifiers | This paper presents how to leak private information from a wireless signal classifier by launching an over-the-air membership inference attack (MIA). As machine learning (ML) algorithms are used to process wireless signals to make decisions such as PHY-layer authentication, the training data characteristics (e.g., devi... | ['Yalin E. Sagduyu', 'Kemal Davaslioglu', 'Yi Shi'] | 2020-06-25 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 8.11921299e-01 1.41851991e-01 -1.59401134e-01 -6.99452311e-02
-1.00610387e+00 -1.14413989e+00 1.50343418e-01 2.31858805e-01
-1.25662088e-01 4.69121844e-01 -5.96117318e-01 -9.29447293e-01
-5.19478545e-02 -1.10463119e+00 -8.31147373e-01 -1.09838021e+00
-7.24886954e-01 -2.67335594e-01 -2.72408962e-01 2.25936219... | [13.885719299316406, 5.821480751037598] |
bcdf643b-f97b-48b6-9695-cb69c3898f9e | a-method-for-automatically-animating-children | 2303.12741 | null | https://arxiv.org/abs/2303.12741v2 | https://arxiv.org/pdf/2303.12741v2.pdf | A Method for Animating Children's Drawings of the Human Figure | Children's drawings have a wonderful inventiveness, creativity, and variety to them. We present a system that automatically animates children's drawings of the human figure, is robust to the variance inherent in these depictions, and is simple and straightforward enough for anyone to use. We demonstrate the value and b... | ['Jessica K. Hodgins', 'Somya Jain', 'Yifei Li', 'Qingyuan Zheng', 'Harrison Jesse Smith'] | 2023-03-07 | null | null | null | null | ['image-to-video'] | ['computer-vision'] | [ 8.34034681e-02 1.45358935e-01 3.03262860e-01 -4.35438544e-01
-2.09492579e-01 -1.18989182e+00 6.81527853e-01 -6.58829585e-02
-8.63293111e-02 3.26260448e-01 2.53799170e-01 4.85591888e-02
5.61462268e-02 -5.75359404e-01 -5.25509119e-01 -8.61617401e-02
-6.46388903e-02 6.88113093e-01 4.56862003e-01 -3.01651597... | [11.699957847595215, -0.2516021430492401] |
166c18ad-d9fc-4d16-86ea-69be490ef1d8 | dynamic-knowledge-distillation-with-a-single | 2106.09517 | null | https://arxiv.org/abs/2106.09517v3 | https://arxiv.org/pdf/2106.09517v3.pdf | Dynamic Knowledge Distillation With Noise Elimination for RGB-D Salient Object Detection | RGB-D salient object detection (SOD) demonstrates its superiority on detecting in complex environments due to the additional depth information introduced in the data. Inevitably, an independent stream is introduced to extract features from depth images, leading to extra computation and parameters. This methodology sacr... | ['Tania Stathaki', 'Hengyan Liu', 'Yinxiao Yu', 'Guangyu Ren'] | 2021-06-17 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 2.75421977e-01 -1.41298428e-01 1.13526091e-01 -4.38357472e-01
-4.94468540e-01 -7.32311904e-02 2.88910866e-01 3.22591782e-01
-8.39528739e-01 3.82911950e-01 -8.49850178e-02 -2.66074836e-01
-6.47219941e-02 -7.89619207e-01 -6.20595217e-01 -9.09905851e-01
2.74249613e-01 -1.11722626e-01 9.13694918e-01 7.71449432... | [9.589129447937012, -0.8303123116493225] |
aeff4033-3358-4c7c-b32c-966b3aea1dd6 | a-case-study-of-empirical-bayes-in-user-movie | 1707.02294 | null | http://arxiv.org/abs/1707.02294v1 | http://arxiv.org/pdf/1707.02294v1.pdf | A case study of Empirical Bayes in User-Movie Recommendation system | In this article we provide a formulation of empirical bayes described by
Atchade (2011) to tune the hyperparameters of priors used in bayesian set up of
collaborative filter. We implement the same in MovieLens small dataset. We see
that it can be used to get a good initial choice for the parameters. It can
also be used... | ['Raghav Somani', 'Sreangsu Acharyya', 'Arabin Kumar Dey'] | 2017-07-07 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-4.37014073e-01 -1.92283839e-01 1.02370270e-01 -5.19830167e-01
-5.95743060e-01 -1.03469634e+00 8.01618934e-01 1.14316247e-01
-6.84667945e-01 8.06647956e-01 2.69090831e-01 -3.21710736e-01
-4.15832937e-01 -8.76100361e-01 -3.47822070e-01 -8.51459742e-01
1.31075069e-01 9.96652365e-01 8.43492866e-01 -2.56810516... | [6.703224182128906, 4.007948875427246] |
cf744abf-355f-40f2-a575-2b4820cda0a4 | thin-plate-spline-motion-model-for-image | 2203.14367 | null | https://arxiv.org/abs/2203.14367v2 | https://arxiv.org/pdf/2203.14367v2.pdf | Thin-Plate Spline Motion Model for Image Animation | Image animation brings life to the static object in the source image according to the driving video. Recent works attempt to perform motion transfer on arbitrary objects through unsupervised methods without using a priori knowledge. However, it remains a significant challenge for current unsupervised methods when there... | ['HUI ZHANG', 'Jian Zhao'] | 2022-03-27 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhao_Thin-Plate_Spline_Motion_Model_for_Image_Animation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhao_Thin-Plate_Spline_Motion_Model_for_Image_Animation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['image-animation'] | ['computer-vision'] | [ 2.33340845e-01 -1.94365377e-04 2.47108918e-02 -2.94929206e-01
-4.83355373e-01 -2.99956977e-01 3.73874784e-01 -7.56404638e-01
-1.79119393e-01 7.15507627e-01 2.32444242e-01 3.43926698e-01
2.07499921e-01 -6.63430154e-01 -8.09583008e-01 -7.34561265e-01
2.39624754e-01 2.77877927e-01 3.99360567e-01 -2.41596267... | [10.908447265625, -0.8358219265937805] |
c6d37c04-60fa-4bf4-828c-9cfe4703d0c9 | apsnet-attention-based-point-cloud-sampling | 2210.05638 | null | https://arxiv.org/abs/2210.05638v1 | https://arxiv.org/pdf/2210.05638v1.pdf | APSNet: Attention Based Point Cloud Sampling | Processing large point clouds is a challenging task. Therefore, the data is often downsampled to a smaller size such that it can be stored, transmitted and processed more efficiently without incurring significant performance degradation. Traditional task-agnostic sampling methods, such as farthest point sampling (FPS),... | ['Shihao Ji', 'Xiulong Yang', 'Yang Ye'] | 2022-10-11 | null | null | null | null | ['3d-point-cloud-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [ 1.77188084e-01 -1.51821047e-01 -1.86560750e-01 -3.74937713e-01
-1.11862266e+00 -1.34830445e-01 4.43574369e-01 2.10253835e-01
-1.75213024e-01 6.69521093e-01 -1.87396318e-01 8.67350698e-02
-4.33104672e-02 -1.15084124e+00 -1.04111302e+00 -7.20081449e-01
5.81238195e-02 1.01280451e+00 2.83945352e-01 1.73864424... | [8.253817558288574, -3.519660472869873] |
5bcb77c2-4941-46f6-b9af-77855b81eb95 | fraunhofer-sit-at-checkthat-2023-mixing | 2307.00610 | null | https://arxiv.org/abs/2307.00610v1 | https://arxiv.org/pdf/2307.00610v1.pdf | Fraunhofer SIT at CheckThat! 2023: Mixing Single-Modal Classifiers to Estimate the Check-Worthiness of Multi-Modal Tweets | The option of sharing images, videos and audio files on social media opens up new possibilities for distinguishing between false information and fake news on the Internet. Due to the vast amount of data shared every second on social media, not all data can be verified by a computer or a human expert. Here, a check-wort... | ['Inna Vogel', 'Raphael Frick'] | 2023-07-02 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 7.62966573e-02 4.03800666e-01 -4.87681963e-02 -7.74215534e-02
-9.65310454e-01 -6.63455009e-01 8.90069366e-01 6.42698407e-01
-6.03069782e-01 6.01338983e-01 -2.26082996e-01 -2.82429338e-01
1.66183457e-01 -7.13897765e-01 -6.06782854e-01 -6.03205144e-01
3.57852690e-02 3.42381060e-01 6.62347496e-01 -1.07800633... | [8.148065567016602, 10.224099159240723] |
4d624737-535b-45c8-80a5-60ec24b28c49 | a-survey-of-deep-learning-techniques-for-the | 2202.06372 | null | https://arxiv.org/abs/2202.06372v2 | https://arxiv.org/pdf/2202.06372v2.pdf | A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron | The Coronavirus (COVID-19) outbreak in December 2019 has become an ongoing threat to humans worldwide, creating a health crisis that infected millions of lives, as well as devastating the global economy. Deep learning (DL) techniques have proved helpful in analysis and delineation of infectious regions in radiological ... | ['Muhammad Waleed Khan', 'Anabia Sohail', 'Asiya Batool', 'Mahrukh Saif', 'Saddam Hussain Khan', 'Asifullah Khan'] | 2022-02-13 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 3.76249999e-01 -3.79833549e-01 -2.49322236e-01 -2.38315195e-01
-9.24441278e-01 -6.73903525e-01 1.57360941e-01 5.24212241e-01
-4.63739604e-01 3.63135755e-01 3.12232375e-02 -5.86183369e-01
-3.22788805e-01 -4.93402779e-01 -2.97169030e-01 -9.14918959e-01
-5.17363131e-01 9.34338808e-01 -4.71192002e-02 3.55057448... | [15.536113739013672, -1.7459293603897095] |
bbb8fd15-872f-4484-bc6a-152f55ee4680 | video-diffusion-models-with-local-global | 2306.02562 | null | https://arxiv.org/abs/2306.02562v1 | https://arxiv.org/pdf/2306.02562v1.pdf | Video Diffusion Models with Local-Global Context Guidance | Diffusion models have emerged as a powerful paradigm in video synthesis tasks including prediction, generation, and interpolation. Due to the limitation of the computational budget, existing methods usually implement conditional diffusion models with an autoregressive inference pipeline, in which the future fragment is... | ['You He', 'Zhizhuo Jiang', 'Yu Liu', 'Lu Zhang', 'Siyuan Yang'] | 2023-06-05 | null | null | null | null | ['video-generation', 'video-prediction', 'unconditional-video-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-9.59778130e-02 -2.78766543e-01 -2.15039670e-01 -3.35696697e-01
-4.72317576e-01 8.47486034e-03 6.70681834e-01 -3.62005860e-01
8.16481337e-02 5.83748102e-01 7.64645934e-01 -9.16245133e-02
2.56138146e-01 -6.52496696e-01 -7.78961003e-01 -7.44286001e-01
2.36822769e-01 -3.63383979e-01 3.83743376e-01 -5.45413606... | [10.718304634094238, -0.6861099600791931] |
7c98b1e5-288c-4674-9045-dd4d78eec392 | answering-complex-logical-queries-on | 2212.09567 | null | https://arxiv.org/abs/2212.09567v3 | https://arxiv.org/pdf/2212.09567v3.pdf | Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization | Answering complex logical queries on incomplete knowledge graphs is a challenging task, and has been widely studied. Embedding-based methods require training on complex queries, and cannot generalize well to out-of-distribution query structures. Recent work frames this task as an end-to-end optimization problem, and it... | ['Lei Hou', 'Juanzi Li', 'Xin Lv', 'Yushi Bai'] | 2022-12-19 | null | null | null | null | ['complex-query-answering'] | ['knowledge-base'] | [-1.90685123e-01 1.49711862e-01 -6.32607639e-01 -2.92874575e-01
-1.09372592e+00 -5.75629473e-01 4.28380147e-02 4.90929276e-01
-3.63180041e-01 4.81607676e-01 6.57476112e-02 -3.82809967e-01
-3.05017412e-01 -1.12273538e+00 -1.02715874e+00 -1.44336894e-01
5.96616082e-02 9.80021656e-01 5.91323316e-01 -1.69487000... | [9.324214935302734, 7.716960430145264] |
9fa80e15-1e7c-4479-baa8-0b26b1335c09 | cic-lt-edi-acl2022-are-transformers-the-only | null | null | https://aclanthology.org/2022.ltedi-1.28 | https://aclanthology.org/2022.ltedi-1.28.pdf | CIC@LT-EDI-ACL2022: Are transformers the only hope? Hope speech detection for Spanish and English comments | Hope is an inherent part of human life and essential for improving the quality of life. Hope increases happiness and reduces stress and feelings of helplessness. Hope speech is the desired outcome for better and can be studied using text from various online sources where people express their desires and outcomes. In th... | ['Alexander Gelbukh', 'Grigori Sidorov', 'Sabur Butt', 'Fazlourrahman Balouchzahi'] | null | null | null | null | ltedi-acl-2022-5 | ['hope-speech-detection'] | ['natural-language-processing'] | [-1.05028617e+00 3.49820137e-01 -8.35201323e-01 -3.28049809e-01
-7.36450195e-01 1.97356850e-01 7.63235152e-01 6.58360541e-01
-3.14175457e-01 9.53120053e-01 1.49173141e+00 2.64883220e-01
1.12303108e-01 -6.49422646e-01 3.84658635e-01 -1.02866471e-01
-9.79642272e-02 1.22115813e-01 -7.29314029e-01 -6.42864347... | [9.034977912902832, 10.748452186584473] |
4e6a9aed-010f-4914-951f-3df3c4dd2bf1 | a-systematic-review-of-transfer-learning | 2105.13793 | null | https://arxiv.org/abs/2105.13793v1 | https://arxiv.org/pdf/2105.13793v1.pdf | A systematic review of transfer learning based approaches for diabetic retinopathy detection | Cases of diabetes and related diabetic retinopathy (DR) have been increasing at an alarming rate in modern times. Early detection of DR is an important problem since it may cause permanent blindness in the late stages. In the last two decades, many different approaches have been applied in DR detection. Reviewing acade... | ['Atilla Özgür', 'Hamit Erdem', 'Büşra Kübra Karaca', 'Burcu Oltu'] | 2021-05-28 | null | null | null | null | ['diabetic-retinopathy-detection'] | ['medical'] | [-2.75187641e-02 -1.08830400e-01 -2.08900243e-01 -4.69072104e-01
-1.37496784e-01 -1.07603677e-01 3.35487098e-01 -8.96664783e-02
-5.83378375e-01 8.74845326e-01 1.53285742e-01 -3.56546760e-01
-8.38758945e-02 -8.19864452e-01 -7.28334039e-02 -6.85684621e-01
8.65062997e-02 3.14404845e-01 1.04635231e-01 -8.79383311... | [15.83752727508545, -3.98468017578125] |
b70d536c-13ef-4b46-864e-9a10769eaddf | unsupervised-text-summarization-of-long | null | null | https://aclanthology.org/2022.rocling-1.3 | https://aclanthology.org/2022.rocling-1.3.pdf | Unsupervised Text Summarization of Long Documents using Dependency-based Noun Phrases and Contextual Order Arrangement | Unsupervised extractive summarization has recently gained importance since it does not require labeled data. Among unsupervised methods, graph-based approaches have achieved outstanding results. These methods represent each document by a graph, with sentences as nodes and word-level similarity among sentences as edges.... | ['Yi-Shin Chen', 'Hsiao-Yen Lan', 'Yen-Hao Huang'] | null | null | null | null | rocling-2022-11 | ['unsupervised-extractive-summarization', 'extractive-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.97995389e-01 3.14968348e-01 -4.18932855e-01 -2.42936343e-01
-4.78088200e-01 -5.65330446e-01 4.41356540e-01 9.59447324e-01
-3.69688660e-01 6.61228836e-01 1.02658582e+00 7.89179206e-02
-1.91654146e-01 -8.38556707e-01 -7.86161795e-02 -5.76667249e-01
-1.11880720e-01 1.82998374e-01 3.58291566e-01 -3.35622370... | [12.493996620178223, 9.540916442871094] |
9993fc6b-0ac9-4af2-81a4-baecb4f49e08 | hybrid-multimodal-fusion-for-humor-detection | 2209.11949 | null | https://arxiv.org/abs/2209.11949v1 | https://arxiv.org/pdf/2209.11949v1.pdf | Hybrid Multimodal Fusion for Humor Detection | In this paper, we present our solution to the MuSe-Humor sub-challenge of the Multimodal Emotional Challenge (MuSe) 2022. The goal of the MuSe-Humor sub-challenge is to detect humor and calculate AUC from audiovisual recordings of German football Bundesliga press conferences. It is annotated for humor displayed by the ... | ['Meng Wang', 'Xiao Sun', 'Yunwei Shi', 'Yasi Peng', 'Yu Feng', 'Mingzheng Li', 'Jingwei Liu', 'Weifeng Liu', 'Haojie Xu'] | 2022-09-24 | null | null | null | null | ['humor-detection'] | ['natural-language-processing'] | [-4.25275683e-01 -2.85359234e-01 2.00324684e-01 1.52051389e-01
-1.03730834e+00 -3.28560561e-01 3.96061093e-01 -1.00258484e-01
-3.74799639e-01 4.53580737e-01 6.92889929e-01 3.21683347e-01
2.42376477e-01 -1.80080190e-01 -3.18168104e-01 -2.99760073e-01
2.53943324e-01 -8.78280774e-02 1.34861737e-01 -4.33966190... | [13.223134994506836, 5.124037742614746] |
6a4a8d54-ab4b-4f17-969a-e20bfb9e43d3 | freehand-ultrasound-image-simulation-with | 1707.05392 | null | http://arxiv.org/abs/1707.05392v1 | http://arxiv.org/pdf/1707.05392v1.pdf | Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks | Sonography synthesis has a wide range of applications, including medical
procedure simulation, clinical training and multimodality image registration.
In this paper, we propose a machine learning approach to simulate ultrasound
images at given 3D spatial locations (relative to the patient anatomy), based
on conditional... | ['Li-Lin Lee', 'Yipeng Hu', 'Tom Vercauteren', 'Weidi Xie', 'Eli Gibson', 'Dean C. Barratt', 'J. Alison Noble'] | 2017-07-17 | null | null | null | null | ['medical-procedure'] | ['medical'] | [ 5.44344246e-01 5.95964134e-01 4.45155948e-01 -1.06225386e-01
-8.95765305e-01 -6.47600174e-01 3.10662925e-01 -4.49495614e-01
-3.19577217e-01 8.04979742e-01 2.36808900e-02 -3.83616954e-01
-8.68156701e-02 -6.93337679e-01 -1.01748884e+00 -9.76284504e-01
-3.65979224e-01 3.91478151e-01 -8.35165158e-02 2.61865016... | [14.13900089263916, -2.0197365283966064] |
e93a208b-c497-42cc-80c1-ecf282109af0 | re-embedding-words | null | null | https://aclanthology.org/P13-2087 | https://aclanthology.org/P13-2087.pdf | Re-embedding words | null | ['Igor Labutov', 'Hod Lipson'] | 2013-08-01 | null | null | null | acl-2013-8 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.445281505584717, 3.647340774536133] |
7866dc14-7415-4785-aafc-1136af25b80f | the-surprising-effectiveness-of-diffusion | 2306.01923 | null | https://arxiv.org/abs/2306.01923v1 | https://arxiv.org/pdf/2306.01923v1.pdf | The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation | Denoising diffusion probabilistic models have transformed image generation with their impressive fidelity and diversity. We show that they also excel in estimating optical flow and monocular depth, surprisingly, without task-specific architectures and loss functions that are predominant for these tasks. Compared to the... | ['David J. Fleet', 'Deqing Sun', 'Mohammad Norouzi', 'Abhishek Kar', 'Junhwa Hur', 'Charles Herrmann', 'Saurabh Saxena'] | 2023-06-02 | null | null | null | null | ['monocular-depth-estimation'] | ['computer-vision'] | [-6.21068291e-02 -1.07890576e-01 1.68073587e-02 -3.42033319e-02
-9.73068774e-01 -5.46769023e-01 7.26704180e-01 -4.94063497e-01
-4.95363772e-01 1.05461884e+00 3.50992262e-01 -1.26290426e-01
1.34115843e-02 -5.93594491e-01 -6.42137945e-01 -9.11791921e-01
-8.45621452e-02 4.35226381e-01 1.22098990e-01 2.94622600... | [8.786431312561035, -2.2106196880340576] |
53a59cbc-3cb7-4b78-8a10-fcabc61e95f6 | association-metrics-in-neural-transition | null | null | https://aclanthology.org/W19-7722 | https://aclanthology.org/W19-7722.pdf | Association Metrics in Neural Transition-Based Dependency Parsing | null | ['Dani{\\"e}l de Kok', 'Sebastian P{\\"u}tz', 'Patricia Fischer'] | 2019-08-01 | null | null | null | ws-2019-8 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.21635627746582, 3.826930046081543] |
d77dad25-2102-4c05-bc37-c21a2aa1563d | decoupling-features-in-hierarchical | 2210.09782 | null | https://arxiv.org/abs/2210.09782v3 | https://arxiv.org/pdf/2210.09782v3.pdf | Decoupling Features in Hierarchical Propagation for Video Object Segmentation | This paper focuses on developing a more effective method of hierarchical propagation for semi-supervised Video Object Segmentation (VOS). Based on vision transformers, the recently-developed Associating Objects with Transformers (AOT) approach introduces hierarchical propagation into VOS and has shown promising results... | ['Yi Yang', 'Zongxin Yang'] | 2022-10-18 | null | null | null | null | ['semi-supervised-video-object-segmentation', 'video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [-3.37479323e-01 -2.00871885e-01 -2.69610196e-01 -2.19857305e-01
-4.74346191e-01 -2.65505314e-01 2.30726823e-01 -6.24057464e-02
-5.32998979e-01 3.54414344e-01 3.64993438e-02 -1.95242912e-01
3.44831407e-01 -7.56250620e-01 -8.68487000e-01 -5.47714889e-01
3.12256329e-02 9.72872525e-02 7.81856179e-01 2.29383871... | [9.317607879638672, -0.08782043308019638] |
81c680c9-fd79-4777-8c86-6f28598f56a9 | sentu-sentiment-analysis-of-tweets-by | null | null | https://aclanthology.org/S15-2108 | https://aclanthology.org/S15-2108.pdf | SeNTU: Sentiment Analysis of Tweets by Combining a Rule-based Classifier with Supervised Learning | null | ['Soujanya Poria', 'Prerna Chikersal', 'Erik Cambria'] | 2015-06-01 | null | null | null | semeval-2015-6 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.295368671417236, 3.6487231254577637] |
9d6f42cd-e9f0-4056-8c9c-81af77b09cd2 | asc-net-unsupervised-medical-anomaly | 2112.09135 | null | https://arxiv.org/abs/2112.09135v1 | https://arxiv.org/pdf/2112.09135v1.pdf | ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network | In this paper we consider the problem of unsupervised anomaly segmentation in medical images, which has attracted increasing attention in recent years due to the expensive pixel-level annotations from experts and the existence of a large amount of unannotated normal and abnormal image scans. We introduce a segmentation... | ['Yi Hong', 'Haibo Xu', 'Wenbo Sun', 'Raunak Dey'] | 2021-12-16 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 5.19059539e-01 6.71162784e-01 -3.98341753e-02 -5.92807174e-01
-7.98116326e-01 -4.85739827e-01 2.75513351e-01 3.68684709e-01
-6.23196840e-01 2.18692958e-01 -2.10282028e-01 -4.82923210e-01
3.07576507e-01 -7.00546503e-01 -6.93430960e-01 -8.95963609e-01
-1.80948302e-01 8.10278118e-01 4.84323204e-01 5.51540330... | [14.513944625854492, -2.0344181060791016] |
e866a317-15d4-4f11-ab75-c1533656d923 | osan-a-one-stage-alignment-network-to-unify | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_OSAN_A_One-Stage_Alignment_Network_To_Unify_Multimodal_Alignment_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_OSAN_A_One-Stage_Alignment_Network_To_Unify_Multimodal_Alignment_and_CVPR_2023_paper.pdf | OSAN: A One-Stage Alignment Network To Unify Multimodal Alignment and Unsupervised Domain Adaptation | Extending from unimodal to multimodal is a critical challenge for unsupervised domain adaptation (UDA). Two major problems emerge in unsupervised multimodal domain adaptation: domain adaptation and modality alignment. An intuitive way to handle these two problems is to fulfill these tasks in two separate stages: al... | ['Bo Ren', 'Haoyuan Peng', 'Chen Lin', 'Di Yin', 'Changchong Lu', 'Lingfeng Qiao', 'Ye Liu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['unsupervised-domain-adaptation'] | ['methodology'] | [ 3.98643196e-01 -2.29484200e-01 -6.08698130e-01 -5.04748702e-01
-5.72614849e-01 -8.24836433e-01 7.88948417e-01 -9.43172947e-02
-1.59609437e-01 6.17711246e-01 4.47383165e-01 8.22103322e-02
1.30929232e-01 -5.56122124e-01 -3.35585415e-01 -7.16038644e-01
3.49671602e-01 3.60847712e-01 1.26566738e-01 -4.28109616... | [10.386143684387207, 3.145720958709717] |
e07b5f69-6288-468d-aad9-31e605ea508f | understanding-the-semantics-of-narratives-of | null | null | https://aclanthology.org/W17-1801 | https://aclanthology.org/W17-1801.pdf | Understanding the Semantics of Narratives of Interpersonal Violence through Reader Annotations and Physiological Reactions | Interpersonal violence (IPV) is a prominent sociological problem that affects people of all demographic backgrounds. By analyzing how readers interpret, perceive, and react to experiences narrated in social media posts, we explore an understudied source for discourse about abuse. We asked readers to annotate Reddit pos... | ['er', 'Elizabeth A. Pruett', 'Alex Calderwood', 'Christopher Homan', 'Raymond Ptucha', 'Cecilia Ovesdotter Alm'] | 2017-04-01 | null | null | null | ws-2017-4 | ['text-annotation'] | ['natural-language-processing'] | [ 2.93124795e-01 7.63502479e-01 -7.08073378e-01 -4.52266425e-01
-5.44505000e-01 -1.11520231e+00 8.61757576e-01 8.84207606e-01
-3.57095480e-01 5.86039007e-01 1.67074263e+00 8.31890404e-02
1.29946291e-01 -5.34494519e-01 8.38025939e-03 -3.32228720e-01
3.09087068e-01 1.69235915e-01 -4.35845047e-01 -6.79536700... | [8.631144523620605, 10.429652214050293] |
ffe3fdf0-4d4b-4090-9dcf-ecb5d8030768 | deep-attention-guided-hashing | 1812.01404 | null | http://arxiv.org/abs/1812.01404v2 | http://arxiv.org/pdf/1812.01404v2.pdf | Deep Attention-guided Hashing | With the rapid growth of multimedia data (e.g., image, audio and video etc.)
on the web, learning-based hashing techniques such as Deep Supervised Hashing
(DSH) have proven to be very efficient for large-scale multimedia search. The
recent successes seen in Learning-based hashing methods are largely due to the
success ... | ['Jun Long', 'Wuqing Sun', 'Zhan Yang', 'Osolo Ian Raymond'] | 2018-12-04 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-1.77918985e-01 -1.14367135e-01 -1.63428262e-01 -2.01540530e-01
-1.14741004e+00 -1.54206976e-01 2.92860270e-01 1.36560306e-01
-3.86913806e-01 3.69207680e-01 3.43434125e-01 1.47090927e-01
2.83449292e-01 -8.71369183e-01 -8.98948729e-01 -7.90911198e-01
-1.53203681e-01 3.65469456e-01 3.93479615e-01 -1.00978367... | [11.282415390014648, 0.9500383138656616] |
12c68deb-3403-4e92-b69d-b9024bcc764b | consistent-cross-view-matching-for | 1908.10486 | null | https://arxiv.org/abs/1908.10486v3 | https://arxiv.org/pdf/1908.10486v3.pdf | Exploiting Global Camera Network Constraints for Unsupervised Video Person Re-identification | Many unsupervised approaches have been proposed recently for the video-based re-identification problem since annotations of samples across cameras are time-consuming. However, higher-order relationships across the entire camera network are ignored by these methods, leading to contradictory outputs when matching results... | ['Amit K. Roy-Chowdhury', 'Xueping Wang', 'Min Liu', 'Yaonan Wang', 'Rameswar Panda'] | 2019-08-27 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 2.33367473e-01 -4.00464356e-01 -5.49854696e-01 -3.92955273e-01
-5.47327638e-01 -5.14532268e-01 3.73029232e-01 1.10078841e-01
-2.72384018e-01 2.75688767e-01 8.98394138e-02 3.50346148e-01
-4.36322004e-01 -4.16145235e-01 -5.53131461e-01 -6.00464702e-01
7.47987181e-02 2.81502038e-01 2.69081652e-01 2.61577606... | [14.7605619430542, 1.0264275074005127] |
12ff0ae6-a4f6-4713-a746-d867f2061134 | consistent-representation-learning-for | 2203.02721 | null | https://arxiv.org/abs/2203.02721v2 | https://arxiv.org/pdf/2203.02721v2.pdf | Consistent Representation Learning for Continual Relation Extraction | Continual relation extraction (CRE) aims to continuously train a model on data with new relations while avoiding forgetting old ones. Some previous work has proved that storing a few typical samples of old relations and replaying them when learning new relations can effectively avoid forgetting. However, these memory-b... | ['Kai Gao', 'Jiangong Yang', 'Hua Xu', 'Kang Zhao'] | 2022-03-05 | null | https://aclanthology.org/2022.findings-acl.268 | https://aclanthology.org/2022.findings-acl.268.pdf | findings-acl-2022-5 | ['continual-relation-extraction'] | ['natural-language-processing'] | [-6.46033883e-02 3.55419397e-01 -5.88162720e-01 -3.09376001e-01
-2.67313540e-01 1.41142756e-01 4.79649812e-01 4.37094241e-01
-3.61111581e-01 1.08701515e+00 1.91011146e-01 -1.55194595e-01
-1.25848293e-01 -1.21348679e+00 -7.80869484e-01 -7.06208706e-01
5.70264049e-02 6.90560937e-01 3.75487059e-01 -4.31083202... | [9.158329010009766, 8.509424209594727] |
72328f5c-fedb-4e7c-91f5-7d582fda19c9 | statistical-estimation-for-covariance | 2305.11282 | null | https://arxiv.org/abs/2305.11282v1 | https://arxiv.org/pdf/2305.11282v1.pdf | Statistical Estimation for Covariance Structures with Tail Estimates using Nodewise Quantile Predictive Regression Models | This paper considers the specification of covariance structures with tail estimates. We focus on two aspects: (i) the estimation of the VaR-CoVaR risk matrix in the case of larger number of time series observations than assets in a portfolio using quantile predictive regression models without assuming the presence of n... | ['Christis Katsouris'] | 2023-05-18 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 1.30570263e-01 -1.94334373e-01 1.50445811e-02 -1.33242950e-01
-1.28484219e-01 -5.41736662e-01 4.43696320e-01 1.23404130e-01
-1.58872292e-03 7.93964028e-01 5.39238639e-02 -8.06718409e-01
-1.13889229e+00 -9.06146109e-01 -2.47351304e-01 -5.96489608e-01
-5.87922096e-01 4.77944344e-01 -4.75234725e-02 9.50857177... | [5.168966770172119, 4.061452388763428] |
23b99097-6c48-4aa2-a5fe-4e61ce1864da | compound-probabilistic-context-free-grammars | 1906.10225 | null | https://arxiv.org/abs/1906.10225v9 | https://arxiv.org/pdf/1906.10225v9.pdf | Compound Probabilistic Context-Free Grammars for Grammar Induction | We study a formalization of the grammar induction problem that models sentences as being generated by a compound probabilistic context-free grammar. In contrast to traditional formulations which learn a single stochastic grammar, our grammar's rule probabilities are modulated by a per-sentence continuous latent variabl... | ['Yoon Kim', 'Alexander M. Rush', 'Chris Dyer'] | 2019-06-24 | compound-probabilistic-context-free-grammars-1 | https://aclanthology.org/P19-1228 | https://aclanthology.org/P19-1228.pdf | acl-2019-7 | ['constituency-grammar-induction'] | ['natural-language-processing'] | [ 3.58141422e-01 7.17688143e-01 -1.61610842e-01 -7.69495249e-01
-1.17454660e+00 -5.94089687e-01 7.48800337e-01 -5.26386276e-02
-3.80235940e-01 8.95830929e-01 2.76382446e-01 -6.93137586e-01
1.44614175e-01 -8.11554611e-01 -9.18125808e-01 -8.99485588e-01
-6.10207021e-02 1.06537759e+00 1.38385892e-01 9.66430083... | [10.426616668701172, 9.649282455444336] |
ec30a64a-ad22-4a96-98a1-91d8e8633cff | grad-cam-improved-visual-explanations-for | 1710.11063 | null | http://arxiv.org/abs/1710.11063v3 | http://arxiv.org/pdf/1710.11063v3.pdf | Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks | Over the last decade, Convolutional Neural Network (CNN) models have been
highly successful in solving complex vision problems. However, these deep
models are perceived as "black box" methods considering the lack of
understanding of their internal functioning. There has been a significant
recent interest in developing ... | ['Vineeth N. Balasubramanian', 'Anirban Sarkar', 'Prantik Howlader', 'Aditya Chattopadhyay'] | 2017-10-30 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 2.81234115e-01 6.57787681e-01 -1.44709632e-01 -5.90377986e-01
-9.88169387e-02 -2.29690731e-01 8.30178857e-01 8.31183493e-02
5.05726524e-02 5.53295016e-01 3.25579256e-01 -3.12970996e-01
-1.71569198e-01 -5.54773450e-01 -9.39501345e-01 -5.09591758e-01
2.72146136e-01 3.50146174e-01 -1.42032339e-03 -6.51824474... | [8.991789817810059, 5.420103549957275] |
4bbbe4f0-ae04-4109-8caa-0b9ced5a2850 | an-accelerated-pipeline-for-multi-label-renal | 2305.14566 | null | https://arxiv.org/abs/2305.14566v1 | https://arxiv.org/pdf/2305.14566v1.pdf | An Accelerated Pipeline for Multi-label Renal Pathology Image Segmentation at the Whole Slide Image Level | Deep-learning techniques have been used widely to alleviate the labour-intensive and time-consuming manual annotation required for pixel-level tissue characterization. Our previous study introduced an efficient single dynamic network - Omni-Seg - that achieved multi-class multi-scale pathological segmentation with less... | ['Yuankai Huo', 'Lipeng Wan', 'Haichun Yang', 'R. Michael Womick', 'Zuhayr Asad', 'Ruining Deng', 'Haoju Leng'] | 2023-05-23 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 3.23938429e-01 7.58056901e-03 5.14861057e-03 -3.57504964e-01
-1.07610667e+00 -4.36969161e-01 -1.92965213e-02 5.33620954e-01
-5.38407624e-01 4.68917251e-01 -4.35717046e-01 -5.31412959e-01
-1.60438776e-01 -8.59117627e-01 -4.52640444e-01 -8.93070400e-01
1.50000649e-02 6.22526765e-01 5.96515596e-01 3.05886596... | [14.952760696411133, -2.9504849910736084] |
9ed249a6-54cb-4832-9e59-760abb98727d | iterative-loop-learning-combining-self | 2301.13361 | null | https://arxiv.org/abs/2301.13361v4 | https://arxiv.org/pdf/2301.13361v4.pdf | Iterative Loop Method Combining Active and Semi-Supervised Learning for Domain Adaptive Semantic Segmentation | Semantic segmentation is an important technique for environment perception in intelligent transportation systems. With the rapid development of convolutional neural networks (CNNs), road scene analysis can usually achieve satisfactory results in the source domain. However, guaranteeing good generalization to different ... | ['Xue Yuan', 'Licong Guan'] | 2023-01-31 | null | null | null | null | ['semi-supervised-semantic-segmentation'] | ['computer-vision'] | [ 1.60120875e-01 2.40195215e-01 -7.04931200e-01 -6.11431301e-01
-1.08940578e+00 -3.22798431e-01 3.02790344e-01 1.18440278e-01
-7.22918749e-01 9.52860475e-01 -3.12143624e-01 -2.01768890e-01
-1.32606313e-01 -9.98716891e-01 -6.10046685e-01 -8.09998631e-01
2.38619909e-01 1.00320315e+00 8.92453492e-01 6.14176430... | [9.507773399353027, 0.7021668553352356] |
1e20544a-072f-48bd-870b-25a9d189c298 | actionable-recourse-via-gans-for-mobile | 2211.06525 | null | https://arxiv.org/abs/2211.06525v1 | https://arxiv.org/pdf/2211.06525v1.pdf | Actionable Recourse via GANs for Mobile Health | Mobile health apps provide a unique means of collecting data that can be used to deliver adaptive interventions.The predicted outcomes considerably influence the selection of such interventions. Recourse via counterfactuals provides tangible mechanisms to modify user predictions. By identifying plausible actions that i... | ['Lauren Bellhouse', 'Africa Perianez', 'Ana Fernandez del Rio', 'Anna Guitart', 'Jennifer Chien'] | 2022-11-12 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 7.49382496e-01 1.03002012e+00 -9.20533359e-01 -5.76960266e-01
-8.51725638e-01 -2.25289062e-01 4.79718626e-01 2.66249001e-01
1.84221819e-01 9.11178350e-01 1.21671903e+00 -9.12989318e-01
-1.82977647e-01 -1.07422888e+00 -8.97704303e-01 -1.38757885e-01
-1.67491198e-01 4.48795408e-02 -6.14616275e-01 -7.46409893... | [8.213264465332031, 5.552174091339111] |
e59abea5-8c19-486d-b593-4264cda463c8 | aang-automating-auxiliary-learning | 2205.14082 | null | https://arxiv.org/abs/2205.14082v2 | https://arxiv.org/pdf/2205.14082v2.pdf | AANG: Automating Auxiliary Learning | Auxiliary objectives, supplementary learning signals that are introduced to help aid learning on data-starved or highly complex end-tasks, are commonplace in machine learning. Whilst much work has been done to formulate useful auxiliary objectives, their construction is still an art which proceeds by slow and tedious h... | ['Ameet Talwalkar', 'Graham Neubig', 'Mikhail Khodak', 'Paul Michel', 'Lucio M. Dery'] | 2022-05-27 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 7.22953618e-01 4.43165898e-01 -3.25707674e-01 -4.67403769e-01
-1.11802912e+00 -7.62967288e-01 6.78662837e-01 4.95568335e-01
-6.45844579e-01 6.75647676e-01 5.94306409e-01 -6.10519946e-01
-1.85992062e-01 -2.85208642e-01 -6.83541298e-01 -5.77009797e-01
5.90606220e-02 6.38743460e-01 -1.58574097e-02 -2.28512540... | [10.577397346496582, 8.296758651733398] |
3a5eea4d-4c6e-45ad-875b-8d3d3b6af9ed | charformer-fast-character-transformers-via | 2106.12672 | null | https://arxiv.org/abs/2106.12672v3 | https://arxiv.org/pdf/2106.12672v3.pdf | Charformer: Fast Character Transformers via Gradient-based Subword Tokenization | State-of-the-art models in natural language processing rely on separate rigid subword tokenization algorithms, which limit their generalization ability and adaptation to new settings. In this paper, we propose a new model inductive bias that learns a subword tokenization end-to-end as part of the model. To this end, we... | ['Donald Metzler', 'Cong Yu', 'Simon Baumgartner', 'Zhen Qin', 'Dara Bahri', 'Hyung Won Chung', 'Jai Gupta', 'Sebastian Ruder', 'Vinh Q. Tran', 'Yi Tay'] | 2021-06-23 | charformer-fast-character-transformers-via-1 | https://openreview.net/forum?id=JtBRnrlOEFN | https://openreview.net/pdf?id=JtBRnrlOEFN | iclr-2022-4 | ['paraphrase-identification', 'linguistic-acceptability'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.70257509e-01 -9.58077908e-02 -5.18327296e-01 -5.04442036e-01
-1.42762744e+00 -6.76634431e-01 6.62122071e-01 4.54343319e-01
-1.02646029e+00 4.31296378e-01 5.53222537e-01 -6.55259490e-01
5.99976718e-01 -6.39359951e-01 -9.62897897e-01 -5.68754792e-01
-1.94520541e-02 3.86536807e-01 1.43268734e-01 -1.99624956... | [10.787893295288086, 8.645148277282715] |
b80eff6f-7098-4702-a769-14b4a12e52cd | a-series-of-unfortunate-counterfactual-events | 2010.04687 | null | https://arxiv.org/abs/2010.04687v2 | https://arxiv.org/pdf/2010.04687v2.pdf | A Series of Unfortunate Counterfactual Events: the Role of Time in Counterfactual Explanations | Counterfactual explanations are a prominent example of post-hoc interpretability methods in the explainable Artificial Intelligence research domain. They provide individuals with alternative scenarios and a set of recommendations to achieve a sought-after machine learning model outcome. Recently, the literature has ide... | ['Michele Loi', 'Andrea Ferrario'] | 2020-10-09 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 2.23435000e-01 8.61961484e-01 -3.62823784e-01 -2.13522673e-01
-2.35565193e-03 -3.86230797e-01 7.22360373e-01 1.51231363e-01
-3.42202455e-01 1.30353463e+00 6.28038108e-01 -7.77492344e-01
-5.42086363e-01 -4.71921831e-01 -7.07439721e-01 -2.44346127e-01
-6.54325634e-02 3.99760097e-01 -6.96122527e-01 1.36186361... | [8.739544868469238, 5.679496765136719] |
9d21acdf-e337-480c-9138-618e4ce00941 | improving-eeg-based-emotion-recognition-by | 2303.11421 | null | https://arxiv.org/abs/2303.11421v1 | https://arxiv.org/pdf/2303.11421v1.pdf | Improving EEG-based Emotion Recognition by Fusing Time-frequency And Spatial Representations | Using deep learning methods to classify EEG signals can accurately identify people's emotions. However, existing studies have rarely considered the application of the information in another domain's representations to feature selection in the time-frequency domain. We propose a classification network of EEG signals bas... | ['Jing Xiao', 'Ning Cheng', 'Jianzong Wang', 'xulong Zhang', 'Kexin Zhu'] | 2023-03-14 | null | null | null | null | ['eeg-emotion-recognition'] | ['miscellaneous'] | [-8.33322108e-02 -8.11995506e-01 3.08805048e-01 -6.01903796e-01
-3.94316733e-01 3.25198434e-02 1.95168525e-01 1.36268297e-02
-4.86076057e-01 9.96607006e-01 3.19138914e-01 3.72492880e-01
-6.70520782e-01 -7.39893436e-01 -9.62791070e-02 -6.71581686e-01
-2.18642130e-01 -3.49294782e-01 -1.77753493e-01 -2.34121323... | [13.16024398803711, 3.4683995246887207] |
92440b6a-d83e-4462-b5f9-5775bb6a90ff | transductive-matrix-completion-with | 2302.09834 | null | https://arxiv.org/abs/2302.09834v1 | https://arxiv.org/pdf/2302.09834v1.pdf | Transductive Matrix Completion with Calibration for Multi-Task Learning | Multi-task learning has attracted much attention due to growing multi-purpose research with multiple related data sources. Moreover, transduction with matrix completion is a useful method in multi-label learning. In this paper, we propose a transductive matrix completion algorithm that incorporates a calibration constr... | ['Zhonglei Wang', 'Xiaojun Mao', 'Yasi Zhang', 'Hengfang Wang'] | 2023-02-20 | null | null | null | null | ['matrix-completion', 'multi-label-learning'] | ['methodology', 'methodology'] | [ 2.41224855e-01 -3.25940043e-01 -2.26246595e-01 -3.50595504e-01
-1.25443304e+00 -3.17684054e-01 1.67973995e-01 6.21551499e-02
-3.82835120e-01 6.21234715e-01 2.17039645e-01 3.06350231e-01
-3.38934451e-01 -2.29856521e-01 -6.23792827e-01 -9.85842228e-01
4.04665291e-01 2.13363051e-01 -3.71076643e-01 4.88044918... | [8.037503242492676, 4.512552261352539] |
6a1776a7-6e60-4f25-bcd9-ca61078f1299 | joint-noise-tolerant-learning-and-meta-camera | 2103.04618 | null | https://arxiv.org/abs/2103.04618v1 | https://arxiv.org/pdf/2103.04618v1.pdf | Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification | This paper considers the problem of unsupervised person re-identification (re-ID), which aims to learn discriminative models with unlabeled data. One popular method is to obtain pseudo-label by clustering and use them to optimize the model. Although this kind of approach has shown promising accuracy, it is hampered by ... | ['Nicu Sebe', 'Shaozi Li', 'Yaojin Lin', 'Yuanzheng Cai', 'Zhiming Luo', 'Zhun Zhong', 'Fengxiang Yang'] | 2021-03-08 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Yang_Joint_Noise-Tolerant_Learning_and_Meta_Camera_Shift_Adaptation_for_Unsupervised_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Yang_Joint_Noise-Tolerant_Learning_and_Meta_Camera_Shift_Adaptation_for_Unsupervised_CVPR_2021_paper.pdf | cvpr-2021-1 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 1.58304930e-01 -3.65072876e-01 -8.13860819e-02 -5.96466780e-01
-6.02402091e-01 -3.86786401e-01 6.21823788e-01 -4.10956740e-02
-6.66501641e-01 5.60111105e-01 1.21454515e-01 4.16369289e-01
2.94434391e-02 -3.94853741e-01 -3.82569313e-01 -9.06581700e-01
4.52424020e-01 5.65289438e-01 9.68145281e-02 3.51467818... | [14.806422233581543, 1.0976905822753906] |
f70977f0-8118-42df-8342-1f9d07d0cc8e | fully-automated-2d-and-3d-convolutional | 2103.14734 | null | https://arxiv.org/abs/2103.14734v2 | https://arxiv.org/pdf/2103.14734v2.pdf | Fully Automated 2D and 3D Convolutional Neural Networks Pipeline for Video Segmentation and Myocardial Infarction Detection in Echocardiography | Cardiac imaging known as echocardiography is a non-invasive tool utilized to produce data including images and videos, which cardiologists use to diagnose cardiac abnormalities in general and myocardial infarction (MI) in particular. Echocardiography machines can deliver abundant amounts of data that need to be quickly... | ['Tahir Hamid', 'Rashid Mazhar', 'Ridha Hamila', 'Serkan Kiranyaz', 'Christopher J. Henry', 'Sheela Ramanna', 'Oumaima Hamila'] | 2021-03-26 | null | null | null | null | ['myocardial-infarction-detection'] | ['medical'] | [ 3.11702788e-01 -1.59317926e-01 6.92690760e-02 -3.46369624e-01
-4.43816394e-01 -8.15842688e-01 -2.37743124e-01 1.08353205e-01
-3.15419704e-01 3.98669183e-01 -2.71444172e-01 -7.65102625e-01
1.34368762e-01 -6.06891930e-01 -4.39309955e-01 -5.20737052e-01
-4.63281333e-01 6.04792118e-01 -7.50617832e-02 3.85085255... | [14.203413009643555, -2.4207427501678467] |
247ac899-0a25-4093-b894-c390e9719b3c | contrastive-learning-of-global-and-local-2 | null | null | http://proceedings.neurips.cc/paper/2021/hash/38ef4b66cb25e92abe4d594acb841471-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/38ef4b66cb25e92abe4d594acb841471-Paper.pdf | Contrastive Learning of Global and Local Video Representations | Contrastive learning has delivered impressive results for various tasks in the self-supervised regime. However, existing approaches optimize for learning representations specific to downstream scenarios, i.e., global representations suitable for tasks such as classification or local representations for tasks such as de... | ['Yale Song', 'Daniel McDuff', 'Zhaoyang Zeng', 'Shuang Ma'] | 2021-12-01 | null | https://openreview.net/forum?id=txWfwhc6gi | https://openreview.net/pdf?id=txWfwhc6gi | neurips-2021-12 | ['sound-classification'] | ['audio'] | [ 4.17223066e-01 -2.31452912e-01 -4.01041538e-01 -2.97606438e-01
-1.18477404e+00 -5.58999419e-01 5.92831135e-01 2.01505259e-01
-2.11285338e-01 5.11912644e-01 4.52967107e-01 -9.79702473e-02
-8.79831165e-02 -4.44487959e-01 -8.18557799e-01 -6.64933681e-01
-2.05210343e-01 -2.41859425e-02 1.80879384e-01 1.83319803... | [14.647201538085938, 4.938182353973389] |
c17bf0a0-8d8b-4337-b62d-6f5ad05d0f1e | lion-latent-point-diffusion-models-for-3d | 2210.06978 | null | https://arxiv.org/abs/2210.06978v1 | https://arxiv.org/pdf/2210.06978v1.pdf | LION: Latent Point Diffusion Models for 3D Shape Generation | Denoising diffusion models (DDMs) have shown promising results in 3D point cloud synthesis. To advance 3D DDMs and make them useful for digital artists, we require (i) high generation quality, (ii) flexibility for manipulation and applications such as conditional synthesis and shape interpolation, and (iii) the ability... | ['Karsten Kreis', 'Sanja Fidler', 'Or Litany', 'Zan Gojcic', 'Francis Williams', 'Arash Vahdat', 'Xiaohui Zeng'] | 2022-10-12 | null | null | null | null | ['3d-shape-generation', 'point-cloud-generation'] | ['computer-vision', 'computer-vision'] | [-7.21040368e-02 -3.91775183e-03 2.68338650e-01 4.54411171e-02
-8.50118279e-01 -5.94331980e-01 7.67835736e-01 -2.70084888e-01
3.20168912e-01 3.75271529e-01 1.86622486e-01 -2.99000442e-01
2.41558895e-01 -1.37447333e+00 -9.55846429e-01 -6.79041564e-01
1.46056488e-01 9.48096871e-01 2.75987182e-02 -2.50358433... | [8.939461708068848, -3.6220602989196777] |
d5cb9e8b-7159-4d17-b704-a6470fd4842b | transformerfusion-monocular-rgb-scene | 2107.02191 | null | https://arxiv.org/abs/2107.02191v1 | https://arxiv.org/pdf/2107.02191v1.pdf | TransformerFusion: Monocular RGB Scene Reconstruction using Transformers | We introduce TransformerFusion, a transformer-based 3D scene reconstruction approach. From an input monocular RGB video, the video frames are processed by a transformer network that fuses the observations into a volumetric feature grid representing the scene; this feature grid is then decoded into an implicit 3D scene ... | ['Matthias Nießner', 'Angela Dai', 'Justus Thies', 'Pablo Palafox', 'Aljaž Božič'] | 2021-07-05 | null | http://proceedings.neurips.cc/paper/2021/hash/0a87257e5308197df43230edf4ad1dae-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/0a87257e5308197df43230edf4ad1dae-Paper.pdf | neurips-2021-12 | ['stereo-depth-estimation', '3d-scene-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 6.16693616e-01 7.22326338e-02 1.47223592e-01 -2.70532519e-01
-1.11123610e+00 -3.42891455e-01 5.90216517e-01 -1.34013832e-01
-2.87482798e-01 4.40592438e-01 4.47938740e-01 1.14446782e-01
8.66500661e-02 -1.08648896e+00 -1.09415615e+00 -6.83978558e-01
1.60128430e-01 6.03900194e-01 4.39376980e-01 -3.19368183... | [8.655566215515137, -2.8701303005218506] |
19a16e18-322e-4c34-8cee-6d24ec3c75b0 | task-aware-multi-task-learning-for-speech-to | null | null | https://ieeexplore.ieee.org/document/9414703 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9414703 | TASK AWARE MULTI-TASK LEARNING FOR SPEECH TO TEXT TASKS | In general, the direct Speech-to-text translation (ST) is jointly trained with Automatic Speech Recognition (ASR), and Machine Translation (MT) tasks. However, the issues with the current joint learning strategies inhibit the knowledge transfer across these tasks. We propose a task modulation network which allows the m... | ['Inchul Hwang', 'Chanwoo Kim', 'Sangha Kim', 'Seokchan Ahn', 'Hyojung Han', 'Beomseok Lee', 'Nikhil Kumar Lakumarapu', 'Mohd Abbas Zaidi', 'Sathish Indurthi'] | 2021-06-10 | null | null | null | icassp-2021-6 | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 5.03800631e-01 3.34754825e-01 -3.72154146e-01 -4.40956533e-01
-1.66767001e+00 -5.21057069e-01 1.06475353e+00 -5.23700655e-01
-6.85771227e-01 9.18413103e-01 2.64061570e-01 -7.85721660e-01
4.17488068e-01 5.48173711e-02 -6.71723008e-01 -6.08658075e-01
5.35373688e-01 6.61867499e-01 -6.70133252e-03 -2.39108592... | [14.491800308227539, 7.230256080627441] |
15759296-399d-4098-8f3d-7dc3dfb1f7dc | multilingual-coreference-resolution-in | 2208.01307 | null | https://arxiv.org/abs/2208.01307v2 | https://arxiv.org/pdf/2208.01307v2.pdf | Multilingual Coreference Resolution in Multiparty Dialogue | Existing multiparty dialogue datasets for entity coreference resolution are nascent, and many challenges are still unaddressed. We create a large-scale dataset, Multilingual Multiparty Coref (MMC), for this task based on TV transcripts. Due to the availability of gold-quality subtitles in multiple languages, we propose... | ['Benjamin Van Durme', 'Mahsa Yarmohammadi', 'Patrick Xia', 'Boyuan Zheng'] | 2022-08-02 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [-1.15794204e-02 4.63828325e-01 -3.71150464e-01 -3.58238041e-01
-1.76910233e+00 -1.02884901e+00 7.49626577e-01 -2.93127567e-01
-6.29422188e-01 9.37659979e-01 1.24446416e+00 -7.25627616e-02
2.77014792e-01 -9.91474912e-02 -4.57073241e-01 -1.64017767e-01
3.31159741e-01 1.26128805e+00 2.29562055e-02 -6.92033827... | [9.300653457641602, 9.556510925292969] |
19ab74e9-6e9f-429b-b6f0-59c2eb99ae22 | the-muse-2023-multimodal-sentiment-analysis | 2305.03369 | null | https://arxiv.org/abs/2305.03369v1 | https://arxiv.org/pdf/2305.03369v1.pdf | The MuSe 2023 Multimodal Sentiment Analysis Challenge: Mimicked Emotions, Cross-Cultural Humour, and Personalisation | The MuSe 2023 is a set of shared tasks addressing three different contemporary multimodal affect and sentiment analysis problems: In the Mimicked Emotions Sub-Challenge (MuSe-Mimic), participants predict three continuous emotion targets. This sub-challenge utilises the Hume-Vidmimic dataset comprising of user-generated... | ['Björn W. Schuller', 'Erik Cambria', 'Alan Cowen', 'Andreas König', 'Eva-Maria Meßner', 'Panagiotis Tzirakis', 'Chris Gagne', 'Steffen Klug', 'Niklas Müller', 'Alexander Kathan', 'Alice Baird', 'Shahin Amiriparian', 'Lukas Christ'] | 2023-05-05 | null | null | null | null | ['multimodal-sentiment-analysis', 'multimodal-sentiment-analysis'] | ['computer-vision', 'natural-language-processing'] | [ 4.80360277e-02 -6.50385991e-02 3.94576579e-01 -4.00756240e-01
-8.95541966e-01 -2.88080812e-01 4.05820966e-01 1.61254272e-01
-3.05333048e-01 4.28026587e-01 5.08739114e-01 6.00825548e-01
2.24399850e-01 -7.55158961e-02 -1.23414341e-02 -5.91144323e-01
-4.23742890e-01 -1.09004825e-01 -6.16440892e-01 -6.29232049... | [13.37167739868164, 5.072886943817139] |
7c75211c-2273-4c3d-a330-0f7956c10f59 | sparsity-by-redundancy-solving-l-1-with-a | 2210.01212 | null | https://arxiv.org/abs/2210.01212v4 | https://arxiv.org/pdf/2210.01212v4.pdf | spred: Solving $L_1$ Penalty with SGD | We propose to minimize a generic differentiable objective with $L_1$ constraint using a simple reparametrization and straightforward stochastic gradient descent. Our proposal is the direct generalization of previous ideas that the $L_1$ penalty may be equivalent to a differentiable reparametrization with weight decay. ... | ['ZiHao Wang', 'Liu Ziyin'] | 2022-10-03 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 3.37744743e-01 2.24745691e-01 -1.57998353e-01 -6.19056046e-01
-1.04100251e+00 -1.16884194e-01 6.56420663e-02 -1.36212468e-01
-4.69040811e-01 1.14034855e+00 -6.14888221e-02 -2.08666012e-01
-5.03488660e-01 -5.76591253e-01 -1.04595959e+00 -1.15017736e+00
-2.93780982e-01 2.47510672e-01 -5.46413362e-01 -2.25499496... | [7.955105304718018, 3.8680319786071777] |
dea24688-3ef1-4160-b13b-f4c38fb95146 | modeling-task-interactions-in-document-level | 2205.01909 | null | https://arxiv.org/abs/2205.01909v1 | https://arxiv.org/pdf/2205.01909v1.pdf | Modeling Task Interactions in Document-Level Joint Entity and Relation Extraction | We target on the document-level relation extraction in an end-to-end setting, where the model needs to jointly perform mention extraction, coreference resolution (COREF) and relation extraction (RE) at once, and gets evaluated in an entity-centric way. Especially, we address the two-way interaction between COREF and RE... | ['Jinho D. Choi', 'Liyan Xu'] | 2022-05-04 | null | https://aclanthology.org/2022.naacl-main.395 | https://aclanthology.org/2022.naacl-main.395.pdf | naacl-2022-7 | ['document-level-relation-extraction', 'joint-entity-and-relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.29256332e-01 7.09818304e-01 -1.66039586e-01 -2.09678933e-01
-9.77055013e-01 -5.64336538e-01 8.02653730e-01 2.52077192e-01
-6.58905864e-01 5.87545395e-01 6.26624227e-01 -4.91098493e-01
-1.77602887e-01 -3.54519129e-01 -7.93837428e-01 -2.76356250e-01
-3.34418058e-01 6.02881908e-01 2.49668077e-01 -1.19497858... | [9.275753021240234, 8.854002952575684] |
8e55cbab-ff1e-4e0d-a5c5-77b05df97eac | zero-shot-keyword-spotting-for-visual-speech | 1807.08469 | null | http://arxiv.org/abs/1807.08469v2 | http://arxiv.org/pdf/1807.08469v2.pdf | Zero-shot keyword spotting for visual speech recognition in-the-wild | Visual keyword spotting (KWS) is the problem of estimating whether a text
query occurs in a given recording using only video information. This paper
focuses on visual KWS for words unseen during training, a real-world, practical
setting which so far has received no attention by the community. To this end,
we devise an ... | ['Georgios Tzimiropoulos', 'Themos Stafylakis'] | 2018-07-23 | zero-shot-keyword-spotting-for-visual-speech-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Themos_Stafylakis_Zero-shot_keyword_search_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Themos_Stafylakis_Zero-shot_keyword_search_ECCV_2018_paper.pdf | eccv-2018-9 | ['visual-keyword-spotting'] | ['computer-vision'] | [ 3.74246627e-01 -1.13825127e-01 -4.29164842e-02 -6.26457781e-02
-1.04982483e+00 -5.54363668e-01 8.35201502e-01 -1.49417192e-01
-6.50817335e-01 2.35698253e-01 2.74814934e-01 -6.47772491e-01
5.89423299e-01 -8.21415931e-02 -1.14982629e+00 -7.21537054e-01
1.31723598e-01 1.65354922e-01 1.02090023e-01 -1.10475242... | [10.697278022766113, 1.2371104955673218] |
d31d26a1-4a29-4dae-ac17-db625da5f120 | a-survey-in-adversarial-defences-and | 2203.06414 | null | https://arxiv.org/abs/2203.06414v4 | https://arxiv.org/pdf/2203.06414v4.pdf | A Survey of Adversarial Defences and Robustness in NLP | In the past few years, it has become increasingly evident that deep neural networks are not resilient enough to withstand adversarial perturbations in input data, leaving them vulnerable to attack. Various authors have proposed strong adversarial attacks for computer vision and Natural Language Processing (NLP) tasks. ... | ['Balaraman Ravindran', 'Mitesh M. Khapra', 'Sumanth Doddapaneni', 'Shreya Goyal'] | 2022-03-12 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 2.39749804e-01 3.41265887e-01 2.59703457e-01 -5.05572140e-01
-1.92083061e-01 -1.26116943e+00 6.98743999e-01 1.18276447e-01
-5.81885397e-01 6.81270063e-01 -2.56099273e-02 -4.49567199e-01
1.70566410e-01 -8.97737324e-01 -8.46731663e-01 -7.34455287e-01
-6.64169863e-02 2.24068224e-01 1.27809629e-01 -4.76740271... | [5.708001613616943, 7.837150573730469] |
c1d98c3d-b9e7-4d04-827e-242becfa07fd | gaussian-induced-convolution-for-graphs | 1811.04393 | null | http://arxiv.org/abs/1811.04393v1 | http://arxiv.org/pdf/1811.04393v1.pdf | Gaussian-Induced Convolution for Graphs | Learning representation on graph plays a crucial role in numerous tasks of
pattern recognition. Different from grid-shaped images/videos, on which local
convolution kernels can be lattices, however, graphs are fully coordinate-free
on vertices and edges. In this work, we propose a Gaussian-induced convolution
(GIC) fra... | ['Jian Yang', 'Zhen Cui', 'Jiatao Jiang', 'Chunyan Xu'] | 2018-11-11 | null | null | null | null | ['learning-representation-on-graph'] | ['methodology'] | [-2.39499807e-01 -1.47295278e-02 -1.68110598e-02 -3.07296336e-01
7.11755008e-02 -3.54827285e-01 4.30299282e-01 -9.62788910e-02
1.98825728e-02 8.83689001e-02 -6.21619401e-04 -4.00482655e-01
-1.23268202e-01 -1.21673203e+00 -6.69929028e-01 -7.76162684e-01
-2.81874567e-01 2.57443130e-01 1.98418394e-01 2.15547923... | [7.141427040100098, 6.30488395690918] |
d868e478-2a91-4921-96b5-5608d83bcb0d | slam-for-visually-impaired-people-a-survey | 2212.04745 | null | https://arxiv.org/abs/2212.04745v1 | https://arxiv.org/pdf/2212.04745v1.pdf | SLAM for Visually Impaired People: A Survey | In recent decades, several assistive technologies for visually impaired and blind (VIB) people have been developed to improve their ability to navigate independently and safely. At the same time, simultaneous localization and mapping (SLAM) techniques have become sufficiently robust and efficient to be adopted in the d... | ['Alireza Darvishy', 'Davide Scaramuzza', 'Marziyeh Bamdad'] | 2022-12-09 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-5.21652400e-01 -1.79430351e-01 1.72141552e-01 -3.98369044e-01
-2.31669530e-01 -5.04670918e-01 4.79387432e-01 3.70026156e-02
-8.18420172e-01 1.14061046e+00 5.12398779e-01 -5.34680784e-01
-1.67741805e-01 -3.57023358e-01 1.86916292e-02 -1.89088970e-01
-1.59051791e-01 3.94922157e-04 1.97159201e-01 -4.12999094... | [7.5579328536987305, -1.8506495952606201] |
709e440e-9065-4540-a993-ccb831660e3c | representing-and-learning-functions-invariant | 2306.05261 | null | https://arxiv.org/abs/2306.05261v1 | https://arxiv.org/pdf/2306.05261v1.pdf | Representing and Learning Functions Invariant Under Crystallographic Groups | Crystallographic groups describe the symmetries of crystals and other repetitive structures encountered in nature and the sciences. These groups include the wallpaper and space groups. We derive linear and nonlinear representations of functions that are (1) smooth and (2) invariant under such a group. The linear repres... | ['Peter Orbanz', 'Ryan P. Adams'] | 2023-06-08 | null | null | null | null | ['gaussian-processes'] | ['methodology'] | [ 4.24407959e-01 4.16602433e-01 -5.11357971e-02 -2.29232669e-01
-7.55188391e-02 -6.28013432e-01 1.03192127e+00 -1.09918022e+00
3.85339186e-02 7.64174938e-01 4.15298164e-01 -1.49931997e-01
-3.66129845e-01 -9.57362950e-01 -9.47146297e-01 -1.12115765e+00
-4.80520099e-01 6.90265119e-01 -1.92775995e-01 9.53725912... | [7.370428562164307, 4.551607608795166] |
5453dc12-43c0-4e87-a9eb-00e78fdaffac | causal-inductive-synthesis-corpus | null | null | https://openreview.net/forum?id=rO24tIDmtSr | https://openreview.net/pdf?id=rO24tIDmtSr | Causal Inductive Synthesis Corpus | We introduce the Causal Inductive Synthesis Corpus (CISC) -- a manually constructed collection of interactive domains.
CISC domains abstract core causal concepts present in real world mechanisms and environments. We formulate two synthesis challenges of causal model discovery: the passive discovery of a model of a CIS... | ['Armando Solar-Lezama', 'Joshua B. Tenenbaum', 'Kate Lin', 'Elizabeth Weeks', 'Ria Das', 'Zenna Tavares'] | 2020-10-13 | null | null | null | neurips-workshop-cap-2020-12 | ['model-discovery'] | ['miscellaneous'] | [ 1.78573668e-01 5.98130107e-01 -5.65890551e-01 -4.29321021e-01
-1.93332195e-01 -8.90264273e-01 1.47963858e+00 2.53618419e-01
2.16507971e-01 8.46490741e-01 7.19298899e-01 -6.74108267e-01
-6.41981542e-01 -9.02369380e-01 -8.36245537e-01 -3.22899491e-01
-9.51772988e-01 1.18840289e+00 4.03794616e-01 1.31613314... | [8.211679458618164, 5.854409217834473] |
7f479f63-efd1-4930-b996-adc441b4a405 | matching-based-term-semantics-pre-training | 2303.01341 | null | https://arxiv.org/abs/2303.01341v1 | https://arxiv.org/pdf/2303.01341v1.pdf | Matching-based Term Semantics Pre-training for Spoken Patient Query Understanding | Medical Slot Filling (MSF) task aims to convert medical queries into structured information, playing an essential role in diagnosis dialogue systems. However, the lack of sufficient term semantics learning makes existing approaches hard to capture semantically identical but colloquial expressions of terms in medical co... | ['Bo Xu', 'Shuang Xu', 'Jing Shi', 'Ziyi Ni', 'Minglun Han', 'Haoran Wu', 'Xiuyi Chen', 'Zefa Hu'] | 2023-03-02 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [ 7.54024923e-01 6.48444295e-01 -4.98520494e-01 -6.47317708e-01
-1.03184164e+00 3.73969018e-03 6.04780912e-01 3.68133932e-01
-6.09084845e-01 5.78735828e-01 5.65478265e-01 -5.85200131e-01
2.25490257e-02 -6.80945992e-01 -2.57139713e-01 -3.53867918e-01
1.26171872e-01 9.62078035e-01 1.67029560e-01 -5.85672975... | [8.805784225463867, 8.684979438781738] |
dcd2b604-1bc4-4225-b194-d48b052abed8 | flat-chinese-ner-using-flat-lattice | 2004.11795 | null | https://arxiv.org/abs/2004.11795v2 | https://arxiv.org/pdf/2004.11795v2.pdf | FLAT: Chinese NER Using Flat-Lattice Transformer | Recently, the character-word lattice structure has been proved to be effective for Chinese named entity recognition (NER) by incorporating the word information. However, since the lattice structure is complex and dynamic, most existing lattice-based models are hard to fully utilize the parallel computation of GPUs and ... | ['Xuanjing Huang', 'Xipeng Qiu', 'Xiaonan Li', 'Hang Yan'] | 2020-04-24 | flat-chinese-ner-using-flat-lattice-1 | https://aclanthology.org/2020.acl-main.611 | https://aclanthology.org/2020.acl-main.611.pdf | acl-2020-6 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-0.39726076 -0.4772567 -0.22666173 -0.14534497 -0.8616117 -0.78905714
0.14397582 0.17945808 -0.69959575 0.678722 0.441427 -0.52778167
0.47739026 -1.0837616 -0.38216767 -0.4644934 0.10598721 0.47632322
0.47277325 -0.11466485 0.04483442 0.16543591 -0.8198652 0.1639546
0.9516016 0.49393138 0.4... | [9.794939994812012, 9.772170066833496] |
df55c60f-0772-4121-ac53-6554ed90c02d | learning-governing-physics-from-output-only | 2208.05609 | null | https://arxiv.org/abs/2208.05609v1 | https://arxiv.org/pdf/2208.05609v1.pdf | Learning governing physics from output only measurements | Extracting governing physics from data is a key challenge in many areas of science and technology. The existing techniques for equations discovery are dependent on both input and state measurements; however, in practice, we only have access to the output measurements only. We here propose a novel framework for learning... | ['Souvik Chakraborty', 'Tapas Tripura'] | 2022-08-11 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 2.74746567e-01 -4.21017826e-01 2.68558592e-01 -1.10039942e-01
-7.52464473e-01 -6.81998253e-01 6.58485115e-01 4.90852147e-02
-1.08289540e-01 1.09413540e+00 8.97851288e-02 -9.00088064e-03
-6.04554653e-01 -7.22304046e-01 -5.22132277e-01 -1.15744233e+00
1.22822180e-01 4.28052783e-01 1.17103174e-01 2.12780192... | [6.624414443969727, 3.583439588546753] |
153181eb-9897-431e-aec8-7ecb88e2365b | tpt-an-empirical-term-selection-for-arabic | null | null | https://aclanthology.org/2021.icnlsp-1.26 | https://aclanthology.org/2021.icnlsp-1.26.pdf | TPT: An Empirical Term Selection for Arabic Text Categorization | null | ['Mohamed Lichouri', 'Mourad Abbas'] | null | null | null | null | icnlsp-2021-11 | ['text-categorization'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.256826877593994, 3.518962860107422] |
6408b0a2-2145-492d-a9e9-28c24002d2c4 | connecting-the-dots-loss-aversion-sybil | 2210.15181 | null | https://arxiv.org/abs/2210.15181v2 | https://arxiv.org/pdf/2210.15181v2.pdf | Connecting the Dots: Loss Aversion, Sybil Attacks, and Welfare Maximization | A celebrated known cognitive bias of individuals is that the pain of losing is psychologically higher than the pleasure of gaining. In robust decision making under uncertainty, this approach is typically associated with the selection of safety (aka security) level strategies. We consider a refined notion, which we term... | ['Moshe Tennenholtz', 'Yotam Gafni'] | 2022-10-27 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [-4.18877341e-02 4.86380845e-01 -1.98556140e-01 1.45019636e-01
-1.61485538e-01 -8.90169382e-01 2.08186060e-01 2.10411370e-01
-1.07014000e+00 8.10851634e-01 -7.43999854e-02 -1.03938438e-01
-5.57667911e-01 -9.44799960e-01 -3.99865776e-01 -7.29650617e-01
-3.53035718e-01 1.33357331e-01 -4.03462261e-01 -6.59182668... | [4.322923183441162, 3.0443131923675537] |
13ad3d4f-57f4-4904-b9ca-50f01a4d88b1 | jiuzhang-2-0-a-unified-chinese-pre-trained | 2306.11027 | null | https://arxiv.org/abs/2306.11027v1 | https://arxiv.org/pdf/2306.11027v1.pdf | JiuZhang 2.0: A Unified Chinese Pre-trained Language Model for Multi-task Mathematical Problem Solving | Although pre-trained language models~(PLMs) have recently advanced the research progress in mathematical reasoning, they are not specially designed as a capable multi-task solver, suffering from high cost for multi-task deployment (\eg a model copy for a task) and inferior performance on complex mathematical problems i... | ['Guoping Hu', 'Cong Liu', 'Shijin Wang', 'Jing Sha', 'Ji-Rong Wen', 'Yuanhang Zhou', 'Zhipeng Chen', 'Zheng Gong', 'Beichen Zhang', 'Kun Zhou', 'Wayne Xin Zhao'] | 2023-06-19 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 1.88803189e-02 -3.18168312e-01 1.06144079e-03 -3.95035177e-01
-9.16220307e-01 -5.57158887e-01 1.37290001e-01 -4.15736020e-01
-4.66964662e-01 6.18166864e-01 -1.43216029e-01 -4.40117896e-01
-2.23636493e-01 -5.30680954e-01 -7.72322416e-01 -3.54981214e-01
3.30700845e-01 6.37388945e-01 -6.95544258e-02 -4.00271595... | [10.636126518249512, 8.242810249328613] |
897891e3-34c0-4319-8d1c-de1a4d439dc9 | distributional-variational-autoencoder-to | 2302.11294 | null | https://arxiv.org/abs/2302.11294v2 | https://arxiv.org/pdf/2302.11294v2.pdf | Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation | The Gaussianity assumption has been consistently criticized as a main limitation of the Variational Autoencoder (VAE), despite its efficiency in computational modeling. In this paper, we propose a new approach that expands the model capacity (i.e., expressive power of distributional family) without sacrificing the comp... | ['Jong-June Jeon', 'SeungHwan An'] | 2023-02-22 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [-3.71101618e-01 2.93609947e-01 -1.78340271e-01 -3.05683702e-01
-6.86581910e-01 -5.52474976e-01 2.97856539e-01 -3.31040770e-01
-3.47030163e-01 9.39417064e-01 3.85800116e-02 -4.39017534e-01
-1.36495516e-01 -9.04198408e-01 -8.32998335e-01 -8.45620573e-01
1.72598884e-01 1.33118302e-01 -3.83555144e-01 5.44730127... | [7.243322849273682, 3.9516007900238037] |
95276fa4-4457-4250-b9a3-64e1dbc74b6e | online-video-instance-segmentation-via-robust | 2207.05580 | null | https://arxiv.org/abs/2207.05580v1 | https://arxiv.org/pdf/2207.05580v1.pdf | Online Video Instance Segmentation via Robust Context Fusion | Video instance segmentation (VIS) aims at classifying, segmenting and tracking object instances in video sequences. Recent transformer-based neural networks have demonstrated their powerful capability of modeling spatio-temporal correlations for the VIS task. Relying on video- or clip-level input, they suffer from high... | ['Yan Lu', 'Bhiksha Raj', 'Xiaohao Xu', 'Jinglu Wang', 'Xiang Li'] | 2022-07-12 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 5.53048134e-01 -3.23401570e-01 -3.17962378e-01 -3.27001065e-01
-9.78963077e-01 -4.03653413e-01 5.55771828e-01 -1.10848583e-01
-2.47560844e-01 4.01386976e-01 3.51813912e-01 2.10552499e-01
-3.79573330e-02 -4.49343413e-01 -1.09387589e+00 -8.13443363e-01
-2.30655059e-01 -9.21584889e-02 4.33474630e-01 9.13856402... | [9.410284996032715, 0.23920592665672302] |
967ea30e-13ff-48a9-b052-8ce3678dedc4 | moving-object-detection-for-event-based-2 | 2109.14979 | null | https://arxiv.org/abs/2109.14979v3 | https://arxiv.org/pdf/2109.14979v3.pdf | Moving Object Detection for Event-based vision using Graph Spectral Clustering | Moving object detection has been a central topic of discussion in computer vision for its wide range of applications like in self-driving cars, video surveillance, security, and enforcement. Neuromorphic Vision Sensors (NVS) are bio-inspired sensors that mimic the working of the human eye. Unlike conventional frame-bas... | ['Ananda S. Chowdhury', 'Thierry Bouwmans', 'Jhony H. Giraldo', 'Shashant R', 'Anindya Mondal'] | 2021-09-30 | moving-object-detection-for-event-based-1 | https://www.researchgate.net/publication/354462913_Moving_Object_Detection_for_Event-based_Vision_using_Graph_Spectral_Clustering | https://www.researchgate.net/publication/354462913_Moving_Object_Detection_for_Event-based_Vision_using_Graph_Spectral_Clustering | international-conference-on-computer-vision-5 | ['moving-object-detection', 'event-based-vision'] | ['computer-vision', 'computer-vision'] | [ 5.35901070e-01 -5.65835893e-01 6.08955957e-02 -2.09751025e-01
-1.54111773e-01 -4.26042885e-01 4.96715426e-01 1.87772691e-01
-7.03640640e-01 5.63990891e-01 -3.89751732e-01 1.36575133e-01
-2.58155726e-02 -5.00635207e-01 -5.42639852e-01 -9.65763509e-01
5.93956895e-02 -2.06453726e-01 1.11013150e+00 2.82534093... | [8.608266830444336, -1.207703709602356] |
dbf1ce25-36a8-4a7e-aaf9-0cb5920d3792 | visual-speech-recognition-in-a-driver | null | null | https://eurasip.org/Proceedings/Eusipco/Eusipco2022/pdfs/0001131.pdf | https://eurasip.org/Proceedings/Eusipco/Eusipco2022/pdfs/0001131.pdf | Visual Speech Recognition in a Driver Assistance System | Visual speech recognition or automated lipreading is a field of growing attention. Video data proved its usefulness in multimodal speech recognition, especially when acoustic data is heavily noised or even inaccessible. In this paper, we present a novel method for visual speech recognition. We benchmark it on the famou... | ['Alexey Karpov', 'Alexandr Axyonov', 'Alexey Kashevnik', 'Dmitry Ryumin', 'Denis Ivanko'] | 2022-08-29 | null | null | null | 30th-european-signal-processing-conference | ['lipreading'] | ['computer-vision'] | [ 4.01644170e-01 5.31684011e-02 -2.11146489e-01 -6.66758418e-02
-8.25962305e-01 -4.43374842e-01 1.08098698e+00 -3.18871707e-01
-7.85273135e-01 8.70303750e-01 1.45756125e-01 -5.33435464e-01
1.29080012e-01 3.40852095e-03 -4.40792233e-01 -7.86192358e-01
2.92547852e-01 2.68635005e-01 2.45445460e-01 -1.18853338... | [14.326522827148438, 5.042632102966309] |
53da3c43-b130-472e-a40b-69cf53e68d31 | exploiting-multimodal-synthetic-data-for | 2306.12152 | null | https://arxiv.org/abs/2306.12152v1 | https://arxiv.org/pdf/2306.12152v1.pdf | Exploiting Multimodal Synthetic Data for Egocentric Human-Object Interaction Detection in an Industrial Scenario | In this paper, we tackle the problem of Egocentric Human-Object Interaction (EHOI) detection in an industrial setting. To overcome the lack of public datasets in this context, we propose a pipeline and a tool for generating synthetic images of EHOIs paired with several annotations and data signals (e.g., depth maps or ... | ['Giovanni Maria Farinella', 'Antonino Furnari', 'Francesco Ragusa', 'Rosario Leonardi'] | 2023-06-21 | null | null | null | null | ['human-object-interaction-detection'] | ['computer-vision'] | [ 4.32779908e-01 2.64110059e-01 5.06434083e-01 -3.07274044e-01
-5.63707769e-01 -5.92078805e-01 5.77948749e-01 -3.99779618e-01
-2.28871971e-01 4.64190662e-01 -7.88843036e-02 3.19174558e-01
4.01423015e-02 -6.16264701e-01 -9.03670192e-01 -5.01504719e-01
2.87297487e-01 7.68714428e-01 2.26314545e-01 -1.82973966... | [7.618464946746826, -0.823140561580658] |
ac163c87-e6cc-4911-87e5-e552cc78ae6b | wavemix-lite-a-resource-efficient-neural-1 | null | null | https://openreview.net/forum?id=y_icnxeeUcl | https://openreview.net/pdf?id=y_icnxeeUcl | WaveMix-Lite: A Resource-efficient Neural Network for Image Analysis | Gains in the ability to generalize on image analysis tasks for neural networks have come at the cost of increased number of parameters and layers, dataset sizes, training and test computations, and GPU RAM. We introduce a new architecture -- WaveMix-Lite -- that can generalize on par with contemporary transformers and ... | ['Amit', 'Pranav; Sethi', 'Jeevan'] | 2022-10-13 | null | null | null | iclr-2022-10 | ['scene-classification'] | ['computer-vision'] | [ 9.34417173e-02 -1.97700962e-01 -4.49622311e-02 -4.71402228e-01
-3.72168362e-01 -5.67054451e-01 4.53439981e-01 -5.72783537e-02
-9.19603765e-01 1.68709740e-01 -5.86942077e-01 -6.08688295e-01
2.05718596e-02 -9.33085322e-01 -7.59284258e-01 -7.15895236e-01
-4.69357334e-02 3.21313113e-01 8.02665591e-01 -3.91102284... | [9.075857162475586, 1.6443334817886353] |
d87f64d0-cd6f-4d6d-9b1d-5f3ecc8f2c48 | rapid-extraction-of-respiratory-waveforms | 2212.12578 | null | https://arxiv.org/abs/2212.12578v1 | https://arxiv.org/pdf/2212.12578v1.pdf | Rapid Extraction of Respiratory Waveforms from Photoplethysmography: A Deep Encoder Approach | Much of the information of breathing is contained within the photoplethysmography (PPG) signal, through changes in venous blood flow, heart rate and stroke volume. We aim to leverage this fact, by employing a novel deep learning framework which is a based on a repurposed convolutional autoencoder. Our model aims to enc... | ['Danilo P. Mandic', 'Harry J. Davies'] | 2022-12-22 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 1.63287446e-01 1.80983782e-01 3.44045222e-01 -2.17056423e-01
-5.22442520e-01 -5.91117263e-01 8.47491995e-02 -1.72745466e-01
-2.78218627e-01 6.28417134e-01 4.05721366e-01 -9.74646062e-02
-7.67439604e-02 -5.85378587e-01 -3.66247833e-01 -9.17783201e-01
-1.65538937e-01 -7.77594447e-02 -9.43749547e-02 -7.90722966... | [13.914237976074219, 2.9271230697631836] |
db3543a5-cc1a-4825-85ad-8372b357d681 | real-time-semantic-segmentation-using | 2303.15623 | null | https://arxiv.org/abs/2303.15623v1 | https://arxiv.org/pdf/2303.15623v1.pdf | Real-Time Semantic Segmentation using Hyperspectral Images for Mapping Unstructured and Unknown Environments | Autonomous navigation in unstructured off-road environments is greatly improved by semantic scene understanding. Conventional image processing algorithms are difficult to implement and lack robustness due to a lack of structure and high variability across off-road environments. The use of neural networks and machine le... | ['Swaminathan Gopalswamy', 'Reza Langari', 'Anant Bhamri', 'Anthony Medellin'] | 2023-03-27 | null | null | null | null | ['real-time-semantic-segmentation'] | ['computer-vision'] | [ 1.01550901e+00 9.64078456e-02 4.46012110e-01 -5.13047755e-01
-2.87948042e-01 -6.49906576e-01 3.94442648e-01 3.18645269e-01
-5.00127077e-01 7.32788682e-01 -5.48946857e-01 -3.73397380e-01
-4.00130451e-01 -1.36697173e+00 -5.95400572e-01 -5.11921465e-01
-5.56325093e-02 6.27799928e-01 3.43673795e-01 -2.00314924... | [9.274158477783203, -1.5775575637817383] |
d3c43917-ef30-462c-bff7-b66307426df0 | hybrid-rl-using-both-offline-and-online-data | 2210.06718 | null | https://arxiv.org/abs/2210.06718v3 | https://arxiv.org/pdf/2210.06718v3.pdf | Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient | We consider a hybrid reinforcement learning setting (Hybrid RL), in which an agent has access to an offline dataset and the ability to collect experience via real-world online interaction. The framework mitigates the challenges that arise in both pure offline and online RL settings, allowing for the design of simple an... | ['Wen Sun', 'Akshay Krishnamurthy', 'J. Andrew Bagnell', 'Ayush Sekhari', 'Yifei Zhou', 'Yuda Song'] | 2022-10-13 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-4.02232766e-01 5.84072992e-02 -5.44183254e-01 1.78368479e-01
-1.15507317e+00 -8.51182818e-01 5.96139967e-01 1.48219094e-01
-8.21263671e-01 1.17998636e+00 2.62198776e-01 -6.34023547e-01
-4.24922913e-01 -7.73601234e-01 -1.13374054e+00 -6.93881989e-01
-6.81453049e-01 6.14948392e-01 -1.29116476e-01 -1.85817957... | [4.131948947906494, 2.3615708351135254] |
8685a436-aa58-4d85-bb8b-3372d578c14b | machine-translation-between-spoken-languages | 2210.05404 | null | https://arxiv.org/abs/2210.05404v2 | https://arxiv.org/pdf/2210.05404v2.pdf | Machine Translation between Spoken Languages and Signed Languages Represented in SignWriting | This paper presents work on novel machine translation (MT) systems between spoken and signed languages, where signed languages are represented in SignWriting, a sign language writing system. Our work seeks to address the lack of out-of-the-box support for signed languages in current MT systems and is based on the SignB... | ['Sarah Ebling', 'Mathias Müller', 'Amit Moryossef', 'Zifan Jiang'] | 2022-10-11 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 3.33524406e-01 2.94962466e-01 -5.07568419e-01 -9.34623480e-01
-1.27143073e+00 -1.06029379e+00 9.05949295e-01 -7.79273510e-01
-4.94816482e-01 7.86992133e-01 9.13190424e-01 -5.76017916e-01
3.93390536e-01 -1.81033969e-01 -4.81102675e-01 -1.91292822e-01
5.87183535e-01 7.79311836e-01 -1.53308108e-01 -4.88742709... | [9.195640563964844, -6.522155284881592] |
85a238b8-04aa-40fc-b1e3-92cda034d53a | dota-2-with-large-scale-deep-reinforcement | 1912.06680 | null | https://arxiv.org/abs/1912.06680v1 | https://arxiv.org/pdf/1912.06680v1.pdf | Dota 2 with Large Scale Deep Reinforcement Learning | On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game. The game of Dota 2 presents novel challenges for AI systems such as long time horizons, imperfect information, and complex, continuous state-action spaces, all challenges which will become increasingly central ... | ['Susan Zhang', 'Filip Wolski', 'Jie Tang', 'Henrique Pondé de Oliveira Pinto', 'Rafal Józefowicz', 'Chris Hesse', 'Przemysław Dębiak', 'Vicki Cheung', 'Tim Salimans', 'Szymon Sidor', 'Quirin Fischer', 'Brooke Chan', 'Scott Gray', 'Ilya Sutskever', 'Shariq Hashme', 'Michael Petrov', 'Jonathan Raiman', 'Jeremy Schlatter... | 2019-12-13 | null | null | null | null | ['dota-2'] | ['playing-games'] | [-3.83695334e-01 -1.30386446e-02 -1.19173983e-02 1.08661212e-01
-5.63283563e-01 -7.07883716e-01 2.71600991e-01 -3.42915565e-01
-7.94112742e-01 1.07103229e+00 -4.47276801e-01 -3.05794865e-01
-1.77054048e-01 -5.51388502e-01 -6.95421040e-01 -1.73821911e-01
-7.09360719e-01 6.61620438e-01 4.72420663e-01 -8.32557440... | [3.60996413230896, 1.5209455490112305] |
fc0e4945-4062-4373-82db-00e63f843649 | memory-enriched-computation-and-learning-in | 2205.11276 | null | https://arxiv.org/abs/2205.11276v1 | https://arxiv.org/pdf/2205.11276v1.pdf | Memory-enriched computation and learning in spiking neural networks through Hebbian plasticity | Memory is a key component of biological neural systems that enables the retention of information over a huge range of temporal scales, ranging from hundreds of milliseconds up to years. While Hebbian plasticity is believed to play a pivotal role in biological memory, it has so far been analyzed mostly in the context of... | ['Robert Legenstein', 'Ozan Özdenizci', 'Thomas Limbacher'] | 2022-05-23 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 3.21945488e-01 -2.46167183e-01 1.38478026e-01 -4.85742986e-02
3.16338181e-01 -5.32911301e-01 7.22647309e-01 2.92586029e-01
-7.07560778e-01 1.04205132e+00 -2.12187961e-01 -1.53046563e-01
-3.51138055e-01 -1.02420831e+00 -8.44076753e-01 -1.00746942e+00
-2.16253653e-01 1.81086034e-01 9.86125588e-01 -3.56252134... | [8.158235549926758, 2.5761733055114746] |
9b038787-fd05-44f7-9d8d-c67194d7e4fb | semantic-relationships-guided-representation | 1904.09939 | null | http://arxiv.org/abs/1904.09939v1 | http://arxiv.org/pdf/1904.09939v1.pdf | Semantic Relationships Guided Representation Learning for Facial Action Unit Recognition | Facial action unit (AU) recognition is a crucial task for facial expressions
analysis and has attracted extensive attention in the field of artificial
intelligence and computer vision. Existing works have either focused on
designing or learning complex regional feature representations, or delved into
various types of A... | ['Liang Lin', 'Yirui Zeng', 'Xin Zhu', 'Guanbin Li', 'Qing Wang'] | 2019-04-22 | null | null | null | null | ['facial-action-unit-detection'] | ['computer-vision'] | [ 2.43757680e-01 8.14308152e-02 -2.04946741e-01 -4.67296451e-01
-6.00188002e-02 -1.60467147e-03 3.73022288e-01 -3.46666247e-01
4.38139290e-02 2.76959330e-01 1.57919571e-01 1.87581390e-01
8.07741135e-02 -9.78005111e-01 -5.40744185e-01 -8.67682099e-01
1.24072485e-01 -1.62182882e-01 2.68158503e-02 -5.40605068... | [13.644853591918945, 1.618565320968628] |
2dfa2798-c4b8-499a-93af-608102891dc0 | automated-mobile-attention-kpconv-networks-1 | null | null | https://openreview.net/group?id=ICLR.cc/2022/Conference | https://openreview.net/forum?id=VZC5Lzyl0le | Automated Mobile Attention KPConv Networks via a Wide and Deep Predictor | Kernel Point Convolution (KPConv) achieves cutting-edge performance on 3D point cloud applications. Unfortunately, the large size of KPConv network limits its usage in mobile scenarios. In addition, we observe that KPConv ignores the kernel relationship and treats each kernel point equally when formulating neighbor-ker... | ['Anonymous'] | 2022-09-28 | null | null | null | international-conference-on-learning | ['point-cloud-classification'] | ['computer-vision'] | [-3.63566697e-01 -2.63823539e-01 -3.76825124e-01 -1.91751689e-01
-3.82406533e-01 -4.29503947e-01 1.33968338e-01 -2.58795619e-01
-1.97734594e-01 -4.38337997e-02 6.35533407e-02 -8.75229120e-01
-2.59539455e-01 -9.21853721e-01 -1.00197327e+00 -2.84911036e-01
1.11875117e-01 1.82579339e-01 2.97826678e-01 -1.86846793... | [7.88428258895874, -3.5417747497558594] |
52dd90e8-b533-4479-b5e8-b42955c1a412 | qmul-sds-diacr-ita2020-evaluating | 2011.02935 | null | https://arxiv.org/abs/2011.02935v2 | https://arxiv.org/pdf/2011.02935v2.pdf | QMUL-SDS @ DIACR-Ita: Evaluating Unsupervised Diachronic Lexical Semantics Classification in Italian | In this paper, we present the results and main findings of our system for the DIACR-ITA 2020 Task. Our system focuses on using variations of training sets and different semantic detection methods. The task involves training, aligning and predicting a word's vector change from two diachronic Italian corpora. We demonstr... | ['Maria Liakata', 'Arkaitz Zubiaga', 'Adam Tsakalidis', 'Rabab Alkhalifa'] | 2020-11-05 | null | null | null | null | ['diachronic-word-embeddings'] | ['natural-language-processing'] | [-1.13780782e-01 -1.59272105e-01 -3.68391067e-01 -4.68987614e-01
-8.38293254e-01 -6.17672741e-01 9.58260000e-01 2.92036027e-01
-9.11268890e-01 5.05337656e-01 4.18690950e-01 -2.92084634e-01
1.28607497e-01 -5.77207685e-01 -1.49538293e-01 -4.89648134e-01
-1.63521960e-01 6.97904468e-01 3.45879287e-01 -4.45320040... | [10.148763656616211, 9.040098190307617] |
84256bae-8f19-46aa-90f8-bf96bf3a52b5 | video-text-retrieval-by-supervised-multi | 2302.09473 | null | https://arxiv.org/abs/2302.09473v1 | https://arxiv.org/pdf/2302.09473v1.pdf | Video-Text Retrieval by Supervised Multi-Space Multi-Grained Alignment | While recent progress in video-text retrieval has been advanced by the exploration of better representation learning, in this paper, we present a novel multi-space multi-grained supervised learning framework, SUMA, to learn an aligned representation space shared between the video and the text for video-text retrieval. ... | ['Peng Shi', 'Yimu Wang'] | 2023-02-19 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [ 3.38711619e-01 -6.12328053e-01 -5.46721637e-01 -2.98319101e-01
-1.02483034e+00 -4.22176123e-01 1.05236638e+00 3.68450701e-01
-2.56118208e-01 2.49758780e-01 5.97396791e-01 2.90018857e-01
-4.17578191e-01 -4.15820748e-01 -4.88237619e-01 -7.05210984e-01
3.06509584e-02 5.13808250e-01 2.86140130e-03 1.86711568... | [10.364459991455078, 0.9976592659950256] |
260ef057-23ca-498c-be5e-c2402121b26a | when-source-free-domain-adaptation-meets-1 | 2301.13381 | null | https://arxiv.org/abs/2301.13381v2 | https://arxiv.org/pdf/2301.13381v2.pdf | When Source-Free Domain Adaptation Meets Learning with Noisy Labels | Recent state-of-the-art source-free domain adaptation (SFDA) methods have focused on learning meaningful cluster structures in the feature space, which have succeeded in adapting the knowledge from source domain to unlabeled target domain without accessing the private source data. However, existing methods rely on the ... | ['Boyu Wang', 'A. Ian McLeod', 'Charles Ling', 'Ruizhi Pu', 'Jiaqi Li', 'Pengcheng Xu', 'Gezheng Xu', 'Li Yi'] | 2023-01-31 | null | null | null | null | ['learning-with-noisy-labels', 'source-free-domain-adaptation', 'learning-with-noisy-labels'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 2.85897970e-01 -8.50185007e-02 -2.88739085e-01 -2.95760870e-01
-1.01184535e+00 -8.23219001e-01 6.42836690e-01 -1.87697560e-01
-1.79297775e-01 9.64101017e-01 -3.98650430e-02 -1.16724715e-01
-3.17743242e-01 -6.03948534e-01 -8.14432681e-01 -1.24097943e+00
2.83186436e-01 6.02797568e-01 1.92242116e-01 -8.69614910... | [10.39415454864502, 3.1836228370666504] |
e8c29ee5-69c5-42e3-ac53-77eb27e12746 | learning-mid-level-features-and-modeling | 1401.5535 | null | http://arxiv.org/abs/1401.5535v2 | http://arxiv.org/pdf/1401.5535v2.pdf | Learning Mid-Level Features and Modeling Neuron Selectivity for Image Classification | We now know that mid-level features can greatly enhance the performance of
image learning, but how to automatically learn the image features efficiently
and in an unsupervised manner is still an open question. In this paper, we
present a very efficient mid-level feature learning approach (MidFea), which
only involves s... | ['Shu Kong', 'Qiang Yang', 'Zhuolin Jiang'] | 2014-01-22 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 2.54475564e-01 2.30074325e-03 -1.44866139e-01 -8.55232835e-01
-5.10431826e-01 -1.81990549e-01 5.81473053e-01 3.21272463e-01
-6.56775951e-01 4.94854689e-01 -7.01170266e-02 -3.21685746e-02
-2.60788262e-01 -1.02808952e+00 -8.10369074e-01 -7.39599705e-01
-2.68916488e-01 2.57651627e-01 1.01991773e-01 1.60270661... | [9.472250938415527, 2.7609059810638428] |
b2c9630e-8ff2-48d9-bf94-4b666768e426 | few-shot-class-incremental-learning-via | 2006.15524 | null | https://arxiv.org/abs/2006.15524v3 | https://arxiv.org/pdf/2006.15524v3.pdf | MgSvF: Multi-Grained Slow vs. Fast Framework for Few-Shot Class-Incremental Learning | As a challenging problem, few-shot class-incremental learning (FSCIL) continually learns a sequence of tasks, confronting the dilemma between slow forgetting of old knowledge and fast adaptation to new knowledge. In this paper, we concentrate on this "slow vs. fast" (SvF) dilemma to determine which knowledge components... | ['Fei Wu', 'Qi Tian', 'Mintong Kang', 'Xi Li', 'Yongjian Fu', 'Hanbin Zhao'] | 2020-06-28 | null | null | null | null | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 1.85424253e-01 -4.51305270e-01 -2.28259951e-01 -2.24192426e-01
-4.50636864e-01 -2.82203048e-01 6.55938447e-01 1.87853470e-01
-6.36675358e-01 7.78597534e-01 1.45499825e-01 7.08616897e-02
-5.69632947e-01 -8.81018341e-01 -5.77237666e-01 -8.38434100e-01
2.38060996e-01 2.17423573e-01 8.88436496e-01 -2.10158780... | [9.855484962463379, 3.3703994750976562] |
69dcd5d9-6a77-4fae-91f6-ec1913aff5f0 | handling-normalization-issues-for-part-of | null | null | https://aclanthology.org/L18-1014 | https://aclanthology.org/L18-1014.pdf | Handling Normalization Issues for Part-of-Speech Tagging of Online Conversational Text | null | ["Fr{\\'e}d{\\'e}ric B{\\'e}chet", "G{\\'e}raldine Damnati", 'Jeremy Auguste', 'Delphine Charlet', 'Alexis Nasr', 'Johannes Heinecke'] | 2018-05-01 | handling-normalization-issues-for-part-of-1 | https://aclanthology.org/L18-1014 | https://aclanthology.org/L18-1014.pdf | lrec-2018-5 | ['lexical-normalization'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.299404144287109, 3.713064193725586] |
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