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fc810c22-502c-489f-b848-b745fafee04b | near-zero-shot-suggestion-mining-with-a | 2111.12956 | null | https://arxiv.org/abs/2111.12956v1 | https://arxiv.org/pdf/2111.12956v1.pdf | Near-Zero-Shot Suggestion Mining with a Little Help from WordNet | In this work, we explore the constructive side of online reviews: advice, tips, requests, and suggestions that users provide about goods, venues, services, and other items of interest. To reduce training costs and annotation efforts needed to build a classifier for a specific label set, we present and evaluate several ... | ['Sergey Nikolenko', 'Sejeong Kwon', 'Elena Tutubalina', 'Anton Alekseev'] | 2021-11-25 | null | null | null | null | ['suggestion-mining'] | ['natural-language-processing'] | [ 1.68833449e-01 2.72742629e-01 -7.95757771e-01 -9.60916698e-01
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4.22987312e-01 2.17206329e-01 3.40933576e-02 -3.86621177... | [10.926475524902344, 7.4930739402771] |
a57c1625-2943-49e9-bd39-2def6dc398ba | investigating-emotion-color-association-in | 2011.11058 | null | https://arxiv.org/abs/2011.11058v1 | https://arxiv.org/pdf/2011.11058v1.pdf | Investigating Emotion-Color Association in Deep Neural Networks | It has been found that representations learned by Deep Neural Networks (DNNs) correlate very well to neural responses measured in primates' brains and psychological representations exhibited by human similarity judgment. On another hand, past studies have shown that particular colors can be associated with specific emo... | ['Shashi Kant Gupta', 'Shivi Gupta'] | 2020-11-22 | null | null | null | null | ['emotional-intelligence'] | ['natural-language-processing'] | [ 1.63242772e-01 -2.03752130e-01 1.12630449e-01 -8.01884055e-01
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c37a33a3-be52-4ab1-8f28-6b6de7fe7e20 | syntactic-methods-for-negation-detection-in | null | null | https://aclanthology.org/W16-2921 | https://aclanthology.org/W16-2921.pdf | Syntactic methods for negation detection in radiology reports in Spanish | null | ['Jorge Vivaldi', 'Vanesa Stricker', 'Viviana Cotik', 'Horacio Rodriguez'] | 2016-08-01 | null | null | null | ws-2016-8 | ['negation-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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4d9b0030-dc65-4184-82c8-3113a6147ef5 | deep-reinforced-attention-regression-for | 2111.10917 | null | https://arxiv.org/abs/2111.10917v1 | https://arxiv.org/pdf/2111.10917v1.pdf | Deep Reinforced Attention Regression for Partial Sketch Based Image Retrieval | Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) aims at finding a specific image from a large gallery given a query sketch. Despite the widespread applicability of FG-SBIR in many critical domains (e.g., crime activity tracking), existing approaches still suffer from a low accuracy while being sensitive to external... | ['Qi Yu', 'Xumin Liu', 'Hitesh Sapkota', 'Dingrong Wang'] | 2021-11-21 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 2.19893187e-01 -4.28062975e-01 -5.12211561e-01 -2.32960492e-01
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1.12344243e-01 5.27564645e-01 1.22326970e-01 -6.89377263... | [11.663331985473633, 0.5999932289123535] |
f2f34b32-d913-4816-9639-9382ecf0dda2 | recovering-remote-photoplethysmograph-signal | 1905.02419 | null | https://arxiv.org/abs/1905.02419v2 | https://arxiv.org/pdf/1905.02419v2.pdf | Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks | Recent studies demonstrated that the average heart rate (HR) can be measured from facial videos based on non-contact remote photoplethysmography (rPPG). However for many medical applications (e.g., atrial fibrillation (AF) detection) knowing only the average HR is not sufficient, and measuring precise rPPG signals from... | ['Xiaobai Li', 'Zitong Yu', 'Guoying Zhao'] | 2019-05-07 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 6.88832924e-02 -1.76040202e-01 -4.71120849e-02 -4.64344233e-01
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d0a045fc-2c28-414f-9584-e24017589d98 | spine-a-scalable-log-parser-with-feedback | null | null | https://dl.acm.org/doi/abs/10.1145/3540250.3549176 | https://dl.acm.org/doi/abs/10.1145/3540250.3549176 | SPINE: a scalable log parser with feedback guidance | Log parsing, which extracts log templates and parameters, is a critical prerequisite step for automated log analysis techniques. Though existing log parsers have achieved promising accuracy on public log datasets, they still face many challenges when applied in the industry. Through studying the characteristics of real... | ['Xuheng Wang'] | 2022-11-01 | null | null | null | acm-conferences-2022-11 | ['log-parsing'] | ['computer-code'] | [-2.08885819e-01 -4.08604711e-01 -2.46871606e-01 -4.22430575e-01
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0148579b-e791-4117-a7e1-cab3664d9333 | reproducibility-report-explainable-deep-one | 2206.02598 | null | https://arxiv.org/abs/2206.02598v1 | https://arxiv.org/pdf/2206.02598v1.pdf | [Reproducibility Report] Explainable Deep One-Class Classification | Fully Convolutional Data Description (FCDD), an explainable version of the Hypersphere Classifier (HSC), directly addresses image anomaly detection (AD) and pixel-wise AD without any post-hoc explainer methods. The authors claim that FCDD achieves results comparable with the state-of-the-art in sample-wise AD on Fashio... | ['Etienne Decencière', 'Joao P. C. Bertoldo'] | 2022-06-06 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [-2.03064919e-01 3.44525099e-01 1.38020411e-01 -4.76790667e-01
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a97493ab-5f5c-4191-a9de-994a1d949bb6 | palr-personalization-aware-llms-for | 2305.07622 | null | https://arxiv.org/abs/2305.07622v3 | https://arxiv.org/pdf/2305.07622v3.pdf | PALR: Personalization Aware LLMs for Recommendation | Large language models (LLMs) have recently received significant attention for their exceptional capabilities. Despite extensive efforts in developing general-purpose LLMs that can be utilized in various natural language processing (NLP) tasks, there has been less research exploring their potential in recommender system... | ['Yanbin Lu', 'Xiaojiang Huang', 'Eunah Cho', 'Ziyan Jiang', 'Fan Yang', 'Zheng Chen'] | 2023-05-12 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [ 2.30539069e-01 -5.22893257e-02 -5.88039100e-01 -6.10921443e-01
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2.02437580e-01 8.03332388e-01 1.48466319e-01 -4.05517161... | [10.253395080566406, 5.772123336791992] |
9bea0c7c-d67a-4535-9aca-420be0e191a0 | an-online-semantic-mapping-system-for | 2203.03944 | null | https://arxiv.org/abs/2203.03944v1 | https://arxiv.org/pdf/2203.03944v1.pdf | An Online Semantic Mapping System for Extending and Enhancing Visual SLAM | We present a real-time semantic mapping approach for mobile vision systems with a 2D to 3D object detection pipeline and rapid data association for generated landmarks. Besides the semantic map enrichment the associated detections are further introduced as semantic constraints into a simultaneous localization and mappi... | ['Ayoub Al-Hamadi', 'Thorsten Hempel'] | 2022-03-08 | null | null | null | null | ['semantic-slam'] | ['computer-vision'] | [ 9.82687473e-02 2.77692139e-01 2.38560423e-01 -4.39789772e-01
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03ba9a77-e689-4b4b-8c0a-86b2d55a7bfa | on-the-interplay-between-misspecification-and | 2303.09390 | null | https://arxiv.org/abs/2303.09390v1 | https://arxiv.org/pdf/2303.09390v1.pdf | On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits | We study linear contextual bandits in the misspecified setting, where the expected reward function can be approximated by a linear function class up to a bounded misspecification level $\zeta>0$. We propose an algorithm based on a novel data selection scheme, which only selects the contextual vectors with large uncerta... | ['Quanquan Gu', 'Zhiyuan Fan', 'Jiafan He', 'Weitong Zhang'] | 2023-03-16 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 1.06320819e-02 2.29080111e-01 -6.55532002e-01 -9.81411412e-02
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81a29ff7-3667-4591-a405-50d83f1af467 | disco-a-system-leveraging-semantic-search-in | null | null | https://aclanthology.org/C16-2014 | https://aclanthology.org/C16-2014.pdf | DISCO: A System Leveraging Semantic Search in Document Review | This paper presents Disco, a prototype for supporting knowledge workers in exploring, reviewing and sorting collections of textual data. The goal is to facilitate, accelerate and improve the discovery of information. To this end, it combines Semantic Relatedness techniques with a review workflow developed in a tangible... | ['Fabien Guillot', 'Caroline Privault', 'Ngoc Phuoc An Vo'] | 2016-12-01 | disco-a-system-leveraging-semantic-search-in-1 | https://aclanthology.org/C16-2014 | https://aclanthology.org/C16-2014.pdf | coling-2016-12 | ['text-clustering'] | ['natural-language-processing'] | [-1.70503497e-01 -1.16059273e-01 -2.72637278e-01 -1.42106131e-01
6.23820573e-02 -9.37165558e-01 9.78328347e-01 6.53803945e-01
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3.46090607e-02 5.55029154e-01 4.07830298e-01 -4.14676368... | [11.947903633117676, 7.887519836425781] |
671e8b90-2f75-43bb-bcac-4a1de99c008e | on-cropped-versus-uncropped-training-sets-in | 2110.02933 | null | https://arxiv.org/abs/2110.02933v2 | https://arxiv.org/pdf/2110.02933v2.pdf | On Cropped versus Uncropped Training Sets in Tabular Structure Detection | Automated document processing for tabular information extraction is highly desired in many organizations, from industry to government. Prior works have addressed this problem under table detection and table structure detection tasks. Proposed solutions leveraging deep learning approaches have been giving promising resu... | ['Shahzad Khan', 'Burak Kantarci', 'Murat Simsek', 'Yakup Akkaya'] | 2021-10-06 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [ 1.72310978e-01 5.11735827e-02 -6.12515844e-02 -7.87524208e-02
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-3.88992690e-02 2.96847761e-01 2.56964952e-01 3.73983942... | [11.687959671020508, 3.073248863220215] |
ba135738-d526-4db0-a300-b8a04736a326 | pop-music-transformer-generating-music-with | 2002.00212 | null | https://arxiv.org/abs/2002.00212v3 | https://arxiv.org/pdf/2002.00212v3.pdf | Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions | A great number of deep learning based models have been recently proposed for automatic music composition. Among these models, the Transformer stands out as a prominent approach for generating expressive classical piano performance with a coherent structure of up to one minute. The model is powerful in that it learns ab... | ['Yi-Hsuan Yang', 'Yu-Siang Huang'] | 2020-02-01 | null | null | null | null | ['music-modeling'] | ['music'] | [-7.13016540e-02 1.63597822e-01 -3.68331708e-02 -1.44157887e-01
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f43d11c5-cd5a-49c7-a81f-9603e5834990 | learning-local-displacements-for-point-cloud | 2203.16600 | null | https://arxiv.org/abs/2203.16600v1 | https://arxiv.org/pdf/2203.16600v1.pdf | Learning Local Displacements for Point Cloud Completion | We propose a novel approach aimed at object and semantic scene completion from a partial scan represented as a 3D point cloud. Our architecture relies on three novel layers that are used successively within an encoder-decoder structure and specifically developed for the task at hand. The first one carries out feature e... | ['Federico Tombari', 'Nassir Navab', 'David Joseph Tan', 'Yida Wang'] | 2022-03-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Learning_Local_Displacements_for_Point_Cloud_Completion_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Learning_Local_Displacements_for_Point_Cloud_Completion_CVPR_2022_paper.pdf | cvpr-2022-1 | ['point-cloud-completion'] | ['computer-vision'] | [ 3.24127316e-01 2.27073371e-01 3.51064891e-01 -5.76098740e-01
-7.98429072e-01 -2.58329511e-01 8.92755628e-01 4.25702304e-01
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-9.50775892e-02 4.53847289e-01 5.66080153e-01 -1.33636102... | [8.139472961425781, -3.43686842918396] |
8d901171-6ee7-44e8-9985-df234b30a582 | delving-into-the-cyclic-mechanism-in-semi | 2010.12176 | null | https://arxiv.org/abs/2010.12176v1 | https://arxiv.org/pdf/2010.12176v1.pdf | Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation | In this paper, we address several inadequacies of current video object segmentation pipelines. Firstly, a cyclic mechanism is incorporated to the standard semi-supervised process to produce more robust representations. By relying on the accurate reference mask in the starting frame, we show that the error propagation p... | ['Weiyao Lin', 'John See', 'Jinlong Peng', 'Ning Xu', 'Yuxi Li'] | 2020-10-23 | null | http://proceedings.neurips.cc/paper/2020/hash/0d5bd023a3ee11c7abca5b42a93c4866-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/0d5bd023a3ee11c7abca5b42a93c4866-Paper.pdf | neurips-2020-12 | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 2.62368083e-01 -2.20714901e-02 -1.40132934e-01 -5.18405735e-01
-6.11967146e-01 -5.78758359e-01 3.07851523e-01 -1.71112180e-01
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-1.10796820e-02 -1.25654653e-01 8.47182810e-01 6.54203594... | [9.228978157043457, -0.16529454290866852] |
a2f64b32-8ba2-46a3-bd5d-5e817291ef34 | geomvsnet-learning-multi-view-stereo-with | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_GeoMVSNet_Learning_Multi-View_Stereo_With_Geometry_Perception_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_GeoMVSNet_Learning_Multi-View_Stereo_With_Geometry_Perception_CVPR_2023_paper.pdf | GeoMVSNet: Learning Multi-View Stereo With Geometry Perception | Recent cascade Multi-View Stereo (MVS) methods can efficiently estimate high-resolution depth maps through narrowing hypothesis ranges. However, previous methods ignored the vital geometric information embedded in coarse stages, leading to vulnerable cost matching and sub-optimal reconstruction results. In this pap... | ['Ronggang Wang', 'Yuxi Hu', 'Rui Peng', 'Zhe Zhang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-reconstruction', 'stereo-matching-1'] | ['computer-vision', 'computer-vision'] | [-1.44486055e-01 8.79682973e-02 1.01664357e-01 -3.99162680e-01
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3.78993064e-01 2.00693086e-01 5.22664309e-01 -2.14429110... | [8.9816312789917, -2.741619110107422] |
35e0cb45-3b55-4fc9-9a9a-f1af5d57d33a | unknown-intent-detection-using-multi | null | null | https://aclanthology.org/2021.ranlp-main.127 | https://aclanthology.org/2021.ranlp-main.127.pdf | Unknown Intent Detection Using Multi-Objective Optimization on Deep Learning Classifiers | Modelling and understanding dialogues in a conversation depends on identifying the user intent from the given text. Unknown or new intent detection is a critical task, as in a realistic scenario a user intent may frequently change over time and divert even to an intent previously not encountered. This task of separatin... | ['Roshni Ramnani', 'Sakshi C. Jain', 'Shubhashis Sengupta', 'Asif Ekbal', 'Zishan Ahmad', 'Prerna Prem'] | null | null | https://aclanthology.org/2021.ranlp-1.127 | https://aclanthology.org/2021.ranlp-1.127.pdf | ranlp-2021-9 | ['intent-discovery'] | ['natural-language-processing'] | [ 5.29366791e-01 -1.08470105e-01 -7.55841732e-02 -7.63062179e-01
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-1.21936284e-01 1.03259003e+00 1.21125311e-01 -4.85081732... | [12.514715194702148, 7.574090003967285] |
735322b6-7702-4ae4-a518-4057161e78c7 | quantized-sparse-weight-decomposition-for | 2207.11048 | null | https://arxiv.org/abs/2207.11048v1 | https://arxiv.org/pdf/2207.11048v1.pdf | Quantized Sparse Weight Decomposition for Neural Network Compression | In this paper, we introduce a novel method of neural network weight compression. In our method, we store weight tensors as sparse, quantized matrix factors, whose product is computed on the fly during inference to generate the target model's weights. We use projected gradient descent methods to find quantized and spars... | ['Arash Behboodi', 'Markus Nagel', 'Mart van Baalen', 'Andrey Kuzmin'] | 2022-07-22 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 3.24624956e-01 1.05307914e-01 -4.05401379e-01 -3.79293859e-01
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-3.85437429e-01 -6.39328599e-01 -7.57160962e-01 -6.00947022e-01
-7.68070109e-03 7.02728629e-01 -1.54227056e-02 -1.44145057... | [8.44459342956543, 3.498746871948242] |
93392afd-b820-49a1-b0f0-3e6195de14f1 | unibuckernel-a-kernel-based-learning-method | 1803.07602 | null | http://arxiv.org/abs/1803.07602v4 | http://arxiv.org/pdf/1803.07602v4.pdf | UnibucKernel: A kernel-based learning method for complex word identification | In this paper, we present a kernel-based learning approach for the 2018
Complex Word Identification (CWI) Shared Task. Our approach is based on
combining multiple low-level features, such as character n-grams, with
high-level semantic features that are either automatically learned using word
embeddings or extracted fro... | ['Radu Tudor Ionescu', 'Andrei M. Butnaru'] | 2018-03-20 | unibuckernel-a-kernel-based-learning-method-1 | https://aclanthology.org/W18-0519 | https://aclanthology.org/W18-0519.pdf | ws-2018-6 | ['complex-word-identification'] | ['natural-language-processing'] | [ 1.28500506e-01 3.05726454e-02 -4.01819855e-01 -3.11822563e-01
-5.79707623e-01 -6.38426304e-01 6.45480871e-01 8.12678397e-01
-1.20446122e+00 6.35735035e-01 3.61621797e-01 -4.29754496e-01
-8.74575227e-02 -8.86357725e-01 -3.69003803e-01 -3.37654769e-01
-5.44634350e-02 4.11101997e-01 1.31724209e-01 -1.65054336... | [10.473504066467285, 10.30249309539795] |
3085373d-adfa-4bde-adf9-d8af806e2acb | searcher-shared-embedding-architecture-for | null | null | https://aclanthology.org/2020.clssts-1.4 | https://aclanthology.org/2020.clssts-1.4.pdf | SEARCHER: Shared Embedding Architecture for Effective Retrieval | We describe an approach to cross lingual information retrieval that does not rely on explicit translation of either document or query terms. Instead, both queries and documents are mapped into a shared embedding space where retrieval is performed. We discuss potential advantages of the approach in handling polysemy and... | ['Scott Miller', 'Marjorie Freedman', 'Elizabeth Boschee', 'Joel Barry'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-2.42636636e-01 -4.04709876e-01 -4.68657762e-01 -4.84159768e-01
-1.48726058e+00 -8.28510642e-01 1.11335707e+00 3.16694289e-01
-1.13442957e+00 7.57875860e-01 5.06885409e-01 -3.43226910e-01
-3.58579278e-01 -5.65769613e-01 -1.95813596e-01 -4.82872784e-01
2.24918604e-01 8.58646870e-01 1.37266412e-01 -6.31690741... | [11.318033218383789, 9.8681001663208] |
d27802ee-6bb2-4de7-95d6-58f01dd94944 | topic-discovery-via-latent-space-clustering | 2202.04582 | null | https://arxiv.org/abs/2202.04582v1 | https://arxiv.org/pdf/2202.04582v1.pdf | Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations | Topic models have been the prominent tools for automatic topic discovery from text corpora. Despite their effectiveness, topic models suffer from several limitations including the inability of modeling word ordering information in documents, the difficulty of incorporating external linguistic knowledge, and the lack of... | ['Jiawei Han', 'Yu Zhang', 'Jiaxin Huang', 'Yunyi Zhang', 'Yu Meng'] | 2022-02-09 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-1.67908952e-01 1.01503439e-01 -7.65071690e-01 -4.25635606e-01
-1.04783559e+00 -3.94585133e-01 1.24726188e+00 4.09208596e-01
1.06814161e-01 4.94793981e-01 8.50598812e-01 -3.73419039e-02
-2.88831502e-01 -7.29220331e-01 -2.44545817e-01 -6.40587926e-01
-2.34757528e-01 7.61587381e-01 8.84471759e-02 3.11954133... | [10.38632869720459, 6.955364227294922] |
b10c01df-9c92-4624-ba4f-a7b5cbee57de | learning-functions-to-study-the-benefit-of | 2006.05561 | null | https://arxiv.org/abs/2006.05561v2 | https://arxiv.org/pdf/2006.05561v2.pdf | Learning Functions to Study the Benefit of Multitask Learning | We study and quantify the generalization patterns of multitask learning (MTL) models for sequence labeling tasks. MTL models are trained to optimize a set of related tasks jointly. Although multitask learning has achieved improved performance in some problems, there are also tasks that lose performance when trained tog... | ['Dietrich Klakow', 'Gabriele Bettgenhäuser', 'Michael A. Hedderich'] | 2020-06-09 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [ 3.38355273e-01 2.07063686e-02 -7.74360523e-02 -5.38949490e-01
-6.79537177e-01 -6.43119216e-01 8.41971874e-01 2.70923287e-01
-7.54351377e-01 8.47729921e-01 -7.19924420e-02 -3.75764191e-01
-5.65762460e-01 -7.53377452e-02 -8.52598429e-01 -6.22263670e-01
-4.76184189e-01 4.59822953e-01 3.59768629e-01 -6.27666786... | [9.313990592956543, 4.526829242706299] |
f407a7b0-1405-4747-81f6-0188cba9a235 | beyond-rgb-scene-property-synthesis-with | 2206.04669 | null | https://arxiv.org/abs/2206.04669v1 | https://arxiv.org/pdf/2206.04669v1.pdf | Beyond RGB: Scene-Property Synthesis with Neural Radiance Fields | Comprehensive 3D scene understanding, both geometrically and semantically, is important for real-world applications such as robot perception. Most of the existing work has focused on developing data-driven discriminative models for scene understanding. This paper provides a new approach to scene understanding, from a s... | ['Yu-Xiong Wang', 'Martial Hebert', 'Zhipeng Bao', 'Shuhong Zheng', 'Mingtong Zhang'] | 2022-06-09 | null | null | null | null | ['edge-detection'] | ['computer-vision'] | [ 7.05084145e-01 1.62684254e-03 1.57138824e-01 -5.86862803e-01
-5.91753066e-01 -5.21435618e-01 7.76735723e-01 2.46727332e-01
3.24329808e-02 2.88191408e-01 9.50985122e-03 -3.24435174e-01
-9.19482484e-02 -1.06677866e+00 -8.39094937e-01 -6.73445344e-01
1.99530736e-01 6.04157090e-01 1.37406036e-01 -4.26813960... | [8.570149421691895, -3.1173436641693115] |
ca46993d-bb7a-41a6-a548-30a07531a4b4 | cmir-net-a-deep-learning-based-model-for | 1904.04794 | null | https://arxiv.org/abs/1904.04794v2 | https://arxiv.org/pdf/1904.04794v2.pdf | CMIR-NET : A Deep Learning Based Model For Cross-Modal Retrieval In Remote Sensing | We address the problem of cross-modal information retrieval in the domain of remote sensing. In particular, we are interested in two application scenarios: i) cross-modal retrieval between panchromatic (PAN) and multi-spectral imagery, and ii) multi-label image retrieval between very high resolution (VHR) images and sp... | ['Mihai Datcu', 'Biplab Banerjee', 'Ushasi Chaudhuri', 'Avik Bhattacharya'] | 2019-04-09 | null | null | null | null | ['multi-label-image-retrieval', 'cross-modal-information-retrieval'] | ['computer-vision', 'miscellaneous'] | [ 5.47227740e-01 -8.15220356e-01 -9.28551555e-02 -4.13085729e-01
-1.88223636e+00 -6.31608129e-01 8.50622237e-01 1.43699929e-01
-3.93694192e-01 5.83273768e-01 9.02772248e-02 2.84893457e-02
-7.34769583e-01 -8.55958164e-01 -1.68232784e-01 -1.06476235e+00
3.20472978e-02 5.96603513e-01 -3.09254676e-01 -2.39473462... | [9.770553588867188, -1.440542221069336] |
b16b9256-5408-461b-9a61-80629584945f | semail-eliminating-distractors-in-visual | 2306.10695 | null | https://arxiv.org/abs/2306.10695v1 | https://arxiv.org/pdf/2306.10695v1.pdf | SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models | Model-based imitation learning (MBIL) is a popular reinforcement learning method that improves sample efficiency on high-dimension input sources, such as images and videos. Following the convention of MBIL research, existing algorithms are highly deceptive by task-irrelevant information, especially moving distractors i... | ['De-Chuan Zhan', 'Ruying Chen', 'Minghao Shao', 'Yucen Wang', 'Shenghua Wan'] | 2023-06-19 | null | null | null | null | ['imitation-learning'] | ['methodology'] | [-4.14262488e-02 -9.59966555e-02 -1.59552246e-01 3.63773406e-01
-3.06141734e-01 -5.00616133e-01 6.49291217e-01 -7.41713464e-01
-7.59624004e-01 9.55371439e-01 -8.96476358e-02 -1.10051125e-01
3.06103341e-02 -2.08923340e-01 -7.73669124e-01 -8.76696825e-01
-2.25669914e-03 2.19664767e-01 2.53282905e-01 -1.56808540... | [4.329900741577148, 1.4208500385284424] |
83d90232-92e9-4cf5-ab02-1f06674d98a7 | an-unpaired-sketch-to-photo-translation-model | 1909.08313 | null | https://arxiv.org/abs/1909.08313v3 | https://arxiv.org/pdf/1909.08313v3.pdf | Unsupervised Sketch-to-Photo Synthesis | Humans can envision a realistic photo given a free-hand sketch that is not only spatially imprecise and geometrically distorted but also without colors and visual details. We study unsupervised sketch-to-photo synthesis for the first time, learning from unpaired sketch-photo data where the target photo for a sketch is ... | ['Qian Yu', 'Stella Yu', 'Runtao Liu'] | 2019-09-18 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 6.53684139e-01 6.91169649e-02 1.66931719e-01 -5.51619411e-01
-6.89702809e-01 -1.11887276e+00 8.66957128e-01 -4.84178990e-01
8.76925960e-02 4.93237972e-01 1.06117114e-01 9.11556557e-02
3.04130375e-01 -8.31358790e-01 -9.55716610e-01 -4.08269167e-01
5.78020275e-01 4.86199200e-01 -2.42683783e-01 -1.00735702... | [11.870206832885742, 0.009818816557526588] |
ab2a75c9-49f7-4b2a-9bfb-265d02c1039a | recasting-self-attention-with-holographic | 2305.19534 | null | https://arxiv.org/abs/2305.19534v1 | https://arxiv.org/pdf/2305.19534v1.pdf | Recasting Self-Attention with Holographic Reduced Representations | In recent years, self-attention has become the dominant paradigm for sequence modeling in a variety of domains. However, in domains with very long sequence lengths the $\mathcal{O}(T^2)$ memory and $\mathcal{O}(T^2 H)$ compute costs can make using transformers infeasible. Motivated by problems in malware detection, whe... | ['James Holt', 'Tim Oates', 'Stella Biderman', 'Edward Raff', 'Mohammad Mahmudul Alam'] | 2023-05-31 | null | null | null | null | ['malware-classification'] | ['miscellaneous'] | [ 3.26074749e-01 -3.08751166e-01 1.49026945e-01 -9.85741839e-02
-8.59323800e-01 -6.65886045e-01 3.29724282e-01 1.46920532e-01
-7.91297019e-01 7.60628521e-01 -5.13231397e-01 -7.83068657e-01
1.25178006e-02 -1.01608253e+00 -1.07494724e+00 -7.11466134e-01
-4.13425982e-01 5.18173635e-01 1.49259582e-01 -4.57548738... | [8.658747673034668, 3.351501226425171] |
ba56378d-cf8c-4a34-ba5f-475fef820d58 | no-fear-of-heterogeneity-classifier | 2106.05001 | null | https://arxiv.org/abs/2106.05001v2 | https://arxiv.org/pdf/2106.05001v2.pdf | No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data | A central challenge in training classification models in the real-world federated system is learning with non-IID data. To cope with this, most of the existing works involve enforcing regularization in local optimization or improving the model aggregation scheme at the server. Other works also share public datasets or ... | ['Jiashi Feng', 'Jian Liang', 'Yifan Zhang', 'Dapeng Hu', 'Fei Chen', 'Mi Luo'] | 2021-06-09 | null | http://proceedings.neurips.cc/paper/2021/hash/2f2b265625d76a6704b08093c652fd79-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/2f2b265625d76a6704b08093c652fd79-Paper.pdf | neurips-2021-12 | ['classifier-calibration', 'classifier-calibration'] | ['computer-vision', 'miscellaneous'] | [-4.15715277e-01 -3.28418672e-01 -3.96442264e-01 -8.43750000e-01
-6.38111413e-01 -4.69572902e-01 4.32838589e-01 -1.75687283e-01
-1.74861103e-01 6.65220082e-01 1.97325423e-01 -5.42320907e-01
-1.52967274e-01 -7.05481946e-01 -7.82330632e-01 -7.58389413e-01
-1.39106333e-01 3.96406054e-01 -2.87656724e-01 -7.03492165... | [5.874028205871582, 6.331753730773926] |
aabed015-e3fd-4958-b6c8-20435d4c630b | few-shot-text-classification-with-pre-trained | 1804.02063 | null | http://arxiv.org/abs/1804.02063v1 | http://arxiv.org/pdf/1804.02063v1.pdf | Few-Shot Text Classification with Pre-Trained Word Embeddings and a Human in the Loop | Most of the literature around text classification treats it as a supervised
learning problem: given a corpus of labeled documents, train a classifier such
that it can accurately predict the classes of unseen documents. In industry,
however, it is not uncommon for a business to have entire corpora of documents
where few... | ['Sunny Chopra', 'Katherine Bailey'] | 2018-04-05 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 1.30075648e-01 2.83191562e-01 -3.40199977e-01 -5.28551936e-01
-4.98556882e-01 -7.95824111e-01 8.51872206e-01 7.33254433e-01
-8.08193386e-01 5.85217178e-01 2.18889669e-01 -4.48469400e-01
-4.25245129e-02 -8.83083761e-01 -1.65085196e-01 -6.42677724e-01
1.80929035e-01 7.67497718e-01 3.94456178e-01 -1.86555982... | [10.362604141235352, 7.999029636383057] |
971ca2e0-fe01-499a-aaea-d43d0740dbec | accessible-instruction-following-agent | 2305.06358 | null | https://arxiv.org/abs/2305.06358v1 | https://arxiv.org/pdf/2305.06358v1.pdf | Accessible Instruction-Following Agent | Humans can collaborate and complete tasks based on visual signals and instruction from the environment. Training such a robot is difficult especially due to the understanding of the instruction and the complicated environment. Previous instruction-following agents are biased to English-centric corpus, making it unreali... | ['Kairui Zhou'] | 2023-05-08 | null | null | null | null | ['vision-language-navigation', 'instruction-following'] | ['computer-vision', 'natural-language-processing'] | [-1.11138001e-01 2.23850086e-01 -1.59324422e-01 -2.22473890e-01
-4.15279537e-01 -7.04682171e-01 8.50118935e-01 -4.14243698e-01
-7.91015089e-01 4.89853710e-01 2.18496785e-01 -6.70500875e-01
4.12068844e-01 -6.69121087e-01 -1.28315783e+00 -5.38282812e-01
2.55546629e-01 7.08969593e-01 1.28656447e-01 -5.57694912... | [4.431572914123535, 0.533290684223175] |
0ea95bf7-2940-4e44-a197-c91f440aedda | 3d-modelling-of-survey-scene-from-images | 2111.05541 | null | https://arxiv.org/abs/2111.05541v1 | https://arxiv.org/pdf/2111.05541v1.pdf | 3D modelling of survey scene from images enhanced with a multi-exposure fusion | In current practice, scene survey is carried out by workers using total stations. The method has high accuracy, but it incurs high costs if continuous monitoring is needed. Techniques based on photogrammetry, with the relatively cheaper digital cameras, have gained wide applications in many fields. Besides point measur... | ['Ho-Yin Chan', 'Arthur Wing-Tak Leung', 'Liping Li', 'Kwok-Leung Chan'] | 2021-11-10 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 7.98533499e-01 -4.45906073e-01 4.01122987e-01 -1.45544529e-01
-2.46124864e-01 -2.47789755e-01 3.98851573e-01 1.04224466e-01
-5.78772604e-01 4.24949497e-01 -8.06423500e-02 -1.88546658e-01
-1.86606824e-01 -1.33937800e+00 -5.69382906e-01 -9.11565840e-01
1.89264029e-01 1.56487018e-01 6.34505868e-01 -2.49427423... | [10.723325729370117, -2.5016672611236572] |
730fd741-ba68-4328-9aa2-b7364ca9ae72 | robust-occlusion-aware-pose-estimation-for | 2003.03518 | null | https://arxiv.org/abs/2003.03518v1 | https://arxiv.org/pdf/2003.03518v1.pdf | Robust, Occlusion-aware Pose Estimation for Objects Grasped by Adaptive Hands | Many manipulation tasks, such as placement or within-hand manipulation, require the object's pose relative to a robot hand. The task is difficult when the hand significantly occludes the object. It is especially hard for adaptive hands, for which it is not easy to detect the finger's configuration. In addition, RGB-onl... | ['Kostas E. Bekris', 'Sruthi Soorian', 'Avishai Sintov', 'Chaitanya Mitash', 'Bowen Wen', 'Andrew Kimmel'] | 2020-03-07 | null | null | null | null | ['6d-pose-estimation-using-rgbd', 'hand-object-pose'] | ['computer-vision', 'computer-vision'] | [ 2.96390932e-02 -2.02313289e-01 1.26043618e-01 1.64680313e-02
-6.40008390e-01 -6.54883981e-01 4.72949035e-02 -2.74507664e-02
-1.54399261e-01 2.95325726e-01 -4.43076789e-01 4.83025387e-02
-3.55491728e-01 -2.89610624e-01 -3.72412443e-01 -5.21049440e-01
-1.95550034e-03 1.34710944e+00 5.62333405e-01 -3.18239778... | [6.487014293670654, -1.064592957496643] |
fdfdd821-b784-4556-a2a9-662c9af7dc12 | voxeltrack-multi-person-3d-human-pose | 2108.02452 | null | https://arxiv.org/abs/2108.02452v1 | https://arxiv.org/pdf/2108.02452v1.pdf | VoxelTrack: Multi-Person 3D Human Pose Estimation and Tracking in the Wild | We present VoxelTrack for multi-person 3D pose estimation and tracking from a few cameras which are separated by wide baselines. It employs a multi-branch network to jointly estimate 3D poses and re-identification (Re-ID) features for all people in the environment. In contrast to previous efforts which require to estab... | ['Wenjun Zeng', 'Wenyu Liu', 'Xinggang Wang', 'Chunyu Wang', 'Yifu Zhang'] | 2021-08-05 | null | null | null | null | ['3d-pose-estimation', '3d-multi-person-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-2.30904743e-01 -3.27716559e-01 -6.55294582e-03 -4.19074804e-01
-9.13179874e-01 -8.21627557e-01 5.72122455e-01 -1.66049629e-01
-4.95167166e-01 4.35195357e-01 6.37851417e-01 6.13160729e-01
1.52752042e-01 -4.78478283e-01 -8.20819497e-01 -2.91304022e-01
-7.15272725e-02 1.05649114e+00 5.98559566e-02 2.46824503... | [7.062793731689453, -1.0418163537979126] |
c0509ff1-3a62-4ad4-a70a-363aecf7067b | complexity-based-prompting-for-multi-step | 2210.00720 | null | https://arxiv.org/abs/2210.00720v2 | https://arxiv.org/pdf/2210.00720v2.pdf | Complexity-Based Prompting for Multi-Step Reasoning | We study the task of prompting large-scale language models to perform multi-step reasoning. Existing work shows that when prompted with a chain of thoughts (CoT), sequences of short sentences describing intermediate reasoning steps towards a final answer, large language models can generate new reasoning chains and pred... | ['Tushar Khot', 'Peter Clark', 'Ashish Sabharwal', 'Hao Peng', 'Yao Fu'] | 2022-10-03 | null | null | null | null | ['gsm8k', 'date-understanding'] | ['natural-language-processing', 'reasoning'] | [ 1.76087633e-01 2.50005692e-01 -1.21329464e-01 -6.00245178e-01
-1.43118584e+00 -9.44006920e-01 9.46838260e-01 4.56054509e-01
-3.28209281e-01 6.19460464e-01 5.17096698e-01 -8.21189642e-01
-6.97184652e-02 -1.00237262e+00 -9.13729548e-01 -2.26936460e-01
2.88569123e-01 9.59777474e-01 2.96809852e-01 -3.81531656... | [9.697153091430664, 7.412925720214844] |
9d740d38-0972-47bf-b953-45a570170121 | de-novo-visual-proteomics-in-single-cells | 1512.09347 | null | http://arxiv.org/abs/1512.09347v3 | http://arxiv.org/pdf/1512.09347v3.pdf | De novo visual proteomics in single cells through pattern mining | Cryo-electron tomography enables 3D visualization of cells in a near native
state at molecular resolution. The produced cellular tomograms contain detailed
information about all macromolecular complexes, their structures, their
abundances and their specific spatial locations in the cell. However,
extracting this inform... | [] | 2016-02-11 | null | null | null | null | ['electron-tomography'] | ['medical'] | [ 1.59100667e-01 -5.58645546e-01 -5.12158964e-03 1.46267831e-01
-1.13515839e-01 -5.03115416e-01 3.71645302e-01 5.55620372e-01
-2.88260996e-01 1.04313612e+00 -2.78811097e-01 -2.82064378e-01
1.37992248e-01 -5.70181310e-01 -5.55633962e-01 -9.23986852e-01
-8.75643268e-02 9.79000747e-01 3.99149090e-01 1.38130307... | [13.396769523620605, -3.0905561447143555] |
24282088-3e06-4b9f-85f8-e61b2917aa42 | music-separation-enhancement-with-generative | 2208.12387 | null | https://arxiv.org/abs/2208.12387v1 | https://arxiv.org/pdf/2208.12387v1.pdf | Music Separation Enhancement with Generative Modeling | Despite phenomenal progress in recent years, state-of-the-art music separation systems produce source estimates with significant perceptual shortcomings, such as adding extraneous noise or removing harmonics. We propose a post-processing model (the Make it Sound Good (MSG) post-processor) to enhance the output of music... | ['Bryan Pardo', 'Prem Seetharaman', 'Max Morrison', 'Ethan Manilow', 'Boaz Cogan', 'Noah Schaffer'] | 2022-08-26 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 1.92668721e-01 -4.36825752e-01 3.31952780e-01 1.85982257e-01
-1.25085795e+00 -9.04119372e-01 1.80373937e-01 2.69983649e-01
-2.24397443e-02 5.06261289e-01 6.59513354e-01 1.64275467e-01
-3.15591663e-01 -3.35644007e-01 -4.21774954e-01 -4.33857292e-01
-1.46706834e-01 -7.10132718e-02 3.14938545e-01 -3.00419211... | [15.40837574005127, 5.693741321563721] |
933b53c8-62a0-4e6c-98e1-de4e6f4eb143 | conquer-contextualized-query-reduction-using | 2305.12662 | null | https://arxiv.org/abs/2305.12662v1 | https://arxiv.org/pdf/2305.12662v1.pdf | ConQueR: Contextualized Query Reduction using Search Logs | Query reformulation is a key mechanism to alleviate the linguistic chasm of query in ad-hoc retrieval. Among various solutions, query reduction effectively removes extraneous terms and specifies concise user intent from long queries. However, it is challenging to capture hidden and diverse user intent. This paper propo... | ['Jongwuk Lee', 'Young-In Song', 'Eunseong Choi', 'Sunkyung Lee', 'Minjin Choi', 'Hye-Young Kim'] | 2023-05-22 | null | null | null | null | ['term-extraction'] | ['natural-language-processing'] | [ 2.41178393e-01 -4.04278934e-01 -5.58058858e-01 -1.89685076e-01
-1.58492339e+00 -9.33919311e-01 7.44348884e-01 3.77158850e-01
-7.55401134e-01 3.05822164e-01 5.43767810e-01 -2.02770978e-01
-2.97015697e-01 -5.54876864e-01 -3.07849973e-01 -1.15298748e-01
2.93138564e-01 7.28060067e-01 3.91914368e-01 -7.21776247... | [11.549020767211914, 7.6085124015808105] |
49c7d18f-5504-478a-9973-dc65421aa9ce | discobox-weakly-supervised-instance | 2105.06464 | null | https://arxiv.org/abs/2105.06464v2 | https://arxiv.org/pdf/2105.06464v2.pdf | DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision | We introduce DiscoBox, a novel framework that jointly learns instance segmentation and semantic correspondence using bounding box supervision. Specifically, we propose a self-ensembling framework where instance segmentation and semantic correspondence are jointly guided by a structured teacher in addition to the boundi... | ['Anima Anandkumar', 'Larry S. Davis', 'Yuke Zhu', 'Guilin Liu', 'Subhashree Radhakrishnan', 'Christopher Choy', 'Zhiding Yu', 'Shiyi Lan'] | 2021-05-13 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Lan_DiscoBox_Weakly_Supervised_Instance_Segmentation_and_Semantic_Correspondence_From_Box_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Lan_DiscoBox_Weakly_Supervised_Instance_Segmentation_and_Semantic_Correspondence_From_Box_ICCV_2021_paper.pdf | iccv-2021-1 | ['box-supervised-instance-segmentation', 'weakly-supervised-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.99195051e-01 6.10712051e-01 -3.12742293e-01 -1.00382125e+00
-1.02391791e+00 -7.04624414e-01 6.52902305e-01 -2.31036618e-02
-5.15109539e-01 6.88823462e-01 -6.95348531e-02 2.66815573e-01
1.46888867e-01 -6.06212616e-01 -1.31063843e+00 -6.52844012e-01
1.19554974e-01 1.10310984e+00 5.22992015e-01 -8.86288472... | [9.520328521728516, 0.5258999466896057] |
5d037c99-1fdc-458e-872c-29d3d46524b5 | noise-level-estimation-from-single-color | 1904.02566 | null | http://arxiv.org/abs/1904.02566v1 | http://arxiv.org/pdf/1904.02566v1.pdf | Noise-Level Estimation from Single Color Image Using Correlations Between Textures in RGB Channels | We propose a simple method for estimating noise level from a single color
image. In most image-denoising algorithms, an accurate noise-level estimate
results in good denoising performance; however, it is difficult to estimate
noise level from a single image because it is an ill-posed problem. We tackle
this problem by ... | ['Michihiro Kobayashi', 'Akihiro Nakamura'] | 2019-04-04 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 3.81610036e-01 -7.80126572e-01 5.26329577e-01 -1.90647304e-01
-9.39208329e-01 -2.59782940e-01 8.55253637e-02 -3.58373523e-01
-6.68431222e-01 7.66998589e-01 -1.32074744e-01 2.98768193e-01
1.92674220e-01 -9.32323158e-01 -5.42652786e-01 -1.06865168e+00
2.43017033e-01 -2.98768193e-01 4.44347501e-01 -6.88370094... | [11.427098274230957, -2.436018943786621] |
34506fa6-d7c6-4df1-a373-e03c62cca9d7 | recurrent-u-net-for-resource-constrained | 1906.04913 | null | https://arxiv.org/abs/1906.04913v1 | https://arxiv.org/pdf/1906.04913v1.pdf | Recurrent U-Net for Resource-Constrained Segmentation | State-of-the-art segmentation methods rely on very deep networks that are not always easy to train without very large training datasets and tend to be relatively slow to run on standard GPUs. In this paper, we introduce a novel recurrent U-Net architecture that preserves the compactness of the original U-Net, while sub... | ['Joachim Hugonot', 'Pascal Fua', 'Mathieu Salzmann', 'Wei Wang', 'Kaicheng Yu'] | 2019-06-11 | recurrent-u-net-for-resource-constrained-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Recurrent_U-Net_for_Resource-Constrained_Segmentation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Recurrent_U-Net_for_Resource-Constrained_Segmentation_ICCV_2019_paper.pdf | iccv-2019-10 | ['road-segementation', 'hand-segmentation'] | ['computer-vision', 'computer-vision'] | [-5.03969975e-02 9.52552855e-02 -2.78432935e-01 -2.06333041e-01
-4.20357734e-01 -3.55173081e-01 2.48302266e-01 -1.61887378e-01
-4.44021434e-01 6.17183805e-01 2.55593639e-02 -7.25118577e-01
4.47851270e-01 -1.01967216e+00 -5.79418838e-01 -2.40861699e-01
1.92535430e-01 3.41596156e-01 7.28848219e-01 -1.41826659... | [9.497892379760742, 0.07954994589090347] |
66f0bf78-f959-43dd-9916-7f05963e3ca9 | from-shadow-segmentation-to-shadow-removal | 2008.00267 | null | https://arxiv.org/abs/2008.00267v1 | https://arxiv.org/pdf/2008.00267v1.pdf | From Shadow Segmentation to Shadow Removal | The requirement for paired shadow and shadow-free images limits the size and diversity of shadow removal datasets and hinders the possibility of training large-scale, robust shadow removal algorithms. We propose a shadow removal method that can be trained using only shadow and non-shadow patches cropped from the shadow... | ['Dimitris Samaras', 'Hieu Le'] | 2020-08-01 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1321_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123560256.pdf | eccv-2020-8 | ['shadow-removal'] | ['computer-vision'] | [ 9.06232834e-01 2.46971890e-01 4.42985952e-01 -1.59166589e-01
-3.46065700e-01 -6.55319452e-01 6.42643690e-01 -7.75853455e-01
-1.99941620e-01 9.15966928e-01 -6.13531843e-02 -5.19156933e-01
3.25429678e-01 -4.67919827e-01 -9.76023912e-01 -1.00542414e+00
-1.76270828e-01 3.43766451e-01 1.00716782e+00 -3.81006390... | [10.834246635437012, -4.097268581390381] |
a3c78a13-1124-4cf0-8a76-9c89aded68ae | fast-user-guided-video-object-segmentation-by | 1904.09791 | null | http://arxiv.org/abs/1904.09791v2 | http://arxiv.org/pdf/1904.09791v2.pdf | Fast User-Guided Video Object Segmentation by Interaction-and-Propagation Networks | We present a deep learning method for the interactive video object
segmentation. Our method is built upon two core operations, interaction and
propagation, and each operation is conducted by Convolutional Neural Networks.
The two networks are connected both internally and externally so that the
networks are trained joi... | ['Ning Xu', 'Joon-Young Lee', 'Seoung Wug Oh', 'Seon Joo Kim'] | 2019-04-22 | fast-user-guided-video-object-segmentation-by-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Oh_Fast_User-Guided_Video_Object_Segmentation_by_Interaction-And-Propagation_Networks_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Oh_Fast_User-Guided_Video_Object_Segmentation_by_Interaction-And-Propagation_Networks_CVPR_2019_paper.pdf | cvpr-2019-6 | ['interactive-video-object-segmentation'] | ['computer-vision'] | [ 1.64558277e-01 2.01353561e-02 -1.88016102e-01 -5.45453250e-01
-5.41973650e-01 -6.32473111e-01 1.47334412e-01 -1.96456388e-01
-6.01296604e-01 2.26362675e-01 -1.31162584e-01 -1.77113459e-01
4.26659763e-01 -4.75951076e-01 -9.80392516e-01 -2.98752159e-01
-1.92951649e-01 6.98267043e-01 7.88556874e-01 1.61209360... | [9.241947174072266, -0.07217943668365479] |
cc7b41e6-07b7-4c33-92ee-48127a8cdd6d | repbert-contextualized-text-embeddings-for | 2006.15498 | null | https://arxiv.org/abs/2006.15498v2 | https://arxiv.org/pdf/2006.15498v2.pdf | RepBERT: Contextualized Text Embeddings for First-Stage Retrieval | Although exact term match between queries and documents is the dominant method to perform first-stage retrieval, we propose a different approach, called RepBERT, to represent documents and queries with fixed-length contextualized embeddings. The inner products of query and document embeddings are regarded as relevance ... | ['Min Zhang', 'Yiqun Liu', 'Shaoping Ma', 'Jingtao Zhan', 'Jiaxin Mao'] | 2020-06-28 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [-4.14580345e-01 -7.67998397e-01 -7.00076580e-01 -2.02677146e-01
-1.65377903e+00 -6.44057035e-01 1.19099033e+00 7.61452973e-01
-9.08146858e-01 5.28426170e-01 6.46563113e-01 -7.45171979e-02
-5.73838770e-01 -5.46022594e-01 -3.34219903e-01 -3.13415974e-01
-2.39890337e-01 7.42431462e-01 6.66563809e-01 -5.32997191... | [11.450371742248535, 7.6717705726623535] |
0fae5e52-b35e-4d47-86ac-9ac64b185eec | graphical-representation-for-heterogeneous | 1503.00488 | null | http://arxiv.org/abs/1503.00488v3 | http://arxiv.org/pdf/1503.00488v3.pdf | Graphical Representation for Heterogeneous Face Recognition | Heterogeneous face recognition (HFR) refers to matching face images acquired
from different sources (i.e., different sensors or different wavelengths) for
identification. HFR plays an important role in both biometrics research and
industry. In spite of promising progresses achieved in recent years, HFR is
still a chall... | ['Chunlei Peng', 'Nannan Wang', 'Jie Li', 'Xinbo Gao'] | 2015-03-02 | null | null | null | null | ['heterogeneous-face-recognition'] | ['computer-vision'] | [ 7.22480953e-01 -5.36933601e-01 -4.92220651e-03 -2.93098509e-01
-7.05283523e-01 -4.22336012e-01 5.53150296e-01 -2.48002768e-01
1.55223683e-01 3.68395716e-01 -6.98122308e-02 -1.23436965e-01
-4.13218707e-01 -7.61947393e-01 -1.75053716e-01 -8.97218823e-01
3.96210790e-01 9.12470929e-03 -2.05386460e-01 3.76624465... | [12.952423095703125, 0.43721550703048706] |
52a6ae0c-645d-4ee5-96bc-60ba91d15d9a | private-graph-extraction-via-feature | 2206.14724 | null | https://arxiv.org/abs/2206.14724v1 | https://arxiv.org/pdf/2206.14724v1.pdf | Private Graph Extraction via Feature Explanations | Privacy and interpretability are two of the important ingredients for achieving trustworthy machine learning. We study the interplay of these two aspects in graph machine learning through graph reconstruction attacks. The goal of the adversary here is to reconstruct the graph structure of the training data given access... | ['Megha Khosla', 'Thorben Funke', 'Mandeep Rathee', 'Iyiola E. Olatunji'] | 2022-06-29 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [ 3.47190559e-01 8.16327393e-01 -2.85488933e-01 -2.74420559e-01
-5.50222218e-01 -1.02907193e+00 5.02544820e-01 4.20220733e-01
-2.06823409e-01 6.91320360e-01 2.37719994e-02 -8.15984845e-01
-3.05558145e-01 -8.75919163e-01 -9.69065905e-01 -7.11974382e-01
-1.40523046e-01 1.86061800e-01 -8.77955407e-02 -1.51330888... | [5.961368083953857, 7.147886276245117] |
ff779e50-f93b-4235-b29c-493b2ac98269 | rethinking-label-smoothing-on-multi-hop | 2212.09512 | null | https://arxiv.org/abs/2212.09512v1 | https://arxiv.org/pdf/2212.09512v1.pdf | Rethinking Label Smoothing on Multi-hop Question Answering | Label smoothing is a regularization technique widely used in supervised learning to improve the generalization of models on various tasks, such as image classification and machine translation. However, the effectiveness of label smoothing in multi-hop question answering (MHQA) has yet to be well studied. In this paper,... | ['Xipeng Qiu', 'Xuanjing Huang', 'Zhao Cao', 'Xinyu Zhang', 'Xiannian Hu', 'Hang Yan', 'Yiguang Wu', 'Yuxin Wang', 'Zhangyue Yin'] | 2022-12-19 | null | null | null | null | ['multi-hop-question-answering', 'machine-reading-comprehension'] | ['knowledge-base', 'natural-language-processing'] | [ 4.32340503e-01 3.24072987e-01 -3.06379646e-01 -6.20502532e-01
-1.26707494e+00 -4.80751663e-01 5.26195586e-01 4.72566307e-01
-7.05169797e-01 6.33316576e-01 3.82055312e-01 -5.57388544e-01
2.10545421e-01 -4.24328148e-01 -6.98733270e-01 -5.20785093e-01
3.41705292e-01 1.67058274e-01 4.92315501e-01 -2.26077124... | [11.259909629821777, 8.143522262573242] |
af466257-7cd9-4c82-af4e-45d1d6558e73 | frnet-flattened-residual-network-for-infant | 1904.05578 | null | http://arxiv.org/abs/1904.05578v1 | http://arxiv.org/pdf/1904.05578v1.pdf | FRNET: Flattened Residual Network for Infant MRI Skull Stripping | Skull stripping for brain MR images is a basic segmentation task. Although
many methods have been proposed, most of them focused mainly on the adult MR
images. Skull stripping for infant MR images is more challenging due to the
small size and dynamic intensity changes of brain tissues during the early
ages. In this pap... | ['Qian Zhang', 'Weili Lin', 'Dinggang Shen', 'Xiaopeng Zong', 'Li Wang', 'Gang Li'] | 2019-04-11 | null | null | null | null | ['skull-stripping'] | ['medical'] | [ 2.99961865e-01 1.77845076e-01 3.31842840e-01 -7.87061870e-01
-3.38792473e-01 -6.77092448e-02 7.23167509e-02 6.49872422e-02
-8.32539439e-01 5.79924226e-01 3.99285927e-02 -4.66624722e-02
1.12102091e-01 -6.26062334e-01 -8.23094308e-01 -4.70397294e-01
-2.33331069e-01 1.55821726e-01 6.59627795e-01 3.45136970... | [14.213980674743652, -2.3612921237945557] |
a1dd4b23-8244-4822-8fa3-711819c574ee | learning-non-maximum-suppression | 1705.02950 | null | http://arxiv.org/abs/1705.02950v2 | http://arxiv.org/pdf/1705.02950v2.pdf | Learning non-maximum suppression | Object detectors have hugely profited from moving towards an end-to-end
learning paradigm: proposals, features, and the classifier becoming one neural
network improved results two-fold on general object detection. One
indispensable component is non-maximum suppression (NMS), a post-processing
algorithm responsible for ... | ['Jan Hosang', 'Rodrigo Benenson', 'Bernt Schiele'] | 2017-05-08 | learning-non-maximum-suppression-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Hosang_Learning_Non-Maximum_Suppression_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Hosang_Learning_Non-Maximum_Suppression_CVPR_2017_paper.pdf | cvpr-2017-7 | ['occlusion-handling'] | ['computer-vision'] | [ 4.37727645e-02 7.95262828e-02 5.26881143e-02 -6.29958451e-01
-4.66981202e-01 -2.82398403e-01 5.95687151e-01 2.56132483e-01
-1.10596299e+00 4.88573939e-01 -1.91728026e-01 4.17249985e-02
-1.33427352e-01 -5.82609594e-01 -4.73499924e-01 -6.78264260e-01
-4.48684186e-01 7.61549115e-01 8.01633716e-01 3.67222354... | [8.486103057861328, -0.46467098593711853] |
e7185e4c-275a-48ea-bb1e-cdf2a939ae0d | clelfpc-a-large-open-multi-speaker-corpus-of | null | null | https://aclanthology.org/2022.lrec-1.104 | https://aclanthology.org/2022.lrec-1.104.pdf | CLeLfPC: a Large Open Multi-Speaker Corpus of French Cued Speech | Cued Speech is a communication system developed for deaf people to complement speechreading at the phonetic level with hands. This visual communication mode uses handshapes in different placements near the face in combination with the mouth movements of speech to make the phonemes of spoken language look different from... | ['Carine André', 'Maryvonne Zimmermann', 'Brigitte Bigi'] | null | null | null | null | lrec-2022-6 | ['transliteration'] | ['natural-language-processing'] | [ 1.79958139e-02 2.08542064e-01 -4.45349663e-02 -1.63788155e-01
-6.29120231e-01 -7.87362278e-01 6.79839849e-01 3.70446667e-02
-4.79419768e-01 7.81901538e-01 9.01815057e-01 -5.41692376e-01
3.67591172e-01 2.05597039e-02 -5.69865465e-01 -7.93643057e-01
3.43674511e-01 2.32091486e-01 6.56572223e-01 -3.85122359... | [14.35164737701416, 5.113812446594238] |
8130781c-93d9-42db-9bde-7711079b8a47 | distribution-aware-testing-of-neural-networks | 2102.13602 | null | https://arxiv.org/abs/2102.13602v1 | https://arxiv.org/pdf/2102.13602v1.pdf | Distribution-Aware Testing of Neural Networks Using Generative Models | The reliability of software that has a Deep Neural Network (DNN) as a component is urgently important today given the increasing number of critical applications being deployed with DNNs. The need for reliability raises a need for rigorous testing of the safety and trustworthiness of these systems. In the last few years... | ['Mary Lou Soffa', 'Matthew B. Dwyer', 'Swaroopa Dola'] | 2021-02-26 | null | null | null | null | ['dnn-testing'] | ['adversarial'] | [ 1.37447581e-01 1.58840299e-01 1.43475115e-01 -4.26299721e-01
-3.48967202e-02 -6.97990358e-01 2.93879896e-01 -4.34940815e-01
6.34482577e-02 1.08808362e+00 -4.76669848e-01 -8.85689795e-01
-4.88316745e-01 -1.22438490e+00 -9.85384047e-01 -3.49697918e-01
9.23243240e-02 3.44671637e-01 6.01730585e-01 -1.29328087... | [6.6611199378967285, 7.637300968170166] |
4a4db427-db46-4481-ab02-5bc0fd4f4691 | meet-in-the-middle-multi-scale-upsampling-and | 2211.15225 | null | https://arxiv.org/abs/2211.15225v2 | https://arxiv.org/pdf/2211.15225v2.pdf | Meet-in-the-middle: Multi-scale upsampling and matching for cross-resolution face recognition | In this paper, we aim to address the large domain gap between high-resolution face images, e.g., from professional portrait photography, and low-quality surveillance images, e.g., from security cameras. Establishing an identity match between disparate sources like this is a classical surveillance face identification sc... | ['Hazim Kemal Ekenel', 'Vitomir Štruc', 'Berk Kemal Özata', 'Klemen Grm'] | 2022-11-28 | null | null | null | null | ['face-identification'] | ['computer-vision'] | [ 7.38832116e-01 -4.65464741e-01 1.29904926e-01 -4.49505389e-01
-1.06835747e+00 -7.70918131e-01 7.12352991e-01 -5.49621582e-01
-1.32360741e-01 7.11492062e-01 -9.21408236e-02 2.44708315e-01
-3.34508896e-01 -8.30565870e-01 -6.20790958e-01 -7.81396866e-01
2.93106735e-01 1.68941021e-01 1.06215976e-01 -2.35693112... | [12.966775894165039, 0.46521514654159546] |
2e24c813-84d2-49bd-9c0c-57ad82d34724 | zero-shot-dense-video-captioning-by-jointly | 2307.02682 | null | https://arxiv.org/abs/2307.02682v1 | https://arxiv.org/pdf/2307.02682v1.pdf | Zero-Shot Dense Video Captioning by Jointly Optimizing Text and Moment | Dense video captioning, a task of localizing meaningful moments and generating relevant captions for videos, often requires a large, expensive corpus of annotated video segments paired with text. In an effort to minimize the annotation cost, we propose ZeroTA, a novel method for dense video captioning in a zero-shot ma... | ['Minjoon Seo', 'Hanseok Oh', 'Hyunji Lee', 'Aiden SJ Lee', 'Seongyun Lee', 'Yongrae Jo'] | 2023-07-05 | null | null | null | null | ['video-captioning', 'dense-video-captioning', 'text-generation'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 4.11165327e-01 -2.62140650e-02 -4.36919808e-01 -2.33075127e-01
-1.10755360e+00 -5.14176250e-01 6.94642603e-01 -2.85623848e-01
-2.52664059e-01 6.23820662e-01 4.74962890e-01 1.43299162e-01
4.57853913e-01 -2.49487415e-01 -1.15970397e+00 -5.92116654e-01
-1.99755933e-02 3.90825689e-01 2.52148092e-01 1.23589501... | [10.409172058105469, 0.663163959980011] |
fb4eaf6a-1981-4b07-8a2a-e4628414d72c | a-deep-neural-network-for-chinese-zero | 1604.05800 | null | http://arxiv.org/abs/1604.05800v3 | http://arxiv.org/pdf/1604.05800v3.pdf | A Deep Neural Network for Chinese Zero Pronoun Resolution | Existing approaches for Chinese zero pronoun resolution overlook semantic
information. This is because zero pronouns have no descriptive information,
which results in difficulty in explicitly capturing their semantic similarities
with antecedents. Moreover, when dealing with candidate antecedents,
traditional systems s... | ['Wei-Nan Zhang', 'Qingyu Yin', 'Ting Liu', 'Yu Zhang'] | 2016-04-20 | null | null | null | null | ['chinese-zero-pronoun-resolution'] | ['natural-language-processing'] | [ 3.07952851e-01 1.32503465e-01 -5.03818452e-01 -5.03727615e-01
-1.00629818e+00 -4.53229606e-01 5.06335199e-01 2.80321002e-01
-6.31953418e-01 7.83122361e-01 6.99533403e-01 -4.90242280e-02
-1.20459460e-02 -8.31366420e-01 -3.92289042e-01 -4.03045267e-01
3.14449579e-01 5.36182046e-01 2.97449887e-01 -4.08076733... | [10.25475788116455, 9.246983528137207] |
f208ce83-08a2-4fb3-9b94-2806a839576b | 190406472 | 1904.06472 | null | https://arxiv.org/abs/1904.06472v2 | https://arxiv.org/pdf/1904.06472v2.pdf | A Repository of Conversational Datasets | Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches. To this end, we present a repository of conversational datasets consisting of hundreds of millions of examples, and a standardised evaluation procedure for conversatio... | ['Tsung-Hsien Wen', 'Ivan Vulić', 'Pei-Hao Su', 'Nikola Mrkšić', 'Iñigo Casanueva', 'Paweł Budzianowski', 'Matthew Henderson', 'Girish Kumar', 'Georgios Spithourakis', 'Daniela Gerz', 'Sam Coope'] | 2019-04-13 | a-repository-of-conversational-datasets | https://aclanthology.org/W19-4101 | https://aclanthology.org/W19-4101.pdf | ws-2019-8 | ['dialogue-understanding', 'conversational-response-selection'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.07546139e-01 1.99592095e-02 -3.31376851e-01 -1.00954211e+00
-1.25355256e+00 -5.04015625e-01 8.87894392e-01 2.25177541e-01
-4.82554764e-01 9.09769118e-01 7.85172522e-01 -2.88124084e-01
-3.92339937e-02 -6.53306663e-01 -1.22790799e-01 -3.01121116e-01
9.86200944e-02 9.96202052e-01 5.49324453e-02 -5.76911092... | [12.70113754272461, 8.009961128234863] |
7c501ac9-af20-492a-bf14-9dd906d7445d | stimulating-the-diffusion-model-for-image | 2307.03992 | null | https://arxiv.org/abs/2307.03992v1 | https://arxiv.org/pdf/2307.03992v1.pdf | Stimulating the Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling | Image denoising is a fundamental problem in computational photography, where achieving high-quality perceptual performance with low distortion is highly demanding. Current methods either struggle with perceptual performance or suffer from significant distortion. Recently, the emerging diffusion model achieves state-of-... | ['Hua Huang', 'Zhiwei Xiong', 'Lizhi Wang', 'Hansen Feng', 'Tong Li'] | 2023-07-08 | null | null | null | null | ['image-denoising', 'denoising'] | ['computer-vision', 'computer-vision'] | [ 4.14390981e-01 -2.45493650e-01 4.08543408e-01 -1.68736562e-01
-7.02423930e-01 -2.97652304e-01 5.44130504e-01 1.37707386e-02
-4.02258307e-01 1.83243141e-01 3.49667549e-01 2.24157050e-03
-2.56591022e-01 -8.38316023e-01 -3.14623594e-01 -1.22441924e+00
1.90626606e-01 -3.25970113e-01 3.95759284e-01 -3.03696603... | [11.416037559509277, -2.3013782501220703] |
19292227-f338-474e-94da-691c74f8ef97 | modeling-fine-grained-information-via | 2211.10991 | null | https://arxiv.org/abs/2211.10991v1 | https://arxiv.org/pdf/2211.10991v1.pdf | Modeling Fine-grained Information via Knowledge-aware Hierarchical Graph for Zero-shot Entity Retrieval | Zero-shot entity retrieval, aiming to link mentions to candidate entities under the zero-shot setting, is vital for many tasks in Natural Language Processing. Most existing methods represent mentions/entities via the sentence embeddings of corresponding context from the Pre-trained Language Model. However, we argue tha... | ['Yujiu Yang', 'Siheng Li', 'Weijie Liu', 'Weigang Guo', 'Xingyu Bai', 'Taiqiang Wu'] | 2022-11-20 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [-3.19227725e-01 5.39420664e-01 -2.92380065e-01 -2.82159358e-01
-7.17718601e-01 -3.58301163e-01 5.43551266e-01 6.63149774e-01
-5.90637207e-01 7.15804517e-01 7.21046686e-01 -1.91866755e-01
-1.15234785e-01 -1.21577632e+00 -6.83682084e-01 -4.52367127e-01
-9.22437310e-02 3.38663310e-01 2.33244836e-01 -3.49962562... | [9.060853958129883, 8.339190483093262] |
754e5c1a-3f33-49cb-b5a3-6fef10a67e9e | non-parametric-online-market-regime-detection | 2306.15835 | null | https://arxiv.org/abs/2306.15835v1 | https://arxiv.org/pdf/2306.15835v1.pdf | Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structures | In this work we present a non-parametric online market regime detection method for multidimensional data structures using a path-wise two-sample test derived from a maximum mean discrepancy-based similarity metric on path space that uses rough path signatures as a feature map. The latter similarity metric has been deve... | ['Blanka Horvath', 'Zacharia Issa'] | 2023-06-27 | null | null | null | null | ['clustering', 'outlier-detection'] | ['methodology', 'methodology'] | [-2.43375540e-01 -6.10359371e-01 -8.89491662e-02 7.72350505e-02
-3.75307500e-01 -1.04106092e+00 1.01917398e+00 5.23001432e-01
-4.57726419e-01 6.92089081e-01 5.15620373e-02 -9.46761727e-01
-7.99624503e-01 -8.01001489e-01 -3.64595503e-01 -6.33836865e-01
-9.34036434e-01 1.07789004e+00 4.57134306e-01 -2.92378455... | [4.933896541595459, 4.068650722503662] |
5ca5aa92-cd87-47b7-abb7-64c53b4dbf25 | sequential-batch-learning-in-finite-action | 2004.06321 | null | https://arxiv.org/abs/2004.06321v1 | https://arxiv.org/pdf/2004.06321v1.pdf | Sequential Batch Learning in Finite-Action Linear Contextual Bandits | We study the sequential batch learning problem in linear contextual bandits with finite action sets, where the decision maker is constrained to split incoming individuals into (at most) a fixed number of batches and can only observe outcomes for the individuals within a batch at the batch's end. Compared to both standa... | ['Zhengyuan Zhou', 'Zhengqing Zhou', 'Yinyu Ye', 'Yanjun Han', 'Jose Blanchet', 'Peter W. Glynn'] | 2020-04-14 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 2.96996087e-01 4.18217152e-01 -1.03618836e+00 -3.15593421e-01
-7.42151558e-01 -7.89634466e-01 -5.08951433e-02 3.83771986e-01
-6.27034843e-01 1.17212939e+00 -2.02214606e-02 -6.58988297e-01
-6.26214325e-01 -6.54402852e-01 -1.03727961e+00 -1.04682922e+00
-2.01580286e-01 9.68827903e-01 2.94624586e-02 2.10633829... | [4.488717079162598, 3.2584545612335205] |
e5a277ed-943e-4eda-94ca-9c23c7e2dd91 | continuous-sign-language-recognition-with | 2303.03202 | null | https://arxiv.org/abs/2303.03202v3 | https://arxiv.org/pdf/2303.03202v3.pdf | Continuous Sign Language Recognition with Correlation Network | Human body trajectories are a salient cue to identify actions in the video. Such body trajectories are mainly conveyed by hands and face across consecutive frames in sign language. However, current methods in continuous sign language recognition (CSLR) usually process frames independently, thus failing to capture cross... | ['Wei Feng', 'Zekang Liu', 'Liqing Gao', 'Lianyu Hu'] | 2023-03-06 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Hu_Continuous_Sign_Language_Recognition_With_Correlation_Network_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hu_Continuous_Sign_Language_Recognition_With_Correlation_Network_CVPR_2023_paper.pdf | cvpr-2023-1 | ['sign-language-recognition'] | ['computer-vision'] | [-1.44955724e-01 -4.50942934e-01 -2.36573756e-01 -7.26991594e-02
-1.64760873e-01 -4.82851326e-01 7.60787964e-01 -2.05571726e-01
-3.00523758e-01 2.64218122e-01 7.22372890e-01 2.23722175e-01
-2.23024979e-01 -4.98744547e-01 -2.86539733e-01 -5.06036401e-01
-3.76761198e-01 -2.49186143e-01 6.10745490e-01 -1.54849112... | [9.227987289428711, -6.467686176300049] |
9d764e4d-348b-4bdc-b60b-900b12472115 | few-bit-backward-quantized-gradients-of | 2202.00441 | null | https://arxiv.org/abs/2202.00441v2 | https://arxiv.org/pdf/2202.00441v2.pdf | Few-Bit Backward: Quantized Gradients of Activation Functions for Memory Footprint Reduction | Memory footprint is one of the main limiting factors for large neural network training. In backpropagation, one needs to store the input to each operation in the computational graph. Every modern neural network model has quite a few pointwise nonlinearities in its architecture, and such operation induces additional mem... | ['Ivan Oseledets', 'Denis Dimitrov', 'Alex Shonenkov', 'Julia Gusak', 'Daniel Bershatsky', 'Georgii Novikov'] | 2022-02-01 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [-5.71866706e-02 9.89594981e-02 -1.72299236e-01 -5.16177535e-01
-8.84264112e-02 -4.21070814e-01 2.11559743e-01 3.78214896e-01
-9.01577234e-01 6.09344125e-01 -1.84611142e-01 -6.15857244e-01
-2.27712579e-02 -8.13514709e-01 -1.12268221e+00 -5.99687636e-01
-3.20035905e-01 3.29112232e-01 5.89080453e-01 -2.08495691... | [8.439805030822754, 3.184908866882324] |
a89c8279-82d0-4a08-9d15-6cae58c4f324 | adaptive-residue-wise-profile-fusion-for-low | 2108.04176 | null | https://arxiv.org/abs/2108.04176v1 | https://arxiv.org/pdf/2108.04176v1.pdf | Adaptive Residue-wise Profile Fusion for Low Homologous Protein SecondaryStructure Prediction Using External Knowledge | Protein secondary structure prediction (PSSP) is essential for protein function analysis. However, for low homologous proteins, the PSSP suffers from insufficient input features. In this paper, we explicitly import external self-supervised knowledge for low homologous PSSP under the guidance of residue-wise profile fus... | ['Shuguang Cu', 'Sheng Wang', 'Zhen Li1', 'Boyuan Wang', 'Jun Wei', 'Qin Wang'] | 2021-08-05 | null | null | null | null | ['protein-secondary-structure-prediction'] | ['medical'] | [ 5.70420504e-01 4.82135899e-02 -1.57594010e-01 -5.20348072e-01
-7.60820091e-01 -4.73980367e-01 6.80356994e-02 3.58826369e-01
-2.78675526e-01 1.01986730e+00 2.26048958e-02 -6.26867786e-02
-2.42518201e-01 -3.54433447e-01 -1.03305900e+00 -1.30253661e+00
2.02697650e-01 1.99727580e-01 4.31012809e-01 -2.19610974... | [4.728219509124756, 5.677133083343506] |
079c929c-038c-40ce-8566-8cd5ed0e02a2 | did-you-offend-me-classification-of-offensive | null | null | https://aclanthology.org/W18-5118 | https://aclanthology.org/W18-5118.pdf | Did you offend me? Classification of Offensive Tweets in Hinglish Language | The use of code-switched languages (\textit{e.g.}, Hinglish, which is derived by the blending of Hindi with the English language) is getting much popular on Twitter due to their ease of communication in native languages. However, spelling variations and absence of grammar rules introduce ambiguity and make it difficult... | ['Ramit Sawhney', 'Meghna Ayyar', 'Rajiv Shah', 'Puneet Mathur'] | 2018-10-01 | null | null | null | ws-2018-10 | ['abuse-detection'] | ['natural-language-processing'] | [-1.35176703e-01 9.92820114e-02 -2.35141933e-01 -4.26815957e-01
-7.78946042e-01 -5.87445378e-01 9.83967543e-01 1.57153457e-01
-7.11741328e-01 7.18626618e-01 4.35579836e-01 -4.62850302e-01
3.77098560e-01 -6.89799488e-01 -6.77306771e-01 -5.39166689e-01
-4.22925130e-02 2.80069351e-01 -8.76162946e-02 -5.90317905... | [8.860387802124023, 10.586600303649902] |
bb7d4c2c-82a4-4f2a-86f2-06864acea732 | composing-rnns-and-fsts-for-small-data | null | null | https://openreview.net/forum?id=rkgj0tNfom | https://openreview.net/pdf?id=rkgj0tNfom | Composing RNNs and FSTs for Small Data: Recovering Missing Characters in Old Hawaiian Text | In contrast to the older writing system of the 19th century, modern Hawaiian orthography employs characters for long vowels and glottal stops. These extra characters account for about one-third of the phonemes in Hawaiian, so including them makes a big difference to reading comprehension and pronunciation. However, tra... | ['Anonymous'] | 2018-10-15 | null | null | null | nips-workshop-irasl-2018 | ['transliteration'] | ['natural-language-processing'] | [ 1.98795214e-01 2.77878672e-01 1.20411597e-01 -2.95409381e-01
-9.85955596e-01 -8.77317309e-01 4.85319674e-01 -1.54012755e-01
-8.48919213e-01 6.48779511e-01 4.72586632e-01 -1.12307811e+00
3.21384698e-01 -6.28915071e-01 -6.68316543e-01 -2.68767744e-01
6.27407074e-01 6.81841195e-01 1.27997756e-01 -3.40906501... | [11.06924057006836, 10.214523315429688] |
f5ac5fbe-fd66-4538-9d5f-e245201bf4be | enhancing-medical-image-segmentation-with | 2301.10847 | null | https://arxiv.org/abs/2301.10847v1 | https://arxiv.org/pdf/2301.10847v1.pdf | Enhancing Medical Image Segmentation with TransCeption: A Multi-Scale Feature Fusion Approach | While CNN-based methods have been the cornerstone of medical image segmentation due to their promising performance and robustness, they suffer from limitations in capturing long-range dependencies. Transformer-based approaches are currently prevailing since they enlarge the reception field to model global contextual co... | ['Dorit Merhof', 'Julien Cohen-Adad', 'Ehsan Khodapanah Aghdam', 'Yiwei Jia', 'Reza Azad'] | 2023-01-25 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 2.89005458e-01 1.16631396e-01 -2.66029924e-01 -3.54851753e-01
-7.21653581e-01 -2.31528193e-01 4.63157058e-01 1.74819425e-01
-3.72046202e-01 3.50882322e-01 2.27847710e-01 -1.71850964e-01
-1.11903697e-01 -8.39379370e-01 -6.37417257e-01 -6.44208312e-01
7.07521066e-02 -2.13000119e-01 5.74738145e-01 -3.43191117... | [14.622121810913086, -2.6257779598236084] |
426c314d-294d-494d-a47a-53a7c8e259ed | readprobe-a-demo-of-retrieval-enhanced-large | 2306.07875 | null | https://arxiv.org/abs/2306.07875v1 | https://arxiv.org/pdf/2306.07875v1.pdf | ReadProbe: A Demo of Retrieval-Enhanced Large Language Models to Support Lateral Reading | With the rapid growth and spread of online misinformation, people need tools to help them evaluate the credibility and accuracy of online information. Lateral reading, a strategy that involves cross-referencing information with multiple sources, may be an effective approach to achieving this goal. In this paper, we pre... | ['Ronak Pradeep', 'Dake Zhang'] | 2023-06-13 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [-3.80896747e-01 2.78439224e-01 -1.82316408e-01 -1.51896968e-01
-1.46339440e+00 -9.21908259e-01 7.84011126e-01 7.22827971e-01
-2.80288607e-01 6.33414209e-01 6.63788855e-01 -6.01663768e-01
1.07982233e-01 -8.10928881e-01 -5.18721581e-01 2.31782302e-01
5.32391012e-01 3.71828794e-01 3.78721118e-01 -4.26086843... | [8.434343338012695, 10.042689323425293] |
e56c431c-3af9-49fa-a029-5ae3904b7aaf | evolutionary-generation-of-visual-motion | 2112.13243 | null | https://arxiv.org/abs/2112.13243v1 | https://arxiv.org/pdf/2112.13243v1.pdf | Evolutionary Generation of Visual Motion Illusions | Why do we sometimes perceive static images as if they were moving? Visual motion illusions enjoy a sustained popularity, yet there is no definitive answer to the question of why they work. We present a generative model, the Evolutionary Illusion GENerator (EIGen), that creates new visual motion illusions. The structure... | ['Eiji Watanabe', 'Lana Sinapayen'] | 2021-12-25 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 1.44933715e-01 2.71861345e-01 6.33288398e-02 2.71569267e-02
6.12468362e-01 -5.79214692e-01 8.63953352e-01 -7.93968797e-01
-4.02128696e-01 5.96203208e-01 6.02984786e-01 -5.74430585e-01
2.52044797e-01 -4.05374795e-01 -4.88423944e-01 -8.42445791e-01
2.73688823e-01 -1.30938619e-01 -4.69266810e-03 -3.67956191... | [10.103150367736816, 2.3685266971588135] |
b5261c15-7006-4250-8e56-5034baba0ede | ipg-net-image-pyramid-guidance-network-for | 1912.00632 | null | https://arxiv.org/abs/1912.00632v3 | https://arxiv.org/pdf/1912.00632v3.pdf | IPG-Net: Image Pyramid Guidance Network for Small Object Detection | For Convolutional Neural Network-based object detection, there is a typical dilemma: the spatial information is well kept in the shallow layers which unfortunately do not have enough semantic information, while the deep layers have a high semantic concept but lost a lot of spatial information, resulting in serious info... | ['Guangyu Gao', 'Li Fang', 'Ziming Liu', 'Lin Sun'] | 2019-12-02 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [-1.23027869e-01 -1.36189424e-02 -5.02488529e-03 -3.26409459e-01
-2.38028482e-01 -1.18519114e-02 2.97417909e-01 2.09958375e-01
-5.03027141e-01 2.19761252e-01 -8.17639828e-02 6.93450421e-02
2.47887410e-02 -1.08878458e+00 -7.71164536e-01 -6.98823810e-01
6.97756708e-02 -1.12373851e-01 1.10873187e+00 -5.27247727... | [9.18364429473877, -0.4947846531867981] |
d1dc73c0-b59b-4a79-bd8e-a98e327bce3c | mapfast-a-deep-algorithm-selector-for-multi | 2102.12461 | null | https://arxiv.org/abs/2102.12461v1 | https://arxiv.org/pdf/2102.12461v1.pdf | MAPFAST: A Deep Algorithm Selector for Multi Agent Path Finding using Shortest Path Embeddings | Solving the Multi-Agent Path Finding (MAPF) problem optimally is known to be NP-Hard for both make-span and total arrival time minimization. While many algorithms have been developed to solve MAPF problems, there is no dominating optimal MAPF algorithm that works well in all types of problems and no standard guidelines... | ['Nora Ayanian', 'Baskin Senbaslar', 'Eric Ewing', 'Vikraman Sathiyanarayanan', 'Jingyao Ren'] | 2021-02-24 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-7.66772553e-02 -2.01642439e-01 -4.87809151e-01 -3.87978345e-01
-8.29219937e-01 -1.07357073e+00 3.70621264e-01 5.57608604e-01
-4.69180197e-01 7.90121257e-01 -1.04514118e-02 -6.30259335e-01
-1.25633037e+00 -1.09981012e+00 -9.24791634e-01 -3.79501760e-01
-7.13849723e-01 1.36407614e+00 1.47867277e-01 -2.86685288... | [4.994901657104492, 2.1640865802764893] |
8152701b-7d0a-48a2-84f4-03a7f80f64ed | an-open-source-multi-goal-reinforcement | 2105.05985 | null | https://arxiv.org/abs/2105.05985v1 | https://arxiv.org/pdf/2105.05985v1.pdf | An Open-Source Multi-Goal Reinforcement Learning Environment for Robotic Manipulation with Pybullet | This work re-implements the OpenAI Gym multi-goal robotic manipulation environment, originally based on the commercial Mujoco engine, onto the open-source Pybullet engine. By comparing the performances of the Hindsight Experience Replay-aided Deep Deterministic Policy Gradient agent on both environments, we demonstrate... | ['Yu-Kun Lai', 'Jing Wu', 'Ze Ji', 'Xintong Yang'] | 2021-05-12 | null | null | null | null | ['multi-goal-reinforcement-learning'] | ['methodology'] | [-3.83091360e-01 -2.32364759e-02 8.70535225e-02 -1.42296195e-01
-7.04477370e-01 -5.71592689e-01 4.60952461e-01 -4.12456423e-01
-8.95218313e-01 9.49131787e-01 -8.23367834e-02 -1.16897903e-01
-4.07629639e-01 -4.38468963e-01 -8.44857037e-01 -6.37204111e-01
-5.89650810e-01 7.06514418e-01 3.79080713e-01 -7.15334177... | [4.382195472717285, 1.0877102613449097] |
0e50b341-c60b-4578-a4d3-33ecea95b35d | deepfd-automated-fault-diagnosis-and | 2205.01938 | null | https://arxiv.org/abs/2205.01938v1 | https://arxiv.org/pdf/2205.01938v1.pdf | DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs | As Deep Learning (DL) systems are widely deployed for mission-critical applications, debugging such systems becomes essential. Most existing works identify and repair suspicious neurons on the trained Deep Neural Network (DNN), which, unfortunately, might be a detour. Specifically, several existing studies have reporte... | ['Shing-Chi Cheung', 'Bo Wu', 'Yongqiang Tian', 'Ming Wen', 'Xiao Chen', 'Meiziniu Li', 'Jialun Cao'] | 2022-05-04 | null | null | null | null | ['fault-localization'] | ['computer-code'] | [-2.88294464e-01 4.01669703e-02 -3.33625555e-01 -9.58318934e-02
-3.76924634e-01 -3.34923357e-01 1.01498134e-01 1.05654746e-02
3.25372130e-01 5.89421272e-01 -3.68851453e-01 -5.09418130e-01
-1.74297497e-01 -7.74439335e-01 -1.09179688e+00 -5.54415166e-01
-1.26279637e-01 3.19320381e-01 3.79020244e-01 1.37742683... | [7.2721123695373535, 7.714724063873291] |
397c8e21-0987-4251-9af6-714eac89e46f | a-survey-of-word-embeddings-evaluation | 1801.09536 | null | http://arxiv.org/abs/1801.09536v1 | http://arxiv.org/pdf/1801.09536v1.pdf | A Survey of Word Embeddings Evaluation Methods | Word embeddings are real-valued word representations able to capture lexical
semantics and trained on natural language corpora. Models proposing these
representations have gained popularity in the recent years, but the issue of
the most adequate evaluation method still remains open. This paper presents an
extensive ove... | ['Amir Bakarov'] | 2018-01-21 | null | null | null | null | ['embeddings-evaluation'] | ['natural-language-processing'] | [-9.01810527e-02 -1.11633293e-01 -9.44524944e-01 -4.30895209e-01
-5.40548086e-01 -5.40554106e-01 7.81554341e-01 5.84870815e-01
-1.19551170e+00 5.92297733e-01 6.19506419e-01 -1.13670245e-01
-1.12794824e-01 -7.59738445e-01 1.23163261e-01 -3.46304268e-01
-1.02302276e-01 6.56994879e-01 1.26965463e-01 -6.25147104... | [10.487541198730469, 8.674001693725586] |
3eb19d86-5aa0-4230-9fa9-bcc8ced173ae | beat-a-large-scale-semantic-and-emotional | 2203.05297 | null | https://arxiv.org/abs/2203.05297v5 | https://arxiv.org/pdf/2203.05297v5.pdf | BEAT: A Large-Scale Semantic and Emotional Multi-Modal Dataset for Conversational Gestures Synthesis | Achieving realistic, vivid, and human-like synthesized conversational gestures conditioned on multi-modal data is still an unsolved problem due to the lack of available datasets, models and standard evaluation metrics. To address this, we build Body-Expression-Audio-Text dataset, BEAT, which has i) 76 hours, high-quali... | ['Bo Zheng', 'Elif Bozkurt', 'You Zhou', 'Zhengqing Li', 'Yichen Peng', 'Naoya Iwamoto', 'Zihao Zhu', 'Haiyang Liu'] | 2022-03-10 | null | null | null | null | ['gesture-recognition', 'gesture-generation'] | ['computer-vision', 'robots'] | [ 7.90504292e-02 -2.79006064e-01 -3.54742646e-01 -4.85189348e-01
-9.65236127e-01 -3.19366872e-01 7.63824880e-01 -7.58506536e-01
-1.92852810e-01 4.80190098e-01 1.11589324e+00 5.83947003e-01
1.97368085e-01 -1.65880755e-01 -3.21667880e-01 -7.72337735e-01
6.43379241e-02 2.70381808e-01 -1.56083241e-01 -2.75650114... | [5.656338214874268, -0.13442525267601013] |
f7e43eb5-316c-4b3b-a21d-d4bddeac57b3 | image-label-based-semantic-segmentation | 2303.07892 | null | https://arxiv.org/abs/2303.07892v3 | https://arxiv.org/pdf/2303.07892v3.pdf | SILOP: An Automated Framework for Semantic Segmentation Using Image Labels Based on Object Perimeters | Achieving high-quality semantic segmentation predictions using only image-level labels enables a new level of real-world applicability. Although state-of-the-art networks deliver reliable predictions, the amount of handcrafted pixel-wise annotations to enable these results are not feasible in many real-world applicatio... | ['Muhammad Shafique', 'Bharath Srinivas Prabakaran', 'Erik Ostrowski'] | 2023-03-14 | null | null | null | null | ['edge-detection', 'unsupervised-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 6.78044200e-01 4.73840296e-01 -3.64629060e-01 -4.92402762e-01
-4.95979786e-01 -4.39295828e-01 4.93508786e-01 2.98539847e-01
-4.72187310e-01 5.26477933e-01 -3.76771420e-01 -1.72074839e-01
-5.72098680e-02 -8.85195076e-01 -8.20243180e-01 -4.05008167e-01
9.64195579e-02 2.73049533e-01 1.14334810e+00 -3.08740348... | [9.510296821594238, 0.22981703281402588] |
00e0945d-2a5e-4e0a-86ad-82815b75befa | a-unified-framework-for-tumor-proliferation | 1612.07180 | null | http://arxiv.org/abs/1612.07180v2 | http://arxiv.org/pdf/1612.07180v2.pdf | A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology | We present a unified framework to predict tumor proliferation scores from
breast histopathology whole slide images. Our system offers a fully automated
solution to predicting both a molecular data-based, and a mitosis
counting-based tumor proliferation score. The framework integrates three
modules, each fine-tuned to m... | ['Minsoo Kim', 'Kyunghyun Paeng', 'Sunggyun Park', 'Sangheum Hwang'] | 2016-12-21 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 1.47792518e-01 2.36555442e-01 -5.24136007e-01 -1.51497960e-01
-1.44199908e+00 -1.68374747e-01 3.60423952e-01 6.00587666e-01
-6.58249617e-01 9.06908989e-01 5.12871929e-02 -5.30543268e-01
8.39486942e-02 -9.52954173e-01 -1.76240668e-01 -1.32776093e+00
-3.91741805e-02 6.21390581e-01 2.87473351e-01 1.92788780... | [15.121014595031738, -3.116823673248291] |
163cec3a-a314-4981-bce2-5937640c8d4f | mm-fi-multi-modal-non-intrusive-4d-human | 2305.10345 | null | https://arxiv.org/abs/2305.10345v1 | https://arxiv.org/pdf/2305.10345v1.pdf | MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing | 4D human perception plays an essential role in a myriad of applications, such as home automation and metaverse avatar simulation. However, existing solutions which mainly rely on cameras and wearable devices are either privacy intrusive or inconvenient to use. To address these issues, wireless sensing has emerged as a ... | ['Lihua Xie', 'Chris Xiaoxuan Lu', 'Han Zou', 'Shenghai Yuan', 'Yuecong Xu', 'Xinyan Chen', 'Yunjiao Zhou', 'He Huang', 'Jianfei Yang'] | 2023-05-12 | null | null | null | null | ['action-recognition-in-videos'] | ['computer-vision'] | [ 5.28458714e-01 3.05007286e-02 -1.29865214e-01 -2.39268512e-01
-8.05242956e-01 -2.49338672e-01 1.37497947e-01 -2.52074093e-01
-4.83460218e-01 6.39289737e-01 2.97712058e-01 1.17523618e-01
-5.68726771e-02 -5.18152833e-01 -2.63223946e-01 -6.77002072e-01
1.53917074e-01 1.51631851e-02 1.91749707e-01 1.37905866... | [6.981463432312012, 0.4000723958015442] |
bdb49b8c-7880-415a-b54a-9effafdf3992 | query-focused-sentence-compression-in-linear-1 | null | null | https://aclanthology.org/D19-1612 | https://aclanthology.org/D19-1612.pdf | Query-focused Sentence Compression in Linear Time | Search applications often display shortened sentences which must contain certain query terms and must fit within the space constraints of a user interface. This work introduces a new transition-based sentence compression technique developed for such settings. Our query-focused method constructs length and lexically con... | ["Brendan O{'}Connor", 'H', 'Abram ler'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['sentence-compression'] | ['natural-language-processing'] | [ 5.97805679e-01 5.18634692e-02 -5.97140551e-01 -4.36153859e-01
-1.24248004e+00 -7.03157187e-01 5.68476331e-04 5.81039190e-01
-5.77853560e-01 5.89448452e-01 2.24161208e-01 -8.24090958e-01
3.35544758e-02 -6.83889151e-01 -6.02525055e-01 1.96405277e-01
7.02917576e-02 7.43164182e-01 3.39938343e-01 -3.06880176... | [11.840574264526367, 8.439921379089355] |
db8a5a1f-d355-4d15-9027-ff100059c68e | user-centric-evaluation-of-ocr-systems-for | 2302.13410 | null | https://arxiv.org/abs/2302.13410v1 | https://arxiv.org/pdf/2302.13410v1.pdf | User-Centric Evaluation of OCR Systems for Kwak'wala | There has been recent interest in improving optical character recognition (OCR) for endangered languages, particularly because a large number of documents and books in these languages are not in machine-readable formats. The performance of OCR systems is typically evaluated using automatic metrics such as character and... | ['Graham Neubig', 'Antonios Anastasopoulos', 'Michayla King', 'Daisy Rosenblum', 'Shruti Rijhwani'] | 2023-02-26 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 2.73039311e-01 -4.13292974e-01 2.29292605e-02 -1.64861128e-01
-1.12713051e+00 -1.11596930e+00 4.96412098e-01 3.40076447e-01
-7.50388086e-01 6.29112840e-01 4.03193921e-01 -5.20919085e-01
2.16932014e-01 -1.51371881e-01 -2.71107763e-01 -2.77340114e-01
4.51794952e-01 3.83177787e-01 -1.86249822e-01 -1.47696480... | [11.823478698730469, 2.6297249794006348] |
6612c2c7-bdc6-4b2d-8ea4-8f2849119804 | parameters-sharing-exploration-and-hetero | 2008.06223 | null | https://arxiv.org/abs/2008.06223v2 | https://arxiv.org/pdf/2008.06223v2.pdf | Parameter Sharing Exploration and Hetero-Center based Triplet Loss for Visible-Thermal Person Re-Identification | This paper focuses on the visible-thermal cross-modality person re-identification (VT Re-ID) task, whose goal is to match person images between the daytime visible modality and the nighttime thermal modality. The two-stream network is usually adopted to address the cross-modality discrepancy, the most challenging probl... | ['Xichuan Zhou', 'Xiaoheng Tan', 'Haijun Liu'] | 2020-08-14 | null | null | null | null | ['cross-view-person-re-identification'] | ['computer-vision'] | [-1.13077993e-02 -6.14162326e-01 8.74217153e-02 -2.85200685e-01
-6.69764996e-01 -3.25879484e-01 6.86401486e-01 -3.66002798e-01
-6.41984344e-01 6.04500115e-01 3.45532089e-01 2.70071507e-01
-2.40909874e-01 -5.18947184e-01 -4.55677897e-01 -1.08181894e+00
3.82933021e-01 2.45066911e-01 -1.57674536e-01 -2.75152713... | [14.698814392089844, 0.935853123664856] |
5b72635a-d855-4a88-9952-7c30b5be9d25 | language-understanding-for-text-based-games | 1506.08941 | null | http://arxiv.org/abs/1506.08941v2 | http://arxiv.org/pdf/1506.08941v2.pdf | Language Understanding for Text-based Games Using Deep Reinforcement Learning | In this paper, we consider the task of learning control policies for
text-based games. In these games, all interactions in the virtual world are
through text and the underlying state is not observed. The resulting language
barrier makes such environments challenging for automatic game players. We
employ a deep reinforc... | ['Regina Barzilay', 'tejas kulkarni', 'Karthik Narasimhan'] | 2015-06-30 | language-understanding-for-text-based-games-1 | https://aclanthology.org/D15-1001 | https://aclanthology.org/D15-1001.pdf | emnlp-2015-9 | ['text-based-games'] | ['playing-games'] | [-1.82114661e-01 3.29121649e-01 -4.94058460e-01 -7.35477507e-02
-6.06527984e-01 -8.66334796e-01 1.16013730e+00 1.32246614e-01
-7.41115749e-01 7.59153247e-01 7.76228487e-01 -4.25641239e-01
2.32745305e-01 -1.09314704e+00 -4.45355803e-01 -1.25508562e-01
-2.43756279e-01 7.46160805e-01 3.14783216e-01 -1.04069448... | [3.759563684463501, 1.447199821472168] |
d8fdd113-290d-4968-94f3-77f6e8f87e4d | blessing-of-dimensionality-high-dimensional | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Chen_Blessing_of_Dimensionality_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Chen_Blessing_of_Dimensionality_2013_CVPR_paper.pdf | Blessing of Dimensionality: High-Dimensional Feature and Its Efficient Compression for Face Verification | Making a high-dimensional (e.g., 100K-dim) feature for face recognition seems not a good idea because it will bring difficulties on consequent training, computation, and storage. This prevents further exploration of the use of a highdimensional feature. In this paper, we study the performance of a highdimensional featu... | ['Dong Chen', 'Xudong Cao', 'Jian Sun', 'Fang Wen'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['age-invariant-face-recognition'] | ['computer-vision'] | [ 2.17700228e-01 -4.34358925e-01 -4.85635959e-02 -3.72562468e-01
-8.09018552e-01 -7.65748397e-02 6.19200885e-01 -4.63024110e-01
-1.70929447e-01 5.45153320e-01 -2.88165198e-03 -3.14663559e-01
-3.65613610e-01 -6.99608386e-01 -3.97069067e-01 -8.46621096e-01
-1.62171662e-01 6.60753772e-02 1.41806245e-01 2.86219388... | [12.545366287231445, 0.4506587088108063] |
928b4694-352d-4d3c-bd8c-e6aca892b03d | af-2-s3net-attentive-feature-fusion-with | 2102.04530 | null | https://arxiv.org/abs/2102.04530v1 | https://arxiv.org/pdf/2102.04530v1.pdf | (AF)2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Network | Autonomous robotic systems and self driving cars rely on accurate perception of their surroundings as the safety of the passengers and pedestrians is the top priority. Semantic segmentation is one the essential components of environmental perception that provides semantic information of the scene. Recently, several met... | ['Bingbing Liu', 'Enxu Li', 'Ehsan Taghavi', 'Ryan Razani', 'Ran Cheng'] | 2021-02-08 | af-2-s3net-attentive-feature-fusion-with-1 | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_AF2-S3Net_Attentive_Feature_Fusion_With_Adaptive_Feature_Selection_for_Sparse_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cheng_AF2-S3Net_Attentive_Feature_Fusion_With_Adaptive_Feature_Selection_for_Sparse_CVPR_2021_paper.pdf | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 9.65998918e-02 2.70408988e-02 -1.85946211e-01 -7.71203518e-01
-8.79110515e-01 -2.28932947e-01 5.97692847e-01 1.48983181e-01
-6.41390443e-01 3.04420352e-01 -1.45079076e-01 -1.75289497e-01
2.30797362e-02 -9.36389863e-01 -9.47544158e-01 -4.92412150e-01
4.24949139e-01 9.32709157e-01 1.04570198e+00 -2.09297970... | [8.136804580688477, -2.6585793495178223] |
104156e3-9168-442c-84a2-44a12b0f28d1 | ballistocardiogram-signal-processing-a | 1807.00951 | null | http://arxiv.org/abs/1807.00951v1 | http://arxiv.org/pdf/1807.00951v1.pdf | Ballistocardiogram Signal Processing: A Literature Review | Time-domain algorithms are focused on detecting local maxima or local minima
using a moving window, and therefore finding the interval between the dominant
J-peaks of ballistocardiogram (BCG) signal. However, this approach has many
limitations due to the nonlinear and nonstationary behavior of the BCG signal.
This is b... | ['Ibrahim Sadek'] | 2018-07-03 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 3.16640109e-01 -3.68506521e-01 -8.88466313e-02 1.14272282e-01
-3.33181322e-01 -3.59087408e-01 -2.46815439e-02 3.38797063e-01
-4.22497511e-01 8.42160761e-01 -2.57775038e-01 -8.68494287e-02
-3.08586270e-01 -7.40724444e-01 8.19084141e-03 -1.07855606e+00
-2.87655920e-01 -2.61596171e-03 1.75441995e-01 -4.81043458... | [14.052884101867676, 3.0792012214660645] |
4d785e07-521f-448d-a5d8-41753d746107 | consistent-explanations-by-contrastive | 2110.00527 | null | https://arxiv.org/abs/2110.00527v2 | https://arxiv.org/pdf/2110.00527v2.pdf | Consistent Explanations by Contrastive Learning | Post-hoc explanation methods, e.g., Grad-CAM, enable humans to inspect the spatial regions responsible for a particular network decision. However, it is shown that such explanations are not always consistent with human priors, such as consistency across image transformations. Given an interpretation algorithm, e.g., Gr... | ['Hamed Pirsiavash', 'Dennis Fong', 'Ashley Ouligian', 'Soroush Abbasi Koohpayegani', 'Vipin Pillai'] | 2021-10-01 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Pillai_Consistent_Explanations_by_Contrastive_Learning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Pillai_Consistent_Explanations_by_Contrastive_Learning_CVPR_2022_paper.pdf | cvpr-2022-1 | ['explainable-models'] | ['computer-vision'] | [ 2.61901736e-01 5.50852716e-01 -5.24255097e-01 -6.69705272e-01
-6.02746904e-01 -6.74643517e-01 7.34700024e-01 2.59290457e-01
-3.03022414e-01 5.64359248e-01 3.15106958e-01 -4.11445856e-01
-1.16091967e-01 -6.08836114e-01 -8.88673306e-01 -4.83012140e-01
4.12001282e-01 4.70871150e-01 9.73150954e-02 2.44825482... | [9.008187294006348, 5.604480266571045] |
2838669a-dc0c-4a51-ad08-fb489f357387 | data-efficient-learning-for-sim-to-real | 1906.08989 | null | https://arxiv.org/abs/1906.08989v1 | https://arxiv.org/pdf/1906.08989v1.pdf | Data-Efficient Learning for Sim-to-Real Robotic Grasping using Deep Point Cloud Prediction Networks | Training a deep network policy for robot manipulation is notoriously costly and time consuming as it depends on collecting a significant amount of real world data. To work well in the real world, the policy needs to see many instances of the task, including various object arrangements in the scene as well as variations... | ['Sören Pirk', 'Yuanzheng Gong', 'Yunfei Bai', 'Xinchen Yan', 'Mohi Khansari', 'Honglak Lee', 'Jasmine Hsu'] | 2019-06-21 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [-1.37219816e-01 -1.33585274e-01 1.68844745e-01 -2.50120908e-01
-4.24913675e-01 -9.68717992e-01 2.53521264e-01 1.80568099e-02
-5.69700658e-01 5.02395153e-01 -3.75857115e-01 -1.83404759e-01
-1.21260062e-01 -7.46538401e-01 -1.19920003e+00 -7.83189476e-01
-3.47199053e-01 1.12720895e+00 3.17980647e-01 -1.52500525... | [5.197816848754883, -0.0531473234295845] |
70b5dc3d-17e3-49c3-afc8-d9dc751d9370 | similarity-guided-deep-face-image-retrieval | 2107.05025 | null | https://arxiv.org/abs/2107.05025v1 | https://arxiv.org/pdf/2107.05025v1.pdf | Similarity Guided Deep Face Image Retrieval | Face image retrieval, which searches for images of the same identity from the query input face image, is drawing more attention as the size of the image database increases rapidly. In order to conduct fast and accurate retrieval, a compact hash code-based methods have been proposed, and recently, deep face image hashin... | ['Nam Ik Cho', 'Young Kyun Jang'] | 2021-07-11 | null | null | null | null | ['face-image-retrieval'] | ['computer-vision'] | [ 1.21133476e-01 -5.05185902e-01 -2.29565099e-01 -6.80086255e-01
-1.11025763e+00 -2.80757993e-01 5.20740926e-01 4.04726639e-02
-2.29986876e-01 3.47638249e-01 7.38822576e-03 2.89858669e-01
-3.93139094e-01 -7.66661167e-01 -3.29249948e-01 -9.82796371e-01
-3.39025319e-01 5.90735078e-01 7.12564588e-02 -1.61101744... | [11.441394805908203, 0.8971682190895081] |
419d9287-0cc3-49aa-bfa5-c5188b1eea65 | fault-signature-identification-for-bldc-motor | 2209.03159 | null | https://arxiv.org/abs/2209.03159v1 | https://arxiv.org/pdf/2209.03159v1.pdf | Fault Signature Identification for BLDC motor Drive System -A Statistical Signal Fusion Approach | A hybrid approach based on multirate signal processing and sensory data fusion is proposed for the condition monitoring and identification of fault signal signatures used in the Flight ECS (Engine Control System) unit. Though motor current signature analysis (MCSA) is widely used for fault detection now-a-days, the pro... | ['B. K. Panigrahi', 'Susanta Roy', 'Tribeni Prasad Banerjee'] | 2022-09-07 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 3.24481547e-01 -6.00806653e-01 2.54675239e-01 2.37158880e-01
-1.63533986e-01 -3.46523970e-01 4.39133257e-01 3.87469411e-01
-6.21914826e-02 6.06766403e-01 -5.85183620e-01 -1.36638567e-01
-7.87272096e-01 -4.36738104e-01 -1.82093784e-01 -8.58891845e-01
1.04297474e-01 2.42127374e-01 2.30909660e-01 -2.07445174... | [6.664951801300049, 2.3780887126922607] |
420b3e3f-7834-42ce-88ec-ce15b0914a7c | learning-shared-semantic-space-for-speech-to | 2105.03095 | null | https://arxiv.org/abs/2105.03095v3 | https://arxiv.org/pdf/2105.03095v3.pdf | Learning Shared Semantic Space for Speech-to-Text Translation | Having numerous potential applications and great impact, end-to-end speech translation (ST) has long been treated as an independent task, failing to fully draw strength from the rapid advances of its sibling - text machine translation (MT). With text and audio inputs represented differently, the modality gap has render... | ['Lei LI', 'Heng Ji', 'Mingxuan Wang', 'Chi Han'] | 2021-05-07 | null | https://aclanthology.org/2021.findings-acl.195 | https://aclanthology.org/2021.findings-acl.195.pdf | findings-acl-2021-8 | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 3.68360341e-01 2.24825904e-01 -4.39525962e-01 -2.86189497e-01
-1.46650863e+00 -7.69620240e-01 9.27425563e-01 -2.76278943e-01
-3.11181247e-01 6.58732712e-01 7.23327875e-01 -4.69031781e-01
3.58873993e-01 -1.93941087e-01 -7.19985068e-01 -4.61622298e-01
5.59014380e-01 5.39927781e-01 3.52844293e-03 -3.69716316... | [14.460112571716309, 7.168462753295898] |
53b1f4d8-3de7-4cb8-96a3-2ea06936eca8 | sick-nl-a-dataset-for-dutch-natural-language | null | null | https://aclanthology.org/2021.eacl-main.126 | https://aclanthology.org/2021.eacl-main.126.pdf | SICK-NL: A Dataset for Dutch Natural Language Inference | We present SICK-NL (read: signal), a dataset targeting Natural Language Inference in Dutch. SICK-NL is obtained by translating the SICK dataset of (Marelli et al., 2014) from English into Dutch. Having a parallel inference dataset allows us to compare both monolingual and multilingual NLP models for English and Dutch o... | ['Michael Moortgat', 'Gijs Wijnholds'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['multilingual-nlp'] | ['natural-language-processing'] | [ 1.29959837e-01 4.00957555e-01 -3.77718389e-01 -5.69426119e-01
-7.34059572e-01 -1.05236948e+00 7.25063026e-01 1.70355245e-01
-7.93884695e-01 8.95503998e-01 9.95184898e-01 -4.93612796e-01
1.58511817e-01 -5.55750489e-01 -7.37845182e-01 -1.93976909e-01
4.69499618e-01 8.64663541e-01 -9.45030525e-02 -4.74913150... | [10.89678955078125, 9.843208312988281] |
6094c57e-29a3-49e3-ad2e-a83b2707e66b | incorporating-unlabelled-data-into-bayesian | 2304.01762 | null | https://arxiv.org/abs/2304.01762v2 | https://arxiv.org/pdf/2304.01762v2.pdf | Incorporating Unlabelled Data into Bayesian Neural Networks | Conventional Bayesian Neural Networks (BNNs) cannot leverage unlabelled data to improve their predictions. To overcome this limitation, we introduce Self-Supervised Bayesian Neural Networks, which use unlabelled data to learn improved prior predictive distributions by maximising an evidence lower bound during an unsupe... | ['Vincent Fortuin', 'Yee Whye Teh', 'Tom Rainforth', 'Mrinank Sharma'] | 2023-04-04 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 6.58841193e-01 9.18951750e-01 -6.13861978e-01 -1.02255726e+00
-7.48959124e-01 -2.10801587e-01 9.73069251e-01 -6.37146980e-02
-5.43341398e-01 1.17401373e+00 1.98474109e-01 -7.18956962e-02
-6.72980964e-01 -6.42860353e-01 -7.71329403e-01 -7.83369660e-01
1.57747194e-02 9.56088126e-01 3.70878041e-01 5.58250606... | [7.242447376251221, 3.837890148162842] |
c1f23a17-10b2-45f5-b4b8-a770af2cfb0e | interactive-segmentation-of-radiance-fields | 2212.13545 | null | https://arxiv.org/abs/2212.13545v2 | https://arxiv.org/pdf/2212.13545v2.pdf | Interactive Segmentation of Radiance Fields | Radiance Fields (RF) are popular to represent casually-captured scenes for new view synthesis and several applications beyond it. Mixed reality on personal spaces needs understanding and manipulating scenes represented as RFs, with semantic segmentation of objects as an important step. Prior segmentation efforts show p... | ['PJ Narayanan', 'Saurabh Saini', 'Dhawal Sirikonda', 'Rahul Goel'] | 2022-12-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Goel_Interactive_Segmentation_of_Radiance_Fields_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Goel_Interactive_Segmentation_of_Radiance_Fields_CVPR_2023_paper.pdf | cvpr-2023-1 | ['mixed-reality', 'interactive-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.11414772e-01 -3.26952338e-02 2.87733711e-02 -6.97941661e-01
-6.41728878e-01 -8.69662821e-01 1.51379302e-01 -4.97884691e-01
2.27435455e-01 4.95215327e-01 3.01624179e-01 -9.86072887e-03
-1.06879279e-01 -8.32972765e-01 -5.28743625e-01 -2.76064217e-01
2.29745850e-01 4.39332545e-01 6.36954010e-01 -3.01790178... | [8.91624927520752, -2.998086452484131] |
1064e53f-f5e0-4f79-bb7c-bb3914c60968 | stable-long-term-recurrent-video-super | 2112.08950 | null | https://arxiv.org/abs/2112.08950v1 | https://arxiv.org/pdf/2112.08950v1.pdf | Stable Long-Term Recurrent Video Super-Resolution | Recurrent models have gained popularity in deep learning (DL) based video super-resolution (VSR), due to their increased computational efficiency, temporal receptive field and temporal consistency compared to sliding-window based models. However, when inferring on long video sequences presenting low motion (i.e. in whi... | ['Jean-Luc Starck', 'Joana Frontera-Pons', 'Arnaud Woiselle', 'Benjamin Naoto Chiche'] | 2021-12-16 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chiche_Stable_Long-Term_Recurrent_Video_Super-Resolution_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chiche_Stable_Long-Term_Recurrent_Video_Super-Resolution_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-super-resolution'] | ['computer-vision'] | [ 3.93049061e-01 -3.12648982e-01 -6.32188693e-02 1.37366712e-01
-6.73753440e-01 -2.61077553e-01 4.09517556e-01 -4.24827307e-01
-3.02771717e-01 8.34731877e-01 3.06442380e-01 1.57723516e-01
7.39278048e-02 -4.94277149e-01 -9.01643813e-01 -7.87073016e-01
-5.01768112e-01 -3.93964499e-01 7.09847569e-01 -4.57533091... | [11.03558349609375, -1.812268614768982] |
db21db24-661a-4024-b937-f7a822b2fc1d | paradise-exploiting-parallel-data-for-1 | null | null | https://aclanthology.org/2022.repl4nlp-1.3 | https://aclanthology.org/2022.repl4nlp-1.3.pdf | PARADISE”:" Exploiting Parallel Data for Multilingual Sequence-to-Sequence Pretraining | Despite the success of multilingual sequence-to-sequence pretraining, most existing approaches rely on monolingual corpora and do not make use of the strong cross-lingual signal contained in parallel data. In this paper, we present PARADISE (PARAllel &Denoising Integration in SEquence-to-sequence models), which extends... | ['Mikel Artetxe', 'Machel Reid'] | null | null | null | null | repl4nlp-acl-2022-5 | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [ 3.52549285e-01 -3.34068388e-01 -1.52867123e-01 -3.34989399e-01
-1.41949117e+00 -9.92090225e-01 8.43873501e-01 -6.33749366e-02
-8.16009045e-01 1.01983464e+00 2.81276554e-01 -7.08912373e-01
5.80965579e-01 -2.93642670e-01 -1.13109493e+00 -6.41875446e-01
5.13086200e-01 6.29698515e-01 -1.19706549e-01 -3.89649242... | [11.61941146850586, 10.305656433105469] |
51d458b8-4311-4eb8-8028-6ab49aa019c2 | progressive-image-deraining-networks-a-better | 1901.09221 | null | https://arxiv.org/abs/1901.09221v3 | https://arxiv.org/pdf/1901.09221v3.pdf | Progressive Image Deraining Networks: A Better and Simpler Baseline | Along with the deraining performance improvement of deep networks, their structures and learning become more and more complicated and diverse, making it difficult to analyze the contribution of various network modules when developing new deraining networks. To handle this issue, this paper provides a better and simpler... | ['QinGhua Hu', 'WangMeng Zuo', 'Pengfei Zhu', 'Dongwei Ren', 'Deyu Meng'] | 2019-01-26 | progressive-image-deraining-networks-a-better-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Ren_Progressive_Image_Deraining_Networks_A_Better_and_Simpler_Baseline_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ren_Progressive_Image_Deraining_Networks_A_Better_and_Simpler_Baseline_CVPR_2019_paper.pdf | cvpr-2019-6 | ['single-image-deraining'] | ['computer-vision'] | [ 6.78524673e-02 -1.44485086e-01 2.19925672e-01 -5.07318556e-01
-1.87399283e-01 -1.93906903e-01 3.66718501e-01 -2.95066208e-01
-4.52172220e-01 7.46947229e-01 6.71767956e-03 -2.94855952e-01
9.42715257e-02 -8.31739068e-01 -7.18980789e-01 -9.27350998e-01
-7.16945902e-03 -2.10582733e-01 2.06180260e-01 -2.45329946... | [10.92308235168457, -3.096674919128418] |
2ebff300-d6c9-4296-9ab5-43d4b721cb33 | on-learning-contrastive-representations-for | 2203.01785 | null | https://arxiv.org/abs/2203.01785v3 | https://arxiv.org/pdf/2203.01785v3.pdf | On Learning Contrastive Representations for Learning with Noisy Labels | Deep neural networks are able to memorize noisy labels easily with a softmax cross-entropy (CE) loss. Previous studies attempted to address this issue focus on incorporating a noise-robust loss function to the CE loss. However, the memorization issue is alleviated but still remains due to the non-robust CE loss. To add... | ['Boyu Wang', 'A. Ian McLeod', 'Qi She', 'Sheng Liu', 'Li Yi'] | 2022-03-03 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yi_On_Learning_Contrastive_Representations_for_Learning_With_Noisy_Labels_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yi_On_Learning_Contrastive_Representations_for_Learning_With_Noisy_Labels_CVPR_2022_paper.pdf | cvpr-2022-1 | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 5.48467398e-01 1.65374890e-01 -2.86715431e-03 -5.22921503e-01
-8.82264256e-01 -2.11523205e-01 3.80193293e-01 3.52395028e-01
-5.76410949e-01 9.18334663e-01 -1.11173049e-01 1.39881730e-01
-2.52352595e-01 -6.94870889e-01 -8.79654348e-01 -9.96645331e-01
1.02136083e-01 -1.97517008e-01 -1.99931309e-01 2.40968391... | [9.28393268585205, 3.8145968914031982] |
c287d167-f7b4-4de3-aa73-f1b0f0e3c988 | self-regression-learning-for-blind | 2103.16806 | null | https://arxiv.org/abs/2103.16806v1 | https://arxiv.org/pdf/2103.16806v1.pdf | Self-Regression Learning for Blind Hyperspectral Image Fusion Without Label | Hyperspectral image fusion (HIF) is critical to a wide range of applications in remote sensing and many computer vision applications. Most traditional HIF methods assume that the observation model is predefined or known. However, in real applications, the observation model involved are often complicated and unknown, wh... | ['Xinhao Ding', 'Yue Huang', 'Wu Wang'] | 2021-03-31 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 7.21679330e-01 -3.88632238e-01 2.54081246e-02 -4.49610382e-01
-7.02524245e-01 -9.25549567e-02 2.31280595e-01 -2.91072726e-01
-2.95294840e-02 7.93847501e-01 -1.79232582e-01 -1.66158527e-01
-4.30648834e-01 -7.40280092e-01 -7.44256675e-01 -1.19606209e+00
2.98692137e-01 1.25318188e-02 -2.34271750e-01 -1.05685100... | [10.242990493774414, -1.903991460800171] |
780b8f0a-a833-483a-b122-488b88da3442 | a-comparative-study-of-pretrained-language | 1912.01580 | null | https://arxiv.org/abs/1912.01580v2 | https://arxiv.org/pdf/1912.01580v2.pdf | A Comparative Study of Pretrained Language Models on Thai Social Text Categorization | The ever-growing volume of data of user-generated content on social media provides a nearly unlimited corpus of unlabeled data even in languages where resources are scarce. In this paper, we demonstrate that state-of-the-art results on two Thai social text categorization tasks can be realized by pretraining a language ... | ['Boonserm Kijsirikul', 'Thanapapas Horsuwan', 'Kasidis Kanwatchara', 'Peerapon Vateekul'] | 2019-12-03 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [-2.31557302e-02 2.74685800e-01 -1.76372796e-01 -6.31185234e-01
-8.14779222e-01 -4.20848757e-01 4.48726684e-01 2.04092413e-01
-1.04510939e+00 7.98828125e-01 4.52821195e-01 -8.56679499e-01
3.77771586e-01 -4.15592998e-01 -2.91212350e-01 -2.30213851e-01
-2.00461559e-02 8.16939235e-01 4.05356251e-02 -4.49937820... | [10.82154655456543, 9.256229400634766] |
57c29942-8139-4648-94bb-71dec910a9b5 | convsearch-a-open-domain-conversational | 2204.02659 | null | https://arxiv.org/abs/2204.02659v1 | https://arxiv.org/pdf/2204.02659v1.pdf | ConvSearch: A Open-Domain Conversational Search Behavior Dataset | Conversational Search has been paid much attention recently with the increasing popularity of intelligent user interfaces. However, compared with the endeavour in designing effective conversational search algorithms, relatively much fewer researchers have focused on the construction of benchmark datasets. For most exis... | ['Shaoping Ma', 'Min Zhang', 'Yingye Huang', 'Yiqun Liu', 'Zhihong Wang', 'Zhumin Chu'] | 2022-04-06 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [-1.50296375e-01 -3.57758225e-04 -4.47736233e-01 -4.72648382e-01
-3.55747372e-01 -6.82460368e-01 1.06181657e+00 -6.46844655e-02
-5.68690419e-01 5.59570372e-01 7.15035677e-01 -2.97523767e-01
-1.46743044e-01 -4.89943802e-01 2.70578951e-01 -2.14874327e-01
3.58833522e-01 9.30955350e-01 2.53552139e-01 -5.25961399... | [12.215126037597656, 7.790463447570801] |
35295cae-9bc1-46f3-a8f3-60f4f1c62cc8 | fine-tune-bert-for-extractive-summarization | 1903.10318 | null | https://arxiv.org/abs/1903.10318v2 | https://arxiv.org/pdf/1903.10318v2.pdf | Fine-tune BERT for Extractive Summarization | BERT, a pre-trained Transformer model, has achieved ground-breaking performance on multiple NLP tasks. In this paper, we describe BERTSUM, a simple variant of BERT, for extractive summarization. Our system is the state of the art on the CNN/Dailymail dataset, outperforming the previous best-performed system by 1.65 on ... | ['Yang Liu'] | 2019-03-25 | fine-tune-bert-for-extractive-summarization-1 | null | null | arxiv-2019-3 | ['extractive-document-summarization'] | ['natural-language-processing'] | [-1.41757458e-01 2.27917984e-01 -3.79045427e-01 -1.38502523e-01
-1.35637629e+00 -7.83293188e-01 7.27451265e-01 2.78127283e-01
-5.44693351e-01 9.25487339e-01 9.05554712e-01 -3.71257961e-01
1.35414481e-01 -2.83370256e-01 -8.79530728e-01 -1.57065973e-01
4.74218503e-02 6.79947197e-01 1.50526330e-01 -3.15169483... | [12.19204044342041, 9.209939002990723] |
8d5b1256-6fef-4114-a3cb-e28bc488c0fc | investigation-of-densely-connected | 2112.10108 | null | https://arxiv.org/abs/2112.10108v1 | https://arxiv.org/pdf/2112.10108v1.pdf | Investigation of Densely Connected Convolutional Networks with Domain Adversarial Learning for Noise Robust Speech Recognition | We investigate densely connected convolutional networks (DenseNets) and their extension with domain adversarial training for noise robust speech recognition. DenseNets are very deep, compact convolutional neural networks which have demonstrated incredible improvements over the state-of-the-art results in computer visio... | ['Ngoc Thang Vu', 'Chia Yu Li'] | 2021-12-19 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [-9.58857015e-02 1.90427914e-01 3.00439924e-01 -2.15300918e-01
-4.79233086e-01 -3.51879358e-01 7.05350995e-01 -7.09991157e-01
-4.22415972e-01 6.41791165e-01 4.91328716e-01 -4.48866278e-01
1.23938575e-01 -7.96408832e-01 -7.54622996e-01 -7.33517647e-01
-6.37139827e-02 1.09051183e-01 2.71885782e-01 -3.47198129... | [5.597415447235107, 7.890004634857178] |
f2a751c8-9bee-416b-94c5-d9a8b2f6adca | shadowdiffusion-diffusion-based-shadow | 2211.08089 | null | https://arxiv.org/abs/2211.08089v2 | https://arxiv.org/pdf/2211.08089v2.pdf | DeS3: Attention-driven Self and Soft Shadow Removal using ViT Similarity and Color Convergence | Removing soft and self shadows that lack clear boundaries from a single image is still challenging. Self shadows are shadows that are cast on the object itself. Most existing methods rely on binary shadow masks, without considering the ambiguous boundaries of soft and self shadows. In this paper, we present DeS3, a met... | ['Robby T. Tan', 'Yuan Yuan', 'Wei Ye', 'Wenhan Yang', 'Yeying Jin'] | 2022-11-15 | null | null | null | null | ['shadow-removal', 'image-shadow-removal'] | ['computer-vision', 'computer-vision'] | [ 5.43224573e-01 -1.60815194e-01 2.64198959e-01 -2.42735639e-01
-2.81642586e-01 -5.99414885e-01 6.17205977e-01 -4.88450080e-01
-8.98394585e-02 5.29263377e-01 -1.52742177e-01 -2.17010945e-01
2.90950060e-01 -7.14226544e-01 -7.20668733e-01 -9.98995900e-01
4.46081579e-01 4.95761752e-01 9.00563478e-01 -9.72390398... | [10.834757804870605, -4.040403842926025] |
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