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428c5b22-467d-4332-8f80-62a5a89f1cd2 | are-large-language-models-ready-for | 2304.05368 | null | https://arxiv.org/abs/2304.05368v2 | https://arxiv.org/pdf/2304.05368v2.pdf | Are Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language Understanding | Large language models (LLMs) have made significant progress in various domains, including healthcare. However, the specialized nature of clinical language understanding tasks presents unique challenges and limitations that warrant further investigation. In this study, we conduct a comprehensive evaluation of state-of-t... | ['Linda Petzold', 'Yun Zhao', 'Yuqing Wang'] | 2023-04-09 | null | null | null | null | ['document-classification', 'semantic-textual-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.47246480e-01 5.03604650e-01 -4.46644515e-01 -5.43689370e-01
-1.29903424e+00 -2.04548076e-01 2.05555990e-01 1.12761986e+00
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-7.24803627e-01 -3.28302264e-01 -2.95125097e-01 -1.96639732e-01
-2.15421259e-01 7.53094494e-01 -1.10769697e-01 -1.54139802... | [8.69277572631836, 8.642789840698242] |
41f69380-554b-4456-bf51-169810612f5a | illumination-variation-correction-using-image | 2301.09702 | null | https://arxiv.org/abs/2301.09702v1 | https://arxiv.org/pdf/2301.09702v1.pdf | Illumination Variation Correction Using Image Synthesis For Unsupervised Domain Adaptive Person Re-Identification | Unsupervised domain adaptive (UDA) person re-identification (re-ID) aims to learn identity information from labeled images in source domains and apply it to unlabeled images in a target domain. One major issue with many unsupervised re-identification methods is that they do not perform well relative to large domain var... | ['Edward J. Delp', 'Amy R. Reibman', 'Jiaqi Guo'] | 2023-01-23 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [ 2.60250717e-01 -4.85894948e-01 6.82726875e-02 -4.93345767e-01
-3.74175549e-01 -5.76884449e-01 7.78699338e-01 -3.04335654e-01
-4.46492732e-01 7.88932204e-01 3.14695001e-01 4.62509125e-01
2.04021335e-01 -6.62172854e-01 -4.32837039e-01 -6.30757391e-01
5.72636008e-01 5.61815560e-01 -2.53577292e-01 -1.41010970... | [14.702515602111816, 1.0070621967315674] |
50313e54-a773-48b6-b9c2-d0fdc02d1cb2 | et5-a-novel-end-to-end-framework-for | 2209.11484 | null | https://arxiv.org/abs/2209.11484v1 | https://arxiv.org/pdf/2209.11484v1.pdf | ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension | Conversational machine reading comprehension (CMRC) aims to assist computers to understand an natural language text and thereafter engage in a multi-turn conversation to answer questions related to the text. Existing methods typically require three steps: (1) decision making based on entailment reasoning; (2) span extr... | ['Xian-Ling Mao', 'Zewen Chi', 'Heyan Huang', 'Xiao Zhang'] | 2022-09-23 | null | https://aclanthology.org/2022.coling-1.47 | https://aclanthology.org/2022.coling-1.47.pdf | coling-2022-10 | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 4.87467736e-01 3.58576298e-01 1.68382570e-01 -4.93027806e-01
-1.13949203e+00 -6.17380202e-01 5.53519189e-01 4.42537338e-01
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3.82540524e-01 4.83372271e-01 2.28614002e-01 -4.38757062... | [11.813239097595215, 8.064355850219727] |
1e61d32f-54b5-4732-a1ac-5e824eb2cfcd | robust-coordinated-longitudinal-control-of | 2208.05708 | null | https://arxiv.org/abs/2208.05708v1 | https://arxiv.org/pdf/2208.05708v1.pdf | Robust Coordinated Longitudinal Control of MAV Based on Energy State | Fixed-wing Miniature Air Vehicle (MAV) is not only coupled with longitudinal motion, but also more susceptible to wind disturbance due to its lighter weight, which brings more challenges to its altitude and airspeed controller design. Therefore, in this paper, an improved longitudinal control strategy based on energy s... | ['Haodong Li', 'Dawei Li', 'Chenlong Zhang'] | 2022-08-11 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-2.91552514e-01 -1.46165639e-01 -2.54480064e-01 4.59442735e-01
6.31421626e-01 -5.15320778e-01 5.40362239e-01 -2.11551651e-01
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1.76564351e-01 -2.85179645e-01 -1.48245785e-02 -4.80060130... | [5.379420280456543, 2.439556837081909] |
61c64f38-e5eb-4438-9488-d17b504a69ca | next3d-generative-neural-texture | 2211.11208 | null | https://arxiv.org/abs/2211.11208v2 | https://arxiv.org/pdf/2211.11208v2.pdf | Next3D: Generative Neural Texture Rasterization for 3D-Aware Head Avatars | 3D-aware generative adversarial networks (GANs) synthesize high-fidelity and multi-view-consistent facial images using only collections of single-view 2D imagery. Towards fine-grained control over facial attributes, recent efforts incorporate 3D Morphable Face Model (3DMM) to describe deformation in generative radiance... | ['Yebin Liu', 'Hongwen Zhang', 'Yong Zhang', 'Xiaoyu Li', 'Lizhen Wang', 'Xuan Wang', 'Jingxiang Sun'] | 2022-11-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sun_Next3D_Generative_Neural_Texture_Rasterization_for_3D-Aware_Head_Avatars_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sun_Next3D_Generative_Neural_Texture_Rasterization_for_3D-Aware_Head_Avatars_CVPR_2023_paper.pdf | cvpr-2023-1 | ['face-model'] | ['computer-vision'] | [ 1.72628477e-01 3.68360549e-01 -9.12558660e-03 -4.32769090e-01
-4.48031038e-01 -6.89630985e-01 8.69617879e-01 -8.78026307e-01
4.18066829e-01 5.03867686e-01 1.94763556e-01 1.14149615e-01
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2.52702951e-01 6.74600005e-01 -1.81460142e-01 -3.69061887... | [12.614302635192871, -0.39362549781799316] |
42bec710-a1c7-411b-b466-8908617d5e6a | srt3d-a-sparse-region-based-3d-object | 2110.12715 | null | https://arxiv.org/abs/2110.12715v1 | https://arxiv.org/pdf/2110.12715v1.pdf | SRT3D: A Sparse Region-Based 3D Object Tracking Approach for the Real World | Region-based methods have become increasingly popular for model-based, monocular 3D tracking of texture-less objects in cluttered scenes. However, while they achieve state-of-the-art results, most methods are computationally expensive, requiring significant resources to run in real-time. In the following, we build on o... | ['Alin Albu-Schäffer', 'Rudolph Triebel', 'Klaus H. Strobl', 'Martin Pfanne', 'Manuel Stoiber'] | 2021-10-25 | null | null | null | null | ['real-time-visual-tracking', '3d-object-tracking', '3d-multi-object-tracking'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.13884576e-01 -2.83969641e-01 -7.05062822e-02 -1.41551524e-01
-9.44648564e-01 -5.94207764e-01 5.61709940e-01 7.59301186e-02
-3.16984922e-01 4.16296363e-01 -1.69294685e-01 9.52396020e-02
-7.61625692e-02 -3.41785580e-01 -7.07686484e-01 -6.59350991e-01
-5.18638864e-02 7.50787914e-01 6.99843347e-01 2.65315771... | [7.098311901092529, -2.320064067840576] |
61577b08-7c57-48dc-a208-0d3481ca74ae | sacdnet-towards-early-type-2-diabetes | 2301.04844 | null | https://arxiv.org/abs/2301.04844v2 | https://arxiv.org/pdf/2301.04844v2.pdf | SACDNet: Towards Early Type 2 Diabetes Prediction with Uncertainty for Electronic Health Records | Type 2 diabetes mellitus (T2DM) is one of the most common diseases and a leading cause of death. The problem of early diagnosis of T2DM is challenging and necessary to prevent serious complications. This study proposes a novel neural network architecture for early T2DM prediction using multi-headed self-attention and d... | ['Muhammad Kamran Malik', 'Tayyab Nasir'] | 2023-01-12 | null | null | null | null | ['diabetes-prediction'] | ['medical'] | [-5.12953922e-02 1.77304000e-01 -3.85814846e-01 -9.43483770e-01
-8.32304478e-01 3.93976659e-01 2.63830543e-01 4.78248179e-01
-2.47948825e-01 8.75694335e-01 3.14735383e-01 -2.51128048e-01
-4.34569508e-01 -7.49740064e-01 -5.35673201e-01 -5.53508520e-01
-4.31389242e-01 9.37604725e-01 -5.01511991e-01 4.47482526... | [8.005162239074707, 5.88607931137085] |
f895d73d-2ba2-415e-9532-0f63abbfba0d | learning-latent-sub-events-in-activity-videos | 1605.08140 | null | http://arxiv.org/abs/1605.08140v3 | http://arxiv.org/pdf/1605.08140v3.pdf | Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters | In this paper, we newly introduce the concept of temporal attention filters,
and describe how they can be used for human activity recognition from videos.
Many high-level activities are often composed of multiple temporal parts (e.g.,
sub-events) with different duration/speed, and our objective is to make the
model exp... | ['Michael S. Ryoo', 'AJ Piergiovanni', 'Chenyou Fan'] | 2016-05-26 | null | null | null | null | ['activity-recognition-in-videos'] | ['computer-vision'] | [ 3.05785686e-01 -9.63835716e-02 -3.14074785e-01 -2.63842613e-01
-2.53238738e-01 -3.30534607e-01 4.98216748e-01 -2.77959853e-01
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-1.94639608e-01 -4.81162369e-01 -1.02012837e+00 -6.63683653e-01
-8.61957252e-01 -1.61406144e-01 5.62080026e-01 2.36306846... | [8.387042045593262, 0.5654478073120117] |
fa539a32-b09d-4e14-861e-29be03a3e703 | vne-an-effective-method-for-improving-deep | 2304.01434 | null | https://arxiv.org/abs/2304.01434v1 | https://arxiv.org/pdf/2304.01434v1.pdf | VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution | Since the introduction of deep learning, a wide scope of representation properties, such as decorrelation, whitening, disentanglement, rank, isotropy, and mutual information, have been studied to improve the quality of representation. However, manipulating such properties can be challenging in terms of implementational... | ['Wonjong Rhee', 'Jungwook Shin', 'Duhun Hwang', 'Suhyun Kang', 'Jaeill Kim'] | 2023-04-04 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Kim_VNE_An_Effective_Method_for_Improving_Deep_Representation_by_Manipulating_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kim_VNE_An_Effective_Method_for_Improving_Deep_Representation_by_Manipulating_CVPR_2023_paper.pdf | cvpr-2023-1 | ['self-supervised-image-classification', 'semi-supervised-image-classification', 'few-shot-image-classification', 'classification'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology'] | [ 1.30914733e-01 -1.50099665e-01 -2.48301491e-01 -4.11449075e-02
-2.07844064e-01 -5.97689986e-01 7.45139956e-01 2.58836765e-02
-1.96350932e-01 7.01109052e-01 3.75425160e-01 -1.60681739e-01
-6.69020891e-01 -8.43486845e-01 -3.00506324e-01 -8.03697348e-01
-3.93200547e-01 -1.05895519e-01 -3.82066965e-01 -1.40283257... | [8.555490493774414, 3.9971113204956055] |
3292a283-09d4-48b7-87d1-7181cd3372e3 | explore-the-power-of-dropout-on-few-shot | 2301.11015 | null | https://arxiv.org/abs/2301.11015v1 | https://arxiv.org/pdf/2301.11015v1.pdf | Explore the Power of Dropout on Few-shot Learning | The generalization power of the pre-trained model is the key for few-shot deep learning. Dropout is a regularization technique used in traditional deep learning methods. In this paper, we explore the power of dropout on few-shot learning and provide some insights about how to use it. Extensive experiments on the few-sh... | ['Rui Zhao', 'Xingyu Zeng', 'Shaobo Lin'] | 2023-01-26 | null | null | null | null | ['few-shot-image-classification', 'few-shot-object-detection'] | ['computer-vision', 'computer-vision'] | [-2.74839073e-01 -3.16098928e-01 -4.99678224e-01 -5.12079537e-01
-3.74865890e-01 -2.94362940e-02 4.57498878e-01 -2.55340636e-01
-6.79402649e-01 6.53779328e-01 -6.52968213e-02 2.17546478e-01
1.86695233e-01 -8.33183050e-01 -7.87596703e-01 -5.78895450e-01
1.60899177e-01 -1.28268912e-01 7.41709232e-01 -1.26052633... | [9.9823637008667, 2.8619766235351562] |
44c4f07b-8b2c-49dd-ae97-3cb47abeb950 | a-design-flow-for-mapping-spiking-neural | 2108.12444 | null | https://arxiv.org/abs/2108.12444v1 | https://arxiv.org/pdf/2108.12444v1.pdf | A Design Flow for Mapping Spiking Neural Networks to Many-Core Neuromorphic Hardware | The design of many-core neuromorphic hardware is getting more and more complex as these systems are expected to execute large machine learning models. To deal with the design complexity, a predictable design flow is needed to guarantee real-time performance such as latency and throughput without significantly increasin... | ['Nagarajan Kandasamy', 'Anup Das', 'M. Lakshmi Varshika', 'Shihao Song'] | 2021-08-27 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [ 2.65518725e-02 -1.11914888e-01 8.73482451e-02 -1.32238567e-01
2.24574983e-01 -3.88212979e-01 1.68252468e-01 1.79750934e-01
-4.50084001e-01 5.56494653e-01 -1.36101753e-01 -1.59931540e-01
-5.40338874e-01 -7.67177105e-01 -5.78340232e-01 -6.36230111e-01
-2.50791609e-01 4.48820204e-01 5.91259062e-01 7.79323429... | [8.269933700561523, 2.5619332790374756] |
5d99f319-e37d-45b7-864a-e22af7d30147 | leaving-the-lines-behind-vision-based-crop | 2306.05869 | null | https://arxiv.org/abs/2306.05869v1 | https://arxiv.org/pdf/2306.05869v1.pdf | Leaving the Lines Behind: Vision-Based Crop Row Exit for Agricultural Robot Navigation | Usage of purely vision based solutions for row switching is not well explored in existing vision based crop row navigation frameworks. This method only uses RGB images for local feature matching based visual feedback to exit crop row. Depth images were used at crop row end to estimate the navigation distance within hea... | ['Junfeng Gao', 'Grzegorz Cielniak', 'Rajitha de Silva'] | 2023-06-09 | null | null | null | null | ['navigate', 'robot-navigation'] | ['reasoning', 'robots'] | [ 5.54699861e-02 -1.03383802e-01 -9.44408625e-02 8.00113752e-02
4.45112020e-01 -1.30352163e+00 -1.89968832e-02 4.33417827e-01
-1.65774614e-01 4.79179889e-01 -4.08933789e-01 -9.33118701e-01
4.03745621e-02 -1.07130361e+00 -1.50324881e-01 -4.58343863e-01
7.38244131e-02 -2.52897024e-01 3.30868751e-01 -7.30538785... | [9.068540573120117, -1.6316707134246826] |
cc5942bc-72ea-4aca-836d-daa4e6fe874d | auto-completion-of-user-interface-layout-1 | 2001.05308 | null | https://arxiv.org/abs/2001.05308v1 | https://arxiv.org/pdf/2001.05308v1.pdf | Auto Completion of User Interface Layout Design Using Transformer-Based Tree Decoders | It has been of increasing interest in the field to develop automatic machineries to facilitate the design process. In this paper, we focus on assisting graphical user interface (UI) layout design, a crucial task in app development. Given a partial layout, which a designer has entered, our model learns to complete the l... | ['Yang Li', 'Si Si', 'Samy Bengio', 'Xin Zhou', 'Julien Amelot'] | 2020-01-14 | null | https://openreview.net/forum?id=SylWNC4FPH | https://openreview.net/pdf?id=SylWNC4FPH | null | ['layout-design'] | ['computer-vision'] | [ 2.18800843e-01 1.32510429e-02 -1.58715233e-01 -4.20299679e-01
-4.42298293e-01 -6.74693704e-01 1.86816584e-02 1.48849860e-01
1.39052182e-01 2.95789242e-01 3.43915880e-01 -7.82381535e-01
-8.42999965e-02 -7.49617815e-01 -7.01542079e-01 -1.31540492e-01
-5.49885780e-02 4.43251252e-01 3.46884690e-02 6.32212758... | [11.293722152709961, -0.04070484638214111] |
6b5bf6ae-97f0-4c4a-a304-22df70c81a0c | restoration-of-user-videos-shared-on-social | 2208.08597 | null | https://arxiv.org/abs/2208.08597v2 | https://arxiv.org/pdf/2208.08597v2.pdf | Restoration of User Videos Shared on Social Media | User videos shared on social media platforms usually suffer from degradations caused by unknown proprietary processing procedures, which means that their visual quality is poorer than that of the originals. This paper presents a new general video restoration framework for the restoration of user videos shared on social... | ['Guoping Qiu', 'Kin-Man Lam', 'Fei Zhou', 'Hongming Luo'] | 2022-08-18 | null | null | null | null | ['video-restoration'] | ['computer-vision'] | [ 1.96821526e-01 -2.74979800e-01 -9.93404984e-02 -1.14523470e-01
-5.90323627e-01 -1.94326222e-01 3.91123563e-01 -1.88874185e-01
-7.17891306e-02 6.67548835e-01 7.51452029e-01 4.30374071e-02
9.86545533e-02 -6.97409511e-01 -7.49303997e-01 -7.54057825e-01
-3.83078270e-02 -4.01566148e-01 7.84672201e-02 -3.13195318... | [11.219417572021484, -1.9180203676223755] |
5f1acfb0-07a1-4b28-bec9-e9c0c082ecdd | can-we-achieve-more-with-less-exploring-data | 2007.00875 | null | https://arxiv.org/abs/2007.00875v1 | https://arxiv.org/pdf/2007.00875v1.pdf | Can We Achieve More with Less? Exploring Data Augmentation for Toxic Comment Classification | This paper tackles one of the greatest limitations in Machine Learning: Data Scarcity. Specifically, we explore whether high accuracy classifiers can be built from small datasets, utilizing a combination of data augmentation techniques and machine learning algorithms. In this paper, we experiment with Easy Data Augment... | ['Fang-I Hsiao', 'Chetanya Rastogi', 'Nikka Mofid'] | 2020-07-02 | null | null | null | null | ['toxic-comment-classification'] | ['natural-language-processing'] | [ 2.18623742e-01 2.44857490e-01 -2.89992899e-01 -3.27517539e-01
-6.73030853e-01 -3.68148476e-01 5.99384367e-01 5.05477190e-01
-6.70376182e-01 6.47317767e-01 5.18328130e-01 -8.24353755e-01
1.06007233e-01 -7.68898427e-01 -4.67470556e-01 -1.88180730e-01
1.28522411e-01 1.61048636e-01 -1.96216062e-01 -5.56123197... | [8.83866024017334, 10.477490425109863] |
5206d940-d690-452c-8a38-166f7cc38ff9 | korsal-key-point-detection-based-online-real | 2111.03319 | null | https://arxiv.org/abs/2111.03319v1 | https://arxiv.org/pdf/2111.03319v1.pdf | KORSAL: Key-point Detection based Online Real-Time Spatio-Temporal Action Localization | Real-time and online action localization in a video is a critical yet highly challenging problem. Accurate action localization requires the utilization of both temporal and spatial information. Recent attempts achieve this by using computationally intensive 3D CNN architectures or highly redundant two-stream architectu... | ['Peshala Jayasekara', 'Ranga Rodrigo', 'Sachira Karunasena', 'Sakuna Jayasundara', 'Shechem Sumanthiran', 'Kalana Abeywardena'] | 2021-11-05 | null | null | null | null | ['spatio-temporal-action-localization'] | ['computer-vision'] | [ 1.72580242e-01 -4.11372334e-01 -3.39155465e-01 -3.69558744e-02
-6.01495266e-01 -4.79731858e-01 4.40446794e-01 1.58050060e-01
-7.86505997e-01 5.05543590e-01 3.23470026e-01 -4.21780460e-02
1.82180911e-01 -3.71404886e-01 -7.38898218e-01 -4.10381973e-01
-3.89745593e-01 1.46801472e-01 7.64061511e-01 6.18940331... | [8.398941993713379, 0.3102327883243561] |
973e7987-de43-4194-8bf7-f96dfed3a26b | flowmot-3d-multi-object-tracking-by-scene | 2012.07541 | null | https://arxiv.org/abs/2012.07541v3 | https://arxiv.org/pdf/2012.07541v3.pdf | FlowMOT: 3D Multi-Object Tracking by Scene Flow Association | Most end-to-end Multi-Object Tracking (MOT) methods face the problems of low accuracy and poor generalization ability. Although traditional filter-based methods can achieve better results, they are difficult to be endowed with optimal hyperparameters and often fail in varying scenarios. To alleviate these drawbacks, we... | ['Yong liu', 'Jinhao Cui', 'Zhen Yang', 'Xin Kong', 'Guangyao Zhai'] | 2020-12-14 | null | null | null | null | ['scene-flow-estimation', '3d-multi-object-tracking'] | ['computer-vision', 'computer-vision'] | [-2.14048773e-01 -7.98916280e-01 -2.44752243e-01 -5.07264398e-02
-5.55225372e-01 -2.97434419e-01 3.23953778e-01 -3.69490772e-01
-5.06078124e-01 6.06230080e-01 -1.49294466e-01 -8.80400389e-02
-1.56780273e-01 -6.99857652e-01 -5.48405409e-01 -5.91992497e-01
7.29770586e-02 5.43816507e-01 1.14711821e+00 -5.96849732... | [6.480399131774902, -2.105417490005493] |
c38c226d-d024-4a51-8892-a95e72f1f79b | defend-data-poisoning-attacks-on-voice | 2209.04547 | null | https://arxiv.org/abs/2209.04547v2 | https://arxiv.org/pdf/2209.04547v2.pdf | Defend Data Poisoning Attacks on Voice Authentication | With the advances in deep learning, speaker verification has achieved very high accuracy and is gaining popularity as a type of biometric authentication option in many scenes of our daily life, especially the growing market of web services. Compared to traditional passwords, "vocal passwords" are much more convenient a... | ['Dan Lin', 'Cameron Baird', 'Ke Li'] | 2022-09-09 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [-7.27163404e-02 -4.27430481e-01 -9.01457369e-02 -1.40347973e-01
-3.51047218e-01 -8.56245875e-01 5.38212538e-01 -9.69982520e-02
-5.62058389e-01 4.96805519e-01 3.55237834e-02 -8.39858413e-01
2.79968172e-01 -8.01928282e-01 5.10808975e-02 -6.62236333e-01
4.27070677e-01 4.51822951e-02 3.03063020e-02 -3.18437487... | [14.017158508300781, 5.800908088684082] |
03527940-db7e-4b56-87b4-4d38d344b841 | enhancement-or-super-resolution-learning | 2201.08197 | null | https://arxiv.org/abs/2201.08197v1 | https://arxiv.org/pdf/2201.08197v1.pdf | Enhancement or Super-Resolution: Learning-based Adaptive Video Streaming with Client-Side Video Processing | The rapid development of multimedia and communication technology has resulted in an urgent need for high-quality video streaming. However, robust video streaming under fluctuating network conditions and heterogeneous client computing capabilities remains a challenge. In this paper, we consider an enhancement-enabled vi... | ['Shuoyao Wang', 'Yang Jiang', 'Junyan Yang'] | 2022-01-20 | null | null | null | null | ['video-enhancement'] | ['computer-vision'] | [ 2.13586852e-01 -4.62610364e-01 -2.26420134e-01 -4.78689194e-01
-7.78002858e-01 -2.02905849e-01 -2.22574413e-01 -2.22866684e-01
-4.65580434e-01 7.18039334e-01 1.07202083e-01 -3.43477428e-01
-1.67476609e-01 -7.29757726e-01 -6.37511969e-01 -1.00061131e+00
-7.58678675e-01 -3.05968612e-01 5.33029675e-01 -2.70646960... | [11.156734466552734, -1.6554429531097412] |
f95637f0-84a7-4429-9a86-c269faec08ba | cosmetic-aware-makeup-cleanser | 2004.09147 | null | https://arxiv.org/abs/2004.09147v1 | https://arxiv.org/pdf/2004.09147v1.pdf | Cosmetic-Aware Makeup Cleanser | Face verification aims at determining whether a pair of face images belongs to the same identity. Recent studies have revealed the negative impact of facial makeup on the verification performance. With the rapid development of deep generative models, this paper proposes a semanticaware makeup cleanser (SAMC) to remove ... | ['Yi Li', 'Junchi Yu', 'Tieniu Tan', 'Huaibo Huang', 'Ran He'] | 2020-04-20 | null | null | null | null | ['face-parsing'] | ['computer-vision'] | [ 3.21364671e-01 3.79783571e-01 5.95728755e-02 -7.32606649e-01
-7.38765836e-01 -5.68911970e-01 4.85925376e-01 -6.19111359e-01
3.64336491e-01 4.09987777e-01 3.56403232e-01 1.70291066e-01
1.41066328e-01 -7.51924694e-01 -7.70839751e-01 -9.25582469e-01
4.45829958e-01 -6.54772902e-03 -6.36237741e-01 -1.87425137... | [12.762114524841309, 0.10756748914718628] |
d29aca32-d5f6-4a31-a78f-04a29d352bbe | physics-informed-representation-learning-for | 2304.12586 | null | https://arxiv.org/abs/2304.12586v1 | https://arxiv.org/pdf/2304.12586v1.pdf | Physics-Informed Representation Learning for Emergent Organization in Complex Dynamical Systems | Nonlinearly interacting system components often introduce instabilities that generate phenomena with new properties and at different space-time scales than the components. This is known as spontaneous self-organization and is ubiquitous in systems far from thermodynamic equilibrium. We introduce a theoretically-grounde... | ['James P. Crutchfield', 'Nalini Kumar', 'Karthik Kashinath', 'Adam Rupe'] | 2023-04-25 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-2.24632591e-01 -3.15896332e-01 3.61360669e-01 1.44558772e-01
1.01409182e-01 -9.91696715e-01 1.12276089e+00 1.63750961e-01
2.42085367e-01 9.80640650e-01 5.57794034e-01 -2.07050353e-01
-5.09886384e-01 -8.75253797e-01 -4.27996993e-01 -1.11940753e+00
-1.16206527e+00 2.35061944e-01 2.15146318e-01 -5.07613122... | [6.547235488891602, 3.873445987701416] |
6dc39e90-8d8e-457a-a1f8-8b101fd056ec | triplet-contrastive-learning-for-brain-tumor | 2108.03611 | null | https://arxiv.org/abs/2108.03611v1 | https://arxiv.org/pdf/2108.03611v1.pdf | Triplet Contrastive Learning for Brain Tumor Classification | Brain tumor is a common and fatal form of cancer which affects both adults and children. The classification of brain tumors into different types is hence a crucial task, as it greatly influences the treatment that physicians will prescribe. In light of this, medical imaging techniques, especially those applying deep co... | ['Jiashi Feng', 'Tian Yu Liu'] | 2021-08-08 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 3.29991728e-01 2.71388054e-01 -1.55207053e-01 -4.91494805e-01
-5.25285482e-01 -1.18140355e-01 6.63890183e-01 4.69371945e-01
-8.19850802e-01 6.48857594e-01 2.36001194e-01 -3.82925212e-01
1.29627381e-02 -8.62534285e-01 -4.71352369e-01 -9.18246090e-01
2.25794744e-02 4.70378697e-01 6.99429736e-02 2.90382281... | [14.864892959594727, -2.4847729206085205] |
12ab58f6-182b-4a54-8550-bc0efe1afca2 | sentence-centrality-revisited-for | 1906.03508 | null | https://arxiv.org/abs/1906.03508v1 | https://arxiv.org/pdf/1906.03508v1.pdf | Sentence Centrality Revisited for Unsupervised Summarization | Single document summarization has enjoyed renewed interests in recent years thanks to the popularity of neural network models and the availability of large-scale datasets. In this paper we develop an unsupervised approach arguing that it is unrealistic to expect large-scale and high-quality training data to be availabl... | ['Mirella Lapata', 'Hao Zheng'] | 2019-06-08 | sentence-centrality-revisited-for-1 | https://aclanthology.org/P19-1628 | https://aclanthology.org/P19-1628.pdf | acl-2019-7 | ['unsupervised-extractive-summarization'] | ['natural-language-processing'] | [ 4.70584720e-01 4.79102045e-01 -3.83318305e-01 -5.14197826e-01
-5.68512440e-01 -5.44121265e-01 9.32853401e-01 8.27340305e-01
-3.63943577e-01 8.49513054e-01 1.23918962e+00 -2.68972397e-01
-3.18455964e-01 -9.29422736e-01 -5.43383777e-01 -2.09428936e-01
-1.63927168e-01 5.76597214e-01 1.66091308e-01 -6.07795596... | [12.51528263092041, 9.52285385131836] |
c04e8898-ed8d-4db2-8ae1-88ff4f76000e | disentangling-homophily-community-structure | 2101.02510 | null | https://arxiv.org/abs/2101.02510v3 | https://arxiv.org/pdf/2101.02510v3.pdf | Disentangling homophily, community structure and triadic closure in networks | Network homophily, the tendency of similar nodes to be connected, and transitivity, the tendency of two nodes being connected if they share a common neighbor, are conflated properties in network analysis, since one mechanism can drive the other. Here we present a generative model and corresponding inference procedure t... | ['Tiago P. Peixoto'] | 2021-01-07 | null | null | null | null | ['stochastic-block-model', 'graph-reconstruction'] | ['graphs', 'graphs'] | [ 3.45758684e-02 2.63216883e-01 3.46175954e-02 7.27527961e-02
5.34264028e-01 -6.79642200e-01 9.02308822e-01 3.03017139e-01
1.79190218e-01 7.48643219e-01 5.98972552e-02 -7.75505960e-01
-5.83231032e-01 -1.40502417e+00 -7.56168604e-01 -8.85107696e-01
-5.00728846e-01 8.15584123e-01 5.55177033e-01 -1.83040991... | [6.971022605895996, 5.267307758331299] |
5e121ac8-ad8a-4fd0-97b8-e4d82320b5d4 | uncertainty-guided-mutual-consistency | 2112.02508 | null | https://arxiv.org/abs/2112.02508v2 | https://arxiv.org/pdf/2112.02508v2.pdf | Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation | Medical image segmentation is a fundamental and critical step in many clinical approaches. Semi-supervised learning has been widely applied to medical image segmentation tasks since it alleviates the heavy burden of acquiring expert-examined annotations and takes the advantage of unlabeled data which is much easier to ... | ['Dongyang Li', 'Jicong Zhang', 'Rushi Jiao', 'Qingcheng Liao', 'Yichi Zhang'] | 2021-12-05 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 2.52581209e-01 2.85091877e-01 -5.49294591e-01 -6.24383569e-01
-1.17883575e+00 -4.24284846e-01 1.67353600e-01 2.91074514e-01
-5.65291405e-01 7.95753717e-01 -3.69435549e-02 -2.79290318e-01
-1.83561072e-01 -2.70283073e-01 -4.27677542e-01 -8.86572480e-01
1.37473390e-01 6.14505053e-01 3.25190902e-01 3.18650454... | [14.708378791809082, -2.1769192218780518] |
cf049968-2a16-4dfb-8065-6621381ccb14 | nnmobile-net-rethinking-cnn-design-for-deep | 2306.01289 | null | https://arxiv.org/abs/2306.01289v1 | https://arxiv.org/pdf/2306.01289v1.pdf | NNMobile-Net: Rethinking CNN Design for Deep Learning-Based Retinopathy Research | Retinal diseases (RD) are the leading cause of severe vision loss or blindness. Deep learning-based automated tools play an indispensable role in assisting clinicians in diagnosing and monitoring RD in modern medicine. Recently, an increasing number of works in this field have taken advantage of Vision Transformer to a... | ['Yalin Wang', 'Oana M. Dumitrascu', 'Natasha Lepore', 'Peijie Qiu', 'Wenhui Zhu'] | 2023-06-02 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [-6.29038140e-02 -3.04155499e-01 -4.83764745e-02 -3.85730624e-01
-2.35579237e-01 -9.80740786e-02 9.92862284e-02 -1.41828522e-01
-5.52569151e-01 7.22892225e-01 3.89395654e-02 -8.50216389e-01
-1.30616188e-01 -8.09626520e-01 -4.10556465e-01 -6.64168298e-01
1.46806091e-01 -3.04396115e-02 3.75680447e-01 -7.93970153... | [15.815529823303223, -3.9820003509521484] |
d39fbc49-28a8-4012-bcb2-9e3034ba7b06 | neuralizing-regular-expressions-for-slot | null | null | https://aclanthology.org/2021.emnlp-main.747 | https://aclanthology.org/2021.emnlp-main.747.pdf | Neuralizing Regular Expressions for Slot Filling | Neural models and symbolic rules such as regular expressions have their respective merits and weaknesses. In this paper, we study the integration of the two approaches for the slot filling task by converting regular expressions into neural networks. Specifically, we first convert regular expressions into a special form... | ['Kewei Tu', 'Zijian Jin', 'Chengyue Jiang'] | null | null | null | null | emnlp-2021-11 | ['slot-filling'] | ['natural-language-processing'] | [ 3.19770664e-01 6.34676218e-01 -9.59311604e-01 -5.12054443e-01
-5.73269904e-01 -1.47535041e-01 5.29885769e-01 -1.90477252e-01
-3.20395619e-01 9.23111975e-01 1.66756943e-01 -7.39420116e-01
2.71432936e-01 -1.02790987e+00 -6.25686347e-01 -1.94191962e-01
2.09248066e-03 5.41434944e-01 2.48888448e-01 -2.99174547... | [10.780698776245117, 6.937510013580322] |
b3f1a0e2-cbfc-4b13-bf03-5eb8ea67a616 | learning-facial-representations-from-the | 2108.03427 | null | https://arxiv.org/abs/2108.03427v1 | https://arxiv.org/pdf/2108.03427v1.pdf | Learning Facial Representations from the Cycle-consistency of Face | Faces manifest large variations in many aspects, such as identity, expression, pose, and face styling. Therefore, it is a great challenge to disentangle and extract these characteristics from facial images, especially in an unsupervised manner. In this work, we introduce cycle-consistency in facial characteristics as f... | ['Wei-Chen Chiu', 'Yong-Sheng Chen', 'Jia-Ren Chang'] | 2021-08-07 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Chang_Learning_Facial_Representations_From_the_Cycle-Consistency_of_Face_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Chang_Learning_Facial_Representations_From_the_Cycle-Consistency_of_Face_ICCV_2021_paper.pdf | iccv-2021-1 | ['person-recognition', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 8.51346999e-02 9.09735486e-02 -2.90292025e-01 -7.94666708e-01
-4.66458827e-01 -6.45988524e-01 6.50907278e-01 -8.49474072e-01
-9.46517065e-02 5.02085805e-01 2.59257048e-01 3.39365721e-01
1.42959699e-01 -3.04901212e-01 -6.67050958e-01 -1.14090276e+00
9.38375369e-02 1.84289187e-01 -7.22684860e-01 -1.31881684... | [13.024592399597168, 0.24281394481658936] |
a125cc63-18c3-4b5b-9ff8-adad6eebe7a6 | mean-reversion-and-optimization | 1408.2217 | null | http://arxiv.org/abs/1408.2217v3 | http://arxiv.org/pdf/1408.2217v3.pdf | Mean-Reversion and Optimization | The purpose of these notes is to provide a systematic quantitative framework
- in what is intended to be a "pedagogical" fashion - for discussing
mean-reversion and optimization. We start with pair trading and add complexity
by following the sequence "mean-reversion via demeaning -> regression ->
weighted regression ->... | [] | 2016-02-12 | null | null | null | null | ['pair-trading'] | ['time-series'] | [-9.94567201e-02 2.03224495e-01 -2.79482722e-01 -3.39603573e-01
-9.05718863e-01 -7.36723602e-01 4.83037114e-01 -2.25056648e-01
-4.52597171e-01 9.49340045e-01 1.79258913e-01 -9.77109253e-01
-5.89849055e-01 -2.73241103e-01 -5.92149079e-01 -7.36621201e-01
-2.31433973e-01 3.38794202e-01 -2.67571121e-01 -4.64420170... | [5.25218391418457, 3.9066262245178223] |
fe9f19ca-ea29-4537-8428-a88c4cf0348f | srl-scaling-distributed-reinforcement | 2306.16688 | null | https://arxiv.org/abs/2306.16688v2 | https://arxiv.org/pdf/2306.16688v2.pdf | SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores | The ever-growing complexity of reinforcement learning (RL) tasks demands a distributed RL system to efficiently generate and process a massive amount of data to train intelligent agents. However, existing open-source libraries suffer from various limitations, which impede their practical use in challenging scenarios wh... | ['Yi Wu', 'Huanchen Zhang', 'Guangju Wang', 'Wei Fu', 'Zhiyu Mei'] | 2023-06-29 | null | null | null | null | ['reinforcement-learning-1'] | ['methodology'] | [-3.29251647e-01 -3.42382938e-01 -2.27274030e-01 -1.81892395e-01
-9.80921924e-01 -7.13712275e-01 4.40503687e-01 -3.01153034e-01
-6.93647563e-01 8.87150586e-01 -2.19695717e-01 -6.66531563e-01
-8.68299529e-02 -8.22302759e-01 -6.84081793e-01 -8.18791211e-01
-1.91205531e-01 7.04012632e-01 1.52038798e-01 -2.68249154... | [3.8627443313598633, 1.660123348236084] |
4615c2b2-1388-4baa-b985-e9549efb7fce | face-image-lighting-enhancement-using-a-3d | 2207.00761 | null | https://arxiv.org/abs/2207.00761v1 | https://arxiv.org/pdf/2207.00761v1.pdf | Face Image Lighting Enhancement Using a 3D Model | Image enhancement helps to generate balanced lighting distributions over faces. Our goal is to get an illuminance-balanced enhanced face image from a single view. Traditionally, image enhancement methods ignore the 3D geometry of the face or require a complicated multi-view geometry. Other methods cause color tone shif... | ['Jan P. Allebach', 'Qiulin Chen'] | 2022-07-02 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [ 1.91625267e-01 -3.62358481e-01 1.69101879e-01 -7.70110786e-01
-1.17582910e-01 -3.87004167e-01 2.36457676e-01 -8.97821665e-01
4.41015400e-02 3.09566915e-01 2.05552444e-01 -5.77341057e-02
2.02271298e-01 -6.46464586e-01 -4.09447521e-01 -7.68660486e-01
5.66592693e-01 -4.91338000e-02 -4.64131951e-01 -1.82492986... | [12.92263412475586, -0.026948630809783936] |
e43d9e89-d3cb-4040-891a-fa00d6487c57 | learning-to-learn-cropping-models-for | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Li_Learning_to_Learn_Cropping_Models_for_Different_Aspect_Ratio_Requirements_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Learning_to_Learn_Cropping_Models_for_Different_Aspect_Ratio_Requirements_CVPR_2020_paper.pdf | Learning to Learn Cropping Models for Different Aspect Ratio Requirements | Image cropping aims at improving the framing of an image by removing its extraneous outer areas, which is widely used in the photography and printing industry. In some cases, the aspect ratio of cropping results is specified depending on some conditions. In this paper, we propose a meta-learning (learning to learn) bas... | [' Kaiqi Huang', ' Junge Zhang', 'Debang Li'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['image-cropping'] | ['computer-vision'] | [ 3.50542665e-01 -2.77860165e-01 -1.52931362e-01 -2.36400977e-01
-3.81591678e-01 -2.45288655e-01 1.68455154e-01 3.91908325e-02
-1.98980257e-01 4.50895458e-01 -3.04747462e-01 -2.57213861e-01
-1.27063870e-01 -1.04032958e+00 -7.90907323e-01 -7.19861805e-01
3.82951766e-01 2.60784358e-01 3.67653340e-01 -1.47235334... | [11.26611328125, -1.0796282291412354] |
6003a15b-d396-4e3c-8a35-d5248d53bab3 | can-chatgpt-detect-intent-evaluating-large | 2305.13512 | null | https://arxiv.org/abs/2305.13512v1 | https://arxiv.org/pdf/2305.13512v1.pdf | Can ChatGPT Detect Intent? Evaluating Large Language Models for Spoken Language Understanding | Recently, large pretrained language models have demonstrated strong language understanding capabilities. This is particularly reflected in their zero-shot and in-context learning abilities on downstream tasks through prompting. To assess their impact on spoken language understanding (SLU), we evaluate several such mode... | ['Philip N. Garner', 'Mutian He'] | 2023-05-22 | null | null | null | null | ['spoken-language-understanding', 'intent-classification', 'slot-filling', 'spoken-language-understanding'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 1.67302385e-01 4.87814367e-01 -1.97600737e-01 -4.80407417e-01
-1.22267842e+00 -6.44670069e-01 6.64300859e-01 1.71109006e-01
-5.07394314e-01 7.50356436e-01 7.74684012e-01 -7.48746097e-01
4.43178676e-02 -4.09538358e-01 -6.08142197e-01 -2.82535583e-01
-5.28472699e-02 8.69960189e-01 9.30462927e-02 -3.95400465... | [11.268059730529785, 8.343341827392578] |
1786cc80-de6c-42a6-84dc-e29507216d4c | early-myocardial-infarction-detection-with | 2204.07253 | null | https://arxiv.org/abs/2204.07253v1 | https://arxiv.org/pdf/2204.07253v1.pdf | Early Myocardial Infarction Detection with One-Class Classification over Multi-view Echocardiography | Myocardial infarction (MI) is the leading cause of mortality and morbidity in the world. Early therapeutics of MI can ensure the prevention of further myocardial necrosis. Echocardiography is the fundamental imaging technique that can reveal the earliest sign of MI. However, the scarcity of echocardiographic datasets f... | ['Moncef Gabbouj', 'Serkan Kiranyaz', 'Fahad Sohrab', 'Aysen Degerli'] | 2022-04-14 | null | null | null | null | ['myocardial-infarction-detection', 'one-class-classification'] | ['medical', 'miscellaneous'] | [ 2.51531869e-01 -4.76730645e-01 -1.63692281e-01 -4.36782129e-02
-1.03042448e+00 -7.69115865e-01 2.23233849e-01 1.46388888e-01
-9.55423713e-02 5.93242168e-01 -8.67076516e-02 -5.90531826e-01
-2.49895334e-01 -5.05024135e-01 -1.65941700e-01 -7.58222401e-01
-4.06061590e-01 4.13425833e-01 1.58620596e-01 5.20314574... | [14.20072078704834, -2.4072790145874023] |
5995b78b-e8b5-4131-8861-d85e14cf84d9 | boundary-aware-u-net-for-glacier-segmentation-1 | 2301.11454 | null | https://arxiv.org/abs/2301.11454v1 | https://arxiv.org/pdf/2301.11454v1.pdf | Boundary Aware U-Net for Glacier Segmentation | Large-scale study of glaciers improves our understanding of global glacier change and is imperative for monitoring the ecological environment, preventing disasters, and studying the effects of global climate change. Glaciers in the Hindu Kush Himalaya (HKH) are particularly interesting as the HKH is one of the world's ... | ['Olac Fuentes', 'Sergio A. Vargas Zesati', 'Katie E. Miles', 'Bibek Aryal'] | 2023-01-26 | boundary-aware-u-net-for-glacier-segmentation | https://septentrio.uit.no/index.php/nldl/article/view/6789 | https://septentrio.uit.no/index.php/nldl/article/view/6789/7028 | proceedings-of-the-northern-lights-deep | ['self-learning'] | ['natural-language-processing'] | [ 1.38895065e-02 -1.71048015e-01 8.03173184e-02 -3.30778629e-01
-7.27357924e-01 -5.36219895e-01 2.53016174e-01 1.07177563e-01
-5.26224196e-01 9.02785838e-01 3.69363368e-01 -4.95502889e-01
1.72571048e-01 -1.04121494e+00 -8.14789593e-01 -8.30548882e-01
-8.78336489e-01 1.92960516e-01 1.73067357e-02 -5.49457312... | [9.578673362731934, -1.4993782043457031] |
107804c0-9a4b-4cea-984f-ab89f92f9030 | learn-to-understand-negation-in-video | 2205.00132 | null | https://arxiv.org/abs/2205.00132v2 | https://arxiv.org/pdf/2205.00132v2.pdf | Learn to Understand Negation in Video Retrieval | Negation is a common linguistic skill that allows human to express what we do NOT want. Naturally, one might expect video retrieval to support natural-language queries with negation, e.g., finding shots of kids sitting on the floor and not playing with a dog. However, the state-of-the-art deep learning based video retr... | ['Xirong Li', 'Fan Hu', 'Aozhu Chen', 'Ziyue Wang'] | 2022-04-30 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 6.31148368e-02 -4.35009211e-01 -5.45464277e-01 -4.95154172e-01
-7.04094470e-01 -5.99607944e-01 5.15731454e-01 -5.29082455e-02
-6.60173535e-01 4.27098989e-01 1.63918659e-01 -5.86813092e-02
1.59070596e-01 -6.58559501e-01 -1.14779341e+00 -4.77838278e-01
9.96471867e-02 3.59405994e-01 1.74807355e-01 -3.64619553... | [10.379281044006348, 0.9364314675331116] |
d7f2c09d-59f4-4949-9be3-916be67e0709 | pgmpy-a-python-toolkit-for-bayesian-networks | 2304.08639 | null | https://arxiv.org/abs/2304.08639v1 | https://arxiv.org/pdf/2304.08639v1.pdf | pgmpy: A Python Toolkit for Bayesian Networks | Bayesian Networks (BNs) are used in various fields for modeling, prediction, and decision making. pgmpy is a python package that provides a collection of algorithms and tools to work with BNs and related models. It implements algorithms for structure learning, parameter estimation, approximate and exact inference, caus... | ['Johannes Textor', 'Ankur Ankan'] | 2023-04-17 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [-4.63038743e-01 -7.76792690e-02 -7.01522470e-01 -4.84349430e-01
-2.12077111e-01 -2.51277655e-01 4.83252853e-01 1.12208210e-01
1.17282175e-01 7.86137819e-01 1.66057155e-01 -7.67285347e-01
-4.13105309e-01 -1.06874120e+00 -4.42827731e-01 -4.53696162e-01
-2.76621431e-01 5.11537373e-01 3.18005115e-01 5.72400801... | [7.511124134063721, 4.419738292694092] |
817d5ffc-903c-47ff-9968-3b14b2d946e0 | object-detection-by-labeling-superpixels | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Yan_Object_Detection_by_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Yan_Object_Detection_by_2015_CVPR_paper.pdf | Object Detection by Labeling Superpixels | Object detection is always conducted by object proposal generation and classification sequentially. This paper handles object detection in a superpixel oriented manner instead of the proposal oriented. Specially, this paper takes object detection as a multi-label superpixel labeling problem by minimizing an energy func... | ['Zhen Lei', 'Stan Z. Li', 'Yinan Yu', 'Xiangyu Zhu', 'Junjie Yan'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['object-proposal-generation'] | ['computer-vision'] | [ 4.31913674e-01 2.33024344e-01 -2.83089250e-01 -4.23173189e-01
-5.48079252e-01 -3.58715594e-01 4.43263590e-01 9.88791883e-02
-6.79178238e-01 6.35704875e-01 -5.52613318e-01 -5.84889799e-02
4.54312116e-01 -6.72920704e-01 -8.84006619e-01 -8.27731371e-01
3.22561979e-01 3.76121163e-01 8.67007375e-01 3.01822931... | [9.271199226379395, 0.7917369604110718] |
951008e9-4ffb-406a-9d32-d4b7d0005265 | multi-view-representation-learning-in-multi | 2201.05829 | null | https://arxiv.org/abs/2201.05829v1 | https://arxiv.org/pdf/2201.05829v1.pdf | Multi-View representation learning in Multi-Task Scene | Over recent decades have witnessed considerable progress in whether multi-task learning or multi-view learning, but the situation that consider both learning scenes simultaneously has received not too much attention. How to utilize multiple views latent representation of each single task to improve each learning task p... | ['Xin Zuo', 'Si-ming Lian', 'Jian-wei Liu', 'Run-kun Lu'] | 2022-01-15 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 1.35095611e-01 -3.42415035e-01 -1.20485350e-01 -4.05974120e-01
-7.27666140e-01 -3.74333143e-01 6.22370124e-01 -3.09079885e-01
-1.05816811e-01 4.84679252e-01 2.22067863e-01 4.23190445e-01
-3.38614941e-01 -4.73831594e-01 -3.88366461e-01 -1.09108484e+00
4.17101473e-01 4.51422900e-01 7.89049864e-02 1.50830254... | [8.475813865661621, 4.515131950378418] |
7bf29b5e-a3bc-40f2-bd7a-54da2f261bfa | gaussian-kernel-smoothing | 2007.09539 | null | https://arxiv.org/abs/2007.09539v4 | https://arxiv.org/pdf/2007.09539v4.pdf | Gaussian kernel smoothing | Image acquisition and segmentation are likely to introduce noise. Further image processing such as image registration and parameterization can introduce additional noise. It is thus imperative to reduce noise measurements and boost signal. In order to increase the signal-to-noise ratio (SNR) and smoothness of data requ... | ['Moo. K. Chung'] | 2020-07-19 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [ 1.86179638e-01 -1.17370859e-01 6.04370423e-02 -5.64679503e-01
-5.60952783e-01 -1.91691443e-01 3.36185038e-01 4.78264928e-01
-8.89847219e-01 7.70535111e-01 1.31149679e-01 -1.75186232e-01
-2.42723823e-01 -5.86412311e-01 -2.14427039e-01 -1.03744161e+00
-3.30048680e-01 -1.10514641e-01 5.59889197e-01 2.35495761... | [13.997464179992676, -2.369415044784546] |
7ecb2385-5d85-4dd2-af26-256bb757b220 | deep-neural-networks-improve-radiologists | 1903.08297 | null | http://arxiv.org/abs/1903.08297v1 | http://arxiv.org/pdf/1903.08297v1.pdf | Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening | We present a deep convolutional neural network for breast cancer screening
exam classification, trained and evaluated on over 200,000 exams (over
1,000,000 images). Our network achieves an AUC of 0.895 in predicting whether
there is a cancer in the breast, when tested on the screening population. We
attribute the high ... | ['Naziya Samreen', 'Beatriu Reig', 'Leng Leng Young Lin', 'Thibault Févry', 'Stanisław Jastrzębski', 'Ujas Parikh', 'Nan Wu', 'Laura Heacock', 'Kyunghyun Cho', 'Krzysztof J. Geras', 'Krystal Airola', 'Jungkyu Park', 'Joshua D. Weinstein', 'Joe Katsnelson', 'Alana Lewin', 'Yiming Gao', 'Stacey Wolfson', 'Linda Moy', 'Ka... | 2019-03-20 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 5.37230670e-01 6.43352449e-01 -4.53101158e-01 -5.03402114e-01
-1.05830920e+00 -5.61702430e-01 1.64296776e-02 6.70806646e-01
-4.69376862e-01 3.21799994e-01 8.98110196e-02 -1.22603095e+00
-7.97214359e-02 -1.02968299e+00 -1.15012741e+00 -3.61809522e-01
-2.48776302e-01 3.52147698e-01 3.77891362e-01 3.93903404... | [15.210101127624512, -2.4319264888763428] |
4fc4d797-5ea5-4333-b076-440ab074cd62 | structured-label-inference-for-visual | 1802.06459 | null | http://arxiv.org/abs/1802.06459v1 | http://arxiv.org/pdf/1802.06459v1.pdf | Structured Label Inference for Visual Understanding | Visual data such as images and videos contain a rich source of structured
semantic labels as well as a wide range of interacting components. Visual
content could be assigned with fine-grained labels describing major components,
coarse-grained labels depicting high level abstractions, or a set of labels
revealing attrib... | ['Greg Mori', 'Zicheng Liao', 'Guang-Tong Zhou', 'Hexiang Hu', 'Zhiwei Deng', 'Nelson Nauata'] | 2018-02-18 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 6.44722760e-01 -1.72841817e-01 -6.37137115e-01 -6.73126519e-01
-4.33292955e-01 -6.57843888e-01 7.34165251e-01 1.39181197e-01
-2.56429434e-01 4.07093853e-01 5.06900072e-01 -1.79372400e-01
-1.33144200e-01 -3.79065841e-01 -7.99856246e-01 -4.62855339e-01
-4.26531374e-01 2.86084712e-01 2.05431297e-01 2.00139701... | [8.595206260681152, 0.7977591753005981] |
96df2840-c256-47ca-ab50-5f544e3f04d9 | word-sense-induction-with-knowledge-1 | 2304.10642 | null | https://arxiv.org/abs/2304.10642v1 | https://arxiv.org/pdf/2304.10642v1.pdf | Word Sense Induction with Knowledge Distillation from BERT | Pre-trained contextual language models are ubiquitously employed for language understanding tasks, but are unsuitable for resource-constrained systems. Noncontextual word embeddings are an efficient alternative in these settings. Such methods typically use one vector to encode multiple different meanings of a word, and... | ['Bulent Yener', 'Alex Gittens', 'Anik Saha'] | 2023-04-20 | word-sense-induction-with-knowledge | https://openreview.net/forum?id=-29uFS4FiDZ | https://openreview.net/pdf?id=-29uFS4FiDZ | null | ['word-sense-induction', 'word-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.52241123e-01 -2.30244651e-01 -3.92251045e-01 -4.93492693e-01
-7.55240321e-01 -6.50261760e-01 7.85819948e-01 5.75630009e-01
-1.06682181e+00 3.36543083e-01 4.82427686e-01 -5.81266522e-01
1.41919434e-01 -7.95357943e-01 -1.75577074e-01 -5.78711092e-01
2.52277136e-01 4.16860193e-01 1.25132710e-01 -5.82844615... | [10.401629447937012, 8.883500099182129] |
6ed41c8a-a4d9-4ec9-bae6-eb622795a70c | learning-online-data-association-1 | 2011.03183 | null | https://arxiv.org/abs/2011.03183v4 | https://arxiv.org/pdf/2011.03183v4.pdf | Learning Object-Based State Estimators for Household Robots | A robot operating in a household makes observations of multiple objects as it moves around over the course of days or weeks. The objects may be moved by inhabitants, but not completely at random. The robot may be called upon later to retrieve objects and will need a long-term object-based memory in order to know how to... | ['Tomas Lozano-Perez', 'Leslie Kaelbling', 'Yilun Du'] | 2020-11-06 | learning-online-data-association | https://openreview.net/forum?id=KjR-3lBYB3y | https://openreview.net/pdf?id=KjR-3lBYB3y | null | ['semantic-slam'] | ['computer-vision'] | [ 6.19669817e-02 9.11440849e-02 6.33975651e-05 -3.52126867e-01
-3.37044775e-01 -1.35404736e-01 4.26339686e-01 3.58689249e-01
-4.74569470e-01 6.71781421e-01 1.19451635e-01 9.39273834e-02
-2.39930704e-01 -6.93119049e-01 -1.01491153e+00 -4.75721627e-01
-6.45350993e-01 1.21178806e+00 3.92690629e-01 -9.31706131... | [4.710126876831055, 0.6224068403244019] |
1cfc24b9-fc7e-444d-97d2-27ddd17853e9 | improving-native-language-identification-by | null | null | https://aclanthology.org/P17-2086 | https://aclanthology.org/P17-2086.pdf | Improving Native Language Identification by Using Spelling Errors | In this paper, we explore spelling errors as a source of information for detecting the native language of a writer, a previously under-explored area. We note that character n-grams from misspelled words are very indicative of the native language of the author. In combination with other lexical features, spelling error ... | ['Vivi Nastase', 'Lingzhen Chen', 'Carlo Strapparava'] | 2017-07-01 | null | null | null | acl-2017-7 | ['native-language-identification'] | ['natural-language-processing'] | [ 3.15727711e-01 -2.45442003e-01 -3.30487698e-01 -1.84153125e-01
-1.02421367e+00 -1.08949852e+00 9.53656852e-01 3.51404637e-01
-7.73741722e-01 7.18145490e-01 3.52699429e-01 -7.05024302e-01
1.67770520e-01 -3.93542022e-01 -2.45992377e-01 -1.90323114e-01
6.82368219e-01 5.19847333e-01 2.25508655e-03 -2.02799752... | [10.429397583007812, 10.515497207641602] |
44d5ba20-1f46-4aa8-9189-408c445afaa7 | pcpnet-an-efficient-and-semantic-enhanced | 2304.07773 | null | https://arxiv.org/abs/2304.07773v1 | https://arxiv.org/pdf/2304.07773v1.pdf | PCPNet: An Efficient and Semantic-Enhanced Transformer Network for Point Cloud Prediction | The ability to predict future structure features of environments based on past perception information is extremely needed by autonomous vehicles, which helps to make the following decision-making and path planning more reasonable. Recently, point cloud prediction (PCP) is utilized to predict and describe future environ... | ['Guangming Xiong', 'Zijie Zhou', 'Junyi Ma', 'Zhen Luo'] | 2023-04-16 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [-9.01176259e-02 -4.38910365e-01 -3.32722992e-01 -7.56735921e-01
-1.96522877e-01 -2.95549273e-01 5.44538915e-01 -3.91807109e-02
-1.09855272e-01 6.55260921e-01 -2.42066056e-01 -5.58920860e-01
-2.50970963e-02 -1.16138494e+00 -7.95415819e-01 -4.32955056e-01
-6.73005730e-02 6.40396655e-01 7.56714702e-01 -2.25356430... | [8.137896537780762, -2.5844075679779053] |
3831d169-06b7-4c20-9893-8d2ac4be8d09 | convolution-channel-separation-and-frequency | 2211.01599 | null | https://arxiv.org/abs/2211.01599v1 | https://arxiv.org/pdf/2211.01599v1.pdf | Convolution channel separation and frequency sub-bands aggregation for music genre classification | In music, short-term features such as pitch and tempo constitute long-term semantic features such as melody and narrative. A music genre classification (MGC) system should be able to analyze these features. In this research, we propose a novel framework that can extract and aggregate both short- and long-term features ... | ['Ha-Jin Yu', 'Chan-yeong Lim', 'Ju-ho Kim', 'Hyun-seo Shin', 'Jungwoo Heo'] | 2022-11-03 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 7.62404175e-03 -5.87648511e-01 1.21352956e-01 -2.30899349e-01
-6.40515327e-01 -6.82223916e-01 2.69130141e-01 6.93326211e-03
-5.52211940e-01 4.17345881e-01 3.84131074e-01 1.68448761e-01
-4.20954376e-01 -1.02576387e+00 -2.90002733e-01 -4.95674878e-01
-4.84761924e-01 -3.60320091e-01 1.60970196e-01 -3.49614352... | [15.775984764099121, 5.255076885223389] |
b7e68e2c-be71-4c74-a05b-cb5ae2f8038e | duluth-at-semeval-2017-task-6-language-models | 1704.08390 | null | http://arxiv.org/abs/1704.08390v1 | http://arxiv.org/pdf/1704.08390v1.pdf | Duluth at SemEval-2017 Task 6: Language Models in Humor Detection | This paper describes the Duluth system that participated in SemEval-2017 Task
6 #HashtagWars: Learning a Sense of Humor. The system participated in Subtasks
A and B using N-gram language models, ranking highly in the task evaluation.
This paper discusses the results of our system in the development and
evaluation stage... | ['Ted Pedersen', 'Xinru Yan'] | 2017-04-27 | duluth-at-semeval-2017-task-6-language-models-1 | https://aclanthology.org/S17-2064 | https://aclanthology.org/S17-2064.pdf | semeval-2017-8 | ['humor-detection'] | ['natural-language-processing'] | [-5.90452015e-01 2.00824440e-01 7.20285699e-02 -4.06182945e-01
-8.32138777e-01 -5.62856972e-01 8.59983563e-01 3.67494643e-01
-8.14584494e-01 4.34591919e-01 1.11963773e+00 -3.24874371e-01
4.16578919e-01 -3.75375569e-01 -2.17841849e-01 -8.41157734e-02
-3.01035464e-01 6.29771173e-01 3.68064940e-01 -9.56141710... | [8.86762523651123, 11.057747840881348] |
8ebb399e-601b-404f-b065-b13139b62115 | ts-net-combining-modality-specific-and-common | 1806.01550 | null | http://arxiv.org/abs/1806.01550v1 | http://arxiv.org/pdf/1806.01550v1.pdf | TS-Net: Combining modality specific and common features for multimodal patch matching | Multimodal patch matching addresses the problem of finding the
correspondences between image patches from two different modalities, e.g. RGB
vs sketch or RGB vs near-infrared. The comparison of patches of different
modalities can be done by discovering the information common to both modalities
(Siamese like approaches)... | ['Frédéric Jurie', 'Sovann En', 'Alexis Lechervy'] | 2018-06-05 | null | null | null | null | ['multimodal-patch-matching', 'patch-matching'] | ['computer-vision', 'computer-vision'] | [ 2.98397928e-01 -1.74252614e-01 -1.42325372e-01 -3.78636688e-01
-8.90141189e-01 -7.05286086e-01 7.88025320e-01 8.42013657e-02
-4.71942306e-01 4.04313564e-01 1.17252372e-01 7.91289359e-02
-3.03709507e-01 -6.24205887e-01 -8.31296265e-01 -5.12853026e-01
1.09141685e-01 2.20259681e-01 4.80430007e-01 -2.37242118... | [7.877450942993164, -2.072140693664551] |
71faa12a-8f5d-45df-8abf-73d8d051b2c7 | is-chatgpt-a-general-purpose-natural-language | 2302.06476 | null | https://arxiv.org/abs/2302.06476v2 | https://arxiv.org/pdf/2302.06476v2.pdf | Is ChatGPT a General-Purpose Natural Language Processing Task Solver? | Spurred by advancements in scale, large language models (LLMs) have demonstrated the ability to perform a variety of natural language processing (NLP) tasks zero-shot -- i.e., without adaptation on downstream data. Recently, the debut of ChatGPT has drawn a great deal of attention from the natural language processing (... | ['Diyi Yang', 'Michihiro Yasunaga', 'Jiaao Chen', 'Zhuosheng Zhang', 'Aston Zhang', 'Chengwei Qin'] | 2023-02-08 | null | null | null | null | ['arithmetic-reasoning'] | ['reasoning'] | [ 2.30594397e-01 4.61791128e-01 -9.65432227e-02 -4.17219549e-01
-1.16872871e+00 -5.01870096e-01 4.37007695e-01 4.32188243e-01
-4.62049186e-01 6.98840857e-01 6.89999580e-01 -6.49242103e-01
9.29882377e-03 -5.86269796e-01 -4.35716480e-01 -1.57994688e-01
2.52388805e-01 7.18466222e-01 1.34970054e-01 -5.34543455... | [11.195412635803223, 8.415201187133789] |
f187ea8b-4dbe-46f6-935c-1338b7720de4 | deep-network-embedding-for-graph | 1901.01718 | null | http://arxiv.org/abs/1901.01718v1 | http://arxiv.org/pdf/1901.01718v1.pdf | Deep Network Embedding for Graph Representation Learning in Signed Networks | Network embedding has attracted an increasing attention over the past few
years. As an effective approach to solve graph mining problems, network
embedding aims to learn a low-dimensional feature vector representation for
each node of a given network. The vast majority of existing network embedding
algorithms, however,... | ['Fu-Lai Chung', 'Xiao Shen'] | 2019-01-07 | null | null | null | null | ['link-sign-prediction'] | ['graphs'] | [-1.76975261e-02 4.54278171e-01 -5.06193578e-01 -4.44661945e-01
5.20529747e-01 -3.78684640e-01 3.64537328e-01 7.87640437e-02
1.34851635e-01 4.89710987e-01 2.73227036e-01 -2.99176753e-01
-6.77608132e-01 -1.17238700e+00 -4.68045920e-01 -6.19973779e-01
-5.27529120e-01 4.73675460e-01 8.84652212e-02 -3.68029296... | [7.211602210998535, 6.206033229827881] |
744cee47-2391-4cef-ba38-e8fd86c54415 | proto-value-networks-scaling-representation | 2304.12567 | null | https://arxiv.org/abs/2304.12567v1 | https://arxiv.org/pdf/2304.12567v1.pdf | Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks | Auxiliary tasks improve the representations learned by deep reinforcement learning agents. Analytically, their effect is reasonably well understood; in practice, however, their primary use remains in support of a main learning objective, rather than as a method for learning representations. This is perhaps surprising g... | ['Marc G. Bellemare', 'Pablo Samuel Castro', 'Ross Goroshin', 'Charline Le Lan', 'Rishabh Agarwal', 'Joshua Greaves', 'Jesse Farebrother'] | 2023-04-25 | null | null | null | null | ['atari-games'] | ['playing-games'] | [ 2.48466894e-01 4.43898350e-01 -1.80249304e-01 5.62714525e-02
-3.95103514e-01 -5.94171047e-01 9.98120010e-01 3.82355064e-01
-8.36530864e-01 9.39219832e-01 1.75372094e-01 -2.60400921e-01
-5.17383695e-01 -8.76691759e-01 -8.34298611e-01 -9.98690784e-01
-3.92756104e-01 6.72095239e-01 1.68817192e-01 -5.44967294... | [4.074431896209717, 1.6780434846878052] |
f043fba3-da36-4a4d-96f6-ff4739f9400e | artificial-bandwidth-extension-using-deep | 2108.13326 | null | https://arxiv.org/abs/2108.13326v1 | https://arxiv.org/pdf/2108.13326v1.pdf | Artificial bandwidth extension using deep neural network and $H^\infty$ sampled-data control theory | Artificial bandwidth extension is applied to speech signals to improve their quality in narrowband telephonic communication. For accomplishing this, the missing high-frequency (high-band) components of speech signals are recovered by utilizing a new extrapolation process based on sampled-data control theory and deep ne... | ['Hanumant Singh Shekhawat', 'Deepika Gupta'] | 2021-08-30 | null | null | null | null | ['bandwidth-extension', 'bandwidth-extension'] | ['audio', 'speech'] | [ 7.13601410e-02 4.23678905e-02 4.79400195e-02 -2.79965878e-01
-6.12158775e-01 -1.75671771e-01 1.82240512e-02 -3.38284373e-01
-1.31249517e-01 8.20487380e-01 3.02747101e-01 -4.79543716e-01
-5.10094523e-01 -5.14099419e-01 -2.90570468e-01 -9.88002837e-01
-4.76242788e-03 -1.21964868e-02 -2.56088763e-01 -2.07917050... | [15.026348114013672, 5.897350788116455] |
8239fdf0-8aba-427a-b0e6-86504f2be455 | a-physics-informed-ai-method-for-calculating | 2306.13345 | null | https://arxiv.org/abs/2306.13345v1 | https://arxiv.org/pdf/2306.13345v1.pdf | A physics-informed AI method for calculating melting points with uncertainty control and optimal sampling | We present an artificial intelligence (AI) method for automatically computing the melting point based on coexistence simulations in the NPT ensemble. Given the interatomic interaction model, the method makes decisions regarding the number of atoms and temperature at which to conduct simulations, and based on the collec... | ['Alexander Shapeev', 'Timofei Miryashkin', 'Olga Klimanova'] | 2023-06-23 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 2.16418043e-01 -1.37358412e-01 -3.06190878e-01 -1.62594751e-01
-5.07991552e-01 -2.45263249e-01 4.90434080e-01 4.09856260e-01
-3.58458191e-01 9.70335960e-01 -1.56813949e-01 -4.92352575e-01
-2.54647970e-01 -9.10281956e-01 -6.14415407e-01 -1.06503201e+00
4.34195548e-02 1.18029320e+00 -1.09214783e-01 -2.97893226... | [5.5447773933410645, 4.693122386932373] |
2efddcd0-3c7f-47f6-831e-6676f81c0c19 | scene-text-magnifier | 1907.00693 | null | https://arxiv.org/abs/1907.00693v2 | https://arxiv.org/pdf/1907.00693v2.pdf | Scene Text Magnifier | Scene text magnifier aims to magnify text in natural scene images without recognition. It could help the special groups, who have myopia or dyslexia to better understand the scene. In this paper, we design the scene text magnifier through interacted four CNN-based networks: character erasing, character extraction, char... | ['Toshiki Nakamura', 'Seiichi Uchida', 'Anna Zhu'] | 2019-06-17 | null | null | null | null | ['text-annotation'] | ['natural-language-processing'] | [ 8.28425825e-01 4.32542339e-02 1.17001779e-01 -4.13570970e-01
1.43866181e-01 -8.52356702e-02 5.73905945e-01 -6.27201200e-01
-7.14178503e-01 5.35023987e-01 5.29205561e-01 -1.91652298e-01
4.41677660e-01 -8.66217256e-01 -7.34018922e-01 -5.79702795e-01
8.20061982e-01 1.05815187e-01 4.88936752e-01 -6.25274703... | [11.862859725952148, 2.0419490337371826] |
fcafbb2d-4dd4-48e8-bd7d-74dcd6c858d5 | synthesizing-training-data-for-object | 1702.07836 | null | http://arxiv.org/abs/1702.07836v2 | http://arxiv.org/pdf/1702.07836v2.pdf | Synthesizing Training Data for Object Detection in Indoor Scenes | Detection of objects in cluttered indoor environments is one of the key
enabling functionalities for service robots. The best performing object
detection approaches in computer vision exploit deep Convolutional Neural
Networks (CNN) to simultaneously detect and categorize the objects of interest
in cluttered scenes. Tr... | ['Arsalan Mousavian', 'Jana Kosecka', 'Georgios Georgakis', 'Alexander C. Berg'] | 2017-02-25 | null | null | null | null | ['object-detection-in-indoor-scenes'] | ['computer-vision'] | [ 1.99293375e-01 2.35338822e-01 4.63410497e-01 -3.68361920e-01
-4.60912704e-01 -6.78899050e-01 8.59915078e-01 9.90281701e-02
-6.36318147e-01 2.57086396e-01 -3.87654871e-01 -1.29092857e-01
2.12299153e-01 -5.50931156e-01 -1.07755697e+00 -4.47407573e-01
-1.92009106e-01 8.63884687e-01 9.11665738e-01 -3.15481395... | [7.937332630157471, -1.1587871313095093] |
e95b188d-1fab-4118-8d5a-cc3d0bcb54a7 | a-novel-image-descriptor-with-aggregated | 2202.03677 | null | https://arxiv.org/abs/2202.03677v1 | https://arxiv.org/pdf/2202.03677v1.pdf | A Novel Image Descriptor with Aggregated Semantic Skeleton Representation for Long-term Visual Place Recognition | In a Simultaneous Localization and Mapping (SLAM) system, a loop-closure can eliminate accumulated errors, which is accomplished by Visual Place Recognition (VPR), a task that retrieves the current scene from a set of pre-stored sequential images through matching specific scene-descriptors. In urban scenes, the appeara... | ['Cheng Shuai', 'Hu Jun', 'Liu Wei', 'Pan Feng', 'Xue Dingyu', 'Feng Joe-Mei', 'Nie Jiwei'] | 2022-02-08 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-3.99891138e-02 -7.41271853e-01 -1.01018801e-01 -3.99282038e-01
-6.38888061e-01 -5.39518416e-01 6.57466173e-01 1.96711391e-01
-4.54654843e-01 5.03378928e-01 2.87237260e-02 2.24657163e-01
-3.27088147e-01 -8.46259475e-01 -6.54689431e-01 -6.11910582e-01
4.30992134e-02 2.62294203e-01 8.04866850e-01 -4.37454283... | [7.623830318450928, -1.9522496461868286] |
8fb94c3f-06d7-4693-8108-8941a6bd7df7 | joint-representation-classification-for | 1505.04617 | null | http://arxiv.org/abs/1505.04617v1 | http://arxiv.org/pdf/1505.04617v1.pdf | Joint Representation Classification for Collective Face Recognition | Sparse representation based classification (SRC) is popularly used in many
applications such as face recognition, and implemented in two steps:
representation coding and classification. For a given set of testing images,
SRC codes every image over the base images as a sparse representation then
classifies it to the cla... | ['Songcan Chen', 'Liping Wang'] | 2015-05-18 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 5.15110314e-01 -1.78464338e-01 -2.25806117e-01 -3.91459733e-01
-8.57856691e-01 -6.43638819e-02 -8.35518241e-02 -2.49814808e-01
-3.57322693e-02 5.23891628e-01 -8.57986733e-02 -4.95971702e-02
-3.98055732e-01 -7.80232906e-01 -4.16957587e-01 -8.56284380e-01
3.71705629e-02 1.98273703e-01 -2.77825117e-01 4.30010259... | [12.49701976776123, 0.4112582802772522] |
d46aeeb0-6eab-4618-bf10-4c4e9584f7cb | symbiotic-adversarial-learning-for-attribute | 2007.09609 | null | https://arxiv.org/abs/2007.09609v2 | https://arxiv.org/pdf/2007.09609v2.pdf | Symbiotic Adversarial Learning for Attribute-based Person Search | Attribute-based person search is in significant demand for applications where no detected query images are available, such as identifying a criminal from witness. However, the task itself is quite challenging because there is a huge modality gap between images and physical descriptions of attributes. Often, there may a... | ['DaCheng Tao', 'Yu-Tong Cao', 'Jingya Wang'] | 2020-07-19 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2116_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123590222.pdf | eccv-2020-8 | ['person-search'] | ['computer-vision'] | [ 5.79457358e-03 -3.02227102e-02 -4.19536754e-02 -4.25587565e-01
-8.54723752e-01 -5.48931897e-01 7.38458037e-01 -1.63339227e-01
-3.22574675e-01 7.10126281e-01 1.95373625e-01 2.96236277e-02
1.21927954e-01 -1.03507066e+00 -6.73405766e-01 -8.97411406e-01
3.52569044e-01 6.19425714e-01 -6.05442412e-02 -2.09302738... | [14.63484001159668, 0.9980476498603821] |
236b857b-7507-408d-816b-979102301296 | federated-learning-with-noisy-labels | 2208.09378 | null | https://arxiv.org/abs/2208.09378v3 | https://arxiv.org/pdf/2208.09378v3.pdf | Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labels | Federated Learning (FL) is a distributed machine learning paradigm that enables learning models from decentralized private datasets, where the labeling effort is entrusted to the clients. While most existing FL approaches assume high-quality labels are readily available on users' devices; in reality, label noise can na... | ['Nirvana Meratnia', 'Tanir Ozcelebi', 'Aaqib Saeed', 'Vasileios Tsouvalas'] | 2022-08-19 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 6.88683316e-02 -2.84404129e-01 -4.28827927e-02 -6.01718426e-01
-1.41239417e+00 -7.81281590e-01 5.47474846e-02 -1.59985796e-01
-8.95769745e-02 4.05964762e-01 1.06290758e-01 5.01514934e-02
4.06130590e-02 -2.82864183e-01 -5.62565327e-01 -1.03268564e+00
2.61848897e-01 3.77638310e-01 6.10836968e-02 3.80873680... | [5.881345272064209, 6.264899730682373] |
07864df5-a38a-43ae-9b72-4725c4d2cb70 | shadow-optimization-from-structured-deep-edge | 1505.01589 | null | http://arxiv.org/abs/1505.01589v2 | http://arxiv.org/pdf/1505.01589v2.pdf | Shadow Optimization from Structured Deep Edge Detection | Local structures of shadow boundaries as well as complex interactions of
image regions remain largely unexploited by previous shadow detection
approaches. In this paper, we present a novel learning-based framework for
shadow region recovery from a single image. We exploit the local structures of
shadow edges by using a... | ['Karianto Leman', 'Li Shen', 'Teck Wee Chua'] | 2015-05-07 | shadow-optimization-from-structured-deep-edge-1 | http://openaccess.thecvf.com/content_cvpr_2015/html/Shen_Shadow_Optimization_From_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Shen_Shadow_Optimization_From_2015_CVPR_paper.pdf | cvpr-2015-6 | ['shadow-detection'] | ['computer-vision'] | [ 7.19992518e-01 1.78963169e-01 3.51361409e-02 -5.41207850e-01
-6.13252282e-01 -3.97862583e-01 4.28520560e-01 -2.83324182e-01
-4.13497388e-02 6.65991724e-01 -6.05863221e-02 -1.68171436e-01
2.22478181e-01 -3.95638913e-01 -9.74254370e-01 -1.15437961e+00
4.02673632e-02 3.11736345e-01 9.29039598e-01 -4.05301712... | [10.843816757202148, -4.1017231941223145] |
c118f282-7b43-4241-b96e-239b544743a0 | sleep-stage-classification-using | 1909.11141 | null | http://arxiv.org/abs/1909.11141v1 | http://arxiv.org/pdf/1909.11141v1.pdf | Sleep Stage Classification Using Bidirectional LSTM in Wearable Multi-sensor Systems | Understanding the sleep quality and architecture is essential to human
being's health, which is usually represented using multiple sleep stages. A
standard sleep stage determination requires Electroencephalography (EEG)
signals during the expensive and labor-intensive Polysomnography (PSG) test. To
overcome this inconv... | [] | 2019-09-24 | null | null | null | null | ['sleep-quality-prediction'] | ['medical'] | [ 1.11165307e-01 -3.03558409e-01 4.39442992e-02 -5.83069324e-01
-4.20515597e-01 5.73539287e-02 -1.93733469e-01 -2.75619626e-01
-5.53305566e-01 9.02944028e-01 1.11831315e-01 -1.21305615e-01
-9.85477939e-02 -4.54689056e-01 -1.49245024e-01 -7.00158298e-01
2.50268411e-02 -4.91330512e-02 -2.01439206e-02 8.20029825... | [13.506937980651855, 3.4969656467437744] |
61f1b4bb-4c1f-4341-9bdb-29873c137c02 | learning-hierarchical-cross-modal-association | 2203.13161 | null | https://arxiv.org/abs/2203.13161v1 | https://arxiv.org/pdf/2203.13161v1.pdf | Learning Hierarchical Cross-Modal Association for Co-Speech Gesture Generation | Generating speech-consistent body and gesture movements is a long-standing problem in virtual avatar creation. Previous studies often synthesize pose movement in a holistic manner, where poses of all joints are generated simultaneously. Such a straightforward pipeline fails to generate fine-grained co-speech gestures. ... | ['Bolei Zhou', 'Bo Dai', 'Wayne Wu', 'Xiaowei Zhou', 'Xinyi Lin', 'Rui Qian', 'Yinghao Xu', 'Hang Zhou', 'Qianyi Wu', 'Xian Liu'] | 2022-03-24 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liu_Learning_Hierarchical_Cross-Modal_Association_for_Co-Speech_Gesture_Generation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_Learning_Hierarchical_Cross-Modal_Association_for_Co-Speech_Gesture_Generation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['gesture-generation'] | ['robots'] | [ 2.78657436e-01 1.08130917e-01 -1.02214843e-01 -3.12199503e-01
-1.13100016e+00 -5.04325569e-01 7.36793101e-01 -5.78960419e-01
2.77397484e-01 2.27570295e-01 9.64282632e-01 2.23439604e-01
2.14302853e-01 -3.90088886e-01 -6.73528373e-01 -4.92424011e-01
1.65430889e-01 6.64094925e-01 2.32131988e-01 -4.10829484... | [5.659525394439697, -0.14955255389213562] |
e5ed403d-8c4e-4efb-bbb6-001cb9ad2c5a | graph-neural-networks-for-breast-cancer-data | 2211.15561 | null | https://arxiv.org/abs/2211.15561v1 | https://arxiv.org/pdf/2211.15561v1.pdf | Graph Neural Networks for Breast Cancer Data Integration | International initiatives such as METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) have collected several multigenomic and clinical data sets to identify the undergoing molecular processes taking place throughout the evolution of various cancers. Numerous Machine Learning and statistical models h... | ['Teodora Reu'] | 2022-11-28 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 4.31760103e-01 3.42897445e-01 -2.42499486e-01 -1.86203331e-01
-3.64848316e-01 -2.90870905e-01 7.03860521e-01 8.86251748e-01
-3.00815970e-01 3.38664532e-01 6.14438653e-01 -5.28286695e-01
-2.83910573e-01 -1.04056442e+00 -4.67879206e-01 -8.93144608e-01
-2.24186465e-01 6.01486742e-01 -1.73881233e-01 -5.91131039... | [5.980751991271973, 5.696719169616699] |
28da45ad-0eb4-4296-935e-35c21dbaf9e2 | source-code-summarization-with-structural | 2202.06521 | null | https://arxiv.org/abs/2202.06521v1 | https://arxiv.org/pdf/2202.06521v1.pdf | Source Code Summarization with Structural Relative Position Guided Transformer | Source code summarization aims at generating concise and clear natural language descriptions for programming languages. Well-written code summaries are beneficial for programmers to participate in the software development and maintenance process. To learn the semantic representations of source code, recent efforts focu... | ['Zenglin Xu', 'Yun Peng', 'Wenchao Gu', 'Yasheng Wang', 'Cuiyun Gao', 'Zi Gong'] | 2022-02-14 | null | null | null | null | ['code-summarization'] | ['computer-code'] | [ 1.84822261e-01 2.09806971e-02 -3.98855746e-01 -5.97720861e-01
-5.67265868e-01 -3.45576048e-01 2.12097794e-01 4.18592721e-01
-1.19725995e-01 8.19451585e-02 6.33953154e-01 -4.15459305e-01
1.46102220e-01 -8.50840330e-01 -8.37493658e-01 -4.36426401e-01
1.05718002e-01 6.99473768e-02 2.60285467e-01 -1.52593344... | [7.558337688446045, 7.965846538543701] |
9bc69c89-a232-46e5-950e-9cef1900c6c8 | improving-passage-re-ranking-with-word-n-gram | null | null | https://aclanthology.org/2020.icon-main.21 | https://aclanthology.org/2020.icon-main.21.pdf | Improving Passage Re-Ranking with Word N-Gram Aware Coattention Encoder | In text matching applications, coattentions have proved to be highly effective attention mechanisms. Coattention enables the learning to attend based on computing word level affinity scores between two texts. In this paper, we propose two improvements to coattention mechanism in the context of passage ranking (re-ranki... | ['Manish Shrivastava', 'Chaitanya Alaparthi'] | null | null | null | null | icon-2020-12 | ['passage-ranking', 'passage-re-ranking'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.46392298e-02 -2.79459387e-01 -9.54870805e-02 -2.63985902e-01
-1.23517907e+00 -4.50608313e-01 9.54026163e-01 5.57108700e-01
-9.73458052e-01 5.38182437e-01 6.48377657e-01 -2.93302268e-01
-2.92467594e-01 -6.86549842e-01 -7.60290921e-01 -1.77281454e-01
6.58983365e-02 6.94048166e-01 7.29184687e-01 -7.91537404... | [11.513226509094238, 7.7163519859313965] |
0c78524c-e170-4d77-af10-9885b0f0db7c | gen-ir-sigir-2023-the-first-workshop-on | 2306.02887 | null | https://arxiv.org/abs/2306.02887v2 | https://arxiv.org/pdf/2306.02887v2.pdf | Gen-IR @ SIGIR 2023: The First Workshop on Generative Information Retrieval | Generative information retrieval (IR) has experienced substantial growth across multiple research communities (e.g., information retrieval, computer vision, natural language processing, and machine learning), and has been highly visible in the popular press. Theoretical, empirical, and actual user-facing products have ... | ['Donald Metzler', 'Ruqing Zhang', 'Gabriel Bénédict'] | 2023-06-05 | null | null | null | null | ['information-retrieval', 'answer-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.07891601e-01 2.68622547e-01 -1.74700066e-01 -2.48217031e-01
-1.33177328e+00 -9.31655765e-01 1.09941638e+00 3.18901986e-01
-1.79090604e-01 4.52852517e-01 8.42029750e-01 -2.86622614e-01
-3.11884075e-01 -4.55758482e-01 -9.63104442e-02 -4.31928366e-01
3.55591893e-01 7.50864983e-01 -1.39188811e-01 -4.56856668... | [12.231797218322754, 9.2449951171875] |
754ba408-19c7-49c6-bb0e-ee669f6cbe0b | hero-hierarchical-encoder-for-video-language | 2005.00200 | null | https://arxiv.org/abs/2005.00200v2 | https://arxiv.org/pdf/2005.00200v2.pdf | HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training | We present HERO, a novel framework for large-scale video+language omni-representation learning. HERO encodes multimodal inputs in a hierarchical structure, where local context of a video frame is captured by a Cross-modal Transformer via multimodal fusion, and global video context is captured by a Temporal Transformer.... | ['Jingjing Liu', 'Zhe Gan', 'Yen-Chun Chen', 'Yu Cheng', 'Linjie Li', 'Licheng Yu'] | 2020-05-01 | null | https://aclanthology.org/2020.emnlp-main.161 | https://aclanthology.org/2020.emnlp-main.161.pdf | emnlp-2020-11 | ['moment-retrieval'] | ['computer-vision'] | [ 3.67130429e-01 -2.76680142e-01 -2.95156091e-01 -4.45591420e-01
-1.32335389e+00 -3.88040185e-01 8.55013072e-01 -1.76899672e-01
-3.46112251e-01 3.21414292e-01 6.99752569e-01 -1.30256683e-01
2.39553615e-01 -3.69493186e-01 -1.40415108e+00 -4.05487537e-01
-1.86448887e-01 4.55672413e-01 2.83367008e-01 -1.76792324... | [10.32602596282959, 0.8692918419837952] |
e3528c02-9a0c-4e58-8d7f-3a54337928fa | alp-action-aware-embodied-learning-for | 2306.10190 | null | https://arxiv.org/abs/2306.10190v1 | https://arxiv.org/pdf/2306.10190v1.pdf | ALP: Action-Aware Embodied Learning for Perception | Current methods in training and benchmarking vision models exhibit an over-reliance on passive, curated datasets. Although models trained on these datasets have shown strong performance in a wide variety of tasks such as classification, detection, and segmentation, they fundamentally are unable to generalize to an ever... | ['Pieter Abbeel', 'aditi raghunathan', 'Wilson Yan', 'Anthony Han', 'Xinran Liang'] | 2023-06-16 | null | null | null | null | ['benchmarking', 'benchmarking'] | ['miscellaneous', 'robots'] | [ 3.73007357e-01 1.48501083e-01 -5.94799686e-03 -5.41821241e-01
-6.92615807e-01 -6.79382205e-01 7.70180047e-01 -4.46682535e-02
-8.11140478e-01 4.13150460e-01 1.55896664e-01 -2.46006712e-01
-1.04089051e-01 -5.45361578e-01 -1.29360271e+00 -5.44861972e-01
-5.89901358e-02 6.03293836e-01 2.42129907e-01 -3.30051869... | [4.430741310119629, 0.7804831862449646] |
0f0d4fd4-cff5-41b8-8433-7d46c0fc8346 | attributable-and-scalable-opinion | 2305.11603 | null | https://arxiv.org/abs/2305.11603v1 | https://arxiv.org/pdf/2305.11603v1.pdf | Attributable and Scalable Opinion Summarization | We propose a method for unsupervised opinion summarization that encodes sentences from customer reviews into a hierarchical discrete latent space, then identifies common opinions based on the frequency of their encodings. We are able to generate both abstractive summaries by decoding these frequent encodings, and extra... | ['Mirella Lapata', 'Hao Tang', 'Tom Hosking'] | 2023-05-19 | null | null | null | null | ['unsupervised-opinion-summarization'] | ['natural-language-processing'] | [ 5.98597765e-01 7.29449868e-01 -2.91783929e-01 -5.85214317e-01
-1.14932120e+00 -1.03662968e+00 8.43433142e-01 8.88518333e-01
-2.08752882e-02 8.70811641e-01 1.44525719e+00 4.52919193e-02
1.78784743e-01 -9.20949996e-01 -4.78010356e-01 -4.77112651e-01
2.45115012e-01 8.21385503e-01 -1.31878093e-01 -1.82424068... | [12.465489387512207, 9.36670970916748] |
49700779-00d9-46d5-9a44-0a48e125b445 | conversational-automated-program-repair | 2301.13246 | null | https://arxiv.org/abs/2301.13246v1 | https://arxiv.org/pdf/2301.13246v1.pdf | Conversational Automated Program Repair | Automated Program Repair (APR) can help developers automatically generate patches for bugs. Due to the impressive performance obtained using Large Pre-Trained Language Models (LLMs) on many code related tasks, researchers have started to directly use LLMs for APR. However, prior approaches simply repeatedly sample the ... | ['Lingming Zhang', 'Chunqiu Steven Xia'] | 2023-01-30 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [ 1.92747161e-01 5.36439657e-01 -1.56963378e-01 -2.78096944e-01
-1.14285791e+00 -6.01299107e-01 8.67252201e-02 3.44220340e-01
5.74359596e-01 4.45737123e-01 9.31793004e-02 -6.94728673e-01
5.94002366e-01 -6.26931846e-01 -9.51748550e-01 6.52869493e-02
-2.26429254e-02 -9.54625010e-02 2.92703122e-01 -8.03724900... | [7.658444881439209, 7.730049133300781] |
79b4b199-c3ec-498d-9267-f58fde1b87ab | grammatical-error-detection-using-tagger | null | null | https://aclanthology.org/W14-1706 | https://aclanthology.org/W14-1706.pdf | Grammatical Error Detection Using Tagger Disagreement | null | ['Anubhav Gupta'] | 2014-06-01 | null | null | null | ws-2014-6 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.385798454284668, 3.7128329277038574] |
764fe5fc-ab81-4462-b052-3a466d36aa10 | a-cascade-sequence-to-sequence-model-for | 1908.04917 | null | https://arxiv.org/abs/1908.04917v2 | https://arxiv.org/pdf/1908.04917v2.pdf | A Cascade Sequence-to-Sequence Model for Chinese Mandarin Lip Reading | Lip reading aims at decoding texts from the movement of a speaker's mouth. In recent years, lip reading methods have made great progress for English, at both word-level and sentence-level. Unlike English, however, Chinese Mandarin is a tone-based language and relies on pitches to distinguish lexical or grammatical mean... | ['Rui Xu', 'Mingli Song', 'Ya Zhao'] | 2019-08-14 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 3.87454599e-01 -1.09493136e-01 -7.35355794e-01 -1.59768358e-01
-1.04964614e+00 -8.87469053e-02 3.86547178e-01 -2.65620172e-01
-2.09682778e-01 4.14114386e-01 7.93137014e-01 -4.57324892e-01
6.63707972e-01 -3.51873964e-01 -4.81981575e-01 -4.83800828e-01
5.68707943e-01 -4.59984243e-01 7.93446749e-02 6.44520521... | [14.305388450622559, 4.96316385269165] |
87d25195-c2ef-4ca2-99d1-e7e85fd85afe | limoseg-real-time-bird-s-eye-view-based-lidar | 2111.04875 | null | https://arxiv.org/abs/2111.04875v3 | https://arxiv.org/pdf/2111.04875v3.pdf | LiMoSeg: Real-time Bird's Eye View based LiDAR Motion Segmentation | Moving object detection and segmentation is an essential task in the Autonomous Driving pipeline. Detecting and isolating static and moving components of a vehicle's surroundings are particularly crucial in path planning and localization tasks. This paper proposes a novel real-time architecture for motion segmentation ... | ['Martin Simon', 'Heinrich Gotzig', 'Hazem Rashed', 'Patrick Maeder', 'Stefan Milz', 'Senthil Yogamani', 'Mona Hodaei', 'Sambit Mohapatra'] | 2021-11-08 | null | null | null | null | ['moving-object-detection', 'motion-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.03931913e-01 -3.42894942e-01 -1.85099229e-01 -6.70348704e-01
-4.01235402e-01 -6.84375942e-01 3.71757448e-01 -6.20835973e-03
-6.90001726e-01 3.77761871e-01 -6.21158481e-01 -8.17941487e-01
2.55233437e-01 -6.79318368e-01 -7.77549148e-01 -3.35983396e-01
9.07518938e-02 5.54904163e-01 1.04700661e+00 -1.01924717... | [8.047029495239258, -1.5997437238693237] |
ace1360e-779b-4358-9461-3289cea7ca54 | emgse-acoustic-emg-fusion-for-multimodal | 2202.06507 | null | https://arxiv.org/abs/2202.06507v1 | https://arxiv.org/pdf/2202.06507v1.pdf | EMGSE: Acoustic/EMG Fusion for Multimodal Speech Enhancement | Multimodal learning has been proven to be an effective method to improve speech enhancement (SE) performance, especially in challenging situations such as low signal-to-noise ratios, speech noise, or unseen noise types. In previous studies, several types of auxiliary data have been used to construct multimodal SE syste... | ['Yu Tsao', 'Hsin-Min Wang', 'Kai-Chun Liu', 'Kuan-Chen Wang'] | 2022-02-14 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 3.36829364e-01 -1.91915825e-01 -1.74155518e-01 1.25609249e-01
-1.26330924e+00 -8.27351213e-02 2.05373377e-01 -6.52675092e-01
-3.62214863e-01 6.54430628e-01 4.57085282e-01 2.00418204e-01
-1.66052774e-01 -1.51864424e-01 -3.75894070e-01 -1.03471935e+00
2.97119379e-01 -5.41596293e-01 -1.37175143e-01 -2.24258319... | [14.324756622314453, 5.014816761016846] |
01231e25-4345-4b86-a639-d74a6dccfc30 | toward-robust-diagnosis-a-contour-attention | 2211.16806 | null | https://arxiv.org/abs/2211.16806v1 | https://arxiv.org/pdf/2211.16806v1.pdf | Toward Robust Diagnosis: A Contour Attention Preserving Adversarial Defense for COVID-19 Detection | As the COVID-19 pandemic puts pressure on healthcare systems worldwide, the computed tomography image based AI diagnostic system has become a sustainable solution for early diagnosis. However, the model-wise vulnerability under adversarial perturbation hinders its deployment in practical situation. The existing adversa... | ['Shancheng Jiang', 'Shiqi Deng', 'Haohan Wang', 'Jinpeng Liu', 'Jinwen She', 'Xing Zhang', 'Kun Xiang'] | 2022-11-30 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 1.39019072e-01 6.10118248e-02 7.40116015e-02 -8.60279873e-02
-1.12111652e+00 -5.36832154e-01 3.30799311e-01 -3.64177339e-02
-3.72524798e-01 6.19135380e-01 2.00425223e-01 -4.92746264e-01
-1.44686565e-01 -5.39634943e-01 -4.87989843e-01 -9.47604358e-01
-2.62041897e-01 5.24720311e-01 7.91054443e-02 -8.95276442... | [14.234519004821777, -2.2603821754455566] |
9971cb6b-6e67-4036-8ab8-5e38187829cf | a-generalized-doubly-robust-learning | 2211.06684 | null | https://arxiv.org/abs/2211.06684v1 | https://arxiv.org/pdf/2211.06684v1.pdf | A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction | Post-click conversion rate (CVR) prediction is an essential task for discovering user interests and increasing platform revenues in a range of industrial applications. One of the most challenging problems of this task is the existence of severe selection bias caused by the inherent self-selection behavior of users and ... | ['Jie Sun', 'Rui Zhang', 'Xiao-Hua Zhou', 'Zhenhua Dong', 'Peng Wu', 'Haoxuan Li', 'Quanyu Dai'] | 2022-11-12 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 1.98658602e-03 -6.58771515e-01 -8.33122849e-01 -3.94627035e-01
-8.73024702e-01 -1.35653406e-01 2.20198646e-01 -1.22628674e-01
-7.09829107e-02 8.66039634e-01 -1.73215181e-01 -4.32002634e-01
-4.00972575e-01 -6.62503183e-01 -5.54225624e-01 -7.05173850e-01
2.35137358e-01 1.01782948e-01 1.73644885e-01 -1.09608307... | [9.698671340942383, 5.320231914520264] |
b5b4a879-dbb3-4948-b6bb-1c3022715dd8 | gpt-self-supervision-for-a-better-data | 2306.04349 | null | https://arxiv.org/abs/2306.04349v2 | https://arxiv.org/pdf/2306.04349v2.pdf | GPT Self-Supervision for a Better Data Annotator | The task of annotating data into concise summaries poses a significant challenge across various domains, frequently requiring the allocation of significant time and specialized knowledge by human experts. Despite existing efforts to use large language models for annotation tasks, significant problems such as limited ap... | ['Chang Xu', 'Yanxi Li', 'Xiaohuan Pei'] | 2023-06-07 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 6.84778929e-01 3.94346923e-01 -3.21193486e-01 -4.56950337e-01
-1.33275187e+00 -6.04498804e-01 7.30879247e-01 4.63516384e-01
-2.36241221e-01 8.46333265e-01 4.70100582e-01 1.82786193e-02
1.12988897e-01 -4.88257349e-01 -4.96648490e-01 -5.97292662e-01
4.77200598e-01 8.10172975e-01 5.22192195e-02 -2.25673303... | [11.74892807006836, 8.874403953552246] |
24c6c9cd-0b05-4361-89c0-1f8677de22f8 | arts-eliminating-inconsistency-between-text | 2110.10405 | null | https://arxiv.org/abs/2110.10405v1 | https://arxiv.org/pdf/2110.10405v1.pdf | ARTS: Eliminating Inconsistency between Text Detection and Recognition with Auto-Rectification Text Spotter | Recent approaches for end-to-end text spotting have achieved promising results. However, most of the current spotters were plagued by the inconsistency problem between text detection and recognition. In this work, we introduce and prove the existence of the inconsistency problem and analyze it from two aspects: (1) inc... | ['Tong Lu', 'Cong Yao', 'Zhibo Yang', 'Wenhai Wang', 'Jun Tang', 'Humen Zhong'] | 2021-10-20 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 5.12558281e-01 -5.92596471e-01 7.83351138e-02 -2.85953015e-01
-7.79924870e-01 -4.09534812e-01 3.66054237e-01 -4.46245253e-01
-2.80804843e-01 2.42134362e-01 -7.16115832e-02 -3.58004302e-01
1.11740299e-01 -4.04572785e-01 -5.82495630e-01 -5.82041323e-01
8.71273100e-01 3.95208329e-01 2.32196376e-01 -4.07361500... | [12.006939888000488, 2.216689348220825] |
9d66fb06-47f5-4cdf-8ac2-42d4cb3ae4fc | lidar-snowfall-simulation-for-robust-3d | 2203.15118 | null | https://arxiv.org/abs/2203.15118v2 | https://arxiv.org/pdf/2203.15118v2.pdf | LiDAR Snowfall Simulation for Robust 3D Object Detection | 3D object detection is a central task for applications such as autonomous driving, in which the system needs to localize and classify surrounding traffic agents, even in the presence of adverse weather. In this paper, we address the problem of LiDAR-based 3D object detection under snowfall. Due to the difficulty of col... | ['Luc van Gool', 'Dengxin Dai', 'Fisher Yu', 'Felix Heide', 'Mario Bijelic', 'Christos Sakaridis', 'Martin Hahner'] | 2022-03-28 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Hahner_LiDAR_Snowfall_Simulation_for_Robust_3D_Object_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Hahner_LiDAR_Snowfall_Simulation_for_Robust_3D_Object_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['robust-3d-object-detection', 'physical-simulations'] | ['computer-vision', 'miscellaneous'] | [ 3.60282324e-02 -4.34290916e-01 3.09706688e-01 -2.37670958e-01
-4.28152949e-01 -5.97994685e-01 4.59122449e-01 3.82087938e-02
-4.28152353e-01 7.32202053e-01 -5.53992808e-01 -7.79625595e-01
4.85771835e-01 -1.04552555e+00 -7.50132143e-01 -5.08079231e-01
-1.53234273e-01 8.72035325e-01 7.70963073e-01 -3.43564332... | [7.818630695343018, -2.4845192432403564] |
2b7d59b4-1ebc-43fa-861f-a30c31ef6c1c | delving-deeper-into-the-decoder-for-video | 2001.05614 | null | https://arxiv.org/abs/2001.05614v3 | https://arxiv.org/pdf/2001.05614v3.pdf | Delving Deeper into the Decoder for Video Captioning | Video captioning is an advanced multi-modal task which aims to describe a video clip using a natural language sentence. The encoder-decoder framework is the most popular paradigm for this task in recent years. However, there exist some problems in the decoder of a video captioning model. We make a thorough investigatio... | ['Jianmin Li', 'Xiaolin Hu', 'Haoran Chen'] | 2020-01-16 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 2.42343843e-01 -7.39964768e-02 -2.53088951e-01 -3.74213636e-01
-1.22304940e+00 -2.21201316e-01 5.93913019e-01 -1.04122967e-01
-4.48659569e-01 8.39952648e-01 4.57596481e-01 -1.20276980e-01
3.89608264e-01 -6.79303482e-02 -9.54528987e-01 -4.46162999e-01
2.55712420e-01 3.53393316e-01 3.28454137e-01 -1.68146983... | [10.58139705657959, 0.6758863925933838] |
bf77cfe7-16be-4c01-9c89-0d68bc810856 | casino-a-corpus-of-campsite-negotiation | 2103.15721 | null | https://arxiv.org/abs/2103.15721v2 | https://arxiv.org/pdf/2103.15721v2.pdf | CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems | Automated systems that negotiate with humans have broad applications in pedagogy and conversational AI. To advance the development of practical negotiation systems, we present CaSiNo: a novel corpus of over a thousand negotiation dialogues in English. Participants take the role of campsite neighbors and negotiate for f... | ['Jonathan Gratch', 'Jonathan May', 'Gale Lucas', 'Rene Clever', 'Jaysa Ramirez', 'Kushal Chawla'] | 2021-03-29 | null | https://aclanthology.org/2021.naacl-main.254 | https://aclanthology.org/2021.naacl-main.254.pdf | naacl-2021-4 | ['persuasion-strategies'] | ['computer-vision'] | [-0.07303953 0.44543058 -0.4877368 -0.767216 -0.8288813 -0.88230485
0.9407642 0.20234789 -0.43137273 0.9608061 0.70967776 -0.31585053
-0.16783153 -0.4253675 -0.12780364 -0.44807833 0.1931431 0.99512243
-0.12699191 -0.8787966 0.3432819 -0.19813578 -0.9836948 0.47390231
0.7338399 0.382597 -0.... | [12.78298282623291, 7.988338947296143] |
ff4304c8-b3ac-46b3-b890-fe471c357c65 | evaluating-off-the-shelf-machine-listening | 2110.07410 | null | https://arxiv.org/abs/2110.07410v1 | https://arxiv.org/pdf/2110.07410v1.pdf | Evaluating Off-the-Shelf Machine Listening and Natural Language Models for Automated Audio Captioning | Automated audio captioning (AAC) is the task of automatically generating textual descriptions for general audio signals. A captioning system has to identify various information from the input signal and express it with natural language. Existing works mainly focus on investigating new methods and try to improve their p... | ['Xavier Serra', 'Konstantinos Drossos', 'Xavier Favory', 'Benno Weck'] | 2021-10-14 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 4.18490976e-01 1.21681318e-01 8.49216729e-02 -2.85111666e-01
-1.36482382e+00 -4.80769098e-01 4.71288830e-01 6.66786209e-02
-2.50758111e-01 6.62257373e-01 9.00993228e-01 -1.25195488e-01
2.04896659e-01 -3.92247349e-01 -5.86541533e-01 -2.80631095e-01
-3.72722792e-03 6.24712765e-01 2.05115199e-01 -3.79949868... | [15.2935152053833, 4.884670734405518] |
840af2ce-6e9a-4629-ad1e-4e6d132f3971 | data-augmentation-with-sentence-recombination | null | null | https://openreview.net/forum?id=WSxxdJdByLS | https://openreview.net/pdf?id=WSxxdJdByLS | Data Augmentation with Sentence Recombination Method for Semi-supervised Text Classification | As the need of large amount of time and expertise to obtain enough labeled data, semi-supervised learning has received much attention to utilize both labeled and unlabeled data. In this paper, we present SeRe: a Sentence Recombination method to augment training data for semi-supervised text classification. SeRe makes f... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['semi-supervised-text-classification-1'] | ['natural-language-processing'] | [ 2.75451928e-01 -7.14644343e-02 -4.14376706e-01 -7.49253988e-01
-8.50870311e-01 -4.52968836e-01 5.89903653e-01 2.26626948e-01
-4.64323103e-01 9.79716718e-01 1.53769180e-01 -2.07514122e-01
4.02608931e-01 -4.57234651e-01 -4.07349527e-01 -3.87820363e-01
4.32393909e-01 5.05251408e-01 5.03713340e-02 -1.78121880... | [10.635576248168945, 7.406373977661133] |
1d3fc220-168f-4ac6-a445-d140e7049ecb | algorithm-and-hardware-co-design-of-energy | 2212.02046 | null | https://arxiv.org/abs/2212.02046v1 | https://arxiv.org/pdf/2212.02046v1.pdf | Algorithm and Hardware Co-Design of Energy-Efficient LSTM Networks for Video Recognition with Hierarchical Tucker Tensor Decomposition | Long short-term memory (LSTM) is a type of powerful deep neural network that has been widely used in many sequence analysis and modeling applications. However, the large model size problem of LSTM networks make their practical deployment still very challenging, especially for the video recognition tasks that require hi... | ['Bo Yuan', 'Yang Sui', 'Chunhua Deng', 'Lingyi Huang', 'Miao Yin', 'Yu Gong'] | 2022-12-05 | null | null | null | null | ['video-recognition'] | ['computer-vision'] | [ 1.45540059e-01 -4.43426043e-01 -4.31878895e-01 1.28185311e-02
-4.23588365e-01 7.19592422e-02 1.35574758e-01 -3.99081632e-02
-6.39581740e-01 2.60501057e-01 9.25946981e-03 -8.19133818e-01
8.81444216e-02 -7.24052072e-01 -8.58184218e-01 -8.03843915e-01
-6.92896545e-02 7.35184327e-02 1.94434360e-01 7.06973150... | [8.484189987182617, 2.914473295211792] |
f2f879cf-42e1-400f-b5a5-b2093381ea2b | generalizable-pose-estimation-using-implicit | 2305.17252 | null | https://arxiv.org/abs/2305.17252v1 | https://arxiv.org/pdf/2305.17252v1.pdf | Generalizable Pose Estimation Using Implicit Scene Representations | 6-DoF pose estimation is an essential component of robotic manipulation pipelines. However, it usually suffers from a lack of generalization to new instances and object types. Most widely used methods learn to infer the object pose in a discriminative setup where the model filters useful information to infer the exact ... | ['Yotto Koga', 'Linh Tran', 'Kamal Rahimi Malekshan', 'Vaibhav Saxena'] | 2023-05-26 | null | null | null | null | ['pose-estimation', 'density-estimation'] | ['computer-vision', 'methodology'] | [ 2.46379882e-01 1.35062933e-01 7.03710094e-02 -6.12787664e-01
-5.61196804e-01 -6.00998700e-01 4.98692900e-01 -3.83450426e-02
-3.55568945e-01 4.67174321e-01 -1.45612717e-01 1.32547155e-01
-2.18360871e-01 -9.02039051e-01 -1.23198795e+00 -4.81547564e-01
1.13744803e-01 1.03678143e+00 4.25278485e-01 -5.37003316... | [6.279313087463379, -1.2832573652267456] |
88d87f3d-bd20-4e66-ad98-424e0e20fc35 | danes-deep-neural-network-ensemble | 2302.01756 | null | https://arxiv.org/abs/2302.01756v1 | https://arxiv.org/pdf/2302.01756v1.pdf | DANES: Deep Neural Network Ensemble Architecture for Social and Textual Context-aware Fake News Detection | The growing popularity of social media platforms has simplified the creation and distribution of news articles but also creates a conduit for spreading fake news. In consequence, the need arises for effective context-aware fake news detection mechanisms, where the contextual information can be built either from the tex... | ['Panagiotis Karras', 'Elena-Simona Apostol', 'Ciprian-Octavian Truică'] | 2023-02-01 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-1.32663280e-01 -1.19171605e-01 -4.24704969e-01 -3.72162044e-01
-2.11308509e-01 -4.77369964e-01 1.17180204e+00 5.20379782e-01
-4.22939926e-01 6.41295910e-01 6.58307433e-01 -2.57957369e-01
3.08159709e-01 -1.02428031e+00 -4.07836825e-01 -2.21731439e-01
1.82050347e-01 7.32480735e-02 2.84877747e-01 -8.29183698... | [8.179155349731445, 10.252546310424805] |
25529604-71af-48b3-9145-7e2df69ecc82 | robust-3d-object-detection-in-cold-weather | 2205.11925 | null | https://arxiv.org/abs/2205.11925v2 | https://arxiv.org/pdf/2205.11925v2.pdf | Robust 3D Object Detection in Cold Weather Conditions | Adverse weather conditions can negatively affect LiDAR-based object detectors. In this work, we focus on the phenomenon of vehicle gas exhaust condensation in cold weather conditions. This everyday effect can influence the estimation of object sizes, orientations and introduce ghost object detections, compromising the ... | ['Klaus Dietmayer', 'Johannes Kopp', 'Daniel Meissner', 'Marc Walessa', 'Vinzenz Dallabetta', 'Aldi Piroli'] | 2022-05-24 | null | null | null | null | ['robust-3d-object-detection'] | ['computer-vision'] | [ 1.23072430e-01 -1.40753806e-01 1.80493772e-01 -3.48636389e-01
-5.23696601e-01 -6.77196503e-01 6.92728698e-01 8.51893798e-02
-4.54322755e-01 5.68898082e-01 -5.30466616e-01 -4.20709968e-01
3.40432823e-01 -1.20404232e+00 -9.84296560e-01 -6.97467029e-01
3.57187033e-01 3.96052927e-01 6.88349009e-01 -1.04397707... | [7.930769920349121, -2.5252139568328857] |
b0992e13-4d96-4cee-adb9-a6f45dd68408 | efficient-explainable-face-verification-based | 2304.13409 | null | https://arxiv.org/abs/2304.13409v1 | https://arxiv.org/pdf/2304.13409v1.pdf | Efficient Explainable Face Verification based on Similarity Score Argument Backpropagation | Explainable Face Recognition is gaining growing attention as the use of the technology is gaining ground in security-critical applications. Understanding why two faces images are matched or not matched by a given face recognition system is important to operators, users, anddevelopers to increase trust, accountability, ... | ['Naser Damer', 'Philipp Terhörst', 'Anh Thi Luu', 'Marco Huber'] | 2023-04-26 | null | null | null | null | ['face-recognition', 'face-verification'] | ['computer-vision', 'computer-vision'] | [ 2.88485944e-01 2.60571212e-01 -2.03810647e-01 -1.06536722e+00
-3.76637429e-01 -5.76990366e-01 6.37710631e-01 8.17886740e-02
4.02812541e-01 4.29503351e-01 1.23297505e-01 -5.62326133e-01
-4.18739706e-01 -5.02057612e-01 -5.78017712e-01 -4.16940123e-01
4.40829992e-02 1.96940035e-01 -2.77039200e-01 -7.51359239... | [12.93997859954834, 0.9519627094268799] |
83185a46-5706-468e-854f-0b008a1eb955 | statistical-tomography-of-microscopic-life | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Levis_Statistical_Tomography_of_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Levis_Statistical_Tomography_of_CVPR_2018_paper.pdf | Statistical Tomography of Microscopic Life | We achieve tomography of 3D volumetric natural objects, where each projected 2D image corresponds to a different specimen. Each specimen has unknown random 3D orientation, location, and scale. This imaging scenario is relevant to microscopic and mesoscopic organisms, aerosols and hydrosols viewed naturally by a microsc... | ['Aviad Levis', 'Ronen Talmon', 'Yoav Y. Schechner'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['transparent-objects'] | ['computer-vision'] | [ 2.34285742e-01 -1.99387565e-01 7.44448721e-01 3.39362212e-02
-2.54499555e-01 -6.08707309e-01 5.11870921e-01 1.17417216e-01
-6.89494252e-01 7.82928586e-01 -4.57974702e-01 -5.62976720e-03
9.47307795e-02 -1.02036047e+00 -6.71579719e-01 -1.01518726e+00
-6.48593530e-02 1.21701574e+00 6.33598685e-01 3.15455467... | [13.04355525970459, -2.979276657104492] |
66d80137-8a9d-4324-b184-88855d9b9bb8 | end-to-end-sleep-staging-with-raw-single | 1904.10255 | null | http://arxiv.org/abs/1904.10255v1 | http://arxiv.org/pdf/1904.10255v1.pdf | End-to-end Sleep Staging with Raw Single Channel EEG using Deep Residual ConvNets | Humans approximately spend a third of their life sleeping, which makes
monitoring sleep an integral part of well-being. In this paper, a 34-layer deep
residual ConvNet architecture for end-to-end sleep staging is proposed. The
network takes raw single channel electroencephalogram (Fpz-Cz) signal as input
and yields hyp... | ['Asif Shahriyar Sushmit', 'Taufiq Hasan', 'Ahmed Imtiaz Humayun', 'Mohammed Imamul Hassan Bhuiyan'] | 2019-04-23 | null | null | null | null | ['sleep-staging'] | ['medical'] | [-1.25458762e-02 -6.27553044e-03 -5.54593876e-02 -6.89658344e-01
-4.13977921e-01 3.30782123e-02 -2.30644703e-01 -3.84386592e-02
-8.80497992e-01 1.02662349e+00 3.19739819e-01 -1.08403787e-01
-2.90065426e-02 -7.59032518e-02 7.93824270e-02 -5.20691335e-01
-4.40239936e-01 1.91619724e-01 -1.78051502e-01 -6.56190887... | [13.504759788513184, 3.522120714187622] |
2ec69317-bd84-40ad-8ef8-3a59c833b63c | automatic-method-of-domain-ontology | 1405.1346 | null | http://arxiv.org/abs/1405.1346v1 | http://arxiv.org/pdf/1405.1346v1.pdf | Automatic Method Of Domain Ontology Construction based on Characteristics of Corpora POS-Analysis | It is now widely recognized that ontologies, are one of the fundamental
cornerstones of knowledge-based systems. What is lacking, however, is a
currently accepted strategy of how to build ontology; what kinds of the
resources and techniques are indispensables to optimize the expenses and the
time on the one hand and th... | ['Olena Orobinska'] | 2014-05-06 | null | null | null | null | ['text-annotation'] | ['natural-language-processing'] | [ 3.75864178e-01 3.76321286e-01 1.59116954e-01 2.52276734e-02
-2.64130175e-01 -3.28855187e-01 7.09963083e-01 5.86504996e-01
-4.46802467e-01 7.53263772e-01 3.18628103e-01 -3.91460508e-01
-9.63287532e-01 -9.72228467e-01 -2.22937297e-02 -5.49584150e-01
1.25447541e-01 7.88340330e-01 4.17127192e-01 -3.83737415... | [9.339373588562012, 8.544754981994629] |
4590235a-ee7f-4b4c-b5a0-f72b40f103d7 | visualhints-a-visual-lingual-environment-for | 2010.13839 | null | https://arxiv.org/abs/2010.13839v1 | https://arxiv.org/pdf/2010.13839v1.pdf | VisualHints: A Visual-Lingual Environment for Multimodal Reinforcement Learning | We present VisualHints, a novel environment for multimodal reinforcement learning (RL) involving text-based interactions along with visual hints (obtained from the environment). Real-life problems often demand that agents interact with the environment using both natural language information and visual perception toward... | ['Michiaki Tatsubori', 'Kartik Talamadupula', 'Subhajit Chaudhury', 'Thomas Carta'] | 2020-10-26 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [-1.08767003e-01 3.00172940e-02 2.07631420e-02 -3.24617207e-01
-5.35130858e-01 -9.85851824e-01 8.17142248e-01 8.77062380e-02
-7.30173230e-01 5.01700163e-01 2.74499178e-01 -4.18318331e-01
3.15835536e-01 -6.16643012e-01 -5.74985862e-01 -3.86563450e-01
-9.64493230e-02 4.49930191e-01 1.49800092e-01 -8.50553930... | [4.416485786437988, 0.7930276989936829] |
60453adb-3022-47ac-a0c3-60e126cc9548 | rethinking-data-free-quantization-as-a-zero | 2302.09572 | null | https://arxiv.org/abs/2302.09572v1 | https://arxiv.org/pdf/2302.09572v1.pdf | Rethinking Data-Free Quantization as a Zero-Sum Game | Data-free quantization (DFQ) recovers the performance of quantized network (Q) without accessing the real data, but generates the fake sample via a generator (G) by learning from full-precision network (P) instead. However, such sample generation process is totally independent of Q, specialized as failing to consider t... | ['Meng Wang', 'Richang Hong', 'Yang Wang', 'Biao Qian'] | 2023-02-19 | null | null | null | null | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 9.92787182e-02 4.01040226e-01 -3.69375974e-01 1.42059013e-01
-8.26007128e-01 -7.50328302e-01 1.57855093e-01 -4.02292490e-01
-3.49455148e-01 9.62317586e-01 -1.83615431e-01 -5.10826886e-01
-3.53113651e-01 -1.18488097e+00 -6.15101278e-01 -1.00012600e+00
-3.07061344e-01 4.97947671e-02 6.08448349e-02 -3.96908164... | [8.688275337219238, 2.900325059890747] |
a0187258-bbf0-44c4-97cf-0df33f455b6f | wsense-a-robust-feature-learning-module-for | 2303.17845 | null | https://arxiv.org/abs/2303.17845v1 | https://arxiv.org/pdf/2303.17845v1.pdf | WSense: A Robust Feature Learning Module for Lightweight Human Activity Recognition | In recent times, various modules such as squeeze-and-excitation, and others have been proposed to improve the quality of features learned from wearable sensor signals. However, these modules often cause the number of parameters to be large, which is not suitable for building lightweight human activity recognition model... | ['Mohd Halim Mohd Noor', 'Ayokunle Olalekan Ige'] | 2023-03-31 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 3.05368323e-02 -3.52244586e-01 -2.00308621e-01 -5.36515415e-01
-3.83064508e-01 -7.31655955e-02 3.02343190e-01 8.43346342e-02
-6.62013769e-01 5.26943743e-01 2.81103671e-01 1.61278710e-01
-1.42207155e-02 -8.40478063e-01 -6.33955479e-01 -4.28021282e-01
-2.98639983e-01 -4.67362076e-01 1.85228720e-01 1.34436265... | [7.563937664031982, 0.8184187412261963] |
8fb83dc9-0c14-47f2-8323-5f7e5ea62f91 | shape-complementarity-optimization-of | 2107.07295 | null | https://arxiv.org/abs/2107.07295v4 | https://arxiv.org/pdf/2107.07295v4.pdf | Shape Complementarity Optimization of Antibody-Antigen Interfaces: the Application to SARS-CoV-2 Spike Protein | Many factors influence biomolecules binding, and its assessment constitutes an elusive challenge in computational structural biology. In this respect, the evaluation of shape complementarity at molecular interfaces is one of the main factors to be considered. We focus on the particular case of antibody-antigen complexe... | ['Giancarlo Ruocco', 'Edoardo Milanetti', 'Pier Paolo Olimpieri', 'Mattia Miotto', 'Lorenzo Di Rienzo', 'Alfredo De Lauro'] | 2021-07-15 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 3.90556186e-01 -2.74399996e-01 2.83697218e-01 -3.24547142e-01
-4.91279632e-01 -8.41214836e-01 3.89264315e-01 7.05803752e-01
-6.95059001e-01 1.14852250e+00 -1.55644849e-01 -4.02407497e-01
-1.36955529e-01 -6.34466290e-01 -8.57850015e-01 -8.34992945e-01
-2.75159836e-01 1.02622724e+00 1.55375913e-01 -4.45835471... | [4.769105911254883, 5.343353748321533] |
2bf98a97-b570-406b-8be3-8761bea12241 | addressing-bias-in-face-detectors-using | 2210.16024 | null | https://arxiv.org/abs/2210.16024v1 | https://arxiv.org/pdf/2210.16024v1.pdf | Addressing Bias in Face Detectors using Decentralised Data collection with incentives | Recent developments in machine learning have shown that successful models do not rely only on huge amounts of data but the right kind of data. We show in this paper how this data-centric approach can be facilitated in a decentralized manner to enable efficient data collection for algorithms. Face detectors are a class ... | ['Richard Blythman', 'Robin Lehmann', 'M. R. Ahan'] | 2022-10-28 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [-3.68347466e-01 1.03404447e-01 -1.21666320e-01 -1.05414701e+00
-3.03093642e-01 -5.77787280e-01 7.69131660e-01 1.46121398e-01
-8.41172159e-01 5.29183030e-01 2.41913959e-01 -2.82166954e-02
1.78628415e-01 -6.86164975e-01 -5.17428935e-01 -3.55714649e-01
2.87977934e-01 7.62257993e-01 -2.02316284e-01 -1.67002290... | [13.057777404785156, 0.9427109360694885] |
468a9d9b-f788-4883-8f57-a753cb69316b | empty-cities-a-dynamic-object-invariant-space | 2010.07646 | null | https://arxiv.org/abs/2010.07646v1 | https://arxiv.org/pdf/2010.07646v1.pdf | Empty Cities: a Dynamic-Object-Invariant Space for Visual SLAM | In this paper we present a data-driven approach to obtain the static image of a scene, eliminating dynamic objects that might have been present at the time of traversing the scene with a camera. The general objective is to improve vision-based localization and mapping tasks in dynamic environments, where the presence (... | ['Jose Neira', 'Cesar Cadena', 'Berta Bescos'] | 2020-10-15 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 3.71477723e-01 1.50403261e-01 3.80132198e-01 -1.36437654e-01
-5.02429008e-01 -6.44752979e-01 6.72330081e-01 -4.04259712e-01
-6.06114566e-01 6.54548705e-01 -1.84160039e-01 2.43676156e-02
2.96047717e-01 -9.51167583e-01 -1.30054390e+00 -8.45748425e-01
1.38515443e-01 5.85679412e-01 5.35261631e-01 -3.59454036... | [8.471084594726562, -2.225710391998291] |
7344f4d6-30d6-45d0-bbf1-b9a84698ed25 | dynamic-memory-induction-networks-for-few | 2005.05727 | null | https://arxiv.org/abs/2005.05727v1 | https://arxiv.org/pdf/2005.05727v1.pdf | Dynamic Memory Induction Networks for Few-Shot Text Classification | This paper proposes Dynamic Memory Induction Networks (DMIN) for few-shot text classification. The model utilizes dynamic routing to provide more flexibility to memory-based few-shot learning in order to better adapt the support sets, which is a critical capacity of few-shot classification models. Based on that, we fur... | ['Xiaodan Zhu', 'Yongbin Li', 'Ruiying Geng', 'Jian Sun', 'Binhua Li'] | 2020-05-12 | dynamic-memory-induction-networks-for-few-1 | https://aclanthology.org/2020.acl-main.102 | https://aclanthology.org/2020.acl-main.102.pdf | acl-2020-6 | ['few-shot-text-classification'] | ['natural-language-processing'] | [-2.97610968e-01 -2.77821928e-01 -7.27437556e-01 -3.02720219e-01
-1.46098718e-01 1.42837003e-01 3.96772712e-01 2.35605597e-01
-6.37240231e-01 5.67441642e-01 5.48529997e-02 -1.84101075e-01
-3.71282518e-01 -1.24515522e+00 -1.94902852e-01 -3.62905681e-01
1.51392147e-02 5.23938179e-01 8.07753086e-01 -4.10746843... | [10.190155029296875, 3.540890693664551] |
2266c139-1f38-49bb-8709-23861c5a2b04 | unified-fully-and-timestamp-supervised | 2209.00638 | null | https://arxiv.org/abs/2209.00638v2 | https://arxiv.org/pdf/2209.00638v2.pdf | Unified Fully and Timestamp Supervised Temporal Action Segmentation via Sequence to Sequence Translation | This paper introduces a unified framework for video action segmentation via sequence to sequence (seq2seq) translation in a fully and timestamp supervised setup. In contrast to current state-of-the-art frame-level prediction methods, we view action segmentation as a seq2seq translation task, i.e., mapping a sequence of... | ['Mehdi Noroozi', 'Juergen Gall', 'Zico Kolter', 'S. Alireza Golestaneh', 'Nadine Behrmann'] | 2022-09-01 | null | null | null | null | ['action-segmentation'] | ['computer-vision'] | [ 9.65102434e-01 2.41653472e-01 -3.98423642e-01 -5.68412304e-01
-1.18967032e+00 -6.13866210e-01 7.12433279e-01 -2.54519731e-01
-5.74559808e-01 6.59270227e-01 3.35122675e-01 -7.79277906e-02
4.46967155e-01 -2.69154400e-01 -1.05883479e+00 -6.12001300e-01
2.10067675e-01 4.52821642e-01 4.34199512e-01 1.52996033... | [8.478521347045898, 0.5468856692314148] |
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