paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
405751a7-cb45-461b-ad09-e5718f515d4c | a-cloud-edge-terminal-collaborative-system | 2107.05078 | null | https://arxiv.org/abs/2107.05078v1 | https://arxiv.org/pdf/2107.05078v1.pdf | A Cloud-Edge-Terminal Collaborative System for Temperature Measurement in COVID-19 Prevention | To prevent the spread of coronavirus disease 2019 (COVID-19), preliminary temperature measurement and mask detection in public areas are conducted. However, the existing temperature measurement methods face the problems of safety and deployment. In this paper, to realize safe and accurate temperature measurement even w... | ['Zhiyong Bu', 'Bin Zhou', 'Qingwen Liu', 'Wen Fang', 'Hao Li', 'Zheyi Ma'] | 2021-07-11 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [-8.99493098e-02 -8.43860984e-01 3.89177918e-01 -3.90500337e-01
-2.96902567e-01 -5.34478009e-01 -1.35812105e-03 -5.00200927e-01
-6.96027040e-01 1.17036372e-01 -6.93617165e-01 -3.98297280e-01
1.71437487e-01 -6.41363323e-01 -3.46298218e-01 -1.13114214e+00
4.41789478e-01 1.00052558e-01 -1.53164595e-01 1.83945894... | [7.071809768676758, 0.2769826054573059] |
0ca1218a-932d-4100-8ecd-1578138e53d2 | generalized-wasserstein-dice-loss-test-time | 2112.13054 | null | https://arxiv.org/abs/2112.13054v1 | https://arxiv.org/pdf/2112.13054v1.pdf | Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challenge | Brain tumor segmentation from multiple Magnetic Resonance Imaging (MRI) modalities is a challenging task in medical image computation. The main challenges lie in the generalizability to a variety of scanners and imaging protocols. In this paper, we explore strategies to increase model robustness without increasing infe... | ['Tom Vercauteren', 'Sébastien Ourselin', 'Johannes C. Paetzold', 'Ivan Ezhov', 'Suprosanna Shit', 'Lucas Fidon'] | 2021-12-24 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 1.94087014e-01 1.21374011e-01 -8.31468925e-02 -5.31611443e-01
-1.31266713e+00 -4.43054885e-01 3.55275542e-01 1.95478454e-01
-6.63459301e-01 8.24017525e-01 8.11353847e-02 -4.89793122e-01
-2.23567382e-01 -3.77209723e-01 -6.23246908e-01 -7.92518377e-01
-2.79950172e-01 4.24222499e-01 1.32355526e-01 3.66958678... | [14.421968460083008, -2.3668324947357178] |
d45bee1d-775f-4406-b02b-cec9ca4bc8f1 | multi-kernel-filtering-an-extension-of | 1908.06307 | null | https://arxiv.org/abs/1908.06307v4 | https://arxiv.org/pdf/1908.06307v4.pdf | Multi-Kernel Filtering for Nonstationary Noise: An Extension of Bilateral Filtering Using Image Context | Bilateral filtering (BF) is one of the most classical denoising filters, however, the manually initialized filtering kernel hampers its adaptivity across images with various characteristics. To deal with image variation (i.e., non-stationary noise), in this paper, we propose multi-kernel filter (MKF) which adapts filte... | ['Pew-Thian Yap', 'Jun Feng', 'Dinggang Shen', 'Feihong Liu'] | 2019-08-17 | null | null | null | null | ['image-variation'] | ['computer-vision'] | [ 8.72528479e-02 -5.97606778e-01 4.57182288e-01 -3.32690030e-01
-4.39364046e-01 -5.69645762e-01 2.94415325e-01 -2.61606257e-02
-4.63945210e-01 4.28963065e-01 1.26479819e-01 1.66995093e-01
-4.10715580e-01 -8.24957967e-01 -6.20533109e-01 -1.13818729e+00
1.10050291e-01 -3.19231063e-01 8.11717451e-01 -3.70692760... | [11.058582305908203, -1.4229164123535156] |
7e2833a1-1ff0-41d8-830d-384badacfd49 | real-time-p-qrs-and-t-wave-detection-by-qrs | null | null | https://www.google.com/url?sa=t&source=web&rct=j&url=http://www.ijieee.org.in/volume.php%3Fvolume_id%3D482&ved=2ahUKEwiGmLiWqvvsAhUQO3AKHYGnBGgQFjACegQIAhAB&usg=AOvVaw0g8O8vZTRpQ6bTmjcUhERw | https://www.google.com/url?sa=t&source=web&rct=j&url=https://www.digitalxplore.org/up_proc/pdf/371-15299043251-5.pdf&ved=2ahUKEwj95ZzPqfvsAhUZZ94KHdrjBf0QFjAAegQIBhAB&usg=AOvVaw073g7FKNIvvTvWjz_vgO01&cshid=1605126531345 | Real time P, QRS and T wave detection by QRS matched filter method | ECG signals have always been a key concern for heart disease analysis and heart monitoring system. That’s why
for a very long time, people are to find newer and easier processes for retrieving information from ECG. And still now some
of the methods are quite accurate and implemented successfully all over the world,... | ['Abdullah Al Masud'] | 2018-07-01 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 1.37076631e-01 -1.63239524e-01 5.75599730e-01 -3.28161389e-01
-4.66238171e-01 -5.57205677e-01 -1.91706121e-01 5.31584382e-01
-2.03426316e-01 8.04883242e-01 9.90362093e-03 8.04483891e-02
-3.33152950e-01 -4.87727702e-01 1.17917359e-01 -5.16495109e-01
-1.91818312e-01 4.06454951e-02 1.90825850e-01 -2.92302817... | [14.147150993347168, 3.190702438354492] |
6e03abfe-1c73-46d8-b79f-7097393a8265 | multipath-based-slam-for-non-ideal-reflective | 2304.05680 | null | https://arxiv.org/abs/2304.05680v2 | https://arxiv.org/pdf/2304.05680v2.pdf | Multipath-based SLAM for Non-Ideal Reflective Surfaces Exploiting Multiple-Measurement Data Association | Multipath-based simultaneous localization and mapping (SLAM) is a promising approach to obtain position information of transmitters and receivers as well as information regarding the propagation environments in future mobile communication systems. Usually, specular reflections of the radio signals occurring at flat sur... | ['Erik Leitinger', 'Thomas Wilding', 'Alexander Venus', 'Lukas Wielandner'] | 2023-04-12 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [ 1.82177112e-01 -1.18285611e-01 4.88886297e-01 -2.18252331e-01
-1.10102642e+00 -6.17592514e-01 6.43286049e-01 4.07531083e-01
-3.01066130e-01 9.52498257e-01 -1.69323340e-01 -5.09239584e-02
-2.83339977e-01 -9.27816808e-01 -9.29521024e-01 -8.59497309e-01
-4.02672887e-01 6.64245009e-01 3.59656602e-01 -1.92454562... | [6.234395503997803, 1.0369789600372314] |
d1982a0f-45eb-45b6-859c-3ec267b8e74d | stock-price-direction-prediction-by-directly | 1309.7119 | null | http://arxiv.org/abs/1309.7119v3 | http://arxiv.org/pdf/1309.7119v3.pdf | Stock price direction prediction by directly using prices data: an empirical study on the KOSPI and HSI | The prediction of a stock market direction may serve as an early
recommendation system for short-term investors and as an early financial
distress warning system for long-term shareholders. Many stock prediction
studies focus on using macroeconomic indicators, such as CPI and GDP, to train
the prediction model. However... | ['Yanshan Wang'] | 2013-09-27 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-9.09208596e-01 -3.59548032e-01 -5.19137442e-01 1.28112817e-02
-3.61297220e-01 -4.39061254e-01 6.09984994e-01 -9.37100872e-02
-1.87432379e-01 9.95629907e-01 2.92670101e-01 -7.69861400e-01
3.52077968e-02 -1.32380688e+00 7.56397471e-02 -7.45172739e-01
9.39184725e-02 6.88346475e-02 3.49264801e-01 -2.95617193... | [4.528965950012207, 4.205076694488525] |
52649c37-b58b-4673-a182-6da6ff84defb | shortest-paths-in-hsi-space-for-color-texture | 1904.07429 | null | http://arxiv.org/abs/1904.07429v1 | http://arxiv.org/pdf/1904.07429v1.pdf | Shortest Paths in HSI Space for Color Texture Classification | Color texture representation is an important step in the task of texture
classification. Shortest paths was used to extract color texture features from
RGB and HSV color spaces. In this paper, we propose to use shortest paths in
the HSI space to build a texture representation for classification. In
particular, two undi... | ['Hongyan zhang', 'Tianyu Wang', 'Lintao Zheng', 'Yongsheng Dong', 'Mingxin Jin', 'Lingfei Liang'] | 2019-04-16 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 1.55560598e-01 -7.91487932e-01 1.65893964e-03 -1.45841062e-01
5.34732863e-02 -2.80678332e-01 1.81177706e-01 -1.60733759e-01
-9.51634720e-02 4.67144430e-01 -3.66955549e-01 -1.08944625e-01
-4.26034629e-01 -1.34873605e+00 8.11778381e-02 -9.69739497e-01
-1.66023746e-01 -2.01696590e-01 4.24584806e-01 -2.55031437... | [10.388235092163086, -0.3999563455581665] |
b926f0fe-6a2e-4554-8766-36e4c4b43c4b | composing-pick-and-place-tasks-by-grounding | 2102.08094 | null | https://arxiv.org/abs/2102.08094v1 | https://arxiv.org/pdf/2102.08094v1.pdf | Composing Pick-and-Place Tasks By Grounding Language | Controlling robots to perform tasks via natural language is one of the most challenging topics in human-robot interaction. In this work, we present a robot system that follows unconstrained language instructions to pick and place arbitrary objects and effectively resolves ambiguities through dialogues. Our approach inf... | ['Wolfram Burgard', 'Oier Mees'] | 2021-02-16 | null | null | null | null | ['natural-language-visual-grounding'] | ['reasoning'] | [ 9.83200669e-02 3.32008988e-01 1.19403258e-01 -5.12483299e-01
-1.99290186e-01 -9.56229925e-01 4.51584101e-01 8.92015547e-02
-2.23524868e-01 7.10356534e-01 -1.39397070e-01 -2.87918597e-01
-2.78524488e-01 -4.12016422e-01 -7.17424810e-01 -2.57651061e-01
9.84691307e-02 1.05175090e+00 9.35723856e-02 -6.27586007... | [4.587829113006592, 0.7614148855209351] |
3f87e15a-1246-4449-b25c-68aedcbc68a4 | enhanced-prototypical-learning-for | 2205.11419 | null | https://arxiv.org/abs/2205.11419v1 | https://arxiv.org/pdf/2205.11419v1.pdf | Enhanced Prototypical Learning for Unsupervised Domain Adaptation in LiDAR Semantic Segmentation | Despite its importance, unsupervised domain adaptation (UDA) on LiDAR semantic segmentation is a task that has not received much attention from the research community. Only recently, a completion-based 3D method has been proposed to tackle the problem and formally set up the adaptive scenarios. However, the proposed pi... | ['Junmo Kim', 'JuYoung Yang', 'Eojindl Yi'] | 2022-05-23 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 5.77721238e-01 6.78188130e-02 -1.40782475e-01 -5.88714123e-01
-6.11511350e-01 -5.25278628e-01 7.79619098e-01 1.07091248e-01
-5.29015779e-01 5.86849570e-01 -4.74974424e-01 -2.79742002e-01
-3.58821273e-01 -8.72657597e-01 -5.70380688e-01 -4.55595940e-01
1.69922128e-01 1.27058649e+00 8.01962435e-01 1.23703312... | [8.170758247375488, -2.6629133224487305] |
09a96c43-509a-4eb7-8a3f-9b9d6a1ec680 | maf-multimodal-alignment-framework-for-weakly | 2010.05379 | null | https://arxiv.org/abs/2010.05379v1 | https://arxiv.org/pdf/2010.05379v1.pdf | MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding | Phrase localization is a task that studies the mapping from textual phrases to regions of an image. Given difficulties in annotating phrase-to-object datasets at scale, we develop a Multimodal Alignment Framework (MAF) to leverage more widely-available caption-image datasets, which can then be used as a form of weak su... | ['Zhewei Yao', 'Michael W. Mahoney', 'Sheng Shen', 'Hao Tan', 'Qinxin Wang'] | 2020-10-12 | null | https://aclanthology.org/2020.emnlp-main.159 | https://aclanthology.org/2020.emnlp-main.159.pdf | emnlp-2020-11 | ['phrase-grounding'] | ['natural-language-processing'] | [ 4.36317593e-01 1.13722667e-01 -4.07938212e-01 -3.34817499e-01
-1.47252727e+00 -8.19272220e-01 8.26900840e-01 2.13891804e-01
-5.26104271e-01 4.99880791e-01 6.25470638e-01 -1.23277150e-01
2.26927176e-01 -1.97393894e-01 -9.84105945e-01 -5.24952173e-01
1.71141550e-01 2.75897831e-01 2.96325207e-01 -7.45658204... | [10.650477409362793, 1.4909441471099854] |
48aad668-7639-495a-85aa-f8285402d16c | a-joint-model-for-dropped-pronoun-recovery | 2106.03345 | null | https://arxiv.org/abs/2106.03345v1 | https://arxiv.org/pdf/2106.03345v1.pdf | A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech | In this paper, we present a neural model for joint dropped pronoun recovery (DPR) and conversational discourse parsing (CDP) in Chinese conversational speech. We show that DPR and CDP are closely related, and a joint model benefits both tasks. We refer to our model as DiscProReco, and it first encodes the tokens in eac... | ['Ji-Rong Wen', 'Nianwen Xue', 'Jun Guo', 'Sheng Gao', 'Si Li', 'Jun Xu', 'Kerui Xu', 'Jingxuan Yang'] | 2021-06-07 | null | https://aclanthology.org/2021.acl-long.138 | https://aclanthology.org/2021.acl-long.138.pdf | acl-2021-5 | ['discourse-parsing'] | ['natural-language-processing'] | [ 4.47356611e-01 8.49785089e-01 -1.12871207e-01 -5.93422890e-01
-1.07032514e+00 -4.82214868e-01 7.96165884e-01 -3.62142958e-02
-8.94845054e-02 4.83671963e-01 9.05336440e-01 -3.76999795e-01
4.66416866e-01 -6.66752338e-01 -7.36777902e-01 -5.51485538e-01
-2.18810722e-01 7.70443976e-01 2.00452402e-01 -3.53557557... | [12.4485502243042, 7.966202735900879] |
a9cc7806-27c5-434c-8240-3d88ad2bbdb1 | detecting-aggression-and-toxicity-using-a | null | null | https://aclanthology.org/W19-3517 | https://aclanthology.org/W19-3517.pdf | Detecting Aggression and Toxicity using a Multi Dimension Capsule Network | In the era of social media, hate speech, trolling and verbal abuse have become a common issue. We present an approach to automatically classify such statements, using a new deep learning architecture. Our model comprises of a Multi Dimension Capsule Network that generates the representation of sentences which we use fo... | ['Prerna Khurana', 'Saurabh Srivastava'] | 2019-08-01 | null | null | null | ws-2019-8 | ['toxic-comment-classification'] | ['natural-language-processing'] | [-8.06744024e-02 4.53143865e-01 5.78966774e-02 -5.77886343e-01
-4.39597458e-01 -5.63484788e-01 8.52431118e-01 4.13916677e-01
-3.48554373e-01 8.67105544e-01 7.14316487e-01 -7.06983626e-01
1.11025773e-01 -5.86798370e-01 -2.88932413e-01 -2.96624929e-01
5.15543595e-02 4.01742637e-01 -1.76914379e-01 -5.85854530... | [8.784991264343262, 10.623607635498047] |
bd451a83-0c77-4fb1-b5f9-0e7bd472392f | lambeq-an-efficient-high-level-python-library | 2110.04236 | null | https://arxiv.org/abs/2110.04236v1 | https://arxiv.org/pdf/2110.04236v1.pdf | lambeq: An Efficient High-Level Python Library for Quantum NLP | We present lambeq, the first high-level Python library for Quantum Natural Language Processing (QNLP). The open-source toolkit offers a detailed hierarchy of modules and classes implementing all stages of a pipeline for converting sentences to string diagrams, tensor networks, and quantum circuits ready to be used on a... | ['Bob Coecke', 'Stephen Clark', 'Konstantinos Meichanetzidis', 'Giovanni De Felice', 'Alexis Toumi', 'Robin Lorenz', 'Anna Pearson', 'Richie Yeung', 'Ian Fan', 'Dimitri Kartsaklis'] | 2021-10-08 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 3.69998366e-01 3.38073820e-01 3.05442989e-01 -6.30379200e-01
-8.47350478e-01 -1.03037679e+00 6.61969721e-01 4.81494457e-01
-3.18629414e-01 4.09934670e-01 2.51404852e-01 -9.31872785e-01
2.20649883e-01 -1.25671327e+00 -6.70612931e-01 -4.06605572e-01
-7.35460892e-02 4.62917596e-01 1.30930677e-01 -6.51694417... | [5.578176021575928, 4.947471618652344] |
6a8e8394-2f64-455c-a6a6-afcb5ed34567 | unsupervised-abstractive-meeting | 1805.05271 | null | http://arxiv.org/abs/1805.05271v2 | http://arxiv.org/pdf/1805.05271v2.pdf | Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization | We introduce a novel graph-based framework for abstractive meeting speech
summarization that is fully unsupervised and does not rely on any annotations.
Our work combines the strengths of multiple recent approaches while addressing
their weaknesses. Moreover, we leverage recent advances in word embeddings and
graph deg... | ['Jean-Pierre Lorré', 'Michalis Vazirgiannis', 'Antoine Jean-Pierre Tixier', 'Wensi Ding', 'Zekun Zhang', 'Polykarpos Meladianos', 'Guokan Shang'] | 2018-05-14 | unsupervised-abstractive-meeting-1 | https://aclanthology.org/P18-1062 | https://aclanthology.org/P18-1062.pdf | acl-2018-7 | ['dialogue-understanding', 'meeting-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.99267405e-01 4.69228595e-01 -3.42020422e-01 -3.46364260e-01
-9.25612032e-01 -5.81391037e-01 5.66841304e-01 5.15683293e-01
-2.18877986e-01 7.17270672e-01 1.14326417e+00 -3.52302670e-01
-2.24586517e-01 -4.46755230e-01 -1.83814421e-01 -3.33381265e-01
-2.35677660e-01 7.44213939e-01 3.72774035e-01 -5.39882421... | [12.52270793914795, 9.510708808898926] |
b51e9cb0-ccac-4802-a2ea-16f95420e00a | deep-cognitive-reasoning-network-for-multi | null | null | https://aclanthology.org/2021.findings-acl.19 | https://aclanthology.org/2021.findings-acl.19.pdf | Deep Cognitive Reasoning Network for Multi-hop Question Answering over Knowledge Graphs | null | ['Jie Wang', 'Feng Wu', 'Zhanqiu Zhang', 'Jianyu Cai'] | null | null | null | null | findings-acl-2021-8 | ['multi-hop-question-answering'] | ['knowledge-base'] | [-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... | [-7.251688003540039, 3.7372524738311768] |
4081fa85-fe5f-4510-aa43-a164a6e966a2 | the-first-shared-task-on-discourse-1 | 2005.13399 | null | https://arxiv.org/abs/2005.13399v1 | https://arxiv.org/pdf/2005.13399v1.pdf | The First Shared Task on Discourse Representation Structure Parsing | The paper presents the IWCS 2019 shared task on semantic parsing where the goal is to produce Discourse Representation Structures (DRSs) for English sentences. DRSs originate from Discourse Representation Theory and represent scoped meaning representations that capture the semantics of negation, modals, quantification,... | ['Rik van Noord', 'Johan Bos', 'Hessel Haagsma', 'Lasha Abzianidze'] | 2020-05-27 | the-first-shared-task-on-discourse | https://aclanthology.org/W19-1201 | https://aclanthology.org/W19-1201.pdf | ws-2019-5 | ['drs-parsing'] | ['natural-language-processing'] | [ 3.64446342e-01 9.34006631e-01 -4.31120694e-01 -6.67133212e-01
-6.99359596e-01 -7.15143025e-01 6.62751615e-01 6.76359475e-01
-1.09524682e-01 8.98638010e-01 9.69037056e-01 -3.17065358e-01
1.32547364e-01 -1.10897028e+00 -4.80713755e-01 -1.58379734e-01
-1.16431946e-02 5.57940364e-01 6.34278595e-01 -8.59556973... | [10.320425987243652, 9.298236846923828] |
b5b92f11-fa06-41b1-8c46-adb3c776c744 | topic-guided-variational-autoencoders-for | 1903.07137 | null | http://arxiv.org/abs/1903.07137v1 | http://arxiv.org/pdf/1903.07137v1.pdf | Topic-Guided Variational Autoencoders for Text Generation | We propose a topic-guided variational autoencoder (TGVAE) model for text
generation. Distinct from existing variational autoencoder (VAE) based
approaches, which assume a simple Gaussian prior for the latent code, our model
specifies the prior as a Gaussian mixture model (GMM) parametrized by a neural
topic module. Eac... | ['Lawrence Carin', 'Wenlin Wang', 'Changyou Chen', 'Zhe Gan', 'Hongteng Xu', 'Ruiyi Zhang', 'Guoyin Wang', 'Dinghan Shen'] | 2019-03-17 | null | null | null | null | ['conditional-text-generation'] | ['natural-language-processing'] | [-5.95331490e-02 6.53500974e-01 -7.75469020e-02 -2.66959667e-01
-9.28415656e-01 -5.12549400e-01 1.17503417e+00 -3.68105233e-01
1.64533257e-01 6.77692831e-01 6.95503771e-01 -2.29831621e-01
4.72861081e-01 -1.07979846e+00 -8.77727389e-01 -7.81889677e-01
4.57740456e-01 6.92393959e-01 -3.49335849e-01 -1.25308976... | [11.850859642028809, 9.040060043334961] |
b23a2975-7b37-4cce-8475-757cba37b9a4 | kalman-based-spectro-temporal-ecg-analysis | 1812.05555 | null | http://arxiv.org/abs/1812.05555v1 | http://arxiv.org/pdf/1812.05555v1.pdf | Kalman-based Spectro-Temporal ECG Analysis using Deep Convolutional Networks for Atrial Fibrillation Detection | In this article, we propose a novel ECG classification framework for atrial
fibrillation (AF) detection using spectro-temporal representation (i.e., time
varying spectrum) and deep convolutional networks. In the first step we use a
Bayesian spectro-temporal representation based on the estimation of
time-varying coeffic... | ['Simo Särkkä', 'Zheng Zhao', 'Ali Bahrami Rad'] | 2018-12-12 | null | null | null | null | ['ecg-classification', 'atrial-fibrillation-detection'] | ['medical', 'medical'] | [ 2.47400135e-01 -4.69905674e-01 3.22989881e-01 -2.35469714e-01
-7.76120901e-01 -4.84459102e-01 -1.62814632e-01 2.49589115e-01
-4.86391008e-01 8.86612058e-01 -3.63368839e-02 -3.91733348e-01
-5.12032330e-01 -3.24724376e-01 -2.03522563e-01 -7.12112010e-01
-7.80727565e-01 -6.91959932e-02 -4.05159473e-01 2.42130086... | [14.290655136108398, 3.2768101692199707] |
5a41d528-85d6-4979-8e32-7f8a9f82ddc4 | mwp-bert-a-strong-baseline-for-math-word | 2107.13435 | null | https://arxiv.org/abs/2107.13435v2 | https://arxiv.org/pdf/2107.13435v2.pdf | MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving | Math word problem (MWP) solving faces a dilemma in number representation learning. In order to avoid the number representation issue and reduce the search space of feasible solutions, existing works striving for MWP solving usually replace real numbers with symbolic placeholders to focus on logic reasoning. However, di... | ['Jie Shao', 'Yunshi Lan', 'Wei Qin', 'Lei Wang', 'Xiangliang Zhang', 'Jipeng Zhang', 'Zhenwen Liang'] | 2021-07-28 | null | https://aclanthology.org/2022.findings-naacl.74 | https://aclanthology.org/2022.findings-naacl.74.pdf | findings-naacl-2022-7 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 1.06010400e-01 3.03437978e-01 -5.13071954e-01 -1.96091518e-01
-4.20575917e-01 -6.53697670e-01 2.47127891e-01 3.84026140e-01
-3.28238726e-01 7.23655224e-01 2.25597173e-01 -9.45433557e-01
-1.74469918e-01 -1.58177495e+00 -9.00254250e-01 -1.01307549e-01
1.53146505e-01 3.92102122e-01 9.71403942e-02 -5.06534696... | [9.491168022155762, 7.369532585144043] |
3269aa66-e864-4a72-b25c-5e1637b0278a | oracle-efficient-smoothed-online-learning-for | 2302.05430 | null | https://arxiv.org/abs/2302.05430v1 | https://arxiv.org/pdf/2302.05430v1.pdf | Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making | Smoothed online learning has emerged as a popular framework to mitigate the substantial loss in statistical and computational complexity that arises when one moves from classical to adversarial learning. Unfortunately, for some spaces, it has been shown that efficient algorithms suffer an exponentially worse regret tha... | ['Max Simchowitz', 'Alexander Rakhlin', 'Adam Block'] | 2023-02-10 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [ 2.51825064e-01 6.55201197e-01 -1.06619976e-01 -2.85911858e-01
-1.45885754e+00 -1.02105570e+00 2.18181744e-01 4.01690722e-01
-8.13070774e-01 1.06723738e+00 -1.86911359e-01 -5.82514465e-01
-4.21072543e-01 -6.55425310e-01 -1.34969246e+00 -9.71802533e-01
-5.49560905e-01 2.26626486e-01 -1.39562972e-02 -1.99654177... | [4.7900896072387695, 3.4681856632232666] |
e6057a66-52c8-4ad7-bab1-64182b6a9c06 | hscnet-hierarchical-scene-coordinate | 2305.03595 | null | https://arxiv.org/abs/2305.03595v1 | https://arxiv.org/pdf/2305.03595v1.pdf | HSCNet++: Hierarchical Scene Coordinate Classification and Regression for Visual Localization with Transformer | Visual localization is critical to many applications in computer vision and robotics. To address single-image RGB localization, state-of-the-art feature-based methods match local descriptors between a query image and a pre-built 3D model. Recently, deep neural networks have been exploited to regress the mapping between... | ['Juho Kannala', 'Giorgos Tolias', 'Yi Zhao', 'Xiaotian Li', 'Iaroslav Melekhov', 'Zakaria Laskar', 'Shuzhe Wang'] | 2023-05-05 | null | null | null | null | ['visual-localization'] | ['computer-vision'] | [ 1.61747143e-01 -4.09038723e-01 4.71952558e-02 -7.24798143e-01
-8.97703111e-01 -3.48896801e-01 5.11875570e-01 -2.10806318e-02
-8.52114081e-01 3.91875178e-01 -2.81287909e-01 -3.50389667e-02
-1.04484474e-03 -7.08293974e-01 -1.18556452e+00 -5.35890639e-01
2.30695456e-01 4.90116507e-01 3.42228830e-01 -4.04375717... | [7.687151908874512, -2.159194231033325] |
c4c624d8-1714-4c80-915a-df978eed627f | human-de-occlusion-invisible-perception-and | 2103.11597 | null | https://arxiv.org/abs/2103.11597v1 | https://arxiv.org/pdf/2103.11597v1.pdf | Human De-occlusion: Invisible Perception and Recovery for Humans | In this paper, we tackle the problem of human de-occlusion which reasons about occluded segmentation masks and invisible appearance content of humans. In particular, a two-stage framework is proposed to estimate the invisible portions and recover the content inside. For the stage of mask completion, a stacked network s... | ['Xinggang Wang', 'Zilong Huang', 'Yitong Wang', 'Shiyin Wang', 'Qiang Zhou'] | 2021-03-22 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhou_Human_De-Occlusion_Invisible_Perception_and_Recovery_for_Humans_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhou_Human_De-Occlusion_Invisible_Perception_and_Recovery_for_Humans_CVPR_2021_paper.pdf | cvpr-2021-1 | ['human-parsing'] | ['computer-vision'] | [ 5.41474164e-01 4.54368800e-01 -1.70214608e-01 -4.30553824e-01
-5.75859845e-01 -1.76885426e-01 1.59175903e-01 -2.20126852e-01
-3.26965392e-01 5.13541400e-01 3.44989121e-01 2.02038497e-01
3.43097419e-01 -4.04567599e-01 -7.03168452e-01 -4.14530128e-01
3.71417493e-01 3.12381625e-01 3.60635608e-01 -9.76065770... | [8.473052024841309, -0.16174066066741943] |
38753511-06f4-447c-839a-741013e639a9 | mbrain-a-multi-channel-self-supervised | 2306.13102 | null | https://arxiv.org/abs/2306.13102v1 | https://arxiv.org/pdf/2306.13102v1.pdf | MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals | Brain signals are important quantitative data for understanding physiological activities and diseases of human brain. Most existing studies pay attention to supervised learning methods, which, however, require high-cost clinical labels. In addition, the huge difference in the clinical patterns of brain signals measured... | ['Yafeng Li', 'Teng Liu', 'Yang Yang', 'Junru Chen', 'Donghong Cai'] | 2023-06-15 | null | null | null | null | ['self-supervised-learning', 'seizure-detection'] | ['computer-vision', 'medical'] | [ 1.84332013e-01 -2.08052605e-01 -3.13542709e-02 -3.63242954e-01
-3.38183314e-01 -2.69726217e-01 2.00217709e-01 -7.42630139e-02
8.17383081e-03 6.59528911e-01 6.29852489e-02 -5.79635911e-02
-7.08627403e-01 -4.06525820e-01 -4.75657076e-01 -9.43612158e-01
-6.68266773e-01 7.96036050e-02 7.04522710e-03 7.49932509... | [13.139824867248535, 3.5001022815704346] |
e3d15fc8-675b-460c-a2cc-9685dde1daf0 | graph-based-multi-robot-path-finding-and | 2206.11319 | null | https://arxiv.org/abs/2206.11319v1 | https://arxiv.org/pdf/2206.11319v1.pdf | Graph-Based Multi-Robot Path Finding and Planning | Purpose of Review Planning collision-free paths for multiple robots is important for real-world multi-robot systems and has been studied as an optimization problem on graphs, called Multi-Agent Path Finding (MAPF). This review surveys different categories of classic and state-of-the-art MAPF algorithms and different re... | ['Hang Ma'] | 2022-06-22 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-1.00473829e-01 9.75273773e-02 6.09445888e-05 -3.14108968e-01
-1.94307223e-01 -9.11219716e-01 2.32681017e-02 5.64595520e-01
-5.03205121e-01 9.47842598e-01 -7.64840126e-01 -4.81263191e-01
-1.07981098e+00 -8.58339131e-01 -6.79169357e-01 -4.31389719e-01
-7.86988258e-01 1.48549342e+00 3.69603544e-01 -1.09232426... | [4.950806617736816, 1.6422604322433472] |
dd5afcb3-d8f8-45f3-891f-ec01432522ea | bi-mix-bidirectional-mixing-for-domain | 2111.10339 | null | https://arxiv.org/abs/2111.10339v1 | https://arxiv.org/pdf/2111.10339v1.pdf | Bi-Mix: Bidirectional Mixing for Domain Adaptive Nighttime Semantic Segmentation | In autonomous driving, learning a segmentation model that can adapt to various environmental conditions is crucial. In particular, copying with severe illumination changes is an impelling need, as models trained on daylight data will perform poorly at nighttime. In this paper, we study the problem of Domain Adaptive Ni... | ['Elisa Ricci', 'Nicu Sebe', 'Mingli Ding', 'Hao Tang', 'Zhun Zhong', 'Guanglei Yang'] | 2021-11-19 | null | null | null | null | ['image-relighting'] | ['computer-vision'] | [ 5.91168217e-02 -2.00595856e-01 -3.89149822e-02 -8.13769639e-01
-7.88620114e-01 -6.87918842e-01 5.50894976e-01 -4.42528635e-01
-3.57379943e-01 5.26217639e-01 -1.04373932e-01 -4.21881944e-01
2.68330127e-01 -6.15951538e-01 -7.92998850e-01 -9.80661273e-01
7.87786484e-01 4.68655318e-01 3.12485576e-01 -3.26384217... | [8.845301628112793, -1.4951142072677612] |
8c905b10-5407-48e4-8183-07d8299fa2e2 | divinet-3d-reconstruction-from-disparate | 2306.04699 | null | https://arxiv.org/abs/2306.04699v3 | https://arxiv.org/pdf/2306.04699v3.pdf | DiViNeT: 3D Reconstruction from Disparate Views via Neural Template Regularization | We present a volume rendering-based neural surface reconstruction method that takes as few as three disparate RGB images as input. Our key idea is to regularize the reconstruction, which is severely ill-posed and leaving significant gaps between the sparse views, by learning a set of neural templates that act as surfac... | ['Hao Zhang', 'Akshay Gadi Patil', 'Aditya Vora'] | 2023-06-07 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [ 5.06814420e-01 3.48033130e-01 3.02647322e-01 -4.19794351e-01
-9.77380753e-01 -5.15323579e-01 5.49700260e-01 -4.75597590e-01
1.57803655e-01 4.63598549e-01 2.89731413e-01 -5.17455228e-02
2.21484751e-01 -8.43168795e-01 -1.07720077e+00 -5.06820917e-01
3.87951225e-01 7.01151788e-01 2.08252892e-01 -2.39105359... | [9.035650253295898, -3.1954963207244873] |
c7f89996-3d9d-4a9f-a97c-6ca4578636c0 | improving-multiple-pedestrian-tracking-by | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Stadler_Improving_Multiple_Pedestrian_Tracking_by_Track_Management_and_Occlusion_Handling_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Stadler_Improving_Multiple_Pedestrian_Tracking_by_Track_Management_and_Occlusion_Handling_CVPR_2021_paper.pdf | Improving Multiple Pedestrian Tracking by Track Management and Occlusion Handling | Multi-pedestrian trackers perform well when targets are clearly visible making the association task quite easy. However, when heavy occlusions are present, a mechanism to reidentify persons is needed. The common approach is to extract visual features from new detections and compare them with the features of previou... | ['Jurgen Beyerer', 'Daniel Stadler'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['occlusion-handling'] | ['computer-vision'] | [-2.47252271e-01 -3.76473010e-01 1.44072041e-01 -8.33306164e-02
-3.52086902e-01 -5.81195295e-01 6.39813006e-01 6.29226923e-01
-7.28556514e-01 9.51660693e-01 1.65099418e-03 1.02479704e-01
2.00813301e-02 -7.88278937e-01 -7.74150252e-01 -7.23074317e-01
1.91145279e-02 4.99427348e-01 9.97500658e-01 6.80491328... | [6.465353965759277, -1.9659761190414429] |
65514e46-86d8-4e21-8f8a-53240e80861a | ensemble-classifier-design-tuned-to-dataset | 2205.06177 | null | https://arxiv.org/abs/2205.06177v1 | https://arxiv.org/pdf/2205.06177v1.pdf | Ensemble Classifier Design Tuned to Dataset Characteristics for Network Intrusion Detection | Machine Learning-based supervised approaches require highly customized and fine-tuned methodologies to deliver outstanding performance. This paper presents a dataset-driven design and performance evaluation of a machine learning classifier for the network intrusion dataset UNSW-NB15. Analysis of the dataset suggests th... | ['Gursel Serpen', 'Zeinab Zoghi'] | 2022-05-08 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 1.57782048e-01 -2.58762568e-01 -2.77180552e-01 -8.06258500e-01
-2.55072594e-01 -4.09159034e-01 4.18329626e-01 5.27796924e-01
-2.19755903e-01 1.04820049e+00 3.73151600e-02 -6.99438453e-01
-7.39756048e-01 -8.39828312e-01 1.21885419e-01 -9.35357094e-01
3.69344316e-02 4.19833004e-01 8.17348994e-03 -3.85378659... | [8.371797561645508, 4.572478771209717] |
11c0d1e2-5095-4d4d-a03f-65ba5316edc4 | spelling-correction-for-russian-a-comparative | null | null | https://aclanthology.org/2021.ranlp-main.136 | https://aclanthology.org/2021.ranlp-main.136.pdf | Spelling Correction for Russian: A Comparative Study of Datasets and Methods | We develop a minimally-supervised model for spelling correction and evaluate its performance on three datasets annotated for spelling errors in Russian. The first corpus is a dataset of Russian social media data that was recently used in a shared task on Russian spelling correction. The other two corpora contain texts ... | ['Alla Rozovskaya'] | null | null | https://aclanthology.org/2021.ranlp-1.136 | https://aclanthology.org/2021.ranlp-1.136.pdf | ranlp-2021-9 | ['cross-corpus', 'spelling-correction'] | ['computer-vision', 'natural-language-processing'] | [ 5.57460725e-01 -9.61100161e-02 -1.80686966e-01 -2.46423453e-01
-1.28493369e+00 -6.26273334e-01 7.05033779e-01 7.99090326e-01
-9.38742340e-01 1.01540029e+00 5.69655240e-01 -6.65733159e-01
2.27230862e-01 -4.09772933e-01 -6.89620197e-01 -1.42818108e-01
7.42156267e-01 7.67044783e-01 3.96445870e-01 -6.30130053... | [11.079379081726074, 10.68565559387207] |
645ca4f1-948a-4d44-8860-e903702662e4 | chinese-zero-pronoun-resolution-some-recent | null | null | https://aclanthology.org/D13-1135 | https://aclanthology.org/D13-1135.pdf | Chinese Zero Pronoun Resolution: Some Recent Advances | null | ['Vincent Ng', 'Chen Chen'] | 2013-10-01 | null | null | null | emnlp-2013-10 | ['chinese-zero-pronoun-resolution'] | ['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... | [-7.387261390686035, 3.557269811630249] |
63bb773d-5402-49db-a5ec-9f0b72191e3d | attention-based-memory-video-portrait-matting | 2203.06890 | null | https://arxiv.org/abs/2203.06890v2 | https://arxiv.org/pdf/2203.06890v2.pdf | Attention based Memory video portrait matting | We proposed a novel trimap free video matting method based on the attention mechanism. By the nature of the problem, most existing approaches use either multiple computational expansive modules or complex algorithms to exploit temporal information fully. We designed a temporal aggregation module to compute the temporal... | ['Shufeng Song'] | 2022-03-14 | null | null | null | null | ['image-matting', 'video-matting'] | ['computer-vision', 'computer-vision'] | [-4.55010869e-02 -1.56315431e-01 -1.45655394e-01 -2.16728389e-01
-2.34219730e-01 -8.45779330e-02 4.60910797e-01 -3.31033051e-01
-3.88233602e-01 7.18315363e-01 2.45540828e-01 -6.05324768e-02
2.59727966e-02 -7.93018401e-01 -5.54006636e-01 -5.47892869e-01
-2.14615613e-01 1.02662869e-01 5.22670746e-01 -1.39506608... | [10.554170608520508, -0.9342142343521118] |
24424c0b-0bc4-4627-a6da-489a27be45c4 | ca-net-comprehensive-attention-convolutional | 2009.10549 | null | https://arxiv.org/abs/2009.10549v2 | https://arxiv.org/pdf/2009.10549v2.pdf | CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation | Accurate medical image segmentation is essential for diagnosis and treatment planning of diseases. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they are still challenged by complicated conditions where the segmentation target has larg... | ['Sébastien Ourselin', 'Tao Song', 'Ran Gu', 'Tom Vercauteren', 'Rui Huang', 'Michael Aertsen', 'Guotai Wang', 'Shaoting Zhang', 'Jan Deprest'] | 2020-09-22 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 9.20023769e-02 2.64430493e-01 2.56724600e-02 -4.94146585e-01
-5.02346396e-01 -3.37518394e-01 -2.50503211e-03 1.84654564e-01
-3.32550794e-01 4.02799368e-01 6.20018877e-02 -3.30773950e-01
-7.87462071e-02 -6.06142521e-01 -6.84453189e-01 -6.92622006e-01
8.47731680e-02 3.51007760e-01 4.67461795e-01 -6.26450852... | [14.582379341125488, -2.5332541465759277] |
f63e9de8-83e3-4ca2-b917-c7c7295fe340 | kit-multi-a-translation-oriented-multilingual | null | null | https://aclanthology.org/L18-1616 | https://aclanthology.org/L18-1616.pdf | KIT-Multi: A Translation-Oriented Multilingual Embedding Corpus | null | ['er', 'Thanh-Le Ha', 'Ngoc Quan Pham', 'Alex Waibel', 'Matthias Sperber', 'Jan Niehues'] | 2018-05-01 | kit-multi-a-translation-oriented-multilingual-1 | https://aclanthology.org/L18-1616 | https://aclanthology.org/L18-1616.pdf | lrec-2018-5 | ['multilingual-word-embeddings', 'cross-lingual-document-classification'] | ['methodology', '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... | [-7.105790615081787, 3.5335988998413086] |
308cd02e-06af-4032-a770-baecb61b64c8 | removing-objects-from-neural-radiance-fields | 2212.11966 | null | https://arxiv.org/abs/2212.11966v1 | https://arxiv.org/pdf/2212.11966v1.pdf | Removing Objects From Neural Radiance Fields | Neural Radiance Fields (NeRFs) are emerging as a ubiquitous scene representation that allows for novel view synthesis. Increasingly, NeRFs will be shareable with other people. Before sharing a NeRF, though, it might be desirable to remove personal information or unsightly objects. Such removal is not easily achieved wi... | ['Sara Vicente', 'Michael Firman', 'Gabriel Brostow', 'Marc Pollefeys', 'Aron Monszpart', 'Guillermo Garcia-Hernando', 'Silvan Weder'] | 2022-12-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Weder_Removing_Objects_From_Neural_Radiance_Fields_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Weder_Removing_Objects_From_Neural_Radiance_Fields_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-inpainting'] | ['computer-vision'] | [ 6.88505948e-01 1.17049403e-01 2.36614466e-01 -4.86376762e-01
-8.15933287e-01 -7.31230319e-01 6.05739772e-01 -2.77890623e-01
-1.61242634e-01 7.90035129e-01 6.52172446e-01 8.58465806e-02
-8.42771158e-02 -7.72852778e-01 -9.27790761e-01 -4.20549870e-01
7.23373532e-01 5.40976748e-02 -8.69130269e-02 -3.25117618... | [9.287914276123047, -3.0858051776885986] |
ca4c3ad7-b12e-4b13-9a5b-0c22152bfacd | 191013408 | 1910.13408 | null | https://arxiv.org/abs/1910.13408v2 | https://arxiv.org/pdf/1910.13408v2.pdf | A framework for deep learning emulation of numerical models with a case study in satellite remote sensing | Numerical models based on physics represent the state-of-the-art in earth system modeling and comprise our best tools for generating insights and predictions. Despite rapid growth in computational power, the perceived need for higher model resolutions overwhelms the latest-generation computers, reducing the ability of ... | ['Weile Wang', 'Auroop R. Ganguly', 'Thomas Vandal', 'Ramakrishna Nemani', 'Kate Duffy'] | 2019-10-29 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [-2.71515667e-01 -1.70562446e-01 1.95549369e-01 -1.24106392e-01
-4.96332675e-01 -6.70620024e-01 1.08773768e+00 2.47454390e-01
-4.36547101e-02 9.42487776e-01 2.66668946e-01 -1.11398566e+00
-4.50417697e-01 -1.06720424e+00 -6.49557769e-01 -6.46472573e-01
-5.13674915e-01 7.20891476e-01 -2.06851199e-01 -7.00708151... | [6.584314346313477, 3.1027779579162598] |
1d94d4f7-791f-4db7-a0a1-75eaf4e8503a | explaining-large-language-model-based-neural | 2301.13820 | null | https://arxiv.org/abs/2301.13820v1 | https://arxiv.org/pdf/2301.13820v1.pdf | Explaining Large Language Model-Based Neural Semantic Parsers (Student Abstract) | While large language models (LLMs) have demonstrated strong capability in structured prediction tasks such as semantic parsing, few amounts of research have explored the underlying mechanisms of their success. Our work studies different methods for explaining an LLM-based semantic parser and qualitatively discusses the... | ['Ziyu Yao', 'Bailin Wang', 'Yilun Zhou', 'Daking Rai'] | 2023-01-25 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 2.32086107e-01 1.30004013e+00 -8.49509776e-01 -9.66341496e-01
-6.83934033e-01 -3.10368389e-01 6.08686447e-01 2.66973734e-01
-4.16421294e-02 3.38984758e-01 5.93197167e-01 -1.05907643e+00
2.22677693e-01 -6.48601174e-01 -6.42104149e-01 1.09681070e-01
1.45441353e-01 8.10665846e-01 4.57070112e-01 4.06722389... | [10.496962547302246, 9.333677291870117] |
68370834-6251-4c11-9f7b-791a4a41ff0d | ntu-rgbd-120-a-large-scale-benchmark-for-3d | 1905.04757 | null | https://arxiv.org/abs/1905.04757v2 | https://arxiv.org/pdf/1905.04757v2.pdf | NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding | Research on depth-based human activity analysis achieved outstanding performance and demonstrated the effectiveness of 3D representation for action recognition. The existing depth-based and RGB+D-based action recognition benchmarks have a number of limitations, including the lack of large-scale training samples, realis... | ['Ling-Yu Duan', 'Gang Wang', 'Mauricio Perez', 'Alex C. Kot', 'Amir Shahroudy', 'Jun Liu'] | 2019-05-12 | null | null | null | null | ['one-shot-3d-action-recognition'] | ['computer-vision'] | [ 3.95591736e-01 -3.77199173e-01 -4.60529834e-01 -3.26470613e-01
-6.34295464e-01 -2.79727042e-01 5.05864263e-01 -3.59698534e-01
-1.42747134e-01 4.88056302e-01 7.26596832e-01 2.35228449e-01
-8.21994171e-02 -6.42113388e-01 -3.45522881e-01 -6.87505186e-01
-1.42461970e-01 2.38881692e-01 4.85715389e-01 -1.36459097... | [7.871738433837891, 0.4557730257511139] |
56edc23c-e546-4832-9718-66436db94792 | cosmix-compositional-semantic-mix-for-domain | 2207.09778 | null | https://arxiv.org/abs/2207.09778v1 | https://arxiv.org/pdf/2207.09778v1.pdf | CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation | 3D LiDAR semantic segmentation is fundamental for autonomous driving. Several Unsupervised Domain Adaptation (UDA) methods for point cloud data have been recently proposed to improve model generalization for different sensors and environments. Researchers working on UDA problems in the image domain have shown that samp... | ['Fabio Poiesi', 'Elisa Ricci', 'Nicu Sebe', 'Giuseppe Fiameni', 'Fabio Galasso', 'Cristiano Saltori'] | 2022-07-20 | null | null | null | null | ['lidar-semantic-segmentation', 'point-cloud-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.31079125e-01 8.78079981e-02 -4.39266562e-01 -6.10410333e-01
-9.01647806e-01 -6.15119874e-01 7.93105602e-01 -2.94250362e-02
-4.89726305e-01 5.27144551e-01 -4.02584523e-01 -3.23149443e-01
9.97771323e-02 -1.01043665e+00 -1.06334007e+00 -5.79790354e-01
3.29763174e-01 1.30503368e+00 7.30509043e-01 -8.55062976... | [8.149197578430176, -2.6085610389709473] |
2fd5e3a6-50c4-4d36-aca5-caa811a8354a | efficient-algorithms-for-exact-graph-matching | 2305.19666 | null | https://arxiv.org/abs/2305.19666v2 | https://arxiv.org/pdf/2305.19666v2.pdf | Efficient Algorithms for Exact Graph Matching on Correlated Stochastic Block Models with Constant Correlation | We consider the problem of graph matching, or learning vertex correspondence, between two correlated stochastic block models (SBMs). The graph matching problem arises in various fields, including computer vision, natural language processing and bioinformatics, and in particular, matching graphs with inherent community ... | ['Hye Won Chung', 'Dongpil Shin', 'Joonhyuk Yang'] | 2023-05-31 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 0.41280547 0.29342628 -0.2730768 0.04222735 -0.3929605 -0.6257282
0.38333195 0.7622422 -0.0447631 0.47117275 -0.26477045 -0.34141544
-0.61133116 -1.2922541 -0.50561047 -0.9157914 -0.5094566 1.0153807
0.49358824 -0.04409095 0.02488483 0.4463059 -1.1577901 -0.09344656
0.8983442 0.33292815 -0.13... | [6.918465614318848, 5.194827079772949] |
73e39d1e-1e45-4435-9823-4e25879fa15a | pie-a-parameter-and-inference-efficient | 2204.13957 | null | https://arxiv.org/abs/2204.13957v2 | https://arxiv.org/pdf/2204.13957v2.pdf | PIE: a Parameter and Inference Efficient Solution for Large Scale Knowledge Graph Embedding Reasoning | Knowledge graph (KG) embedding methods which map entities and relations to unique embeddings in the KG have shown promising results on many reasoning tasks. However, the same embedding dimension for both dense entities and sparse entities will cause either over parameterization (sparse entities) or under fitting (dense... | ['Wei Chu', 'Xiexiong Lin', 'Taifeng Wang', 'Linlin Chao'] | 2022-04-29 | null | null | null | null | ['entity-typing'] | ['natural-language-processing'] | [-4.06685650e-01 4.28834587e-01 -3.95707816e-01 -2.22903237e-01
-1.78311363e-01 -5.30131638e-01 2.58057386e-01 3.82730603e-01
-4.25878793e-01 6.80525482e-01 3.34284276e-01 -3.16432387e-01
-6.02386475e-01 -1.27966070e+00 -8.43415797e-01 -4.55461383e-01
-4.20893013e-01 8.94762635e-01 1.97708070e-01 -7.21050277... | [8.733111381530762, 7.877845764160156] |
c4fff2cc-4a48-4a03-87f7-b2abf0e20da1 | scaling-open-vocabulary-object-detection | 2306.09683 | null | https://arxiv.org/abs/2306.09683v1 | https://arxiv.org/pdf/2306.09683v1.pdf | Scaling Open-Vocabulary Object Detection | Open-vocabulary object detection has benefited greatly from pretrained vision-language models, but is still limited by the amount of available detection training data. While detection training data can be expanded by using Web image-text pairs as weak supervision, this has not been done at scales comparable to image-le... | ['Neil Houlsby', 'Alexey Gritsenko', 'Matthias Minderer'] | 2023-06-16 | null | null | null | null | ['open-vocabulary-object-detection'] | ['computer-vision'] | [ 2.11337879e-01 3.32545996e-01 -2.46733993e-01 -3.12822104e-01
-1.14566386e+00 -7.06398666e-01 6.25735939e-01 1.67330474e-01
-9.47466195e-01 3.71611357e-01 -2.44829580e-02 -3.16931456e-01
6.81883991e-01 -3.69628906e-01 -9.83823955e-01 -1.56769276e-01
7.80341849e-02 6.67429328e-01 9.97231424e-01 -1.88503832... | [9.515212059020996, 1.4129977226257324] |
878da011-d608-48c5-a0b9-323cb323b482 | bitmix-data-augmentation-for-image | 2006.16625 | null | https://arxiv.org/abs/2006.16625v1 | https://arxiv.org/pdf/2006.16625v1.pdf | BitMix: Data Augmentation for Image Steganalysis | Convolutional neural networks (CNN) for image steganalysis demonstrate better performances with employing concepts from high-level vision tasks. The major employed concept is to use data augmentation to avoid overfitting due to limited data. To augment data without damaging the message embedding, only rotating multiple... | ['Heung-Kyu Lee', 'Seung-Hun Nam', 'In-Jae Yu', 'Wonhyuk Ahn'] | 2020-06-30 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 9.29417133e-01 2.47348368e-01 -1.29384562e-01 1.77969709e-01
-2.73902595e-01 -1.47585437e-01 4.11521912e-01 -3.74185473e-01
-4.79492843e-01 3.13407153e-01 7.79945701e-02 -5.03997743e-01
5.15853941e-01 -7.49598145e-01 -8.34728897e-01 -1.17717528e+00
3.24435905e-02 -2.94101954e-01 2.37505451e-01 -1.74369290... | [4.2979278564453125, 8.05726432800293] |
b10f7d8c-204d-4a84-b101-51401f6cbaf5 | siamese-nestedunet-networks-for-change | null | null | https://dl.acm.org/doi/abs/10.1145/3437802.3437810 | https://dl.acm.org/doi/abs/10.1145/3437802.3437810 | Siamese NestedUNet Networks for Change Detection of High Resolution Satellite Image | Change detection is an important task in remote sensing (RS) image analysis. With the development of deep learning and the increase of RS data, there are more and more change detection methods based on supervised learning. In this paper, we improve the semantic segmentation network UNet++ and propose a fully convolutio... | ['Sheng Fang', 'Zhe Li', 'Kaiyu Li'] | 2020-10-27 | null | null | null | null | ['change-detection-for-remote-sensing-images'] | ['miscellaneous'] | [ 9.83396918e-02 -4.45847362e-01 1.15401074e-01 -4.88811225e-01
-2.08647639e-01 -4.98761714e-01 5.60833514e-01 -4.80639264e-02
-4.15258318e-01 3.83550942e-01 4.68289927e-02 -1.87508747e-01
-8.81235376e-02 -1.15541661e+00 -5.99892557e-01 -5.75297832e-01
-2.33595759e-01 -5.60733639e-02 6.14423573e-01 -3.79614443... | [9.668091773986816, -1.2758382558822632] |
772c98f4-a24f-41f1-a666-e04701b15d4d | protecting-individual-interests-across | 2105.03714 | null | https://arxiv.org/abs/2105.03714v2 | https://arxiv.org/pdf/2105.03714v2.pdf | Consistency of Constrained Spectral Clustering under Graph Induced Fair Planted Partitions | Spectral clustering is popular among practitioners and theoreticians alike. While performance guarantees for spectral clustering are well understood, recent studies have focused on enforcing ``fairness'' in clusters, requiring them to be ``balanced'' with respect to a categorical sensitive node attribute (e.g. the race... | ['Ambedkar Dukkipati', 'Shubham Gupta'] | 2021-05-08 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 2.71834344e-01 4.07362968e-01 -4.55834955e-01 -6.40958369e-01
-3.23293567e-01 -7.22032428e-01 4.50938880e-01 5.23795605e-01
-4.04299408e-01 5.20945191e-01 1.10598050e-01 -9.17633846e-02
-5.45655370e-01 -9.21536744e-01 -4.36600417e-01 -8.41719389e-01
-2.23210260e-01 8.95893216e-01 -1.08697847e-01 -1.42214447... | [7.1525983810424805, 5.106605529785156] |
5a716cac-57df-4757-a44a-8eb670d7684e | structuring-user-generated-content-on-social | 2210.15377 | null | https://arxiv.org/abs/2210.15377v2 | https://arxiv.org/pdf/2210.15377v2.pdf | Retrieving Users' Opinions on Social Media with Multimodal Aspect-Based Sentiment Analysis | People post their opinions and experiences on social media, yielding rich databases of end-users' sentiments. This paper shows to what extent machine learning can analyze and structure these databases. An automated data analysis pipeline is deployed to provide insights into user-generated content for researchers in oth... | ['Georg Groh', 'Tobias Eder', 'Miriam Anschütz'] | 2022-10-27 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [-4.33314532e-01 3.94513682e-02 3.28478105e-02 -4.48759496e-01
-8.16186249e-01 -1.03644824e+00 8.10834885e-01 6.84399962e-01
-6.08979762e-01 4.78731096e-03 5.62525868e-01 1.04837641e-02
2.12352127e-01 -7.83167005e-01 -2.39865318e-01 -5.67462027e-01
2.12606028e-01 4.07441169e-01 -1.33656813e-02 -5.62269568... | [12.862785339355469, 5.305628299713135] |
a4a2e1a7-0bfb-41b0-bfaa-c37975f44014 | integrated-sensing-and-communication-for-6g | 2208.02157 | null | https://arxiv.org/abs/2208.02157v4 | https://arxiv.org/pdf/2208.02157v4.pdf | Integrated Sensing and Communication for 6G: Ten Key Machine Learning Roles | Integrating sensing and communication is a defining theme for future wireless systems. This is motivated by the promising performance gains, especially as they assist each other, and by the better utilization of the wireless and hardware resources. Realizing these gains in practice, however, is subject to several chall... | ['Ahmed Alkhateeb', 'Umut Demirhan'] | 2022-08-03 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [ 5.46056807e-01 2.60659784e-01 -7.97353745e-01 -2.07344592e-01
-9.52214479e-01 -1.86831281e-01 2.06988100e-02 -2.84961879e-01
-8.86502117e-03 8.94156337e-01 2.35896245e-01 -7.27224231e-01
-3.32109481e-01 -7.20234573e-01 -3.09947789e-01 -9.89239097e-01
-6.78655922e-01 -1.82333514e-02 -3.52200419e-01 1.02895916... | [6.29931640625, 1.3016233444213867] |
f066476a-68f3-4646-a32a-7736ffee87e1 | learning-to-learn-end-to-end-goal-oriented | 2110.15724 | null | https://arxiv.org/abs/2110.15724v1 | https://arxiv.org/pdf/2110.15724v1.pdf | Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks | For each goal-oriented dialog task of interest, large amounts of data need to be collected for end-to-end learning of a neural dialog system. Collecting that data is a costly and time-consuming process. Instead, we show that we can use only a small amount of data, supplemented with data from a related dialog task. Naiv... | ['Satinder Singh', 'Jonathan K. Kummerfeld', 'Janarthanan Rajendran'] | 2021-10-10 | null | https://aclanthology.org/2021.nlp4convai-1.16 | https://aclanthology.org/2021.nlp4convai-1.16.pdf | emnlp-nlp4convai-2021-11 | ['goal-oriented-dialog'] | ['natural-language-processing'] | [ 6.08941391e-02 3.86230230e-01 -3.34065482e-02 -9.85974729e-01
-1.01459324e+00 -7.05272913e-01 5.96864343e-01 2.60919631e-01
-6.56753540e-01 1.00164151e+00 3.65496576e-01 -2.36688375e-01
2.02687964e-01 -4.22821522e-01 -4.37034726e-01 -1.96618259e-01
8.72166753e-02 1.23880804e+00 4.71338123e-01 -7.61015713... | [12.865556716918945, 8.038320541381836] |
d98ef86c-0564-43d1-aaea-7098a0f7cb5d | a-dataset-and-preliminary-results-for-umpire | 1809.06217 | null | http://arxiv.org/abs/1809.06217v1 | http://arxiv.org/pdf/1809.06217v1.pdf | A Dataset and Preliminary Results for Umpire Pose Detection Using SVM Classification of Deep Features | In recent years, there has been increased interest in video summarization and
automatic sports highlights generation. In this work, we introduce a new
dataset, called SNOW, for umpire pose detection in the game of cricket. The
proposed dataset is evaluated as a preliminary aid for developing systems to
automatically ge... | ['Tizhoosh Hamid R.', 'Paul Sruthy', 'Venugopal Harshwin', 'Ravi Aravind'] | 2018-09-11 | null | null | null | null | ['game-of-cricket'] | ['playing-games'] | [ 9.76962373e-02 -3.21911252e-03 2.73244902e-02 6.93033338e-02
-4.42922980e-01 -3.63180757e-01 4.78546709e-01 3.34271282e-01
-5.82359970e-01 5.49046457e-01 3.53873849e-01 2.82787412e-01
-2.84874644e-02 -6.96395457e-01 -5.74100316e-01 -6.19430900e-01
-3.29398721e-01 2.44956866e-01 5.45833528e-01 -6.27459645... | [7.695512771606445, 0.153154194355011] |
4afe073b-d6d7-4c4a-bc81-c244fd30a497 | pai-gcn-permutable-anisotropic-graph | 2004.09995 | null | https://arxiv.org/abs/2004.09995v3 | https://arxiv.org/pdf/2004.09995v3.pdf | Learning Local Neighboring Structure for Robust 3D Shape Representation | Mesh is a powerful data structure for 3D shapes. Representation learning for 3D meshes is important in many computer vision and graphics applications. The recent success of convolutional neural networks (CNNs) for structured data (e.g., images) suggests the value of adapting insight from CNN for 3D shapes. However, 3D ... | ['Juyong Zhang', 'Guangtao Zhai', 'Yiyan Yang', 'Junchi Yan', 'Xiaokang Yang', 'Zhongpai Gao'] | 2020-04-21 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [-1.11708596e-01 2.84725577e-01 3.91995460e-02 -3.91596615e-01
5.14072459e-03 -2.83166468e-01 3.61254245e-01 -4.29585874e-02
1.52811348e-01 1.67808607e-02 5.98169804e-01 -2.56239414e-01
-2.07201634e-02 -1.32005751e+00 -8.73710155e-01 -5.47448456e-01
-3.12064635e-03 5.78756034e-01 1.83437526e-01 -3.01097423... | [8.197859764099121, -3.7360832691192627] |
3d54d9e4-3502-4d0d-b98e-839c7548e924 | hierarchical-semantic-tree-concept-whitening | 2307.04343 | null | https://arxiv.org/abs/2307.04343v1 | https://arxiv.org/pdf/2307.04343v1.pdf | Hierarchical Semantic Tree Concept Whitening for Interpretable Image Classification | With the popularity of deep neural networks (DNNs), model interpretability is becoming a critical concern. Many approaches have been developed to tackle the problem through post-hoc analysis, such as explaining how predictions are made or understanding the meaning of neurons in middle layers. Nevertheless, these method... | ['Dajiang Zhu', 'Tianming Liu', 'Ninghao Liu', 'Changying Li', 'Yanjun Lyu', 'Xiaowei Yu', 'David Liu', 'Zhengliang Liu', 'Zihao Wu', 'Lin Zhao', 'Lu Zhang', 'Haixing Dai'] | 2023-07-10 | null | null | null | null | ['image-classification', 'disentanglement'] | ['computer-vision', 'methodology'] | [ 2.13894457e-01 5.37796199e-01 -1.89075470e-01 -6.42731428e-01
4.53396082e-01 -4.34101224e-01 6.30709827e-01 3.21218103e-01
-1.30799532e-01 3.23348433e-01 4.31465119e-01 -4.00272012e-01
-1.33679315e-01 -7.37695932e-01 -7.35613167e-01 -5.21914542e-01
1.01235241e-01 2.05096453e-01 1.42033547e-02 7.84875825... | [8.985823631286621, 5.788405418395996] |
b0007640-1306-479b-9b13-e341372b6a9c | efficient-object-level-visual-context | 2101.05208 | null | https://arxiv.org/abs/2101.05208v1 | https://arxiv.org/pdf/2101.05208v1.pdf | Efficient Object-Level Visual Context Modeling for Multimodal Machine Translation: Masking Irrelevant Objects Helps Grounding | Visual context provides grounding information for multimodal machine translation (MMT). However, previous MMT models and probing studies on visual features suggest that visual information is less explored in MMT as it is often redundant to textual information. In this paper, we propose an object-level visual context mo... | ['Deyi Xiong', 'Dexin Wang'] | 2020-12-18 | null | null | null | null | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 4.66485620e-01 1.10279601e-02 -3.21653038e-01 -4.63032760e-02
-6.58667803e-01 -4.69989538e-01 7.89573789e-01 1.06261708e-01
-3.52841914e-02 4.17716086e-01 3.86207759e-01 -5.10079563e-01
2.79181898e-01 -2.73174673e-01 -8.02224517e-01 -6.52729988e-01
5.82568467e-01 1.93827406e-01 -1.23570576e-01 -1.26940191... | [11.463756561279297, 1.484451413154602] |
c1803c7c-6612-4a16-965d-5cc66e4f5e0c | exploring-diffusion-models-for-unsupervised | 2304.05841 | null | https://arxiv.org/abs/2304.05841v2 | https://arxiv.org/pdf/2304.05841v2.pdf | Exploring Diffusion Models for Unsupervised Video Anomaly Detection | This paper investigates the performance of diffusion models for video anomaly detection (VAD) within the most challenging but also the most operational scenario in which the data annotations are not used. As being sparse, diverse, contextual, and often ambiguous, detecting abnormal events precisely is a very ambitious ... | ['Elisa Ricci', 'Cigdem Beyan', "Nicola Dall'Asen", 'Anil Osman Tur'] | 2023-04-12 | null | null | null | null | ['video-anomaly-detection'] | ['computer-vision'] | [ 1.53064564e-01 -3.32748234e-01 1.38159111e-01 -1.81309015e-01
-2.39296883e-01 -4.95396882e-01 9.94448543e-01 3.80094051e-01
-2.90973306e-01 3.42681706e-01 2.06446648e-01 -5.00612736e-01
-2.06784070e-01 -6.69926763e-01 -5.32272696e-01 -6.52939558e-01
-5.16848207e-01 5.60393512e-01 7.93486357e-01 -1.34342179... | [7.858697414398193, 1.5687147378921509] |
20888f9e-0eab-42a2-a263-41a9c9c6bbba | quantitative-manipulation-of-custom | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Do_Quantitative_Manipulation_of_Custom_Attributes_on_3D-Aware_Image_Synthesis_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Do_Quantitative_Manipulation_of_Custom_Attributes_on_3D-Aware_Image_Synthesis_CVPR_2023_paper.pdf | Quantitative Manipulation of Custom Attributes on 3D-Aware Image Synthesis | While 3D-based GAN techniques have been successfully applied to render photo-realistic 3D images with a variety of attributes while preserving view consistency, there has been little research on how to fine-control 3D images without limiting to a specific category of objects of their properties. To fill such resear... | ['Jin Young Choi', 'Chul Lee', 'Taehyeong Kim', 'EunKyung Yoo', 'Hoseok Do'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['image-manipulation', '3d-aware-image-synthesis'] | ['computer-vision', 'computer-vision'] | [ 2.18433499e-01 2.37613857e-01 -1.50088057e-01 -3.70562881e-01
-2.71720827e-01 -6.81775689e-01 6.77702844e-01 -4.40932184e-01
3.41151506e-01 6.40116096e-01 3.19767386e-01 -3.11755203e-02
-7.03457892e-02 -1.10186028e+00 -6.82703257e-01 -5.81750691e-01
4.70199138e-01 3.54516000e-01 8.46206918e-02 -2.46539146... | [12.270013809204102, -0.5684561133384705] |
ff43f3bd-b74f-4fbd-a19b-9b02d249071c | adversarial-amendment-is-the-only-force | 2305.10766 | null | https://arxiv.org/abs/2305.10766v1 | https://arxiv.org/pdf/2305.10766v1.pdf | Adversarial Amendment is the Only Force Capable of Transforming an Enemy into a Friend | Adversarial attack is commonly regarded as a huge threat to neural networks because of misleading behavior. This paper presents an opposite perspective: adversarial attacks can be harnessed to improve neural models if amended correctly. Unlike traditional adversarial defense or adversarial training schemes that aim to ... | ['Zhongxue Gan', 'Tao Chen', 'Chong Yu'] | 2023-05-18 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 2.31593624e-01 2.40293071e-01 2.15672374e-01 -1.19386919e-01
-5.42391002e-01 -5.85703313e-01 7.43416488e-01 -1.91380709e-01
-4.14614916e-01 6.34063005e-01 -5.72758764e-02 -2.54945755e-01
-1.71661992e-02 -8.54637325e-01 -8.13121498e-01 -9.44433868e-01
1.73282832e-01 -7.78893456e-02 3.41322303e-01 -5.44783115... | [5.560213565826416, 7.9413065910339355] |
571279a1-d76b-4587-9a12-7772705f1bab | bayesian-renormalization | 2305.10491 | null | https://arxiv.org/abs/2305.10491v2 | https://arxiv.org/pdf/2305.10491v2.pdf | Bayesian Renormalization | In this note we present a fully information theoretic approach to renormalization inspired by Bayesian statistical inference, which we refer to as Bayesian Renormalization. The main insight of Bayesian Renormalization is that the Fisher metric defines a correlation length that plays the role of an emergent RG scale qua... | ['Alexander G. Stapleton', 'Marc S. Klinger', 'David S. Berman'] | 2023-05-17 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 5.00093400e-01 3.14852834e-01 4.35828418e-02 -2.75835693e-01
-1.36907488e-01 -2.99616307e-01 9.46600258e-01 4.02400851e-01
-4.20196235e-01 6.24729276e-01 3.72056395e-01 -2.62976319e-01
-5.14104426e-01 -1.18821621e+00 -5.09293735e-01 -1.34996748e+00
-9.57856551e-02 4.84383762e-01 1.60920039e-01 -5.20923793... | [5.721335411071777, 4.771697998046875] |
af00bcaf-51d7-4f65-954e-38378334e845 | subspace-regularizers-for-few-shot-class-1 | 2110.07059 | null | https://arxiv.org/abs/2110.07059v2 | https://arxiv.org/pdf/2110.07059v2.pdf | Subspace Regularizers for Few-Shot Class Incremental Learning | Few-shot class incremental learning -- the problem of updating a trained classifier to discriminate among an expanded set of classes with limited labeled data -- is a key challenge for machine learning systems deployed in non-stationary environments. Existing approaches to the problem rely on complex model architecture... | ['Derry Tanti Wijaya', 'Jacob Andreas', 'Ekin Akyürek', 'Afra Feyza Akyürek'] | 2021-10-13 | subspace-regularizers-for-few-shot-class | https://openreview.net/forum?id=boJy41J-tnQ | https://openreview.net/pdf?id=boJy41J-tnQ | iclr-2022-4 | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 4.33091432e-01 -1.21658668e-01 -2.95030236e-01 -6.36307001e-01
-7.42293537e-01 -4.51159149e-01 6.59913003e-01 1.02814078e-01
-6.08462632e-01 6.22130334e-01 1.82825346e-02 -1.39446519e-02
2.30458882e-02 -5.00210464e-01 -4.87376869e-01 -5.76780379e-01
-1.79450288e-01 4.32258159e-01 5.99384546e-01 -3.53681028... | [9.93230152130127, 3.020188570022583] |
a2f09d0e-8e0e-4b38-9a2f-d48ba138ba14 | pifpaf-composite-fields-for-human-pose | 1903.06593 | null | http://arxiv.org/abs/1903.06593v2 | http://arxiv.org/pdf/1903.06593v2.pdf | PifPaf: Composite Fields for Human Pose Estimation | We propose a new bottom-up method for multi-person 2D human pose estimation
that is particularly well suited for urban mobility such as self-driving cars
and delivery robots. The new method, PifPaf, uses a Part Intensity Field (PIF)
to localize body parts and a Part Association Field (PAF) to associate body
parts with ... | ['Alexandre Alahi', 'Sven Kreiss', 'Lorenzo Bertoni'] | 2019-03-15 | pifpaf-composite-fields-for-human-pose-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Kreiss_PifPaf_Composite_Fields_for_Human_Pose_Estimation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Kreiss_PifPaf_Composite_Fields_for_Human_Pose_Estimation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['2d-human-pose-estimation'] | ['computer-vision'] | [-1.89831406e-01 2.65644789e-01 1.06067330e-01 -3.64729017e-01
-6.30235076e-01 -3.18908319e-02 7.04134524e-01 -7.73363784e-02
-6.60473049e-01 5.85991681e-01 3.75401378e-01 4.81844097e-01
-2.34317058e-03 -6.58544362e-01 -8.75465333e-01 -2.27695808e-01
-2.24350363e-01 1.30015445e+00 8.13888550e-01 -6.51852489... | [7.070920467376709, -0.856907069683075] |
9f5703d8-dd22-4522-8675-fff43507b483 | kg-fid-infusing-knowledge-graph-in-fusion-in-1 | 2110.04330 | null | https://arxiv.org/abs/2110.04330v2 | https://arxiv.org/pdf/2110.04330v2.pdf | KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering | Current Open-Domain Question Answering (ODQA) model paradigm often contains a retrieving module and a reading module. Given an input question, the reading module predicts the answer from the relevant passages which are retrieved by the retriever. The recent proposed Fusion-in-Decoder (FiD), which is built on top of the... | ['Michael Zeng', 'Yiming Yang', 'Xiang Ren', 'Yichong Xu', 'Shuohang Wang', 'Wenhao Yu', 'Yuwei Fang', 'Chenguang Zhu', 'Donghan Yu'] | 2021-10-08 | kg-fid-infusing-knowledge-graph-in-fusion-in | https://aclanthology.org/2022.acl-long.340 | https://aclanthology.org/2022.acl-long.340.pdf | acl-2022-5 | ['triviaqa'] | ['miscellaneous'] | [ 1.51388124e-01 1.91754878e-01 2.45833337e-01 -1.45921111e-01
-1.33770597e+00 -4.62677002e-01 5.31336367e-01 2.97082841e-01
-4.68587756e-01 5.94379544e-01 5.81016839e-01 -3.08446974e-01
-1.59806117e-01 -1.19051850e+00 -9.91807103e-01 -1.98405966e-01
3.56402665e-01 6.34744167e-01 6.92360640e-01 -6.24097824... | [11.172150611877441, 8.05397891998291] |
c69736fb-85eb-4088-af74-64ea487177a8 | metrics-matter-in-surgical-phase-recognition | 2305.13961 | null | https://arxiv.org/abs/2305.13961v1 | https://arxiv.org/pdf/2305.13961v1.pdf | Metrics Matter in Surgical Phase Recognition | Surgical phase recognition is a basic component for different context-aware applications in computer- and robot-assisted surgery. In recent years, several methods for automatic surgical phase recognition have been proposed, showing promising results. However, a meaningful comparison of these methods is difficult due to... | ['Stefanie Speidel', 'Dominik Rivoir', 'Isabel Funke'] | 2023-05-23 | null | null | null | null | ['surgical-phase-recognition'] | ['computer-vision'] | [ 2.50695735e-01 6.41630143e-02 -7.91255713e-01 -3.80597144e-01
-1.00850403e+00 -6.75031722e-01 3.58126074e-01 8.46770167e-01
-6.96576655e-01 5.95599771e-01 6.35373056e-01 -4.09569412e-01
-5.47241807e-01 -3.32141250e-01 -1.74838468e-01 -7.58429825e-01
-4.74946171e-01 4.82944965e-01 9.73544046e-02 -1.26890391... | [14.082347869873047, -3.3373119831085205] |
3429158f-5757-4562-a39d-391ca1fddc56 | deep-rigid-instance-scene-flow | 1904.08913 | null | http://arxiv.org/abs/1904.08913v1 | http://arxiv.org/pdf/1904.08913v1.pdf | Deep Rigid Instance Scene Flow | In this paper we tackle the problem of scene flow estimation in the context
of self-driving. We leverage deep learning techniques as well as strong priors
as in our application domain the motion of the scene can be composed by the
motion of the robot and the 3D motion of the actors in the scene. We formulate
the proble... | ['Raquel Urtasun', 'Shenlong Wang', 'Wei-Chiu Ma', 'Yuwen Xiong', 'Rui Hu'] | 2019-04-18 | deep-rigid-instance-scene-flow-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Ma_Deep_Rigid_Instance_Scene_Flow_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ma_Deep_Rigid_Instance_Scene_Flow_CVPR_2019_paper.pdf | cvpr-2019-6 | ['scene-flow-estimation'] | ['computer-vision'] | [-1.12160191e-01 -7.62243792e-02 5.92762604e-03 -2.18900777e-02
-2.23378494e-01 -5.00081301e-01 5.70516646e-01 -1.35460541e-01
-7.98570514e-01 4.40401286e-01 1.71977773e-01 -3.08481246e-01
2.80184895e-01 -5.33088148e-01 -8.62848520e-01 -5.89370012e-01
-3.61422747e-02 6.14062488e-01 4.70073819e-01 -1.30888239... | [8.554159164428711, -1.8919517993927002] |
242a6df8-e131-42dc-b48c-c81a56a9399d | transfer-learning-for-scene-text-recognition | 2201.03180 | null | https://arxiv.org/abs/2201.03180v1 | https://arxiv.org/pdf/2201.03180v1.pdf | Transfer Learning for Scene Text Recognition in Indian Languages | Scene text recognition in low-resource Indian languages is challenging because of complexities like multiple scripts, fonts, text size, and orientations. In this work, we investigate the power of transfer learning for all the layers of deep scene text recognition networks from English to two common Indian languages. We... | ['C. V. Jawahar', 'Rohit Saluja', 'Sanjana Gunna'] | 2022-01-10 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 3.84849310e-01 -6.07985914e-01 2.74917006e-01 -5.68230271e-01
-8.41141820e-01 -7.86591828e-01 8.92462015e-01 -2.69345760e-01
-8.07570100e-01 5.66565156e-01 2.80597478e-01 -6.93141699e-01
3.67446095e-01 -6.57704353e-01 -9.78938401e-01 -5.72388172e-01
1.71354473e-01 3.79821569e-01 2.31845781e-01 -2.26434439... | [11.870315551757812, 2.352259635925293] |
bbdfa774-dec7-4bbe-86e3-a15c5671d1c9 | natlogattack-a-framework-for-attacking | 2307.02849 | null | https://arxiv.org/abs/2307.02849v1 | https://arxiv.org/pdf/2307.02849v1.pdf | NatLogAttack: A Framework for Attacking Natural Language Inference Models with Natural Logic | Reasoning has been a central topic in artificial intelligence from the beginning. The recent progress made on distributed representation and neural networks continues to improve the state-of-the-art performance of natural language inference. However, it remains an open question whether the models perform real reasoning... | ['Xiaodan Zhu', "Zi'ou Zheng"] | 2023-07-06 | null | null | null | null | ['natural-language-inference'] | ['natural-language-processing'] | [ 5.51237762e-02 4.98961806e-01 -1.18237145e-01 -1.38346776e-01
-4.41067159e-01 -8.90333295e-01 8.70279610e-01 2.06198916e-01
-7.01711923e-02 7.69437373e-01 4.71115112e-02 -8.37452888e-01
-3.50126743e-01 -1.42257762e+00 -8.35326314e-01 -5.62958717e-01
-1.47321418e-01 6.95301712e-01 4.26900625e-01 -5.99953949... | [8.933648109436035, 7.156285762786865] |
e5e9b04d-8498-4199-b6d7-ba8362286cd1 | evaluating-factual-consistency-of-texts-with | 2305.13309 | null | https://arxiv.org/abs/2305.13309v1 | https://arxiv.org/pdf/2305.13309v1.pdf | Evaluating Factual Consistency of Texts with Semantic Role Labeling | Automated evaluation of text generation systems has recently seen increasing attention, particularly checking whether generated text stays truthful to input sources. Existing methods frequently rely on an evaluation using task-specific language models, which in turn allows for little interpretability of generated score... | ['Michael Gertz', 'Dennis Aumiller', 'Jing Fan'] | 2023-05-22 | null | null | null | null | ['semantic-role-labeling', 'text-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.67512369e-01 5.29407322e-01 -4.13531005e-01 -4.71050173e-01
-1.38019538e+00 -1.01307607e+00 1.11980951e+00 5.34398079e-01
-3.49085331e-01 1.12661171e+00 8.34574342e-01 -4.01538648e-02
-1.01899013e-01 -6.27282619e-01 -3.35523993e-01 -2.32906282e-01
4.27803576e-01 7.56373525e-01 2.27606595e-01 -3.58331114... | [11.974128723144531, 9.226944923400879] |
680852d8-5801-4752-ba18-1a3891b2da87 | a-scalable-architecture-for-web-deployment-of | null | null | https://aclanthology.org/L12-1226 | https://aclanthology.org/L12-1226.pdf | A Scalable Architecture For Web Deployment of Spoken Dialogue Systems | We describe a scalable architecture, particularly well-suited to cloud-based computing, which can be used for Web-deployment of spoken dialogue systems. In common with similar platforms, like WAMI and the Nuance Mobile Developer Platform, we use a client/server approach in which speech recognition is carried out on the... | ['Manny Rayner', 'Matthew Fuchs', 'Nikos Tsourakis'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['dialogue-management'] | ['natural-language-processing'] | [-1.02125414e-01 2.36896083e-01 6.95662200e-01 -2.10429519e-01
-1.05264270e+00 -8.74108970e-01 6.80145860e-01 -3.12133372e-01
-6.10974014e-01 6.25772953e-01 1.90602630e-01 -8.80880415e-01
1.56577229e-01 -2.90872157e-01 1.02964595e-01 -6.64046764e-01
-9.95754972e-02 9.54742491e-01 6.00401044e-01 -7.88101375... | [13.005367279052734, 7.871457576751709] |
f22c4635-551d-4dde-ae1c-9d812fb6545f | robust-statistical-ranking-theory-and | 1408.3467 | null | https://arxiv.org/abs/1408.3467v2 | https://arxiv.org/pdf/1408.3467v2.pdf | Evaluating Visual Properties via Robust HodgeRank | Nowadays, how to effectively evaluate visual properties has become a popular topic for fine-grained visual comprehension. In this paper we study the problem of how to estimate such visual properties from a ranking perspective with the help of the annotators from online crowdsourcing platforms. The main challenges of ou... | ['Xiaochun Cao', 'Yuan YAO', 'Jiechao Xiong', 'Qianqian Xu', 'Qingming Huang'] | 2014-08-15 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [-3.09701283e-02 -3.59333083e-02 6.63120672e-02 -3.99248376e-02
-8.28300416e-01 -5.48367500e-01 2.58092821e-01 3.30670834e-01
-1.86774984e-01 7.70437360e-01 2.12783620e-01 3.03181142e-01
-8.33428055e-02 -3.83340359e-01 -8.76927555e-01 -9.40338314e-01
1.80097520e-01 4.08163220e-01 1.25253081e-01 -2.25958601... | [7.767514228820801, 4.44369649887085] |
e1bc71c1-3ce2-4e6d-90bf-bb15f22ebb61 | a-trillion-genetic-programming-instructions | 2205.03251 | null | https://arxiv.org/abs/2205.03251v1 | https://arxiv.org/pdf/2205.03251v1.pdf | A Trillion Genetic Programming Instructions per Second | We summarise how a 3.0 GHz 16 core AVX512 computer can interpret the equivalent of up to on average 1103370000000 GPop/s. Citations to existing publications are given. Implementation stress is placed on both parallel computing, bandwidth limits and avoiding repeated calculation. Information theory suggests in digital c... | ['W. B. Langdon'] | 2022-05-06 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-9.72483307e-02 -1.20954834e-01 3.32134098e-01 1.84778154e-01
2.62702644e-01 -4.58732754e-01 1.25209779e-01 1.66076511e-01
-4.09819216e-01 9.91022646e-01 -3.60533893e-01 -6.15336537e-01
-5.13475657e-01 -8.14379632e-01 -6.31357282e-02 -7.37422347e-01
-5.67608953e-01 3.36747944e-01 1.94581732e-01 -5.18413007... | [5.655646324157715, 3.9695873260498047] |
9951ca0e-2af8-488b-8903-7be1bfcae872 | direct-dense-pose-estimation | 2204.01263 | null | https://arxiv.org/abs/2204.01263v1 | https://arxiv.org/pdf/2204.01263v1.pdf | Direct Dense Pose Estimation | Dense human pose estimation is the problem of learning dense correspondences between RGB images and the surfaces of human bodies, which finds various applications, such as human body reconstruction, human pose transfer, and human action recognition. Prior dense pose estimation methods are all based on Mask R-CNN framew... | ['Luc van Gool', 'Christian Theobalt', 'Lingjie Liu', 'Liqian Ma'] | 2022-04-04 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 1.80431664e-01 -1.02316372e-01 1.11539394e-01 -5.21280318e-02
-4.68312591e-01 -1.07747652e-01 2.82032818e-01 -3.55179727e-01
-5.11195481e-01 6.46788180e-01 2.64516920e-01 4.90121573e-01
1.88043207e-01 -6.20926678e-01 -7.34820843e-01 -5.72784185e-01
9.11555588e-02 7.05530882e-01 6.08052790e-01 -8.97871405... | [7.089582443237305, -0.9200857877731323] |
a858234b-876b-423b-9b2d-0a1fc152e261 | ccg-supertagging-with-a-recurrent-neural | null | null | https://aclanthology.org/P15-2041 | https://aclanthology.org/P15-2041.pdf | CCG Supertagging with a Recurrent Neural Network | null | ['Stephen Clark', 'Michael Auli', 'Wenduan Xu'] | 2015-07-01 | ccg-supertagging-with-a-recurrent-neural-1 | https://aclanthology.org/P15-2041 | https://aclanthology.org/P15-2041.pdf | ijcnlp-2015-7 | ['ccg-supertagging'] | ['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... | [-7.261399269104004, 3.6752259731292725] |
fbcf7743-aa38-4101-ad3c-96da02daa283 | adversarial-generation-of-time-frequency | 1902.04072 | null | https://arxiv.org/abs/1902.04072v2 | https://arxiv.org/pdf/1902.04072v2.pdf | Adversarial Generation of Time-Frequency Features with application in audio synthesis | Time-frequency (TF) representations provide powerful and intuitive features for the analysis of time series such as audio. But still, generative modeling of audio in the TF domain is a subtle matter. Consequently, neural audio synthesis widely relies on directly modeling the waveform and previous attempts at unconditio... | ['Nathanaël Perraudin', 'Andrés Marafioti', 'Piotr Majdak', 'Nicki Holighaus'] | 2019-02-11 | adversarial-generation-of-time-frequency-1 | https://tifgan.github.io/ | http://proceedings.mlr.press/v97/marafioti19a/marafioti19a.pdf | 36th-international-conference-on-machine | ['audio-generation'] | ['audio'] | [ 6.39929116e-01 3.89886469e-01 4.05632526e-01 -1.50223345e-01
-1.28254747e+00 -7.44969428e-01 6.59367263e-01 -5.91420770e-01
3.20558757e-01 7.91341126e-01 2.42184266e-01 -2.28462398e-01
5.66030070e-02 -8.59035313e-01 -9.24328744e-01 -6.79485142e-01
-1.70792565e-01 1.33788347e-01 -3.10285866e-01 -2.77485728... | [15.6019287109375, 5.932265758514404] |
f4f9c528-1c11-4fcb-a243-cff68ed2df93 | relationship-extraction-for-knowledge-graph | 2201.01647 | null | https://arxiv.org/abs/2201.01647v4 | https://arxiv.org/pdf/2201.01647v4.pdf | Comparison of biomedical relationship extraction methods and models for knowledge graph creation | Biomedical research is growing at such an exponential pace that scientists, researchers, and practitioners are no more able to cope with the amount of published literature in the domain. The knowledge presented in the literature needs to be systematized in such a way that claims and hypotheses can be easily found, acce... | ['Wolfgang Thielemann', 'Nikola Milosevic'] | 2022-01-05 | null | null | null | null | ['key-information-extraction'] | ['natural-language-processing'] | [-1.11820288e-01 4.48325098e-01 -4.69792217e-01 -7.58942962e-02
-4.32180673e-01 -4.87456173e-01 4.67754334e-01 7.84118831e-01
-3.24511915e-01 1.20928693e+00 1.25403315e-01 -6.42651916e-01
-6.62197173e-01 -1.13882697e+00 -7.08872199e-01 -2.89260745e-01
-3.97960236e-03 8.83700788e-01 1.76519901e-01 -1.77408949... | [8.487245559692383, 8.553481101989746] |
8e5978f6-e61a-4e64-8773-55decc631ce9 | watclaimcheck-a-new-dataset-for-claim | null | null | https://aclanthology.org/2022.acl-long.92 | https://aclanthology.org/2022.acl-long.92.pdf | WatClaimCheck: A new Dataset for Claim Entailment and Inference | We contribute a new dataset for the task of automated fact checking and an evaluation of state of the art algorithms. The dataset includes claims (from speeches, interviews, social media and news articles), review articles published by professional fact checkers and premise articles used by those professional fact chec... | ['Pascal Poupart', 'Ruizhe Wang', 'Kashif Khan'] | null | null | null | null | acl-2022-5 | ['passage-retrieval'] | ['natural-language-processing'] | [-2.40730554e-01 5.04795253e-01 -6.20826364e-01 3.19321334e-01
-1.74719167e+00 -7.26943433e-01 8.06576431e-01 8.23116601e-01
-1.75405934e-01 1.12848735e+00 9.10754621e-01 -5.57061315e-01
-2.30950996e-01 -8.80377352e-01 -8.51420939e-01 1.57296047e-01
4.89608258e-01 6.22725964e-01 4.15918142e-01 -5.31232297... | [8.58165168762207, 9.858927726745605] |
992c0a15-f886-4376-b50b-ec62cabbadcb | synergy-with-translation-artifacts-for | 2210.09588 | null | https://arxiv.org/abs/2210.09588v1 | https://arxiv.org/pdf/2210.09588v1.pdf | Synergy with Translation Artifacts for Training and Inference in Multilingual Tasks | Translation has played a crucial role in improving the performance on multilingual tasks: (1) to generate the target language data from the source language data for training and (2) to generate the source language data from the target language data for inference. However, prior works have not considered the use of both... | ['Se-Young Yun', 'Jongwoo Ko', 'Jaehoon Oh'] | 2022-10-18 | null | null | null | null | ['sentence-classification'] | ['natural-language-processing'] | [-2.68715054e-01 -2.82045513e-01 -6.02357268e-01 -3.73250186e-01
-1.23848104e+00 -6.93392098e-01 7.55791306e-01 -2.75528640e-01
-4.97981995e-01 1.12334967e+00 3.86848658e-01 -7.79438376e-01
2.99227148e-01 -4.31540042e-01 -8.27573776e-01 -4.62395877e-01
5.77231348e-01 3.53552610e-01 -2.54956245e-01 -2.87392557... | [11.428635597229004, 10.242596626281738] |
cb5716ec-445a-467f-9a06-36f6e49f21af | geolocation-estimation-of-photos-using-a | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Eric_Muller-Budack_Geolocation_Estimation_of_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Eric_Muller-Budack_Geolocation_Estimation_of_ECCV_2018_paper.pdf | Geolocation Estimation of Photos using a Hierarchical Model and Scene Classification | While the successful estimation of a photo's geolocation enables a number of interesting applications, it is also a very challenging task. Due to the complexity of the problem, most existing approaches are restricted to specific areas, imagery, or worldwide landmarks. Only a few proposals predict GPS coordinates withou... | ['Kader Pustu-Iren', 'Eric Muller-Budack', 'Ralph Ewerth'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['photo-geolocation-estimation'] | ['computer-vision'] | [ 7.79559789e-03 -2.02775896e-01 -1.38961032e-01 -4.62483048e-01
-7.16106057e-01 -6.15999699e-01 8.84029508e-01 3.77673179e-01
-5.81926763e-01 7.67751098e-01 1.91113263e-01 5.70903346e-02
-2.23063350e-01 -1.03809655e+00 -8.18106174e-01 -7.68286526e-01
-2.59354915e-02 2.95313239e-01 9.26129520e-02 9.75653082... | [7.682529926300049, -1.8209214210510254] |
196161f5-f6e6-4d37-b955-42b12f20e2f1 | surrogate-assisted-semi-supervised-inference | 2105.01264 | null | https://arxiv.org/abs/2105.01264v1 | https://arxiv.org/pdf/2105.01264v1.pdf | Surrogate Assisted Semi-supervised Inference for High Dimensional Risk Prediction | Risk modeling with EHR data is challenging due to a lack of direct observations on the disease outcome, and the high dimensionality of the candidate predictors. In this paper, we develop a surrogate assisted semi-supervised-learning (SAS) approach to risk modeling with high dimensional predictors, leveraging a large un... | ['Tianxi Cai', 'Zijian Guo', 'Jue Hou'] | 2021-05-04 | null | null | null | null | ['genetic-risk-prediction'] | ['medical'] | [ 3.12161297e-01 4.34952319e-01 -7.25892425e-01 -9.31959033e-01
-1.14900684e+00 -1.30186096e-01 7.86286294e-02 2.37427101e-01
7.91282207e-02 1.08753622e+00 8.27239275e-01 -3.31962109e-01
-3.41868997e-01 -7.32231259e-01 -8.04521799e-01 -4.96951461e-01
-4.19875413e-01 7.24324942e-01 -8.10530961e-01 4.78302360... | [7.701855659484863, 4.938989162445068] |
8bdf924e-c9fa-4704-9771-d5aeb35c3d3c | a-neural-transition-based-model-for-nested | 1810.01808 | null | http://arxiv.org/abs/1810.01808v1 | http://arxiv.org/pdf/1810.01808v1.pdf | A Neural Transition-based Model for Nested Mention Recognition | It is common that entity mentions can contain other mentions recursively.
This paper introduces a scalable transition-based method to model the nested
structure of mentions. We first map a sentence with nested mentions to a
designated forest where each mention corresponds to a constituent of the
forest. Our shift-reduc... | ['Bailin Wang', 'Yu Wang', 'Wei Lu', 'Hongxia Jin'] | 2018-10-03 | a-neural-transition-based-model-for-nested-1 | https://aclanthology.org/D18-1124 | https://aclanthology.org/D18-1124.pdf | emnlp-2018-10 | ['nested-named-entity-recognition', 'nested-mention-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.81012297e-01 3.40630293e-01 -2.62963384e-01 -6.49798274e-01
-1.01888525e+00 -7.32632160e-01 4.57841158e-01 3.24301839e-01
-2.63366461e-01 7.20663667e-01 4.13948953e-01 -8.00252855e-01
5.13605356e-01 -1.06932032e+00 -1.02918875e+00 -2.87792295e-01
-4.52699453e-01 3.91233951e-01 5.01792789e-01 -6.54235333... | [10.339324951171875, 9.536539077758789] |
e4158ea5-787b-47dc-ae1c-45015579ae26 | online-hybrid-lightweight-representations | 2205.11179 | null | https://arxiv.org/abs/2205.11179v1 | https://arxiv.org/pdf/2205.11179v1.pdf | Online Hybrid Lightweight Representations Learning: Its Application to Visual Tracking | This paper presents a novel hybrid representation learning framework for streaming data, where an image frame in a video is modeled by an ensemble of two distinct deep neural networks; one is a low-bit quantized network and the other is a lightweight full-precision network. The former learns coarse primary information ... | ['Bohyung Han', 'Eunhyeok Park', 'Minji Kim', 'Ilchae Jung'] | 2022-05-23 | null | null | null | null | ['visual-tracking'] | ['computer-vision'] | [ 1.70934007e-01 -1.31141409e-01 -5.97504675e-01 -3.26911844e-02
-6.77428365e-01 -2.82978892e-01 5.83964586e-01 -2.08030343e-01
-4.15542394e-01 5.19288957e-01 -3.79120819e-02 -2.61079371e-01
2.38183647e-01 -5.50134420e-01 -1.12382388e+00 -8.12169015e-01
-5.35238624e-01 1.52738705e-01 5.51941752e-01 2.30278866... | [6.3238396644592285, -2.122180938720703] |
e17c86ed-8b55-45b6-a85f-6d379e818615 | deepirisnet2-learning-deep-iriscodes-from | 1902.05390 | null | http://arxiv.org/abs/1902.05390v1 | http://arxiv.org/pdf/1902.05390v1.pdf | DeepIrisNet2: Learning Deep-IrisCodes from Scratch for Segmentation-Robust Visible Wavelength and Near Infrared Iris Recognition | We first, introduce a deep learning based framework named as DeepIrisNet2 for
visible spectrum and NIR Iris representation. The framework can work without
classical iris normalization step or very accurate iris segmentation; allowing
to work under non-ideal situation. The framework contains spatial transformer
layers t... | ['Akanksha Joshi', 'R. Raghavendra', 'Padmaja Joshi', 'Abhishek Gangwar'] | 2019-02-06 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 3.89861643e-01 1.91067174e-01 -3.51737887e-01 -4.56117451e-01
-6.35499001e-01 -5.17557979e-01 3.42908412e-01 -2.90335268e-01
-1.44529447e-01 3.85125697e-01 7.44979456e-02 -2.97819763e-01
-2.18290046e-01 -6.76579833e-01 -5.38598120e-01 -7.21330881e-01
3.47410023e-01 6.26429200e-01 -1.01463862e-01 -7.81717361... | [3.747234344482422, -3.629610300064087] |
d55b5cdb-d029-47be-a033-873d3b66fadf | navgpt-explicit-reasoning-in-vision-and | 2305.16986 | null | https://arxiv.org/abs/2305.16986v2 | https://arxiv.org/pdf/2305.16986v2.pdf | NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models | Trained with an unprecedented scale of data, large language models (LLMs) like ChatGPT and GPT-4 exhibit the emergence of significant reasoning abilities from model scaling. Such a trend underscored the potential of training LLMs with unlimited language data, advancing the development of a universal embodied agent. In ... | ['Qi Wu', 'Yicong Hong', 'Gengze Zhou'] | 2023-05-26 | null | null | null | null | ['instruction-following', 'vision-and-language-navigation', 'visual-navigation'] | ['natural-language-processing', 'robots', 'robots'] | [ 4.67731357e-02 4.03010398e-01 2.11555269e-02 -1.71239913e-01
-6.97470248e-01 -4.41652715e-01 8.66035581e-01 6.49496391e-02
-5.31831920e-01 2.35258356e-01 7.63369143e-01 -6.64864898e-01
-4.59503904e-02 -9.12266135e-01 -7.47783065e-01 -3.18392426e-01
-2.45932981e-01 8.26453984e-01 1.40355870e-01 -6.70613110... | [4.412583351135254, 0.6926828026771545] |
103ab0db-01ae-4ee8-9a54-b4ff461109ba | evolutionary-preference-learning-via-graph | 2206.12779 | null | https://arxiv.org/abs/2206.12779v2 | https://arxiv.org/pdf/2206.12779v2.pdf | Evolutionary Preference Learning via Graph Nested GRU ODE for Session-based Recommendation | Session-based recommendation (SBR) aims to predict the user next action based on the ongoing sessions. Recently, there has been an increasing interest in modeling the user preference evolution to capture the fine-grained user interests. While latent user preferences behind the sessions drift continuously over time, mos... | ['Sunghun Kim', 'Yan Zhang', 'Xing Xie', 'Chaozhuo Li', 'Peiyan Zhang', 'Jiayan Guo'] | 2022-06-26 | null | null | null | null | ['session-based-recommendations'] | ['miscellaneous'] | [-1.95252284e-01 -4.55283642e-01 -4.73497003e-01 -4.67136294e-01
1.09584503e-01 -7.47612178e-01 2.76706725e-01 3.74040276e-01
-3.87562513e-02 3.34130347e-01 5.42185247e-01 -4.18275774e-01
-6.46889985e-01 -6.50708199e-01 -4.31474686e-01 -5.84349155e-01
-4.62978452e-01 4.33545351e-01 1.44677669e-01 -4.08204347... | [10.190589904785156, 5.589132785797119] |
4e74967e-0e0a-4bea-9d53-e27db7dfbdeb | efficient-gesture-recognition-for-the | 2205.06980 | null | https://arxiv.org/abs/2205.06980v1 | https://arxiv.org/pdf/2205.06980v1.pdf | Efficient Gesture Recognition for the Assistance of Visually Impaired People using Multi-Head Neural Networks | This paper proposes an interactive system for mobile devices controlled by hand gestures aimed at helping people with visual impairments. This system allows the user to interact with the device by making simple static and dynamic hand gestures. Each gesture triggers a different action in the system, such as object reco... | ['Miguel Ángel Lozano', 'Antonio Javier Gallego', 'Samer Alashhab'] | 2022-05-14 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 2.33868539e-01 -3.58425945e-01 3.67208496e-02 -4.41207618e-01
-2.07570463e-01 -3.96796286e-01 6.17341697e-01 -3.92189980e-01
-7.31862843e-01 3.63745660e-01 1.49941579e-01 -1.23155110e-01
3.09864990e-02 -5.18305898e-01 -2.36811861e-01 -7.80358374e-01
1.19438826e-03 8.62554371e-01 5.78775406e-01 -7.93349147... | [6.51102876663208, -0.28187716007232666] |
a6b37fd3-0ab0-4df1-b4e1-7b81736c5cfb | stable-remaster-bridging-the-gap-between-old | 2306.06803 | null | https://arxiv.org/abs/2306.06803v1 | https://arxiv.org/pdf/2306.06803v1.pdf | Stable Remaster: Bridging the Gap Between Old Content and New Displays | The invention of modern displays has enhanced the viewer experience for any kind of content: ranging from sports to movies in 8K high-definition resolution. However, older content developed for CRT or early Plasma screen TVs has become outdated quickly and no longer meets current aspect ratio and resolution standards. ... | ['Yian Wong', 'Shuvam Keshari', 'Nathan Paull'] | 2023-06-11 | null | null | null | null | ['key-point-matching'] | ['natural-language-processing'] | [ 4.07860309e-01 -1.72876582e-01 5.25880337e-01 -2.10266322e-01
-6.00533426e-01 -7.77995408e-01 6.42291903e-01 -1.18946442e-02
-4.21655208e-01 4.01208192e-01 2.60273647e-02 -2.13584095e-01
-2.56547090e-02 -6.84954703e-01 -2.09747672e-01 -3.46618056e-01
8.47757980e-03 5.11423945e-01 1.11491752e+00 -3.39757830... | [11.024794578552246, -2.116525888442993] |
a6d87bac-2024-466a-9382-07fbe99fb13e | environmental-noise-embeddings-for-robust | 1601.02553 | null | http://arxiv.org/abs/1601.02553v2 | http://arxiv.org/pdf/1601.02553v2.pdf | Environmental Noise Embeddings for Robust Speech Recognition | We propose a novel deep neural network architecture for speech recognition
that explicitly employs knowledge of the background environmental noise within
a deep neural network acoustic model. A deep neural network is used to predict
the acoustic environment in which the system in being used. The discriminative
embeddin... | ['Bhiksha Raj', 'Suyoun Kim', 'Ian Lane'] | 2016-01-11 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 1.27578259e-01 -5.48367739e-01 6.85192764e-01 -5.79986036e-01
-1.03565180e+00 -2.49450073e-01 3.11060846e-01 -2.06731215e-01
-6.13494217e-01 2.09743872e-01 4.64708954e-01 -6.96534336e-01
-1.30665138e-01 -5.91206312e-01 -5.90591431e-01 -7.09248066e-01
-9.03761312e-02 1.75886184e-01 -8.30052942e-02 -1.66845188... | [14.850733757019043, 6.045862197875977] |
ab9bc604-51ec-40cd-8201-7f046dc15af0 | anchor-transform-learning-sparse-1 | 2003.08197 | null | https://arxiv.org/abs/2003.08197v4 | https://arxiv.org/pdf/2003.08197v4.pdf | Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies | Learning continuous representations of discrete objects such as text, users, movies, and URLs lies at the heart of many applications including language and user modeling. When using discrete objects as input to neural networks, we often ignore the underlying structures (e.g., natural groupings and similarities) and emb... | ['Yu-An Wang', 'Paul Pu Liang', 'Manzil Zaheer', 'Amr Ahmed'] | 2020-03-18 | anchor-transform-learning-sparse-embeddings | https://openreview.net/forum?id=Vd7lCMvtLqg | https://openreview.net/pdf?id=Vd7lCMvtLqg | iclr-2021-1 | ['movie-recommendation'] | ['miscellaneous'] | [ 3.13683897e-02 -1.25169471e-01 -5.58441162e-01 -5.11671066e-01
-5.44811904e-01 -7.34957874e-01 6.93814814e-01 5.36865830e-01
-4.78996158e-01 2.00498998e-01 5.69642842e-01 -3.23045284e-01
-3.61495584e-01 -7.68374026e-01 -9.00751591e-01 -6.04341209e-01
-1.97080344e-01 7.34154999e-01 4.43414859e-02 7.46345222... | [8.92679214477539, 4.57017707824707] |
ffd54eb5-9da6-44cc-95d2-86a2a8156b88 | generating-highly-realistic-images-of-skin | 1809.01410 | null | http://arxiv.org/abs/1809.01410v2 | http://arxiv.org/pdf/1809.01410v2.pdf | Generating Highly Realistic Images of Skin Lesions with GANs | As many other machine learning driven medical image analysis tasks, skin
image analysis suffers from a chronic lack of labeled data and skewed class
distributions, which poses problems for the training of robust and
well-generalizing models. The ability to synthesize realistic looking images of
skin lesions could act a... | ['Nassir Navab', 'Shadi Albarqouni', 'Christoph Baur'] | 2018-09-05 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 8.22648168e-01 5.82526565e-01 -1.95608940e-02 -1.53961241e-01
-9.68339980e-01 -5.71788788e-01 5.46099722e-01 -2.47362956e-01
-1.73016310e-01 9.12089646e-01 -1.35319993e-01 -2.11002409e-01
1.23563848e-01 -8.55823398e-01 -5.86223960e-01 -1.03768086e+00
4.27810967e-01 3.60720575e-01 4.86777499e-02 -2.63333887... | [14.19669246673584, -1.9991936683654785] |
8198dd11-3aeb-4364-b67f-0454f4fd8032 | subspace-sparse-representation | 1507.01307 | null | http://arxiv.org/abs/1507.01307v1 | http://arxiv.org/pdf/1507.01307v1.pdf | Subspace-Sparse Representation | Given an overcomplete dictionary $A$ and a signal $b$ that is a linear
combination of a few linearly independent columns of $A$, classical sparse
recovery theory deals with the problem of recovering the unique sparse
representation $x$ such that $b = A x$. It is known that under certain
conditions on $A$, $x$ can be re... | ['R. Vidal', 'C. You'] | 2015-07-06 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 3.15271318e-01 -1.87916085e-01 -2.70693719e-01 2.29357220e-02
-6.11581624e-01 -4.98165935e-01 -1.18071057e-01 -4.55239058e-01
2.23905072e-01 6.26650274e-01 2.68827885e-01 -9.67907682e-02
-4.16652381e-01 -7.58331895e-01 -7.45454252e-01 -1.03158724e+00
-2.89012313e-01 2.25996822e-01 -5.72187901e-01 -4.72241253... | [7.3438496589660645, 4.3992133140563965] |
b3ae61c5-da89-473e-9084-a96395d5b155 | semantic-head-enhanced-pedestrian-detection | 1911.11985 | null | https://arxiv.org/abs/1911.11985v1 | https://arxiv.org/pdf/1911.11985v1.pdf | Semantic Head Enhanced Pedestrian Detection in a Crowd | Pedestrian detection in the crowd is a challenging task because of intra-class occlusion. More prior information is needed for the detector to be robust against it. Human head area is naturally a strong cue because of its stable appearance, visibility and relative location to body. Inspired by it, we adopt an extra bra... | ['Huimin Ma', 'Ruiqi Lu'] | 2019-11-27 | null | null | null | null | ['head-detection'] | ['computer-vision'] | [-2.81473517e-01 2.08883330e-01 4.30775136e-02 -4.78939205e-01
-9.26356763e-02 -5.35776876e-02 4.76579756e-01 -2.63965223e-02
-5.83117664e-01 4.53205884e-01 3.35410535e-01 1.82461441e-01
7.08004355e-01 -6.89245403e-01 -6.32589877e-01 -8.12332571e-01
4.11430120e-01 7.66041651e-02 9.62165952e-01 -1.63587049... | [7.973312854766846, -0.5777560472488403] |
e803c8cb-b92b-4885-8f25-20b02d4f6cdd | patch-autoaugment | 2103.11099 | null | https://arxiv.org/abs/2103.11099v2 | https://arxiv.org/pdf/2103.11099v2.pdf | Local Patch AutoAugment with Multi-Agent Collaboration | Data augmentation (DA) plays a critical role in improving the generalization of deep learning models. Recent works on automatically searching for DA policies from data have achieved great success. However, existing automated DA methods generally perform the search at the image level, which limits the exploration of div... | ['Zhibo Chen', 'Xin Jin', 'Xin Li', 'Ruoyu Feng', 'Tao Yu', 'Shiqi Lin'] | 2021-03-20 | local-patch-autoaugment-with-multi-agent | https://openreview.net/forum?id=RuC5ilX2m6O | https://openreview.net/pdf?id=RuC5ilX2m6O | null | ['fine-grained-image-recognition'] | ['computer-vision'] | [-2.23591849e-01 4.76350123e-03 -1.97138026e-01 -1.43794492e-01
-8.20521116e-01 -3.48562270e-01 4.05785263e-01 8.91199335e-02
-5.54557741e-01 8.09425890e-01 3.79831307e-02 5.79267517e-02
9.14349034e-02 -7.94149518e-01 -1.04961205e+00 -1.00010192e+00
2.80689895e-02 7.25553572e-01 7.47996643e-02 -2.52266347... | [9.778618812561035, 2.3983757495880127] |
0e7b8e5d-f087-47ee-bbac-c6af4908b071 | revisiting-stereo-depth-estimation-from-a | 2011.02910 | null | https://arxiv.org/abs/2011.02910v4 | https://arxiv.org/pdf/2011.02910v4.pdf | Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers | Stereo depth estimation relies on optimal correspondence matching between pixels on epipolar lines in the left and right images to infer depth. In this work, we revisit the problem from a sequence-to-sequence correspondence perspective to replace cost volume construction with dense pixel matching using position informa... | ['Francis X. Creighton', 'Andy Ding', 'Nathan Drenkow', 'Mathias Unberath', 'Russell H. Taylor', 'Xingtong Liu', 'Zhaoshuo Li'] | 2020-11-05 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Li_Revisiting_Stereo_Depth_Estimation_From_a_Sequence-to-Sequence_Perspective_With_Transformers_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Li_Revisiting_Stereo_Depth_Estimation_From_a_Sequence-to-Sequence_Perspective_With_Transformers_ICCV_2021_paper.pdf | iccv-2021-1 | ['stereo-depth-estimation'] | ['computer-vision'] | [ 4.47921544e-01 -1.38241768e-01 -3.21774296e-02 -3.38897079e-01
-6.30811930e-01 -5.11537373e-01 4.79338199e-01 -1.82467267e-01
-4.02949601e-01 9.21067834e-01 2.09803239e-01 -1.79558173e-02
2.51885623e-01 -7.47938275e-01 -6.32411003e-01 -3.61547291e-01
3.98560971e-01 2.95665354e-01 5.88344157e-01 6.27056509... | [8.896645545959473, -2.466895341873169] |
4ee99238-d19d-4c81-8203-8b04f4ef6436 | keypose-multi-view-3d-labeling-and-keypoint | 1912.02805 | null | https://arxiv.org/abs/1912.02805v2 | https://arxiv.org/pdf/1912.02805v2.pdf | KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects | Estimating the 3D pose of desktop objects is crucial for applications such as robotic manipulation. Many existing approaches to this problem require a depth map of the object for both training and prediction, which restricts them to opaque, lambertian objects that produce good returns in an RGBD sensor. In this paper w... | ['Anelia Angelova', 'Xingyu Liu', 'Rico Jonschkowski', 'Kurt Konolige'] | 2019-12-05 | keypose-multi-view-3d-labeling-and-keypoint-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Liu_KeyPose_Multi-View_3D_Labeling_and_Keypoint_Estimation_for_Transparent_Objects_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_KeyPose_Multi-View_3D_Labeling_and_Keypoint_Estimation_for_Transparent_Objects_CVPR_2020_paper.pdf | cvpr-2020-6 | ['transparent-objects'] | ['computer-vision'] | [ 7.93351978e-02 -5.88257983e-02 9.94145796e-02 -4.98684317e-01
-7.18272746e-01 -9.12550509e-01 4.66759205e-01 -1.79626063e-01
-2.13889003e-01 2.08473995e-01 -7.78666735e-02 -2.20427006e-01
8.67483690e-02 -6.73009634e-01 -1.25579870e+00 -3.32093358e-01
5.23495413e-02 8.58321369e-01 5.80336928e-01 1.38931572... | [7.012991905212402, -2.1491899490356445] |
b844a6b7-e260-44ca-a9ff-a69ea5f57576 | dmm-net-differentiable-mask-matching-network | 1909.12471 | null | https://arxiv.org/abs/1909.12471v1 | https://arxiv.org/pdf/1909.12471v1.pdf | DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation | In this paper, we propose the differentiable mask-matching network (DMM-Net) for solving the video object segmentation problem where the initial object masks are provided. Relying on the Mask R-CNN backbone, we extract mask proposals per frame and formulate the matching between object templates and proposals at one tim... | ['Sanja Fidler', 'Raquel Urtasun', 'Li Gu', 'Yuwen Xiong', 'Xiaohui Zeng', 'Renjie Liao'] | 2019-09-27 | dmm-net-differentiable-mask-matching-network-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Zeng_DMM-Net_Differentiable_Mask-Matching_Network_for_Video_Object_Segmentation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Zeng_DMM-Net_Differentiable_Mask-Matching_Network_for_Video_Object_Segmentation_ICCV_2019_paper.pdf | iccv-2019-10 | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 1.07805863e-01 2.75477588e-01 -3.93837273e-01 -3.52448761e-01
-8.91350389e-01 -4.97647703e-01 8.47086534e-02 -4.35030192e-01
-5.34724057e-01 2.12290853e-01 -4.01304215e-02 -3.60146880e-01
2.62974441e-01 -5.13997853e-01 -1.28945434e+00 -3.80339891e-01
1.04567535e-01 4.39906478e-01 4.49934930e-01 2.29968831... | [9.201544761657715, -0.10571889579296112] |
e7de6d47-6836-4aea-b771-f0e480ad19a9 | dreeam-guiding-attention-with-evidence-for | 2302.08675 | null | https://arxiv.org/abs/2302.08675v1 | https://arxiv.org/pdf/2302.08675v1.pdf | DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction | Document-level relation extraction (DocRE) is the task of identifying all relations between each entity pair in a document. Evidence, defined as sentences containing clues for the relationship between an entity pair, has been shown to help DocRE systems focus on relevant texts, thus improving relation extraction. Howev... | ['Naoaki Okazaki', 'An Wang', 'Youmi Ma'] | 2023-02-17 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 9.29583609e-02 5.58068037e-01 -5.15079439e-01 4.35494818e-03
-9.00762796e-01 -2.14467421e-01 7.49051571e-01 8.09899092e-01
-6.17237866e-01 9.34004188e-01 1.27509013e-01 -3.52299869e-01
-4.16146010e-01 -7.83545315e-01 -7.94034600e-01 -1.67878151e-01
-1.09979838e-01 5.83143175e-01 4.35036451e-01 -3.13925058... | [9.3016996383667, 8.690985679626465] |
d696b102-97ac-4117-97ee-9d1a04b99c04 | lidar-gait-benchmarking-3d-gait-recognition | 2211.10598 | null | https://arxiv.org/abs/2211.10598v2 | https://arxiv.org/pdf/2211.10598v2.pdf | LidarGait: Benchmarking 3D Gait Recognition with Point Clouds | Video-based gait recognition has achieved impressive results in constrained scenarios. However, visual cameras neglect human 3D structure information, which limits the feasibility of gait recognition in the 3D wild world. Instead of extracting gait features from images, this work explores precise 3D gait features from ... | ['Shiqi Yu', 'George Q. Huang', 'Rui Wang', 'Wei Wu', 'Chao Fan', 'Chuanfu Shen'] | 2022-11-19 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Shen_LidarGait_Benchmarking_3D_Gait_Recognition_With_Point_Clouds_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shen_LidarGait_Benchmarking_3D_Gait_Recognition_With_Point_Clouds_CVPR_2023_paper.pdf | cvpr-2023-1 | ['gait-recognition-in-the-wild', 'gait-recognition'] | ['computer-vision', 'computer-vision'] | [-3.83988559e-01 -8.57075036e-01 -6.57755136e-02 -2.82063723e-01
-3.80072474e-01 -3.04942280e-01 1.51381224e-01 -2.34232828e-01
-3.16763580e-01 4.33113039e-01 -6.06472325e-03 3.41906250e-01
1.52024657e-01 -8.19549978e-01 -5.40627718e-01 -7.22543716e-01
-2.86884785e-01 4.35868949e-01 5.25364935e-01 -2.11273655... | [14.241941452026367, 1.419130563735962] |
c17b62fb-a139-4595-8b13-311d4d3fb07c | revisit-knowledge-distillation-a-teacher-free | 1909.11723 | null | https://arxiv.org/abs/1909.11723v3 | https://arxiv.org/pdf/1909.11723v3.pdf | Revisiting Knowledge Distillation via Label Smoothing Regularization | Knowledge Distillation (KD) aims to distill the knowledge of a cumbersome teacher model into a lightweight student model. Its success is generally attributed to the privileged information on similarities among categories provided by the teacher model, and in this sense, only strong teacher models are deployed to teach ... | ['Tao Wang', 'Li Yuan', 'Guilin Li', 'Francis E. H. Tay', 'Jiashi Feng'] | 2019-09-25 | revisiting-knowledge-distillation-via-label | http://openaccess.thecvf.com/content_CVPR_2020/html/Yuan_Revisiting_Knowledge_Distillation_via_Label_Smoothing_Regularization_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Yuan_Revisiting_Knowledge_Distillation_via_Label_Smoothing_Regularization_CVPR_2020_paper.pdf | cvpr-2020-6 | ['self-knowledge-distillation'] | ['computer-vision'] | [ 1.07077420e-01 5.83123684e-01 -3.60224634e-01 -3.35414618e-01
-4.31621641e-01 -6.27629459e-01 4.89480436e-01 2.07956150e-01
-4.09711123e-01 6.57469511e-01 6.40953481e-02 -3.14665616e-01
-1.86450988e-01 -7.30561137e-01 -8.78665924e-01 -8.99478555e-01
3.82389337e-01 2.51068681e-01 4.63651747e-01 -2.95030177... | [9.513175964355469, 3.334073066711426] |
259fe715-bcc8-4145-9540-44c956152cb8 | searching-for-alignment-in-face-recognition | 2102.05447 | null | https://arxiv.org/abs/2102.05447v2 | https://arxiv.org/pdf/2102.05447v2.pdf | Searching for Alignment in Face Recognition | A standard pipeline of current face recognition frameworks consists of four individual steps: locating a face with a rough bounding box and several fiducial landmarks, aligning the face image using a pre-defined template, extracting representations and comparing. Among them, face detection, landmark detection and repre... | ['Zhen Lei', 'Feng Zhou', 'Chenxu Zhao', 'Jianzhu Guo', 'Yunxiao Qin', 'Qiang Meng', 'Xiaqing Xu'] | 2021-02-10 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [ 1.62752613e-01 -3.96509141e-01 -2.58336216e-01 -5.31919658e-01
-5.67050934e-01 -4.72843021e-01 6.91039979e-01 -4.05537963e-01
-1.33350506e-01 2.73063421e-01 -8.27226043e-02 1.43516943e-01
-2.23734275e-01 -5.22865057e-01 -3.77614319e-01 -7.09294975e-01
4.27113846e-02 2.73963630e-01 5.45449592e-02 2.62284994... | [13.267894744873047, 0.49896031618118286] |
ecc498e2-267b-4e3b-822c-8e01be0c3bdc | towards-continual-adaptation-in-industrial | null | null | https://dl.acm.org/doi/10.1145/3503161.3548232 | https://dl.acm.org/doi/pdf/10.1145/3503161.3548232?casa_token=bIrjY1SPABwAAAAA:JO9_iH9AVEo6zmYgk_axB5lgXWEpHSdy0FfrVYR0UAIhZ0JejPh2U0HcQffNC8ffDw04NG1z07034Q | Towards Continual Adaptation in Industrial Anomaly Detection | Anomaly detection (AD) has gained widespread attention due to its ability to identify defects in industrial scenarios using only normal samples. Although traditional AD methods achieved acceptable performance, they mainly focus on the current set of examples solely, leading to catastrophic forgetting of previously lear... | ['Feng Zheng', 'Chengjie Wang', 'Jun Liu', 'Bin-Bin Gao', 'Bizhong Xia', 'Jinbao Wang', 'Jiawei Zhan', 'Wujin Li'] | 2022-10-10 | null | null | null | acmmm-2022-10 | ['continual-anomaly-detection'] | ['computer-vision'] | [ 2.36166418e-01 -2.03016013e-01 1.87295750e-01 -1.51960999e-01
-2.73317516e-01 -2.44882628e-01 4.80327934e-01 3.00699383e-01
-1.92402467e-01 5.94590724e-01 -3.68497521e-01 -2.74822205e-01
-1.81566402e-01 -7.10406899e-01 -6.09222949e-01 -5.77096403e-01
-4.44462337e-02 2.22483695e-01 3.70637327e-01 -1.39236897... | [7.5176615715026855, 2.13736891746521] |
a02baec8-597b-4f7f-8ba8-96566312380a | cross-paced-representation-learning-with | 1803.01504 | null | http://arxiv.org/abs/1803.01504v1 | http://arxiv.org/pdf/1803.01504v1.pdf | Cross-Paced Representation Learning with Partial Curricula for Sketch-based Image Retrieval | In this paper we address the problem of learning robust cross-domain
representations for sketch-based image retrieval (SBIR). While most SBIR
approaches focus on extracting low- and mid-level descriptors for direct
feature matching, recent works have shown the benefit of learning coupled
feature representations to desc... | ['Nicu Sebe', 'Xavier Alameda-Pineda', 'Jingkuan Song', 'Elisa Ricci', 'Dan Xu'] | 2018-03-05 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 9.07828733e-02 -5.40336311e-01 -6.20456755e-01 -3.46891433e-02
-1.35162342e+00 -6.94734216e-01 9.57456172e-01 2.94272035e-01
-4.35765356e-01 5.35034060e-01 2.78036028e-01 1.31274641e-01
-7.50150919e-01 -6.46496952e-01 -7.76240528e-01 -5.77065825e-01
1.25429645e-01 3.88120502e-01 -1.14214011e-01 -3.40999424... | [11.456860542297363, 0.8131424188613892] |
c8082f6b-3ffb-484b-94b3-f53acf0ed79f | client-driven-animated-gif-generation | null | null | https://link.springer.com/article/10.1007/s11042-020-10236-6#Abs1 | https://link.springer.com/article/10.1007/s11042-020-10236-6 | Client-driven Animated GIF Generation Framework Using an Acoustic Feature | This paper proposes a novel, lightweight method to generate animated graphical interchange format images (GIFs) using the computational resources of a client device. The method analyzes an acoustic feature from the climax section of an audio file to estimate the timestamp corresponding to the maximum pitch. Further, it... | ['Eun-Seok Ryu', 'Jaehyoun Kim', 'Sangsoon Lee', 'Ghulam Mujtaba'] | 2021-02-12 | null | null | null | null | ['animated-gif-generation', 'music-genre-recognition'] | ['computer-vision', 'music'] | [ 2.19008625e-01 -3.27921748e-01 1.55963823e-01 -2.39808679e-01
-6.73759103e-01 -6.22957468e-01 3.53427142e-01 6.29506037e-02
-3.15379649e-01 2.97532946e-01 1.77561715e-02 -1.89562276e-01
2.32822672e-01 -6.34729624e-01 -6.34954810e-01 -4.02609289e-01
-9.67803448e-02 -1.31122708e-01 4.97828692e-01 1.97944865... | [10.579748153686523, -1.0129733085632324] |
34ffe750-2af0-4871-aeb1-09cf03722c50 | partial-inference-in-structured-prediction | 2306.03949 | null | https://arxiv.org/abs/2306.03949v1 | https://arxiv.org/pdf/2306.03949v1.pdf | Partial Inference in Structured Prediction | In this paper, we examine the problem of partial inference in the context of structured prediction. Using a generative model approach, we consider the task of maximizing a score function with unary and pairwise potentials in the space of labels on graphs. Employing a two-stage convex optimization algorithm for label re... | ['Jean Honorio', 'Chuyang Ke'] | 2023-06-06 | null | null | null | null | ['structured-prediction'] | ['methodology'] | [ 6.09617710e-01 6.57594681e-01 -6.75357878e-01 -5.92808664e-01
-1.33514428e+00 -6.93959951e-01 2.10340485e-01 -6.35003522e-02
7.84749091e-02 1.02765453e+00 2.45003134e-01 -2.60457963e-01
-5.33482730e-01 -4.62214798e-01 -9.14775431e-01 -8.60658169e-01
-2.05130845e-01 8.03824604e-01 -3.70669365e-01 3.80606711... | [7.525025844573975, 4.229300498962402] |
3cdbe7f8-dcca-466a-8330-8a36a5803011 | multi-modal-facial-expression-recognition | 2303.08419 | null | https://arxiv.org/abs/2303.08419v2 | https://arxiv.org/pdf/2303.08419v2.pdf | Multi Modal Facial Expression Recognition with Transformer-Based Fusion Networks and Dynamic Sampling | Facial expression recognition is an essential task for various applications, including emotion detection, mental health analysis, and human-machine interactions. In this paper, we propose a multi-modal facial expression recognition method that exploits audio information along with facial images to provide a crucial clu... | ['Chee Sun Won', 'NamHo Kim', 'Jun-Hwa Kim'] | 2023-03-15 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 3.59575987e-01 -3.05500329e-01 -3.19419242e-02 -7.16337740e-01
-1.01898146e+00 -2.60443270e-01 3.41619670e-01 2.62421686e-02
-2.74618357e-01 5.63503265e-01 3.95776421e-01 4.52281803e-01
-3.52547392e-02 -1.78933352e-01 -7.54720494e-02 -8.71099532e-01
8.28493759e-02 -1.33808702e-01 -4.71766323e-01 -3.93228382... | [13.54776382446289, 2.127455472946167] |
c5a9657a-be9d-4454-8ae6-1212455ba033 | text-data-augmentation-made-simple-by | 1812.04718 | null | https://arxiv.org/abs/1812.04718v1 | https://arxiv.org/pdf/1812.04718v1.pdf | Text Data Augmentation Made Simple By Leveraging NLP Cloud APIs | In practice, it is common to find oneself with far too little text data to train a deep neural network. This "Big Data Wall" represents a challenge for minority language communities on the Internet, organizations, laboratories and companies that compete the GAFAM (Google, Amazon, Facebook, Apple, Microsoft). While most... | ['Claude Coulombe'] | 2018-12-05 | null | null | null | null | ['text-augmentation'] | ['natural-language-processing'] | [ 3.68719071e-01 3.32054406e-01 -2.27765605e-01 -2.88803279e-01
-2.85329819e-01 -1.03002638e-01 8.71729136e-01 3.79392743e-01
-6.69109821e-01 9.19837773e-01 3.46904099e-01 -5.53682506e-01
1.45019457e-01 -7.90260434e-01 -5.42875707e-01 -5.76482475e-01
4.80136037e-01 6.89039767e-01 -1.45141318e-01 -6.88047290... | [10.967259407043457, 7.276968479156494] |
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