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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
59bf922b-bed0-426f-9525-b1308939656d | cot-misr-marrying-convolution-and-transformer | 2303.06548 | null | https://arxiv.org/abs/2303.06548v1 | https://arxiv.org/pdf/2303.06548v1.pdf | CoT-MISR:Marrying Convolution and Transformer for Multi-Image Super-Resolution | As a method of image restoration, image super-resolution has been extensively studied at first. How to transform a low-resolution image to restore its high-resolution image information is a problem that researchers have been exploring. In the early physical transformation methods, the high-resolution pictures generated... | ['Chun Liu', 'Qing Song', 'Yang Nie', 'Mingming Xiu'] | 2023-03-12 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 3.05522203e-01 -2.48795733e-01 1.51735500e-01 -6.55120611e-02
-5.96910655e-01 2.24266306e-01 3.48740816e-01 -7.79183507e-01
-7.79175833e-02 8.24099720e-01 4.54514086e-01 3.04815441e-01
-1.91183880e-01 -1.04597640e+00 -4.87218887e-01 -7.47175753e-01
2.78125912e-01 -1.13816932e-01 4.89646316e-01 -7.00175762... | [11.008358001708984, -2.07560396194458] |
ff8404c8-f3a2-4378-b5c1-adc0f6fdd31e | explaining-hate-speech-classification-with | 2306.00021 | null | https://arxiv.org/abs/2306.00021v1 | https://arxiv.org/pdf/2306.00021v1.pdf | Explaining Hate Speech Classification with Model Agnostic Methods | There have been remarkable breakthroughs in Machine Learning and Artificial Intelligence, notably in the areas of Natural Language Processing and Deep Learning. Additionally, hate speech detection in dialogues has been gaining popularity among Natural Language Processing researchers with the increased use of social med... | ['Ute Schmid', 'Durgesh Nandini'] | 2023-05-30 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [ 2.77964234e-01 7.34786570e-01 -1.53614789e-01 -3.21483672e-01
-4.23984975e-02 -4.00346339e-01 9.93524611e-01 3.46412271e-01
-3.98090705e-02 3.73724878e-01 5.40372670e-01 -4.85316336e-01
-1.62401736e-01 -3.05402249e-01 -1.04797073e-01 -4.19263035e-01
3.39960828e-02 3.54242474e-01 -2.56094307e-01 -4.34199423... | [9.290568351745605, 6.604339599609375] |
8a19635d-0f55-45d4-9d61-f1354ac74e56 | accelerated-mr-fingerprinting-with-low-rank | 2305.10651 | null | https://arxiv.org/abs/2305.10651v2 | https://arxiv.org/pdf/2305.10651v2.pdf | Accelerated MR Fingerprinting with Low-Rank and Generative Subspace Modeling | Magnetic Resonance (MR) Fingerprinting is an emerging multi-parametric quantitative MR imaging technique, for which image reconstruction methods utilizing low-rank and subspace constraints have achieved state-of-the-art performance. However, this class of methods often suffers from an ill-conditioned model-fitting issu... | ['Bo Zhao', 'Lawrence L. Wald', 'Huihui Ye', 'Hengfa Lu'] | 2023-05-18 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 4.72804576e-01 -2.96411157e-01 3.28190960e-02 -2.51094908e-01
-8.90666366e-01 -1.49097472e-01 2.65306741e-01 -5.20218313e-01
-6.03386283e-01 7.67314792e-01 1.01456665e-01 -8.82454216e-02
-5.51613212e-01 -1.74669698e-01 -6.49171352e-01 -1.12554979e+00
-1.24957867e-01 4.32489038e-01 -1.94238514e-01 2.80868951... | [13.47022819519043, -2.390057325363159] |
77c776e3-46ee-4346-aeb8-c09be83d5f66 | generalization-of-the-dark-channel-prior-for | null | null | https://ieeexplore.ieee.org/abstract/document/8307410/ | https://ieeexplore.ieee.org/abstract/document/8307410/ | Generalization of the Dark Channel Prior for Single Image Restoration | Abstract— Images degraded by light scattering and absorption, such as hazy, sandstorm, and underwater images, often suffer color distortion and low contrast because of light traveling through turbid media. In order to enhance and restore such images, we first estimate ambient light using the depth-dependent color chang... | ['IEEE', 'Fellow', 'and Pamela C. Cosman', 'Keming Cao', 'Yan-Tsung Peng'] | 2019-03-07 | null | null | null | ieee-transactions-on-image-processing-2019-3 | ['underwater-image-restoration'] | ['computer-vision'] | [ 7.09488988e-01 -3.92574310e-01 9.32761312e-01 -2.63718396e-01
-2.23992676e-01 -3.45756888e-01 2.52568215e-01 -3.31202090e-01
-5.17912865e-01 9.73489642e-01 1.06185153e-01 -5.62486462e-02
1.64777219e-01 -7.09656596e-01 -4.33956742e-01 -1.31886792e+00
2.43688270e-01 -2.33718846e-02 2.32380167e-01 -3.59385550... | [10.781746864318848, -3.242655038833618] |
b7f0c350-d90d-42b9-a7bf-9aa029b3530e | deep-transfer-learning-for-cross-domain | 1807.07963 | null | http://arxiv.org/abs/1807.07963v2 | http://arxiv.org/pdf/1807.07963v2.pdf | Deep Transfer Learning for Cross-domain Activity Recognition | Human activity recognition plays an important role in people's daily life.
However, it is often expensive and time-consuming to acquire sufficient labeled
activity data. To solve this problem, transfer learning leverages the labeled
samples from the source domain to annotate the target domain which has few or
none labe... | ['Vincent W. Zheng', 'Meiyu Huang', 'Yiqiang Chen', 'Jindong Wang'] | 2018-07-20 | null | null | null | null | ['cross-domain-activity-recognition'] | ['computer-vision'] | [ 3.58717144e-01 -4.09866393e-01 -7.66874075e-01 -2.89067656e-01
-8.20003748e-01 -4.21291113e-01 3.70833129e-01 -1.76574484e-01
-3.03301156e-01 1.04885769e+00 3.77176523e-01 1.72377110e-01
-1.65944576e-01 -9.91112649e-01 -6.56317890e-01 -7.50739038e-01
2.17588488e-02 3.16192508e-01 2.79604644e-01 7.65761212... | [8.02088737487793, 1.003119945526123] |
6786511b-0726-43f5-82c4-7461ebdd8442 | meta-self-learning-for-multi-source-domain | 2108.10840 | null | https://arxiv.org/abs/2108.10840v1 | https://arxiv.org/pdf/2108.10840v1.pdf | Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark | In recent years, deep learning-based methods have shown promising results in computer vision area. However, a common deep learning model requires a large amount of labeled data, which is labor-intensive to collect and label. What's more, the model can be ruined due to the domain shift between training data and testing ... | ['Wenli Zhou', 'Chuang Zhu', 'Shuhao Qiu'] | 2021-08-24 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 2.34335855e-01 -7.33525574e-01 -3.33300799e-01 -4.27114218e-01
-4.17100698e-01 -2.99211234e-01 6.18877351e-01 -1.28857061e-01
-2.58154452e-01 5.72300732e-01 -2.19170121e-03 2.60432325e-02
2.40601510e-01 -6.85044885e-01 -5.06753623e-01 -7.64255822e-01
6.25698447e-01 4.92605299e-01 2.37167090e-01 -2.25314766... | [11.833162307739258, 2.1425111293792725] |
3a3784f5-b7f3-47a9-a028-d8a0e6c77fb9 | mimo-mutual-integration-of-patient-journey | 2107.09288 | null | https://arxiv.org/abs/2107.09288v4 | https://arxiv.org/pdf/2107.09288v4.pdf | MIPO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning | Healthcare representation learning on the Electronic Health Records is crucial for downstream medical prediction tasks in health informatics. Many NLP techniques, such as RNN and self-attention, have been adapted to learn medical representations from hierarchical and time-stamped EHRs data, but fail when they lack eith... | ['Clement Schlegel', 'Allison Clarke', 'Jing Jiang', 'Guodong Long', 'Chengqi Zhang', 'Sen Wang', 'Xueping Peng'] | 2021-07-20 | null | null | null | null | ['ontology-embedding'] | ['knowledge-base'] | [ 2.67825574e-01 5.38125932e-01 -4.79589015e-01 -3.53563249e-01
-5.86202085e-01 1.39096946e-01 1.47678688e-01 6.60966575e-01
-1.42910406e-01 4.36831146e-01 7.93293715e-01 -3.44260156e-01
-7.14903057e-01 -9.14410174e-01 -4.32169288e-01 -6.33742809e-01
-2.27038205e-01 6.58978105e-01 -1.77575454e-01 -2.42572755... | [7.842689037322998, 6.507523059844971] |
f2cde338-e23b-4f15-aa9a-11a09fd79742 | background-suppression-network-for-weakly | 1911.09963 | null | https://arxiv.org/abs/1911.09963v1 | https://arxiv.org/pdf/1911.09963v1.pdf | Background Suppression Network for Weakly-supervised Temporal Action Localization | Weakly-supervised temporal action localization is a very challenging problem because frame-wise labels are not given in the training stage while the only hint is video-level labels: whether each video contains action frames of interest. Previous methods aggregate frame-level class scores to produce video-level predicti... | ['Youngjung Uh', 'Pilhyeon Lee', 'Hyeran Byun'] | 2019-11-22 | null | null | null | null | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action'] | ['computer-vision', 'computer-vision'] | [ 4.35380727e-01 -1.93723217e-01 -7.82183409e-01 -3.07768077e-01
-5.75142801e-01 -1.81660354e-01 4.79581386e-01 -3.60671818e-01
-4.83552426e-01 7.43907571e-01 2.98159033e-01 -9.13627446e-02
5.92692912e-01 -3.44521880e-01 -7.24907815e-01 -8.38428855e-01
-1.54311091e-01 -8.36403370e-02 9.38195586e-01 1.47820771... | [8.491143226623535, 0.547023594379425] |
54808efa-aef3-49d6-b260-936cea2b633e | unsupervised-part-of-speech-tagging-in-noisy | null | null | https://aclanthology.org/W12-0601 | https://aclanthology.org/W12-0601.pdf | Unsupervised Part-of-Speech Tagging in Noisy and Esoteric Domains With a Syntactic-Semantic Bayesian HMM | null | ['William M. Darling', 'Michael J. Paul', 'Fei Song'] | 2012-04-01 | null | null | null | ws-2012-4 | ['unsupervised-part-of-speech-tagging'] | ['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... | [-7.365813255310059, 3.70330548286438] |
01914f63-60ea-4495-9ad6-e7997b488263 | joint-adaptive-neighbours-and-metric-learning | 1709.03656 | null | http://arxiv.org/abs/1709.03656v1 | http://arxiv.org/pdf/1709.03656v1.pdf | Joint Adaptive Neighbours and Metric Learning for Multi-view Subspace Clustering | Due to the existence of various views or representations in many real-world
data, multi-view learning has drawn much attention recently. Multi-view
spectral clustering methods based on similarity matrixes or graphs are pretty
popular. Generally, these algorithms learn informative graphs by directly
utilizing original d... | ['Xiangyang Luo', 'Nan Xu', 'Jiujun Wang', 'Yanqing Guo', 'Ran He'] | 2017-09-12 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-1.77713364e-01 -4.68139350e-01 2.20793001e-02 -2.56222636e-01
-5.11005223e-01 -6.95356548e-01 2.69313902e-01 -6.93041692e-03
1.78597867e-01 3.05750102e-01 3.14445496e-01 3.16413641e-01
-5.00226617e-01 -5.01035750e-01 -3.31480622e-01 -1.05983460e+00
1.91817239e-01 3.62129450e-01 6.09549507e-02 5.12062162... | [8.181979179382324, 4.65186882019043] |
6b8ceb80-7bb9-48be-959f-d75dc5f788bb | research-on-attention-memory-networks-as-a | null | null | https://aclanthology.org/W16-5902 | https://aclanthology.org/W16-5902.pdf | Research on attention memory networks as a model for learning natural language inference | null | ['Jing Zhang', 'Zhuang Liu', 'Kaiyu Huang', 'Degen Huang'] | 2016-11-01 | null | null | null | ws-2016-11 | ['sentence-pair-modeling'] | ['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... | [-7.324551105499268, 3.7082862854003906] |
2f187e15-c208-451d-92d7-7d03e02662ba | testing-neural-programs | 1908.10711 | null | https://arxiv.org/abs/1908.10711v2 | https://arxiv.org/pdf/1908.10711v2.pdf | Testing Neural Program Analyzers | Deep neural networks have been increasingly used in software engineering and program analysis tasks. They usually take a program and make some predictions about it, e.g., bug prediction. We call these models neural program analyzers. The reliability of neural programs can impact the reliability of the encompassing anal... | ['Md. Rafiqul Islam Rabin', 'Mohammad Amin Alipour', 'Ke Wang'] | 2019-08-25 | null | null | null | null | ['method-name-prediction'] | ['natural-language-processing'] | [-5.32145053e-02 1.16978034e-01 -2.33201474e-01 -5.03525138e-01
-2.28441909e-01 -3.48730147e-01 -3.10564101e-01 2.34546270e-02
1.05119079e-01 3.57760519e-01 -2.79104620e-01 -9.50902522e-01
4.71990615e-01 -1.02871776e+00 -1.22709966e+00 1.56916440e-01
-2.91149050e-01 -1.01301201e-01 4.26589072e-01 -1.60972670... | [7.522695064544678, 7.693163871765137] |
912dcf87-733b-4941-ad11-67824a8c143e | coupled-gradient-flows-for-strategic-non | 2307.01166 | null | https://arxiv.org/abs/2307.01166v2 | https://arxiv.org/pdf/2307.01166v2.pdf | Coupled Gradient Flows for Strategic Non-Local Distribution Shift | We propose a novel framework for analyzing the dynamics of distribution shift in real-world systems that captures the feedback loop between learning algorithms and the distributions on which they are deployed. Prior work largely models feedback-induced distribution shift as adversarial or via an overly simplistic distr... | ['Lillian Ratliff', 'Eric Mazumdar', 'Franca Hoffmann', 'Lauren Conger'] | 2023-07-03 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 1.75030023e-01 -1.39527932e-01 -5.27051911e-02 3.15436810e-01
-2.82333672e-01 -1.30488694e+00 7.62485027e-01 1.31804526e-01
-5.57055116e-01 9.49276567e-01 -5.34540974e-03 -4.05846506e-01
-5.18026114e-01 -5.19949257e-01 -9.12146032e-01 -1.16366971e+00
-2.00968206e-01 7.39380956e-01 -9.67633054e-02 -3.32214803... | [4.230816841125488, 2.626802682876587] |
c60ff0d9-dc4f-4d09-9118-a60604e6c848 | when-person-re-identification-meets-changing | 2003.04070 | null | https://arxiv.org/abs/2003.04070v3 | https://arxiv.org/pdf/2003.04070v3.pdf | When Person Re-identification Meets Changing Clothes | Person re-identification (ReID) is now an active research topic for AI-based video surveillance applications such as specific person search, but the practical issue that the target person(s) may change clothes (clothes inconsistency problem) has been overlooked for long. For the first time, this paper systematically st... | ['Yanwei Fu', 'Yang Wu', 'Xuelin Qian', 'Yixiong Chen', 'Fangbin Wan'] | 2020-03-09 | null | null | null | null | ['person-search'] | ['computer-vision'] | [ 1.59865543e-01 -1.92689776e-01 -1.31543010e-01 -5.01454711e-01
-1.50689840e-01 -4.60819870e-01 7.00915337e-01 -3.42159718e-01
-4.89188731e-01 7.46308804e-01 1.04723923e-01 1.26722813e-01
-1.07598133e-01 -5.25700808e-01 -7.13832021e-01 -7.19146609e-01
-1.33423924e-01 2.68257380e-01 2.50203192e-01 -2.66000241... | [14.579614639282227, 0.9926351308822632] |
d4e750b8-dc44-41ca-a205-ef97fc760d00 | generic-instance-search-and-re-identification | 1605.07104 | null | http://arxiv.org/abs/1605.07104v1 | http://arxiv.org/pdf/1605.07104v1.pdf | Generic Instance Search and Re-identification from One Example via Attributes and Categories | This paper aims for generic instance search from one example where the
instance can be an arbitrary object like shoes, not just near-planar and
one-sided instances like buildings and logos. First, we evaluate
state-of-the-art instance search methods on this problem. We observe that what
works for buildings loses its ge... | ['Shih-Fu Chang', 'Arnold W. M. Smeulders', 'Ran Tao'] | 2016-05-23 | null | null | null | null | ['instance-search'] | ['computer-vision'] | [ 3.55263725e-02 -1.57849118e-01 -2.27535799e-01 -3.02326262e-01
-9.41547453e-01 -7.86727130e-01 7.52223313e-01 1.44344360e-01
-4.26432729e-01 5.45789599e-01 -4.23276611e-02 2.23312423e-01
-6.96583569e-01 -7.48617947e-01 -7.36073375e-01 -5.76268911e-01
-2.09180247e-02 1.21039343e+00 5.20480275e-01 -2.79833943... | [9.628376007080078, 1.1641433238983154] |
94463f7c-731c-44cd-b43d-eed2351de8fd | easyspider-a-no-code-visual-system-for | null | null | https://dl.acm.org/doi/abs/10.1145/3543873.3587345 | https://dl.acm.org/doi/pdf/10.1145/3543873.3587345 | EasySpider: A No-Code Visual System for Crawling the Web | The web is a treasure trove for data that is increasingly used by computer scientists for building large machine learning models as well as non-computer scientists for social studies or marketing analyses. As such, web-crawling is an essential tool for both computational and non-computational scientists to conduct rese... | ['See-Kiong Ng', 'Jianwei Yin', 'Wenjie Feng', 'Naibo Wang'] | 2023-04-30 | null | null | null | acm-the-web-conference-2023-4 | ['data-integration', 'marketing'] | ['knowledge-base', 'miscellaneous'] | [-5.04635215e-01 -2.44577125e-01 -9.09167081e-02 -2.06997856e-01
-4.86972719e-01 -1.08286119e+00 5.09672165e-01 2.73638248e-01
-4.57200527e-01 3.03116232e-01 -5.26794076e-01 -8.37592125e-01
-1.11803394e-02 -9.71147835e-01 -2.65751153e-01 -4.30620939e-01
1.99635506e-01 3.15387428e-01 7.15078354e-01 -1.36943027... | [9.370826721191406, 8.350706100463867] |
e5fbd2ef-7832-45c4-9de4-5371ee5e3717 | entities-dates-and-languages-zero-shot-on | 2204.05211 | null | https://arxiv.org/abs/2204.05211v1 | https://arxiv.org/pdf/2204.05211v1.pdf | Entities, Dates, and Languages: Zero-Shot on Historical Texts with T0 | In this work, we explore whether the recently demonstrated zero-shot abilities of the T0 model extend to Named Entity Recognition for out-of-distribution languages and time periods. Using a historical newspaper corpus in 3 languages as test-bed, we use prompts to extract possible named entities. Our results show that a... | ['Daniel van Strien', 'Stefan Schweter', 'Enrique Manjavacas', 'Clémentine Fourrier', 'Javier de la Rosa', 'Christopher Akiki', 'Francesco De Toni'] | 2022-04-11 | null | https://aclanthology.org/2022.bigscience-1.7 | https://aclanthology.org/2022.bigscience-1.7.pdf | bigscience-acl-2022-5 | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-4.75446492e-01 8.76035318e-02 -5.08987010e-01 -3.97797018e-01
-1.13231301e+00 -9.37780917e-01 1.04372299e+00 3.24849337e-01
-8.12675238e-01 8.44742119e-01 3.93440932e-01 -7.05928445e-01
5.41087911e-02 -7.72310019e-01 -4.61485803e-01 -8.92997161e-02
-2.30947807e-01 5.25437176e-01 2.97478259e-01 -4.17986333... | [9.828740119934082, 9.693143844604492] |
2441a152-8af5-472c-b61d-3a37c39688fc | eaten-entity-aware-attention-for-single-shot | 1909.09380 | null | https://arxiv.org/abs/1909.09380v1 | https://arxiv.org/pdf/1909.09380v1.pdf | EATEN: Entity-aware Attention for Single Shot Visual Text Extraction | Extracting entity from images is a crucial part of many OCR applications, such as entity recognition of cards, invoices, and receipts. Most of the existing works employ classical detection and recognition paradigm. This paper proposes an Entity-aware Attention Text Extraction Network called EATEN, which is an end-to-en... | ['Junyu Han', 'Errui Ding', 'Jingtuo Liu', 'Xiameng Qin', 'Jiaming Liu', 'He guo'] | 2019-09-20 | null | null | null | null | ['entity-extraction'] | ['natural-language-processing'] | [ 5.89184538e-02 -1.05076730e-01 -1.48361728e-01 -2.96404004e-01
-7.89499104e-01 -6.36464298e-01 6.67927861e-01 1.63773209e-01
-7.37634361e-01 4.82271314e-01 -5.34839965e-02 -2.59081244e-01
2.65929043e-01 -7.69900501e-01 -9.17645454e-01 -3.71709019e-01
3.24898005e-01 2.75833398e-01 2.22254544e-01 5.77916838... | [11.462964057922363, 2.3001677989959717] |
9e67e4b1-f085-42ca-8e1c-af28f638feb2 | linearized-optimal-transport-for-collider | 2008.08604 | null | https://arxiv.org/abs/2008.08604v1 | https://arxiv.org/pdf/2008.08604v1.pdf | Linearized Optimal Transport for Collider Events | We introduce an efficient framework for computing the distance between collider events using the tools of Linearized Optimal Transport (LOT). This preserves many of the advantages of the recently-introduced Energy Mover's Distance, which quantifies the "work" required to rearrange one event into another, while signific... | ['Katy Craig', 'Junyi Cheng', 'Tianji Cai', 'Nathaniel Craig'] | 2020-08-19 | null | null | null | null | ['jet-tagging'] | ['graphs'] | [-2.00011894e-01 -2.88867593e-01 -2.47400731e-01 -1.39694020e-01
-5.56941688e-01 -9.03405786e-01 9.65414703e-01 4.97117758e-01
-6.96943641e-01 6.48831546e-01 6.44873008e-02 -6.93932176e-01
-5.49004316e-01 -7.15852141e-01 -3.28732222e-01 -1.05279171e+00
-6.87854350e-01 5.80468774e-01 1.99228242e-01 -4.61990714... | [15.6827392578125, 2.9224867820739746] |
b7ab3727-d52d-4d90-a13b-39d40bdfacdf | camel-tools-an-open-source-python-toolkit-for | null | null | https://aclanthology.org/2020.lrec-1.868 | https://aclanthology.org/2020.lrec-1.868.pdf | CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing | We present CAMeL Tools, a collection of open-source tools for Arabic natural language processing in Python. CAMeL Tools currently provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and Sentiment Analysis. In this paper, we describe the design of CAMeL Tools a... | ['Nizar Habash', 'er', 'Mai Oudah', 'Salam Khalifa', 'Ossama Obeid', 'Go Inoue', 'Dima Taji', 'Bashar Alhafni', 'Alex Erdmann', 'Nasser Zalmout', 'Fadhl Eryani'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['arabic-sentiment-analysis', 'arabic-text-diacritization'] | ['natural-language-processing', 'natural-language-processing'] | [-8.85592163e-01 -5.80340385e-01 3.00843745e-01 -8.46453428e-01
-7.48170257e-01 -1.21405864e+00 3.68966639e-01 8.06353569e-01
-5.63072979e-01 5.84540963e-01 2.82327652e-01 -4.60392505e-01
3.52537453e-01 -1.05940139e+00 5.15563935e-02 -2.16963470e-01
-3.46167058e-01 4.22345966e-01 -1.10620804e-01 -8.12310219... | [10.346405982971191, 10.369450569152832] |
2271f934-5aa3-4a6d-85f5-321622efb724 | monocular-visual-odometry-with-a-rolling | 1704.07163 | null | http://arxiv.org/abs/1704.07163v1 | http://arxiv.org/pdf/1704.07163v1.pdf | Monocular Visual Odometry with a Rolling Shutter Camera | Rolling Shutter (RS) cameras have become popularized because of low-cost
imaging capability. However, the RS cameras suffer from undesirable artifacts
when the camera or the subject is moving, or illumination condition changes.
For that reason, Monocular Visual Odometry (MVO) with RS cameras produces
inaccurate ego-mot... | ['Kuk-Jin Yoon', 'Chang-Ryeol Lee'] | 2017-04-24 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [ 1.21188410e-01 -6.06880665e-01 -2.75937617e-01 5.86647391e-02
-4.71090898e-02 -5.11417091e-01 4.47274208e-01 -6.28548682e-01
-3.37774247e-01 4.70191568e-01 2.18800027e-02 3.72722410e-02
1.25727341e-01 -3.33279103e-01 -7.02162683e-01 -7.42490947e-01
3.80259037e-01 -1.03476807e-01 3.39648217e-01 2.73887031... | [8.048389434814453, -2.1677029132843018] |
e22c4229-22dd-4c36-adaf-5cfde14fff08 | adir-adaptive-diffusion-for-image | 2212.03221 | null | https://arxiv.org/abs/2212.03221v1 | https://arxiv.org/pdf/2212.03221v1.pdf | ADIR: Adaptive Diffusion for Image Reconstruction | In recent years, denoising diffusion models have demonstrated outstanding image generation performance. The information on natural images captured by these models is useful for many image reconstruction applications, where the task is to restore a clean image from its degraded observations. In this work, we propose a c... | ['Raja Giryes', 'Tom Tirer', 'Shady Abu-Hussein'] | 2022-12-06 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 6.01110697e-01 -1.75635651e-01 2.60679960e-01 -2.43378699e-01
-9.14471030e-01 -4.15671587e-01 8.12617362e-01 -3.25529099e-01
-5.08279979e-01 6.69678330e-01 6.73696816e-01 4.19891328e-02
-1.20741680e-01 -5.42409301e-01 -6.02829874e-01 -1.01427507e+00
3.34038734e-01 1.17316969e-01 3.96818817e-01 -2.30689749... | [11.648002624511719, -2.2320380210876465] |
6664b7ed-2252-43c9-a37c-e9d4b910c2bf | from-indoor-to-outdoor-unsupervised-domain | 2211.11155 | null | https://arxiv.org/abs/2211.11155v1 | https://arxiv.org/pdf/2211.11155v1.pdf | From Indoor To Outdoor: Unsupervised Domain Adaptive Gait Recognition | Gait recognition is an important AI task, which has been progressed rapidly with the development of deep learning. However, existing learning based gait recognition methods mainly focus on the single domain, especially the constrained laboratory environment. In this paper, we study a new problem of unsupervised domain ... | ['Song Wang', 'Wei Feng', 'Ruize Han', 'Likai Wang'] | 2022-11-21 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [ 5.71274385e-02 -6.58540905e-01 1.67371318e-01 -4.48151529e-01
-5.73394597e-01 2.79570166e-02 -9.31123495e-02 -4.15337384e-01
-2.13645369e-01 8.83004487e-01 1.50406212e-01 5.39275050e-01
-1.86955124e-01 -5.95673919e-01 -3.69505405e-01 -1.13782740e+00
-2.64201581e-01 4.32004899e-01 2.25928679e-01 1.60404757... | [14.312410354614258, 1.4116252660751343] |
404afc3f-8860-48ef-b827-cfa8552765ae | joint-pruning-quantization-for-extremely | 2010.01892 | null | https://arxiv.org/abs/2010.01892v1 | https://arxiv.org/pdf/2010.01892v1.pdf | Joint Pruning & Quantization for Extremely Sparse Neural Networks | We investigate pruning and quantization for deep neural networks. Our goal is to achieve extremely high sparsity for quantized networks to enable implementation on low cost and low power accelerator hardware. In a practical scenario, there are particularly many applications for dense prediction tasks, hence we choose s... | ['Shao-Yi Chien', 'Liang-Gee Chen', 'Jan P. Klopp', 'Sih-Sian Wu', 'Po-Hsiang Yu'] | 2020-10-05 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [ 8.50372985e-02 2.24360511e-01 -3.23613808e-02 -5.22256434e-01
-2.07503870e-01 1.04571119e-01 4.05527711e-01 4.25725840e-02
-8.29415619e-01 5.45271933e-01 1.36216834e-01 -5.16316354e-01
1.48149937e-01 -9.84599710e-01 -7.17041850e-01 -4.39436138e-01
5.58762960e-02 1.69154610e-02 5.14503539e-01 -8.82663131... | [8.565573692321777, 3.007598876953125] |
40b11d38-f7c7-4dc6-a538-4580686b0516 | functional-magnetic-resonance-imaging-data | 2107.06104 | null | https://arxiv.org/abs/2107.06104v2 | https://arxiv.org/pdf/2107.06104v2.pdf | Functional Magnetic Resonance Imaging data augmentation through conditional ICA | Advances in computational cognitive neuroimaging research are related to the availability of large amounts of labeled brain imaging data, but such data are scarce and expensive to generate. While powerful data generation mechanisms, such as Generative Adversarial Networks (GANs), have been designed in the last decade f... | ['Bertrand Thirion', 'Hugo Richard', 'Badr Tajini'] | 2021-07-11 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 6.73697412e-01 3.44688922e-01 8.54963586e-02 -5.66350162e-01
-9.07431960e-01 -5.51516831e-01 6.83734179e-01 -3.58931035e-01
-3.92075092e-01 1.08365059e+00 4.66846168e-01 -1.92755312e-01
-6.83201198e-03 -5.52743018e-01 -7.58115351e-01 -7.35952199e-01
-7.10922405e-02 6.74656749e-01 -5.00025630e-01 1.15374610... | [14.128299713134766, -1.8259291648864746] |
5085ff2d-2365-4423-8a8a-c059b0f4bcd8 | neural-lens-modeling | 2304.04848 | null | https://arxiv.org/abs/2304.04848v1 | https://arxiv.org/pdf/2304.04848v1.pdf | Neural Lens Modeling | Recent methods for 3D reconstruction and rendering increasingly benefit from end-to-end optimization of the entire image formation process. However, this approach is currently limited: effects of the optical hardware stack and in particular lenses are hard to model in a unified way. This limits the quality that can be ... | ['Christoph Lassner', 'Noah Snavely', 'Aljaž Božič', 'Wenqi Xian'] | 2023-04-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xian_Neural_Lens_Modeling_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xian_Neural_Lens_Modeling_CVPR_2023_paper.pdf | cvpr-2023-1 | ['camera-calibration'] | ['computer-vision'] | [ 9.26332697e-02 -3.06740582e-01 4.75912988e-01 -5.33933520e-01
-3.52614731e-01 -9.06388819e-01 6.67818546e-01 -9.51032788e-02
-2.12472111e-01 2.10312933e-01 7.04964697e-02 -2.65261471e-01
-6.25157058e-02 -5.81264973e-01 -9.69946563e-01 -5.06301105e-01
2.64888257e-01 6.94316924e-01 3.64873677e-01 -5.65057322... | [9.526339530944824, -3.027308464050293] |
129d26f5-8453-4b22-8387-a0ea39b2390c | revisiting-learnable-affines-for-batch-norm | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yazdanpanah_Revisiting_Learnable_Affines_for_Batch_Norm_in_Few-Shot_Transfer_Learning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yazdanpanah_Revisiting_Learnable_Affines_for_Batch_Norm_in_Few-Shot_Transfer_Learning_CVPR_2022_paper.pdf | Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning | Batch Normalization is a staple of computer vision models, including those employed in few-shot learning. Batch Normalization layers in convolutional neural networks are composed of a normalization step, followed by a shift and scale of these normalized features applied via the per-channel trainable affine paramete... | ['Samira Ebrahimi Kahou', 'Eugene Belilovsky', 'Mohammad Havaei', 'Christian Desrosiers', 'Muawiz Chaudhary', 'Aamer Abdul Rahman', 'Moslem Yazdanpanah'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 4.80688423e-01 -8.74021873e-02 -8.96806642e-02 -6.35596454e-01
-6.37290835e-01 -4.06125516e-01 1.02225387e+00 -6.70670569e-02
-9.71300781e-01 5.16465664e-01 3.54838043e-01 5.09442277e-02
1.03233894e-02 -6.70626819e-01 -7.03850508e-01 -8.12872171e-01
-2.16399767e-02 -1.11256540e-02 5.10836899e-01 -5.93912423... | [10.024577140808105, 2.7746808528900146] |
56104d8b-d95a-4191-b538-38b0b26d84e5 | tiny-hr-towards-an-interpretable-machine | 2208.07981 | null | https://arxiv.org/abs/2208.07981v1 | https://arxiv.org/pdf/2208.07981v1.pdf | Tiny-HR: Towards an interpretable machine learning pipeline for heart rate estimation on edge devices | The focus of this paper is a proof of concept, machine learning (ML) pipeline that extracts heart rate from pressure sensor data acquired on low-power edge devices. The ML pipeline consists an upsampler neural network, a signal quality classifier, and a 1D-convolutional neural network optimized for efficient and accura... | ['Nilanjan Ray', 'Ganesh Tata', 'Shailesh Nanisetty', 'Preetam Anbukarasu'] | 2022-08-16 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 4.24171329e-01 8.11413750e-02 -1.31349787e-01 -3.10343385e-01
-4.12057191e-01 -2.67436981e-01 -3.49695116e-01 4.85145390e-01
-5.96922576e-01 5.09234130e-01 -2.96969444e-01 -3.23304355e-01
3.58395308e-01 -6.30259037e-01 -5.07778883e-01 -3.62094074e-01
-3.68766606e-01 -1.91708773e-01 8.75892341e-02 5.56894004... | [13.972968101501465, 3.1197683811187744] |
158209da-0002-4dc2-bcdf-b7b3d51bde0e | a-separation-logic-for-sequences-in-pointer | 2301.06237 | null | https://arxiv.org/abs/2301.06237v1 | https://arxiv.org/pdf/2301.06237v1.pdf | A separation logic for sequences in pointer programs and its decidability | Separation logic and its variants can describe various properties on pointer programs. However, when it comes to properties on sequences, one may find it hard to formalize. To deal with properties on variable-length sequences and multilevel data structures, we propose sequence-heap separation logic which integrates seq... | ['Hanpin Wang', 'Yongzhi Cao', 'Zhao Jin', 'BoWen Zhang', 'Tianyue Cao'] | 2023-01-16 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 4.53224123e-01 2.53405690e-01 -6.28771245e-01 6.12104759e-02
-2.87306011e-01 -9.31294858e-01 2.40164503e-01 2.80308098e-01
-1.83900267e-01 1.00965357e+00 -1.56469103e-02 -1.21614671e+00
-2.77396739e-01 -1.29754806e+00 -6.76091969e-01 -5.67628324e-01
-7.96445251e-01 2.64731258e-01 9.46171284e-01 -1.60302326... | [8.703289985656738, 6.807203769683838] |
43c8cd09-1a4d-4e19-8da8-ebeec139e3b6 | parallel-instance-query-network-for-named | 2203.10545 | null | https://arxiv.org/abs/2203.10545v1 | https://arxiv.org/pdf/2203.10545v1.pdf | Parallel Instance Query Network for Named Entity Recognition | Named entity recognition (NER) is a fundamental task in natural language processing. Recent works treat named entity recognition as a reading comprehension task, constructing type-specific queries manually to extract entities. This paradigm suffers from three issues. First, type-specific queries can only extract one ty... | ['Yueting Zhuang', 'Weiming Lu', 'Fei Huang', 'Pengjun Xie', 'Guangwei Xu', 'Zeqi Tan', 'Xiaobin Wang', 'Yongliang Shen'] | 2022-03-20 | null | https://aclanthology.org/2022.acl-long.67 | https://aclanthology.org/2022.acl-long.67.pdf | acl-2022-5 | ['nested-named-entity-recognition', 'chinese-named-entity-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [-1.11701470e-02 3.27631325e-01 -1.98075756e-01 -5.94956875e-01
-9.71579134e-01 -9.71899211e-01 1.81433320e-01 5.46974719e-01
-1.01955044e+00 7.95394599e-01 -1.03732750e-01 -1.88533515e-01
-1.82284657e-02 -1.35090649e+00 -8.87081444e-01 -4.96515222e-02
1.53812870e-01 9.44594622e-01 5.65925241e-01 -1.68998405... | [9.569990158081055, 9.263631820678711] |
10b1af57-9b73-401d-bbd0-79cef5a51906 | fjmp-factorized-joint-multi-agent-motion | 2211.16197 | null | https://arxiv.org/abs/2211.16197v2 | https://arxiv.org/pdf/2211.16197v2.pdf | FJMP: Factorized Joint Multi-Agent Motion Prediction over Learned Directed Acyclic Interaction Graphs | Predicting the future motion of road agents is a critical task in an autonomous driving pipeline. In this work, we address the problem of generating a set of scene-level, or joint, future trajectory predictions in multi-agent driving scenarios. To this end, we propose FJMP, a Factorized Joint Motion Prediction framewor... | ['Krzysztof Czarnecki', 'Eli-Henry Dykhne', 'Martin Ethier', 'Luke Rowe'] | 2022-11-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Rowe_FJMP_Factorized_Joint_Multi-Agent_Motion_Prediction_Over_Learned_Directed_Acyclic_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Rowe_FJMP_Factorized_Joint_Multi-Agent_Motion_Prediction_Over_Learned_Directed_Acyclic_CVPR_2023_paper.pdf | cvpr-2023-1 | ['motion-prediction'] | ['computer-vision'] | [-9.64484550e-03 3.07701766e-01 -9.92456079e-02 -6.85729265e-01
-6.28791332e-01 -4.60184693e-01 9.65025663e-01 -2.86724001e-01
-1.55543610e-01 7.33645141e-01 5.59763610e-01 -5.33100605e-01
-1.64210260e-01 -7.79010475e-01 -9.35683131e-01 -4.38335121e-01
-5.25905371e-01 1.01591444e+00 7.40699828e-01 -3.48592877... | [5.869652271270752, 0.8081386089324951] |
bdf0639e-f143-488c-839f-b864a5e1f66e | fusion-volumetric-object-level-slam | 1808.08378 | null | http://arxiv.org/abs/1808.08378v2 | http://arxiv.org/pdf/1808.08378v2.pdf | Fusion++: Volumetric Object-Level SLAM | We propose an online object-level SLAM system which builds a persistent and
accurate 3D graph map of arbitrary reconstructed objects. As an RGB-D camera
browses a cluttered indoor scene, Mask-RCNN instance segmentations are used to
initialise compact per-object Truncated Signed Distance Function (TSDF)
reconstructions ... | ['Stefan Leutenegger', 'Ronald Clark', 'Michael Bloesch', 'John McCormac', 'Andrew J. Davison'] | 2018-08-25 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [ 4.92217541e-01 1.88930199e-01 2.30779961e-01 -3.58310491e-01
-8.59789968e-01 -7.67171919e-01 3.36065382e-01 4.01717037e-01
-4.61769640e-01 3.89011025e-01 -3.38511527e-01 -1.32071093e-01
-2.15486035e-01 -5.22771537e-01 -1.16475296e+00 -3.48837793e-01
-3.92078549e-01 1.15232706e+00 1.09558213e+00 3.92556489... | [7.3467559814453125, -2.3643128871917725] |
ad0532bd-3c13-42c7-bc51-b1907b20c2cb | contour-detection-and-characterization-for | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Barranco_Contour_Detection_and_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Barranco_Contour_Detection_and_ICCV_2015_paper.pdf | Contour Detection and Characterization for Asynchronous Event Sensors | The bio-inspired, asynchronous event-based dynamic vision sensor records temporal changes in the luminance of the scene at high temporal resolution. Since events are only triggered at significant luminance changes, most events occur at the boundary of objects and their parts. The detection of these contours is an essen... | ['Cornelia Fermuller', 'Francisco Barranco', 'Yiannis Aloimonos', 'Ching L. Teo'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['contour-detection'] | ['computer-vision'] | [ 7.14848161e-01 -6.82061195e-01 1.60636678e-01 -4.74517524e-01
-3.09373826e-01 -5.85979879e-01 4.64658380e-01 5.82379818e-01
-4.81332570e-01 7.64484704e-01 -3.06977391e-01 1.80698082e-01
2.64950097e-02 -9.09216821e-01 -5.17640591e-01 -8.13029408e-01
-4.82940584e-01 6.91842288e-02 9.70005989e-01 3.38592291... | [8.67093563079834, -1.2675095796585083] |
e16c7d7a-9f29-4c63-910f-8c62e170799e | encoding-spatial-distribution-of | null | null | http://proceedings.neurips.cc/paper/2021/hash/c04c19c2c2474dbf5f7ac4372c5b9af1-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/c04c19c2c2474dbf5f7ac4372c5b9af1-Paper.pdf | Encoding Spatial Distribution of Convolutional Features for Texture Representation | Existing convolutional neural networks (CNNs) often use global average pooling (GAP) to aggregate feature maps into a single representation. However, GAP cannot well characterize complex distributive patterns of spatial features while such patterns play an important role in texture-oriented applications, e.g., material... | ['Yuhui Quan', 'Jinxiu Liang', 'Zhile Chen', 'Feng Li', 'Yong Xu'] | 2021-12-01 | null | https://openreview.net/forum?id=KnN6mh23cSX | https://openreview.net/pdf?id=KnN6mh23cSX | neurips-2021-12 | ['material-recognition', 'texture-classification'] | ['computer-vision', 'computer-vision'] | [ 2.36560404e-02 -3.58014941e-01 -1.16745960e-02 -4.33837563e-01
-4.38416928e-01 -4.19196457e-01 5.97494721e-01 6.06215298e-01
-3.10276628e-01 1.71803892e-01 1.65955514e-01 -2.15958413e-02
-5.25420070e-01 -1.49335253e+00 -6.35691762e-01 -8.13436866e-01
-5.83804131e-01 -5.18248230e-02 2.95487702e-01 -5.81054211... | [10.27814769744873, -0.25859883427619934] |
74e65524-7aa4-4c96-9237-111436373040 | a-framework-for-dynamically-meeting | 2306.14178 | null | https://arxiv.org/abs/2306.14178v1 | https://arxiv.org/pdf/2306.14178v1.pdf | A Framework for dynamically meeting performance objectives on a service mesh | We present a framework for achieving end-to-end management objectives for multiple services that concurrently execute on a service mesh. We apply reinforcement learning (RL) techniques to train an agent that periodically performs control actions to reallocate resources. We develop and evaluate the framework using a lab... | ['Rolf Stadler', 'Forough Shahab Samani'] | 2023-06-25 | null | null | null | null | ['management'] | ['miscellaneous'] | [-2.98742115e-01 -1.97756752e-01 -2.21076876e-01 -4.25464243e-01
-4.15042818e-01 -5.79052985e-01 4.19813812e-01 -5.61283752e-02
-4.47012216e-01 9.90904570e-01 -4.47790772e-01 -6.77674234e-01
-5.29608727e-01 -6.15710974e-01 -4.86648589e-01 -7.36119628e-01
-9.10165250e-01 1.07160568e+00 6.32474124e-01 -1.05255939... | [4.865922927856445, 2.049434185028076] |
02e8eb3c-e9ce-449d-bba7-82d1abc14722 | a-base-camp-for-scaling-ai | 1612.07896 | null | http://arxiv.org/abs/1612.07896v1 | http://arxiv.org/pdf/1612.07896v1.pdf | A Base Camp for Scaling AI | Modern statistical machine learning (SML) methods share a major limitation
with the early approaches to AI: there is no scalable way to adapt them to new
domains. Human learning solves this in part by leveraging a rich, shared,
updateable world model. Such scalability requires modularity: updating part of
the world mod... | ['R. W. White', 'Z. Yang', 'C. J. C. Burges', 'T. Hart', 'S. Cucerzan', 'J. Lewis', 'A. Pastusiak'] | 2016-12-23 | null | null | null | null | ['dialog-learning'] | ['natural-language-processing'] | [ 6.73499927e-02 7.32935131e-01 -4.44216095e-02 -4.00712103e-01
-6.54015779e-01 -8.18100393e-01 6.36875272e-01 1.20392047e-01
-3.26075763e-01 7.36716449e-01 3.92681718e-01 -5.58115900e-01
-1.28337413e-01 -5.88564992e-01 -7.32861221e-01 -1.17650330e-01
1.73448101e-01 9.00662482e-01 3.17314863e-01 -4.13692296... | [12.648484230041504, 7.887949466705322] |
6d6f1fe0-f7c8-4674-b577-cb086eba9e4b | babyai-towards-grounded-language-learning | 2004.07200 | null | https://arxiv.org/abs/2004.07200v2 | https://arxiv.org/pdf/2004.07200v2.pdf | Zero-Shot Compositional Policy Learning via Language Grounding | Despite recent breakthroughs in reinforcement learning (RL) and imitation learning (IL), existing algorithms fail to generalize beyond the training environments. In reality, humans can adapt to new tasks quickly by leveraging prior knowledge about the world such as language descriptions. To facilitate the research on l... | ['Yining Zhang', 'Jingkang Wang', 'Tianshi Cao', 'Sivabalan Manivasagam'] | 2020-04-15 | null | null | null | null | ['grounded-language-learning'] | ['natural-language-processing'] | [-2.66652912e-01 -4.75838065e-01 5.59423938e-02 -2.47841254e-01
-4.79834050e-01 -7.63494492e-01 9.26882565e-01 -4.58138168e-01
-6.99335098e-01 8.24407101e-01 1.25991791e-01 2.32695807e-02
1.29269943e-01 -5.53249419e-01 -8.79482508e-01 -8.70359838e-01
-9.09982100e-02 6.71050847e-01 3.95001769e-02 -4.92315769... | [4.374476432800293, 0.9175587296485901] |
41eccd79-e72a-4be2-b817-c32a64c12b6d | mparrottts-multilingual-multi-speaker-text-to | 2305.11926 | null | https://arxiv.org/abs/2305.11926v1 | https://arxiv.org/pdf/2305.11926v1.pdf | MParrotTTS: Multilingual Multi-speaker Text to Speech Synthesis in Low Resource Setting | We present MParrotTTS, a unified multilingual, multi-speaker text-to-speech (TTS) synthesis model that can produce high-quality speech. Benefiting from a modularized training paradigm exploiting self-supervised speech representations, MParrotTTS adapts to a new language with minimal supervised data and generalizes to l... | ['Vineet Gandhi', 'Niranjan Pedanekar', 'Saiteja Kosgi', 'Vishal Tambrahalli', 'Neil Shah'] | 2023-05-19 | null | null | null | null | ['text-to-speech-synthesis', 'speech-synthesis'] | ['speech', 'speech'] | [ 1.40440892e-02 2.93486804e-01 -2.67108351e-01 -6.31452322e-01
-1.23050988e+00 -8.14122736e-01 5.12577176e-01 -3.56696159e-01
5.10618382e-04 6.91458821e-01 4.51222539e-01 -7.24591315e-01
4.92049366e-01 -3.07789534e-01 -8.87077451e-01 -4.36321050e-01
1.70397937e-01 7.48007596e-01 5.43568842e-02 -6.27281427... | [14.619803428649902, 6.91439962387085] |
426a61d3-5cd1-43db-bf34-f3c93b81691f | multimodal-sentiment-analysis-using | 1806.06228 | null | http://arxiv.org/abs/1806.06228v1 | http://arxiv.org/pdf/1806.06228v1.pdf | Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling | Multimodal sentiment analysis is a very actively growing field of research. A
promising area of opportunity in this field is to improve the multimodal fusion
mechanism. We present a novel feature fusion strategy that proceeds in a
hierarchical fashion, first fusing the modalities two in two and only then
fusing all thr... | ['N. Majumder', 'D. Hazarika', 'A. Gelbukh', 'E. Cambria', 'S. Poria'] | 2018-06-16 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 4.99658614e-01 7.38804564e-02 1.67710081e-01 -4.93812382e-01
-1.30895448e+00 -6.38908267e-01 6.41671121e-01 5.39693654e-01
-5.65107524e-01 4.50457662e-01 4.31843847e-01 1.04877621e-01
3.23017269e-01 -3.47812712e-01 -4.16333705e-01 -7.60908306e-01
2.28762105e-01 -3.34183685e-02 2.62100279e-01 -4.46730733... | [13.159317970275879, 5.2041144371032715] |
c7896eee-52f3-4d4b-ad5e-9d53b2137439 | otfpf-optimal-transport-based-feature-pyramid | 2205.04684 | null | https://arxiv.org/abs/2205.04684v2 | https://arxiv.org/pdf/2205.04684v2.pdf | OTFPF: Optimal Transport-Based Feature Pyramid Fusion Network for Brain Age Estimation with 3D Overlapped ConvNeXt | Chronological age of healthy brain is able to be predicted using deep neural networks from T1-weighted magnetic resonance images (T1 MRIs), and the predicted brain age could serve as an effective biomarker for detecting aging-related diseases or disorders. In this paper, we propose an end-to-end neural network architec... | ['Cheng Zhuo', 'Yiyu Shi', 'Qianqian Yang', 'Xunzhao Yin', 'Le Xue', 'Shunjie Dong', 'Yalin Wang', 'Yanyan Huang', 'Yu Fu'] | 2022-05-10 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-4.20058936e-01 -1.21587785e-02 -8.76907334e-02 -4.41218436e-01
-2.13095710e-01 3.29564571e-01 2.79393822e-01 1.48103416e-01
-6.97259486e-01 8.15728307e-01 2.65730858e-01 -2.48076692e-02
-2.35896468e-01 -6.71366513e-01 -4.92672205e-01 -6.72119617e-01
-9.18890774e-01 1.93229288e-01 2.80563831e-01 9.49493647... | [14.09220027923584, -1.5637959241867065] |
b5159bf7-a486-46ff-bdac-164157c99dcf | probabilistic-forecasting-with-temporal | 1906.04397 | null | https://arxiv.org/abs/1906.04397v3 | https://arxiv.org/pdf/1906.04397v3.pdf | Probabilistic Forecasting with Temporal Convolutional Neural Network | We present a probabilistic forecasting framework based on convolutional neural network for multiple related time series forecasting. The framework can be applied to estimate probability density under both parametric and non-parametric settings. More specifically, stacked residual blocks based on dilated causal convolut... | ['Zizhuo Wang', 'Yixiong Chen', 'Yitian Chen', 'Yanfei Kang'] | 2019-06-11 | null | null | null | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [-3.85928601e-01 -4.89423484e-01 -5.50903440e-01 -7.29234397e-01
-5.93029082e-01 -4.19182420e-01 7.41778851e-01 -1.65682316e-01
1.96566612e-01 7.92339146e-01 7.86246181e-01 -5.60461342e-01
-2.55991340e-01 -1.05832362e+00 -1.11115456e+00 -6.45493269e-01
-7.55730927e-01 2.09101498e-01 -3.07124555e-01 -9.06958506... | [6.906900882720947, 3.1170542240142822] |
6bb99e08-6885-4e37-80b4-386a591ecdbd | provable-benefits-of-general-coverage | 2304.12886 | null | https://arxiv.org/abs/2304.12886v2 | https://arxiv.org/pdf/2304.12886v2.pdf | What can online reinforcement learning with function approximation benefit from general coverage conditions? | In online reinforcement learning (RL), instead of employing standard structural assumptions on Markov decision processes (MDPs), using a certain coverage condition (original from offline RL) is enough to ensure sample-efficient guarantees (Xie et al. 2023). In this work, we focus on this new direction by digging more p... | ['Volkan Cevher', 'Luca Viano', 'Fanghui Liu'] | 2023-04-25 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [ 7.28318393e-02 4.92517650e-01 -5.80112159e-01 -1.00847870e-01
-9.00801837e-01 -7.90580273e-01 -5.01556098e-02 1.76035836e-01
-5.15307665e-01 1.31939852e+00 -2.11193025e-01 -6.31392956e-01
-7.28433311e-01 -9.10599768e-01 -8.15683782e-01 -9.28613245e-01
-4.29577440e-01 3.43012214e-01 1.22729670e-02 -8.91248807... | [4.3876190185546875, 2.8723256587982178] |
a917c8a9-0798-4b81-975c-ee67e46cb018 | malware-traffic-classification-evaluation-of | 2010.11627 | null | https://arxiv.org/abs/2010.11627v2 | https://arxiv.org/pdf/2010.11627v2.pdf | Malware Traffic Classification: Evaluation of Algorithms and an Automated Ground-truth Generation Pipeline | Identifying threats in a network traffic flow which is encrypted is uniquely challenging. On one hand it is extremely difficult to simply decrypt the traffic due to modern encryption algorithms. On the other hand, passing such an encrypted stream through pattern matching algorithms is useless because encryption ensures... | ['Juan Caballero', 'Syed Muhammad Kumail Raza'] | 2020-10-22 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 2.90360481e-01 -1.95705369e-01 -7.30199516e-02 -2.99516976e-01
-3.74965966e-01 -8.24375570e-01 9.08358455e-01 5.87798417e-01
-3.31592113e-01 5.53566039e-01 -2.81557024e-01 -6.76913619e-01
-2.05765665e-01 -1.18146980e+00 -2.74911225e-01 -6.38188064e-01
-2.58855104e-01 6.48289621e-01 5.51333606e-01 -1.70323923... | [5.250848770141602, 7.244238376617432] |
de028dcb-5152-4768-a557-59125e54a185 | analysing-the-impact-of-audio-quality-on-the | 2305.01965 | null | https://arxiv.org/abs/2305.01965v1 | https://arxiv.org/pdf/2305.01965v1.pdf | Analysing the Impact of Audio Quality on the Use of Naturalistic Long-Form Recordings for Infant-Directed Speech Research | Modelling of early language acquisition aims to understand how infants bootstrap their language skills. The modelling encompasses properties of the input data used for training the models, the cognitive hypotheses and their algorithmic implementations being tested, and the evaluation methodologies to compare models to ... | ['Okko Räsänen', 'Alejandrina Cristia', 'María Andrea Cruz Blandón'] | 2023-05-03 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 2.59155363e-01 2.05468953e-01 5.96773088e-01 -6.21587753e-01
-8.55758011e-01 -4.69146311e-01 6.07972622e-01 4.36145157e-01
-7.00256348e-01 2.00473055e-01 4.99305248e-01 -2.90532619e-01
-3.56604993e-01 -4.67229068e-01 -7.45488882e-01 -4.56710100e-01
-2.14088559e-01 5.30109525e-01 4.92900699e-01 -5.01901954... | [14.34271240234375, 6.3614912033081055] |
a15220f8-ae79-424f-859c-c582a22afaa1 | unsupervised-pre-training-of-graph | 2207.10603 | null | https://arxiv.org/abs/2207.10603v1 | https://arxiv.org/pdf/2207.10603v1.pdf | Unsupervised pre-training of graph transformers on patient population graphs | Pre-training has shown success in different areas of machine learning, such as Computer Vision, Natural Language Processing (NLP), and medical imaging. However, it has not been fully explored for clinical data analysis. An immense amount of clinical records are recorded, but still, data and labels can be scarce for dat... | ['Anees Kazi', 'Nassir Navab', 'Chantal Pellegrini'] | 2022-07-21 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 4.95797306e-01 3.33668023e-01 -2.38424256e-01 -4.57214445e-01
-7.94237077e-01 -1.57175690e-01 1.69387892e-01 8.97897661e-01
-2.95540839e-01 5.83805919e-01 3.23631227e-01 -6.89029992e-01
-3.05520326e-01 -8.02052319e-01 -5.21112859e-01 -5.29636741e-01
-5.26673853e-01 9.72963214e-01 5.21377511e-02 -3.33375037... | [7.947263240814209, 6.437857627868652] |
98760b5b-3b4b-4e85-827e-a9337bb45917 | weakly-semi-supervised-neural-topic-models | null | null | https://openreview.net/forum?id=BJlRKgkDwN | https://openreview.net/pdf?id=BJlRKgkDwN | WEAKLY SEMI-SUPERVISED NEURAL TOPIC MODELS | We consider the problem of topic modeling in a weakly semi-supervised setting. In this scenario, we assume that the user knows a priori a subset of the topics she wants the model to learn and is able to provide a few exemplar documents for those topics. In addition, while each document may typically consist of multiple... | ['Bing Xiang', 'Feng Nan', 'Ran Ding', 'Ramesh Nallapati', 'Ian Gemp'] | 2019-03-13 | null | null | null | iclr-workshop-lld-2019 | ['topic-models'] | ['natural-language-processing'] | [-6.38680384e-02 6.94402218e-01 -5.12833476e-01 -5.50633967e-01
-8.93049359e-01 -4.37295705e-01 1.08928800e+00 4.76813838e-02
-2.31273934e-01 5.50353944e-01 3.97378594e-01 -1.55141696e-01
-6.67753210e-03 -8.18297565e-01 -8.80015433e-01 -6.17492676e-01
1.48008972e-01 1.15882683e+00 9.74388942e-02 6.70319647... | [10.365682601928711, 6.9236249923706055] |
816f42b0-ad29-4d17-a95b-6f99348e621b | robustness-out-of-the-box-compositional | 2012.00558 | null | https://arxiv.org/abs/2012.00558v1 | https://arxiv.org/pdf/2012.00558v1.pdf | Robustness Out of the Box: Compositional Representations Naturally Defend Against Black-Box Patch Attacks | Patch-based adversarial attacks introduce a perceptible but localized change to the input that induces misclassification. While progress has been made in defending against imperceptible attacks, it remains unclear how patch-based attacks can be resisted. In this work, we study two different approaches for defending aga... | ['Alan Yuille', 'Chenglin Yang', 'Adam Kortylewski', 'Christian Cosgrove'] | 2020-12-01 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 5.16199172e-01 -1.88293710e-01 -2.19450817e-01 -2.06597924e-01
-7.97738910e-01 -1.32039070e+00 7.12595344e-01 -5.14150739e-01
-4.58172522e-02 4.56654489e-01 -1.79095849e-01 -8.38096559e-01
3.54244187e-02 -8.67217183e-01 -1.26032710e+00 -7.77986705e-01
-1.00577716e-02 1.29664421e-01 5.95607698e-01 -5.37167013... | [5.557440757751465, 7.911334991455078] |
c76ab8d0-eb79-4ab9-8017-cc964e970c74 | vifi-loc-multi-modal-pedestrian-localization | 2211.12021 | null | https://arxiv.org/abs/2211.12021v1 | https://arxiv.org/pdf/2211.12021v1.pdf | ViFi-Loc: Multi-modal Pedestrian Localization using GAN with Camera-Phone Correspondences | In Smart City and Vehicle-to-Everything (V2X) systems, acquiring pedestrians' accurate locations is crucial to traffic safety. Current systems adopt cameras and wireless sensors to detect and estimate people's locations via sensor fusion. Standard fusion algorithms, however, become inapplicable when multi-modal data is... | ['HongSheng Lu', 'Marco Gruteser', 'Kristin Dana', 'Hansi Liu'] | 2022-11-22 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [-1.48966551e-01 -9.90330726e-02 -7.04196915e-02 -5.05561292e-01
-1.28052890e+00 -7.50265718e-01 6.10684037e-01 2.77580973e-02
-3.53912085e-01 1.08886719e+00 2.67729402e-01 -6.00595400e-02
4.84972566e-01 -1.10162807e+00 -1.09695399e+00 -6.07857406e-01
2.85473734e-01 3.81956369e-01 1.60110280e-01 9.43780988... | [6.79966402053833, 0.4938651919364929] |
8a405f7f-7d1e-4ea5-9157-322aa43759c5 | deep-abstract-q-networks | 1710.00459 | null | http://arxiv.org/abs/1710.00459v2 | http://arxiv.org/pdf/1710.00459v2.pdf | Deep Abstract Q-Networks | We examine the problem of learning and planning on high-dimensional domains
with long horizons and sparse rewards. Recent approaches have shown great
successes in many Atari 2600 domains. However, domains with long horizons and
sparse rewards, such as Montezuma's Revenge and Venture, remain challenging for
existing met... | ['Christopher Grimm', 'Stefanie Tellex', 'Melrose Roderick'] | 2017-10-02 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-3.31427395e-01 3.83770823e-01 -6.14138246e-01 1.12794377e-01
-8.69062841e-01 -6.27939999e-01 7.74984360e-01 -1.86672192e-02
-4.83377844e-01 1.53338122e+00 2.32653365e-01 -5.72067916e-01
-5.85748971e-01 -7.34821916e-01 -7.72201836e-01 -4.43003803e-01
-7.34256089e-01 8.76801014e-01 2.63327032e-01 -6.59551382... | [4.045840263366699, 1.830581784248352] |
47bb35d7-3787-4e63-a1c5-4cea2012fa97 | sarcasm-detection-building-a-contextual | null | null | https://aclanthology.org/W16-4313 | https://aclanthology.org/W16-4313.pdf | Sarcasm Detection : Building a Contextual Hierarchy | The conundrum of understanding and classifying sarcasm has been dealt with by the traditional theorists as an analysis of a sarcastic utterance and the ironic situation that surrounds it. The problem with such an approach is that it is too narrow, as it is unable to sufficiently utilize the two indispensable agents in ... | ['Navjyoti Singh', 'Taradheesh Bali'] | 2016-12-01 | null | null | null | ws-2016-12 | ['lexical-analysis'] | ['natural-language-processing'] | [-2.56364364e-02 5.08091748e-01 -2.41214707e-01 -2.35269099e-01
-1.18278794e-01 -4.14974779e-01 8.56858134e-01 5.88803947e-01
-2.61358529e-01 2.37040982e-01 1.06425858e+00 -4.60618675e-01
-7.45234406e-03 -6.85427845e-01 -9.41433460e-02 -5.12343824e-01
7.39776254e-01 3.20226789e-01 1.81839854e-01 -6.81545556... | [9.265239715576172, 10.436478614807129] |
38e76646-8984-4f6f-88d2-a60f3baec537 | reconstruction-and-quantification-of-3d-iris | 2006.05179 | null | https://arxiv.org/abs/2006.05179v1 | https://arxiv.org/pdf/2006.05179v1.pdf | Reconstruction and Quantification of 3D Iris Surface for Angle-Closure Glaucoma Detection in Anterior Segment OCT | Precise characterization and analysis of iris shape from Anterior Segment OCT (AS-OCT) are of great importance in facilitating diagnosis of angle-closure-related diseases. Existing methods focus solely on analyzing structural properties identified from the 2D slice, while accurate characterization of morphological chan... | ['Jinkui Hao', 'Jiang Liu', 'Huazhu Fu', 'Yitian Zhao', 'Yan Hu', 'Xiulan Zhang', 'Yanwu Xu', 'Fei Li'] | 2020-06-09 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 2.13432476e-01 -1.88128844e-01 -3.76209840e-02 4.66501229e-02
-3.77678096e-01 -2.59464771e-01 1.94826014e-02 2.34059855e-01
-3.24242353e-01 4.49610472e-01 1.74024984e-01 -4.53091592e-01
-4.53094423e-01 -6.92266762e-01 -1.87878497e-02 -7.08687067e-01
-2.35447541e-01 5.71583986e-01 1.25680506e-01 6.85107782... | [15.752470970153809, -3.9671237468719482] |
b2a86ab3-fc36-496d-8abe-7b701078d2f7 | using-explicit-discourse-connectives-in | null | null | https://aclanthology.org/I17-1049 | https://aclanthology.org/I17-1049.pdf | Using Explicit Discourse Connectives in Translation for Implicit Discourse Relation Classification | Implicit discourse relation recognition is an extremely challenging task due to the lack of indicative connectives. Various neural network architectures have been proposed for this task recently, but most of them suffer from the shortage of labeled data. In this paper, we address this problem by procuring additional tr... | ['Raphael Rubino', 'Frances Yung', 'Wei Shi', 'Vera Demberg'] | 2017-11-01 | using-explicit-discourse-connectives-in-1 | https://aclanthology.org/I17-1049 | https://aclanthology.org/I17-1049.pdf | ijcnlp-2017-11 | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [ 5.57002783e-01 8.78895700e-01 -4.23863262e-01 -6.45863712e-01
-7.33282387e-01 -6.67743802e-01 7.29630113e-01 8.68022144e-02
-5.36388397e-01 1.26372027e+00 3.25110823e-01 -6.28945529e-01
4.49016452e-01 -7.09609985e-01 -8.40323389e-01 -3.05118978e-01
3.39136384e-02 9.69181061e-01 1.51492730e-01 -2.71692812... | [10.673955917358398, 9.179931640625] |
d6cb7d40-008c-402a-9e0f-b6ec1a1a1302 | towards-lingua-franca-named-entity | 1912.01389 | null | https://arxiv.org/abs/1912.01389v2 | https://arxiv.org/pdf/1912.01389v2.pdf | Towards Lingua Franca Named Entity Recognition with BERT | Information extraction is an important task in NLP, enabling the automatic extraction of data for relational database filling. Historically, research and data was produced for English text, followed in subsequent years by datasets in Arabic, Chinese (ACE/OntoNotes), Dutch, Spanish, German (CoNLL evaluations), and many ... | ['Taesun Moon', 'Jian Ni', 'Radu Florian', 'Parul Awasthy'] | 2019-11-19 | null | null | null | null | ['cross-lingual-ner'] | ['natural-language-processing'] | [-3.16018254e-01 1.06086813e-01 -3.00063699e-01 -1.99871659e-01
-1.04986203e+00 -6.13116264e-01 4.72133189e-01 3.37381124e-01
-8.94110739e-01 9.59435821e-01 1.44349366e-01 -4.19946879e-01
1.37286276e-01 -5.32723010e-01 -7.03926742e-01 -1.95549294e-01
1.29373714e-01 8.10821593e-01 3.15377086e-01 -2.88612753... | [10.06914234161377, 9.691272735595703] |
122cadd1-135f-4c3e-aad4-23b84a83f8d4 | consistency-driven-sequential-transformers | 2204.00656 | null | https://arxiv.org/abs/2204.00656v1 | https://arxiv.org/pdf/2204.00656v1.pdf | Consistency driven Sequential Transformers Attention Model for Partially Observable Scenes | Most hard attention models initially observe a complete scene to locate and sense informative glimpses, and predict class-label of a scene based on glimpses. However, in many applications (e.g., aerial imaging), observing an entire scene is not always feasible due to the limited time and resources available for acquisi... | ['James J. Clark', 'Chetan L. Srinidhi', 'Samrudhdhi B. Rangrej'] | 2022-04-01 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Rangrej_Consistency_Driven_Sequential_Transformers_Attention_Model_for_Partially_Observable_Scenes_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Rangrej_Consistency_Driven_Sequential_Transformers_Attention_Model_for_Partially_Observable_Scenes_CVPR_2022_paper.pdf | cvpr-2022-1 | ['hard-attention'] | ['methodology'] | [ 2.38663703e-01 3.50583464e-01 -1.13043441e-02 -4.47779417e-01
-5.04078209e-01 -2.94383198e-01 2.70552754e-01 -3.67869847e-02
-6.02155983e-01 8.62645328e-01 -2.45829001e-01 5.73271401e-02
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1.83118582e-01 4.74875212e-01 2.41200313e-01 1.51394248... | [9.554837226867676, 0.13894721865653992] |
77f4c4ea-4487-4d85-91b9-f1db1412f367 | sharp-attention-for-sequence-to-sequence | null | null | https://openreview.net/forum?id=UvNXZgJAOAP | https://openreview.net/pdf?id=UvNXZgJAOAP | Sharp Attention for Sequence to Sequence Learning | Attention mechanism has been widely applied to tasks that output some sequence from an input image. Its success comes from the ability to align relevant parts of the encoded image with the target output. However, most of the existing methods fail to build clear alignment because the aligned parts are unable to well rep... | ['Hua Liu', 'Pei Zhang'] | 2021-09-29 | null | null | null | null | ['scene-text-recognition', 'hard-attention'] | ['computer-vision', 'methodology'] | [ 6.03018403e-01 5.08662052e-02 -2.66153691e-03 -5.25251806e-01
-4.54253942e-01 -5.57500243e-01 7.53918052e-01 -1.97647855e-01
-2.56794721e-01 5.40799260e-01 3.20839584e-01 7.63533711e-02
4.61988486e-02 -4.63588655e-01 -8.06425512e-01 -6.30885720e-01
6.99292839e-01 3.02478552e-01 2.51075536e-01 -2.58298963... | [10.863228797912598, 1.8702524900436401] |
fa23224e-f48c-4e4d-8299-8ed7da348a80 | color-texture-classification-based-on | 1906.11010 | null | https://arxiv.org/abs/1906.11010v1 | https://arxiv.org/pdf/1906.11010v1.pdf | Color Texture Classification Based on Proposed Impulse-Noise Resistant Color Local Binary Patterns and Significant Points Selection Algorithm | The main aim of this paper is to propose a color texture classification approach which uses color sensor information and texture features jointly. High accuracy, low noise sensitivity and low computational complexity are specified aims for our proposed approach. One of the efficient texture analysis operations is local... | ['Shervan Fekri-Ershad', 'Farshad Tajeripour'] | 2019-06-26 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 4.01694626e-01 -8.69203091e-01 -8.10733512e-02 -8.47586095e-02
-3.90491903e-01 -1.02782018e-01 2.23378599e-01 1.87049821e-01
-5.47164798e-01 7.63567209e-01 -3.46404761e-01 1.87336832e-01
-4.67938274e-01 -1.02204812e+00 -1.39115617e-01 -1.00309014e+00
9.14171860e-02 -5.91948535e-03 7.23808229e-01 -1.58754945... | [10.377471923828125, -0.3667498528957367] |
0501a39c-dfb5-48e0-b84f-50928fd5fbfd | example-based-synthesis-of-static-analysis | 2204.08643 | null | https://arxiv.org/abs/2204.08643v1 | https://arxiv.org/pdf/2204.08643v1.pdf | Example-based Synthesis of Static Analysis Rules | Static Analysis tools have rules for several code quality issues and these rules are created by experts manually. In this paper, we address the problem of automatic synthesis of code quality rules from examples. We formulate the rule synthesis problem as synthesizing first order logic formulas over graph representation... | ['Srinivasan Sengamedu SHS', 'Pranav Garg'] | 2022-04-19 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 3.55701149e-01 8.46840024e-01 -5.88937998e-01 -4.51067567e-01
-7.65142202e-01 -6.64349020e-01 3.06515753e-01 4.96752679e-01
5.73012769e-01 2.37839222e-01 1.32460192e-01 -8.67577493e-01
-1.32318527e-01 -8.09639692e-01 -9.39217210e-01 6.51399612e-01
-6.01542182e-02 8.42366077e-04 5.68300903e-01 -4.29437459... | [7.989375591278076, 7.557650566101074] |
eee391c9-5b1f-41d1-ab67-d61396d0bcbc | ontology-guided-semantic-composition-for-zero | 2006.16917 | null | https://arxiv.org/abs/2006.16917v1 | https://arxiv.org/pdf/2006.16917v1.pdf | Ontology-guided Semantic Composition for Zero-Shot Learning | Zero-shot learning (ZSL) is a popular research problem that aims at predicting for those classes that have never appeared in the training stage by utilizing the inter-class relationship with some side information. In this study, we propose to model the compositional and expressive semantics of class labels by an OWL (W... | ['Jeff Z. Pan', 'Freddy Lecue', 'Jiaoyan Chen', 'Huajun Chen', 'Yuxia Geng'] | 2020-06-30 | null | null | null | null | ['ontology-embedding'] | ['knowledge-base'] | [ 2.56505936e-01 3.30854625e-01 -4.82822299e-01 -5.66711605e-01
1.38354346e-01 -1.10526025e-01 6.68185353e-01 3.27405572e-01
-4.20272917e-01 5.75012803e-01 1.25998631e-01 1.70344003e-02
-3.94396603e-01 -1.08049333e+00 -3.75405669e-01 -2.24211931e-01
-2.29339987e-01 7.11214170e-03 6.19916022e-01 -3.54788244... | [10.023003578186035, 2.453230142593384] |
de1bf8dc-95e7-4647-897d-39e8926b9ff5 | mvtn-learning-multi-view-transformations-for | 2212.13462 | null | https://arxiv.org/abs/2212.13462v1 | https://arxiv.org/pdf/2212.13462v1.pdf | MVTN: Learning Multi-View Transformations for 3D Understanding | Multi-view projection techniques have shown themselves to be highly effective in achieving top-performing results in the recognition of 3D shapes. These methods involve learning how to combine information from multiple view-points. However, the camera view-points from which these views are obtained are often fixed for ... | ['Bernard Ghanem', 'Silvio Giancola', 'Faisal AlZahrani', 'Abdullah Hamdi'] | 2022-12-27 | null | null | null | null | ['3d-shape-retrieval', '3d-shape-recognition', '3d-classification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.70670912e-01 -4.64538336e-01 2.33124286e-01 -5.85938692e-01
-8.77662897e-01 -9.99253511e-01 8.17300439e-01 -3.58390808e-01
1.23959966e-01 -1.97575599e-01 -1.00727789e-01 -3.40638846e-01
2.49581978e-01 -9.42543685e-01 -8.59609187e-01 -4.41394329e-01
2.09986299e-01 1.03667212e+00 2.49283060e-01 -1.65042460... | [8.251294136047363, -3.5346157550811768] |
2780cf57-aedc-42cb-a3cb-8bb2c45282dd | batch-constrained-distributional | 2012.08984 | null | https://arxiv.org/abs/2012.08984v1 | https://arxiv.org/pdf/2012.08984v1.pdf | Batch-Constrained Distributional Reinforcement Learning for Session-based Recommendation | Most of the existing deep reinforcement learning (RL) approaches for session-based recommendations either rely on costly online interactions with real users, or rely on potentially biased rule-based or data-driven user-behavior models for learning. In this work, we instead focus on learning recommendation policies in t... | ['Gautam Shroff', 'Lovekesh Vig', 'Pankaj Malhotra', 'Priyanka Gupta', 'Diksha Garg'] | 2020-12-16 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-2.51330316e-01 -3.01107436e-01 -7.90466011e-01 -5.08817077e-01
-7.53037810e-01 -5.98900855e-01 5.90455651e-01 -2.30609160e-02
-5.99681020e-01 8.16233635e-01 4.54739332e-01 -5.55774212e-01
-3.12403649e-01 -6.82296336e-01 -9.07395840e-01 -3.82230848e-01
-5.40265441e-01 7.24383116e-01 -5.65521838e-03 -2.54955053... | [4.145132541656494, 2.328749895095825] |
df452f45-07e0-40b6-89c5-2ac7947a2177 | eamm-one-shot-emotional-talking-face-via | 2205.15278 | null | https://arxiv.org/abs/2205.15278v3 | https://arxiv.org/pdf/2205.15278v3.pdf | EAMM: One-Shot Emotional Talking Face via Audio-Based Emotion-Aware Motion Model | Although significant progress has been made to audio-driven talking face generation, existing methods either neglect facial emotion or cannot be applied to arbitrary subjects. In this paper, we propose the Emotion-Aware Motion Model (EAMM) to generate one-shot emotional talking faces by involving an emotion source vide... | ['Xun Cao', 'Feng Xu', 'Wayne Wu', 'Qianyi Wu', 'Kaisiyuan Wang', 'Hang Zhou', 'Xinya Ji'] | 2022-05-30 | null | null | null | null | ['talking-face-generation'] | ['computer-vision'] | [-9.34938788e-02 2.16154858e-01 1.25022128e-01 -3.95694464e-01
-6.85900211e-01 -2.18582496e-01 5.96079886e-01 -1.07737410e+00
3.72949213e-01 4.48360264e-01 6.37821794e-01 4.62104082e-01
8.13066140e-02 -3.20471972e-01 -4.97732490e-01 -7.18540668e-01
1.11030741e-02 -6.69850186e-02 -5.40737987e-01 -3.65970075... | [13.104826927185059, -0.34004727005958557] |
7513a0fe-862d-4bb1-a22d-8c59e183879d | rank-position-forecasting-in-car-racing | 2010.01707 | null | https://arxiv.org/abs/2010.01707v2 | https://arxiv.org/pdf/2010.01707v2.pdf | Rank Position Forecasting in Car Racing | Forecasting is challenging since uncertainty resulted from exogenous factors exists. This work investigates the rank position forecasting problem in car racing, which predicts the rank positions at the future laps for cars. Among the many factors that bring changes to the rank positions, pit stops are critical but irre... | ['Judy Qiu', 'Ohno Yoshiyuki', 'Takuya Araki', 'Fugang Wang', 'Selahattin Akkas', 'Jiayu Li', 'Bo Peng'] | 2020-10-04 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-1.95688874e-01 -1.20193303e-01 -5.05691648e-01 -1.13975620e+00
-1.04678082e+00 -2.78495461e-01 7.39046574e-01 -4.14256603e-01
1.83049053e-01 6.99137330e-01 5.55541754e-01 -3.71484101e-01
-1.71301305e-01 -6.90225422e-01 -1.12053144e+00 -6.69100285e-01
-4.80580293e-02 7.71883368e-01 4.18597579e-01 -4.81957078... | [6.546148300170898, 1.706516146659851] |
14ea5221-f450-4156-a168-ba4201189f56 | self-supervised-visual-representation-2 | 2205.15288 | null | https://arxiv.org/abs/2205.15288v2 | https://arxiv.org/pdf/2205.15288v2.pdf | Self-Supervised Visual Representation Learning with Semantic Grouping | In this paper, we tackle the problem of learning visual representations from unlabeled scene-centric data. Existing works have demonstrated the potential of utilizing the underlying complex structure within scene-centric data; still, they commonly rely on hand-crafted objectness priors or specialized pretext tasks to b... | ['Xiaojuan Qi', 'Xiangyu Zhang', 'Anlin Zheng', 'Bingchen Zhao', 'Xin Wen'] | 2022-05-30 | null | null | null | null | ['unsupervised-semantic-segmentation', 'unsupervised-pre-training'] | ['computer-vision', 'methodology'] | [ 3.82358670e-01 7.53058717e-02 -3.78249496e-01 -5.86219966e-01
-7.55422115e-01 -3.64663720e-01 5.99809825e-01 2.49659851e-01
-4.99064475e-01 2.84893543e-01 5.53582683e-02 -8.13192129e-02
-5.17556705e-02 -7.81818509e-01 -7.56682158e-01 -6.92301571e-01
8.93952027e-02 3.31943393e-01 4.23878968e-01 1.13958120... | [9.639455795288086, 1.0197519063949585] |
d42141bc-b038-46c6-b719-3ac1063a6358 | beyond-one-glance-gated-recurrent | 1811.10914 | null | http://arxiv.org/abs/1811.10914v3 | http://arxiv.org/pdf/1811.10914v3.pdf | Beyond One Glance: Gated Recurrent Architecture for Hand Segmentation | As mixed reality is gaining increased momentum, the development of effective
and efficient solutions to egocentric hand segmentation is becoming critical.
Traditional segmentation techniques typically follow a one-shot approach, where
the image is passed forward only once through a model that produces a
segmentation ma... | ['Mathieu Salzmann', 'Joachim Hugonot', 'Kaicheng Yu', 'Wei Wang', 'Pascal Fua'] | 2018-11-27 | null | null | null | null | ['road-segementation', 'hand-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.70803714e-01 3.44541818e-01 -1.85049295e-01 -1.17387801e-01
-6.39355004e-01 -6.17046654e-01 4.99190778e-01 -2.01547220e-01
-4.97905910e-01 6.10541582e-01 4.38571423e-01 -2.19670255e-02
2.89261788e-01 -7.24978685e-01 -5.51706970e-01 -4.29346770e-01
1.79839462e-01 8.26932371e-01 6.65348411e-01 -3.30698460... | [9.350666046142578, 0.16243773698806763] |
0b70e0a2-950b-42a5-8573-5abf21b68c63 | imdiffusion-imputed-diffusion-models-for | 2307.00754 | null | https://arxiv.org/abs/2307.00754v1 | https://arxiv.org/pdf/2307.00754v1.pdf | ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection | Anomaly detection in multivariate time series data is of paramount importance for ensuring the efficient operation of large-scale systems across diverse domains. However, accurately detecting anomalies in such data poses significant challenges. Existing approaches, including forecasting and reconstruction-based methods... | ['Dongmei Zhang', 'QIngwei Lin', 'Saravan Rajmohan', 'Shilin He', 'Bowen Li', 'Ruomeng Ding', 'Yudong Liu', 'Minghua Ma', 'Chaoyun Zhang', 'Yuhang Chen'] | 2023-07-03 | null | null | null | null | ['imputation', 'anomaly-detection', 'imputation', 'time-series-anomaly-detection', 'imputation'] | ['computer-vision', 'methodology', 'miscellaneous', 'time-series', 'time-series'] | [-1.24085911e-01 -7.16114402e-01 3.17761511e-01 -1.26774102e-01
-6.33748412e-01 -5.36911845e-01 6.64084852e-01 4.77268368e-01
-1.63740560e-01 2.60550112e-01 1.26950443e-01 -3.89298379e-01
-3.27514142e-01 -9.32523549e-01 -4.57756966e-01 -7.18460739e-01
-5.40027082e-01 1.04781866e-01 1.46287248e-01 -1.25342816... | [7.333265781402588, 2.73496150970459] |
f3235234-0033-404a-92a7-5dee2a94fe35 | chore-contact-human-and-object-reconstruction | 2204.02445 | null | https://arxiv.org/abs/2204.02445v2 | https://arxiv.org/pdf/2204.02445v2.pdf | CHORE: Contact, Human and Object REconstruction from a single RGB image | Most prior works in perceiving 3D humans from images reason human in isolation without their surroundings. However, humans are constantly interacting with the surrounding objects, thus calling for models that can reason about not only the human but also the object and their interaction. The problem is extremely challen... | ['Gerard Pons-Moll', 'Bharat Lal Bhatnagar', 'Xianghui Xie'] | 2022-04-05 | null | null | null | null | ['object-reconstruction'] | ['computer-vision'] | [ 2.7960277e-01 3.3440709e-01 4.9044693e-01 -3.9604607e-01
-1.7974657e-01 -3.4114170e-01 6.4501244e-01 -2.0957412e-01
-3.4819344e-01 5.1628757e-01 -5.5347908e-02 3.0903965e-01
1.0646520e-01 -6.5261972e-01 -9.7772545e-01 -6.7107272e-01
2.1559815e-01 1.2218819e+00 4.2068335e-01 -8.9370832e-02
-4.7298688e-02... | [7.017403602600098, -1.2282941341400146] |
93d8faaf-efac-495e-8289-7367c2e8fa78 | oimnet-prototypical-normalization-and | 2207.10320 | null | https://arxiv.org/abs/2207.10320v1 | https://arxiv.org/pdf/2207.10320v1.pdf | OIMNet++: Prototypical Normalization and Localization-aware Learning for Person Search | We address the task of person search, that is, localizing and re-identifying query persons from a set of raw scene images. Recent approaches are typically built upon OIMNet, a pioneer work on person search, that learns joint person representations for performing both detection and person re-identification (reID) tasks.... | ['Bumsub Ham', 'Junghyup Lee', 'Donghyeon Baek', 'Youngmin Oh', 'SangHoon Lee'] | 2022-07-21 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-1.33661777e-01 -2.63863295e-01 1.89521089e-02 -5.59167862e-01
-4.40889031e-01 -4.41374749e-01 8.96973014e-01 1.93914771e-02
-8.18976760e-01 5.48610508e-01 4.25272077e-01 4.10396427e-01
-5.43607166e-03 -9.14955258e-01 -5.34404218e-01 -5.72777152e-01
1.00385450e-01 7.20405102e-01 2.19437346e-01 2.60138754... | [14.788959503173828, 0.8610552549362183] |
3fea7485-f329-483b-8ce8-3ce9ebd28f55 | neighborhood-collective-estimation-for-noisy | 2208.03207 | null | https://arxiv.org/abs/2208.03207v1 | https://arxiv.org/pdf/2208.03207v1.pdf | Neighborhood Collective Estimation for Noisy Label Identification and Correction | Learning with noisy labels (LNL) aims at designing strategies to improve model performance and generalization by mitigating the effects of model overfitting to noisy labels. The key success of LNL lies in identifying as many clean samples as possible from massive noisy data, while rectifying the wrongly assigned noisy ... | ['Yizhou Yu', 'Feng Liu', 'Guanbin Li', 'Jichang Li'] | 2022-08-05 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 2.24636495e-01 -2.88285762e-01 -5.79090901e-02 -7.17942834e-01
-1.50390911e+00 -5.96972406e-01 3.32623929e-01 2.23280728e-01
-3.79425853e-01 8.21494401e-01 1.81169078e-01 -4.29446101e-02
-1.92811400e-01 -3.04725260e-01 -5.18013239e-01 -1.10234392e+00
3.89809519e-01 1.75286919e-01 -9.10090581e-02 3.68664861... | [9.387531280517578, 3.892132520675659] |
2c976eda-e567-4be7-b8bc-f235291a1ba2 | mixture-of-linear-models-co-supervised-by | 2108.04035 | null | https://arxiv.org/abs/2108.04035v1 | https://arxiv.org/pdf/2108.04035v1.pdf | Mixture of Linear Models Co-supervised by Deep Neural Networks | Deep neural network (DNN) models have achieved phenomenal success for applications in many domains, ranging from academic research in science and engineering to industry and business. The modeling power of DNN is believed to have come from the complexity and over-parameterization of the model, which on the other hand h... | ['Jia Li', 'Lin Lin', 'Beomseok Seo'] | 2021-08-05 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 1.80335879e-01 4.50085133e-01 -3.53151619e-01 -6.34155035e-01
-1.77481189e-01 -4.83071804e-01 4.46115941e-01 7.74870738e-02
-1.38444126e-01 6.86607480e-01 2.45318264e-01 -8.08445990e-01
-4.74134743e-01 -7.37038076e-01 -5.62718868e-01 -6.86848581e-01
4.01882201e-01 5.55358469e-01 -5.59440032e-02 -1.04540616... | [8.767579078674316, 5.535050868988037] |
ef74abfb-9800-4523-934a-73defb59ee55 | content-extraction-and-lexical-analysis-from | null | null | https://aclanthology.org/W18-6118 | https://aclanthology.org/W18-6118.pdf | Content Extraction and Lexical Analysis from Customer-Agent Interactions | In this paper, we provide a lexical comparative analysis of the vocabulary used by customers and agents in an Enterprise Resource Planning (ERP) environment and a potential solution to clean the data and extract relevant content for NLP. As a result, we demonstrate that the actual vocabulary for the language that preva... | ['Sergiu Nisioi', 'Anca Bucur', 'Liviu P. Dinu'] | 2018-11-01 | null | null | null | ws-2018-11 | ['lexical-analysis'] | ['natural-language-processing'] | [-2.78102428e-01 3.29066664e-01 -1.84254646e-01 -1.38240322e-01
-3.18353355e-01 -8.88451874e-01 9.93444443e-01 4.38781321e-01
-6.49505079e-01 7.58820236e-01 5.97988009e-01 -5.05564511e-01
-2.86661774e-01 -6.76394999e-01 -7.24217668e-02 -3.60713333e-01
4.14609998e-01 6.53540492e-01 -5.73927024e-03 -9.14300859... | [10.045528411865234, 9.652057647705078] |
3006f487-591e-4063-b843-3c095efb5f44 | leveraging-adaptive-color-augmentation-in | 2011.00148 | null | https://arxiv.org/abs/2011.00148v1 | https://arxiv.org/pdf/2011.00148v1.pdf | Leveraging Adaptive Color Augmentation in Convolutional Neural Networks for Deep Skin Lesion Segmentation | Fully automatic detection of skin lesions in dermatoscopic images can facilitate early diagnosis and repression of malignant melanoma and non-melanoma skin cancer. Although convolutional neural networks are a powerful solution, they are limited by the illumination spectrum of annotated dermatoscopic screening images, w... | ['Abdullah Thabit', 'Prem Prasad', 'Anindo Saha'] | 2020-10-31 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 4.80220348e-01 1.16807990e-01 -4.87559974e-01 -4.00901407e-01
-4.87419635e-01 -9.10641670e-01 1.72504753e-01 9.53583047e-03
-5.01967072e-01 5.60304999e-01 -2.29848444e-01 -4.92987543e-01
1.57242045e-01 -5.71887314e-01 -3.85278046e-01 -7.56219506e-01
1.02290295e-01 -1.90270901e-01 -2.82728896e-02 1.33609146... | [15.641011238098145, -2.9485630989074707] |
f3c6ea26-c337-48b5-b9e0-0d217e375229 | linguistically-informed-self-attention-for | 1804.08199 | null | http://arxiv.org/abs/1804.08199v3 | http://arxiv.org/pdf/1804.08199v3.pdf | Linguistically-Informed Self-Attention for Semantic Role Labeling | Current state-of-the-art semantic role labeling (SRL) uses a deep neural
network with no explicit linguistic features. However, prior work has shown
that gold syntax trees can dramatically improve SRL decoding, suggesting the
possibility of increased accuracy from explicit modeling of syntax. In this
work, we present l... | ['Andrew McCallum', 'Patrick Verga', 'David Weiss', 'Daniel Andor', 'Emma Strubell'] | 2018-04-23 | linguistically-informed-self-attention-for-1 | https://aclanthology.org/D18-1548 | https://aclanthology.org/D18-1548.pdf | emnlp-2018-10 | ['predicate-detection', 'semantic-role-labeling-predicted-predicates'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.81689668e-01 6.11959338e-01 -4.76445138e-01 -6.77799404e-01
-1.33836520e+00 -7.07946062e-01 3.84670794e-01 5.75850546e-01
-8.77059877e-01 7.89757550e-01 6.43098116e-01 -5.25568485e-01
1.93906963e-01 -6.94562852e-01 -1.01056409e+00 -3.52122158e-01
-3.39394854e-03 6.73175514e-01 2.62284786e-01 -3.32222402... | [10.376916885375977, 9.512594223022461] |
ceb5a5e0-292d-4a67-9dd2-4eb2d9b9aab6 | abinet-autonomous-bidirectional-and-iterative | 2211.10578 | null | https://arxiv.org/abs/2211.10578v2 | https://arxiv.org/pdf/2211.10578v2.pdf | ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spotting | Scene text spotting is of great importance to the computer vision community due to its wide variety of applications. Recent methods attempt to introduce linguistic knowledge for challenging recognition rather than pure visual classification. However, how to effectively model the linguistic rules in end-to-end deep netw... | ['Yongdong Zhang', 'Chenggang Yan', 'Yuxin Wang', 'Hongtao Xie', 'Zhendong Mao', 'Shancheng Fang'] | 2022-11-19 | null | null | null | null | ['text-spotting', 'scene-text-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.41240102e-01 -5.52012026e-01 -1.17138557e-01 -3.23922545e-01
-1.29527792e-01 -2.46864319e-01 7.74530470e-01 -3.79052073e-01
-5.69138885e-01 1.75848156e-01 4.17794943e-01 -4.31975663e-01
2.46971041e-01 -8.14007938e-01 -8.07994127e-01 -4.74943131e-01
7.93752193e-01 2.91789144e-01 2.22432181e-01 -1.87711015... | [11.85139274597168, 2.142874002456665] |
8b6e69c3-1e5a-478a-ba44-33f71a794f17 | evidential-deep-learning-for-open-set-action | 2107.10161 | null | https://arxiv.org/abs/2107.10161v2 | https://arxiv.org/pdf/2107.10161v2.pdf | Evidential Deep Learning for Open Set Action Recognition | In a real-world scenario, human actions are typically out of the distribution from training data, which requires a model to both recognize the known actions and reject the unknown. Different from image data, video actions are more challenging to be recognized in an open-set setting due to the uncertain temporal dynamic... | ['Yu Kong', 'Qi Yu', 'Wentao Bao'] | 2021-07-21 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Bao_Evidential_Deep_Learning_for_Open_Set_Action_Recognition_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Bao_Evidential_Deep_Learning_for_Open_Set_Action_Recognition_ICCV_2021_paper.pdf | iccv-2021-1 | ['open-set-action-recognition'] | ['computer-vision'] | [ 4.28379208e-01 9.50983614e-02 -4.52761531e-01 -3.99121732e-01
-7.14694440e-01 -2.98934639e-01 6.03740454e-01 -6.87486827e-01
-1.42479882e-01 5.31762362e-01 3.05534393e-01 -1.12295724e-01
-9.49676090e-04 -2.19125330e-01 -9.88793969e-01 -7.20931590e-01
1.63285434e-01 2.72334814e-01 5.15291728e-02 1.62977144... | [8.505770683288574, 0.7048065066337585] |
2e2322be-a51a-4ee1-9993-1049357a2f06 | discover-and-mitigate-unknown-biases-with | 2207.10077 | null | https://arxiv.org/abs/2207.10077v2 | https://arxiv.org/pdf/2207.10077v2.pdf | Discover and Mitigate Unknown Biases with Debiasing Alternate Networks | Deep image classifiers have been found to learn biases from datasets. To mitigate the biases, most previous methods require labels of protected attributes (e.g., age, skin tone) as full-supervision, which has two limitations: 1) it is infeasible when the labels are unavailable; 2) they are incapable of mitigating unkno... | ['Chenliang Xu', 'Anthony Hoogs', 'Zhiheng Li'] | 2022-07-20 | null | null | null | null | ['facial-attribute-classification'] | ['computer-vision'] | [ 3.81872505e-01 2.67694443e-01 -3.34402233e-01 -6.11606717e-01
-1.63712859e-01 -5.67454278e-01 3.24031651e-01 -3.41599286e-01
-4.03117925e-01 9.68653381e-01 -9.07961354e-02 -2.84062207e-01
9.26029831e-02 -7.27551162e-01 -8.21415901e-01 -8.76702428e-01
2.28068173e-01 3.38948846e-01 3.06518059e-02 -1.85455963... | [9.162840843200684, 4.195428371429443] |
60247de3-127c-473d-83e3-d9c4f1dbeee3 | supervised-pretraining-can-learn-in-context | 2306.14892 | null | https://arxiv.org/abs/2306.14892v1 | https://arxiv.org/pdf/2306.14892v1.pdf | Supervised Pretraining Can Learn In-Context Reinforcement Learning | Large transformer models trained on diverse datasets have shown a remarkable ability to learn in-context, achieving high few-shot performance on tasks they were not explicitly trained to solve. In this paper, we study the in-context learning capabilities of transformers in decision-making problems, i.e., reinforcement ... | ['Emma Brunskill', 'Ofir Nachum', 'Chelsea Finn', 'Yash Chandak', 'Aldo Pacchiano', 'Annie Xie', 'Jonathan N. Lee'] | 2023-06-26 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 4.45366681e-01 5.25294006e-01 -3.64591599e-01 -3.54262352e-01
-1.05166352e+00 -7.09006608e-01 6.83518887e-01 -2.06851900e-01
-3.44432086e-01 9.41250920e-01 8.76200125e-02 -6.73427641e-01
-4.88615900e-01 -6.80272341e-01 -1.05946529e+00 -9.01327133e-01
-1.70586482e-01 1.08668876e+00 -1.24722563e-01 1.05734877... | [4.142365455627441, 2.0284292697906494] |
ad0cd79a-3687-45d7-b976-675f56f45b31 | random-forest-for-dissimilarity-based-multi | 2007.08377 | null | https://arxiv.org/abs/2007.08377v1 | https://arxiv.org/pdf/2007.08377v1.pdf | Random Forest for Dissimilarity-based Multi-view Learning | Many classification problems are naturally multi-view in the sense their data are described through multiple heterogeneous descriptions. For such tasks, dissimilarity strategies are effective ways to make the different descriptions comparable and to easily merge them, by (i) building intermediate dissimilarity represen... | ['Robert Sabourin', 'Simon Bernard', 'Laurent Heutte', 'Hongliu Cao'] | 2020-07-16 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 1.11317284e-01 -1.47599280e-01 -2.68615484e-01 -6.58966601e-01
-7.85055518e-01 -6.95238590e-01 9.26310837e-01 5.45550883e-01
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-4.19644654e-01 -8.24453115e-01 -1.05655920e-02 -8.35085392e-01
9.45968851e-02 9.19015169e-01 4.64240462e-01 -1.50001347... | [8.46896743774414, 4.4792070388793945] |
35174381-66e9-4c9c-a99f-202b58798717 | knowledge-graph-based-waveform-recommendation | 2202.01926 | null | https://arxiv.org/abs/2202.01926v1 | https://arxiv.org/pdf/2202.01926v1.pdf | Knowledge Graph Based Waveform Recommendation: A New Communication Waveform Design Paradigm | Traditionally, a communication waveform is designed by experts based on communication theory and their experiences on a case-by-case basis, which is usually laborious and time-consuming. In this paper, we investigate the waveform design from a novel perspective and propose a new waveform design paradigm with the knowle... | ['Jun Wang', 'Qihang Peng', 'Yundi Guan', 'Tianfu Qi', 'Wei Huang'] | 2022-01-24 | null | null | null | null | ['intelligent-communication'] | ['time-series'] | [-1.35566041e-01 -4.09410745e-01 1.04454853e-01 -4.29215312e-01
-4.12849873e-01 -2.73659468e-01 -1.26409337e-01 1.16640359e-01
2.17098176e-01 2.69472003e-01 3.65897804e-01 -2.10315585e-01
-1.11512661e+00 -9.61223364e-01 5.10829166e-02 -7.13658512e-01
-1.07744537e-01 8.69474933e-02 -2.29392853e-02 -4.34286356... | [10.087170600891113, 5.589885711669922] |
b4b5a842-1db8-44cf-8d28-8c1d29d61a65 | cardiac-segmentation-from-lge-mri-using-deep | 1906.07347 | null | https://arxiv.org/abs/1906.07347v2 | https://arxiv.org/pdf/1906.07347v2.pdf | Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priors | Cardiac segmentation from late gadolinium enhancement MRI is an important task in clinics to identify and evaluate the infarction of myocardium. The automatic segmentation is however still challenging, due to the heterogeneous intensity distributions and indistinct boundaries in the images. In this paper, we propose a ... | ['Xiahai Zhuang', 'Lingchao Xu', 'Qing Ye', 'Qian Yue', 'Xinzhe Luo'] | 2019-06-18 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 3.49974871e-01 6.35806099e-02 1.46429285e-01 -2.41634190e-01
-7.33370245e-01 -3.74125957e-01 2.73841582e-02 -2.20171567e-02
-7.28915751e-01 7.98385203e-01 -1.20231025e-01 -2.03836828e-01
-5.34766242e-02 -4.57877696e-01 -5.10856271e-01 -1.10406363e+00
1.50147170e-01 4.29655373e-01 4.76664335e-01 1.79656029... | [14.397568702697754, -2.4551761150360107] |
6c79e348-aa32-4195-97a0-099e7caaaf37 | meta-song-evaluation-for-chord-recognition | null | null | https://arxiv.org/abs/1109.0420 | https://arxiv.org/pdf/1109.0420 | Meta-song evaluation for chord recognition | We present a new approach to evaluate chord recognition systems on songs which do not have full annotations. The principle is to use online chord databases to generate high accurate "pseudo annotations" for these songs and compute "pseudo accuracies" of test systems. Statistical models that model the relationship betwe... | ['Raul Santos-Rodriguez', 'Tijl De Bie', 'Yizhao Ni', 'Matt Mcvicar'] | 2011-09-02 | null | null | null | tbd-2011-9 | ['chord-recognition'] | ['audio'] | [-8.37796256e-02 -3.93491387e-01 -1.17196171e-02 -2.62636721e-01
-1.04223192e+00 -1.12287140e+00 4.16795164e-01 5.38956262e-02
-4.51393574e-01 6.64392173e-01 -1.36011481e-01 7.30449036e-02
-4.14438158e-01 -6.23894334e-01 -1.48647949e-01 -3.23967755e-01
-4.38790619e-01 5.24176419e-01 8.37546527e-01 -5.26714027... | [15.907193183898926, 5.284383296966553] |
a227d73d-792e-495a-a33d-11daa832d78f | extended-multilingual-protest-news-detection | 2211.11360 | null | https://arxiv.org/abs/2211.11360v1 | https://arxiv.org/pdf/2211.11360v1.pdf | Extended Multilingual Protest News Detection -- Shared Task 1, CASE 2021 and 2022 | We report results of the CASE 2022 Shared Task 1 on Multilingual Protest Event Detection. This task is a continuation of CASE 2021 that consists of four subtasks that are i) document classification, ii) sentence classification, iii) event sentence coreference identification, and iv) event extraction. The CASE 2022 exte... | ['Erdem Yörük', 'Fatih Beyhan', 'Aaqib Javid', 'Francielle Vargas', 'Milena Slavcheva', 'Tadashi Nomoto', 'Niklas Stoehr', 'Hansi Hettiarachchi', 'Yaoyao Dai', 'Benjamin Radford', 'Alaeddin Selçuk Gürel', 'Onur Uca', 'Fırat Duruşan', 'Osman Mutlu', 'Ali Hürriyetoğlu'] | 2022-11-21 | null | null | null | null | ['event-extraction', 'document-classification', 'sentence-classification'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-2.30013639e-01 6.51942939e-02 3.53954434e-02 -3.23501468e-01
-1.63447118e+00 -9.97455657e-01 9.40667927e-01 4.53107536e-01
-8.86545181e-01 1.17863834e+00 6.20016277e-01 -2.20403939e-01
2.54666418e-01 -4.24305350e-01 -7.41481185e-01 -1.56988576e-01
5.01369946e-02 8.06644261e-01 4.27259058e-01 -4.49932218... | [9.140772819519043, 9.719822883605957] |
264adccf-3df6-4367-b834-1c858d211ba4 | template-free-prompt-tuning-for-few-shot-ner | 2109.13532 | null | https://arxiv.org/abs/2109.13532v3 | https://arxiv.org/pdf/2109.13532v3.pdf | Template-free Prompt Tuning for Few-shot NER | Prompt-based methods have been successfully applied in sentence-level few-shot learning tasks, mostly owing to the sophisticated design of templates and label words. However, when applied to token-level labeling tasks such as NER, it would be time-consuming to enumerate the template queries over all potential entity sp... | ['Xuanjing Huang', 'Qi Zhang', 'Linyang Li', 'Yiding Tan', 'Tao Gui', 'Xin Zhou', 'Ruotian Ma'] | 2021-09-28 | null | https://aclanthology.org/2022.naacl-main.420 | https://aclanthology.org/2022.naacl-main.420.pdf | naacl-2022-7 | ['few-shot-ner'] | ['natural-language-processing'] | [ 2.76299149e-01 7.24787712e-02 -2.02548541e-02 -3.27976555e-01
-1.05602801e+00 -4.49894756e-01 4.51744139e-01 2.18835607e-01
-9.82565939e-01 9.07402039e-01 2.05667838e-01 -3.60321552e-01
-1.21178657e-01 -8.08252871e-01 -3.56264770e-01 -8.34400296e-01
3.30632657e-01 3.93069178e-01 3.94333065e-01 -1.13976099... | [9.741789817810059, 9.416905403137207] |
ccc914cf-0366-442b-ae94-22dd06b9956e | the-brain-tumor-segmentation-brats-challenge | 2305.09011 | null | https://arxiv.org/abs/2305.09011v5 | https://arxiv.org/pdf/2305.09011v5.pdf | The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn) | Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. ... | ['Dominic LaBella', 'Maruf Adewole', 'Jeff Rudie', 'Evan Calabrese', 'Byrone Cole', 'Maria Diaz', 'Syed Muhammad Anwar', 'Jan Kirschke', 'James Eddy', 'Keyvan Farahani', 'Ahmed W. Moawad', 'Anahita Fathi Kazerooni', 'Anastasia Janas', 'Verena Chung', 'Benedikt Wiestler', 'Juan Eugenio Iglesias', 'Bjoern Menze', 'Marius... | 2023-05-15 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 7.58866668e-01 3.03871315e-02 1.18625462e-01 -3.88402760e-01
-9.42436397e-01 -3.77477318e-01 6.98361516e-01 2.92005902e-03
-6.70323133e-01 8.04141343e-01 3.26355010e-01 -4.71395791e-01
-2.29912415e-01 -4.08959389e-01 -2.53768057e-01 -6.22167766e-01
-5.64107001e-02 8.64144206e-01 3.40436786e-01 5.32132797... | [14.276727676391602, -2.3931586742401123] |
2445309f-aecc-4f3d-bf0a-4daf5a597108 | ldc-net-a-unified-framework-for-localization | 2110.04727 | null | https://arxiv.org/abs/2110.04727v1 | https://arxiv.org/pdf/2110.04727v1.pdf | LDC-Net: A Unified Framework for Localization, Detection and Counting in Dense Crowds | The rapid development in visual crowd analysis shows a trend to count people by positioning or even detecting, rather than simply summing a density map. It also enlightens us back to the essence of the field, detection to count, which can give more abundant crowd information and has more practical applications. However... | ['Xuelong Li', 'Yuan Yuan', 'Junyu Gao', 'Tao Han', 'Qi Wang'] | 2021-10-10 | null | null | null | null | ['visual-crowd-analysis'] | ['computer-vision'] | [-5.58957815e-01 -5.85254848e-01 2.92723954e-01 -7.88498223e-02
-2.86222547e-01 -3.82135123e-01 6.44247115e-01 1.89528003e-01
-8.72281194e-01 8.83734286e-01 3.01380187e-01 -1.63630620e-01
3.43725622e-01 -1.02043676e+00 -3.33390832e-01 -4.44189072e-01
-4.01466876e-01 9.49251115e-01 1.00104046e+00 -3.26314032... | [8.357792854309082, -0.36806297302246094] |
781b86ec-b3c1-4550-8853-03dc9ec3bb40 | granger-causality-for-compressively-sensed | 2210.11420 | null | https://arxiv.org/abs/2210.11420v1 | https://arxiv.org/pdf/2210.11420v1.pdf | Granger Causality for Compressively Sensed Sparse Signals | Compressed sensing is a scheme that allows for sparse signals to be acquired, transmitted and stored using far fewer measurements than done by conventional means employing Nyquist sampling theorem. Since many naturally occurring signals are sparse (in some domain), compressed sensing has rapidly seen popularity in a nu... | ['Nithin Nagaraj', 'Aditi Kathpalia'] | 2022-09-23 | null | null | null | null | ['connectivity-estimation', 'quantum-state-tomography'] | ['graphs', 'medical'] | [ 9.16276991e-01 -1.84016883e-01 -2.38484032e-02 -1.38531523e-02
-1.02647930e-01 -4.30737585e-01 5.53180873e-01 2.45794952e-02
-8.58460888e-02 1.23936474e+00 3.83034199e-01 -3.05344194e-01
-6.99198186e-01 -5.05673349e-01 -6.33314967e-01 -9.07397151e-01
-5.83682239e-01 3.58877778e-01 -1.31702453e-01 3.51309590... | [6.9334821701049805, 4.245969772338867] |
edb0adec-e136-4019-8256-592322ea47be | poseformerv2-exploring-frequency-domain-for | 2303.17472 | null | https://arxiv.org/abs/2303.17472v1 | https://arxiv.org/pdf/2303.17472v1.pdf | PoseFormerV2: Exploring Frequency Domain for Efficient and Robust 3D Human Pose Estimation | Recently, transformer-based methods have gained significant success in sequential 2D-to-3D lifting human pose estimation. As a pioneering work, PoseFormer captures spatial relations of human joints in each video frame and human dynamics across frames with cascaded transformer layers and has achieved impressive performa... | ['Chen Chen', 'Pichao Wang', 'Mengyuan Liu', 'Ce Zheng', 'Qitao Zhao'] | 2023-03-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhao_PoseFormerV2_Exploring_Frequency_Domain_for_Efficient_and_Robust_3D_Human_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhao_PoseFormerV2_Exploring_Frequency_Domain_for_Efficient_and_Robust_3D_Human_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-human-pose-estimation', 'human-dynamics'] | ['computer-vision', 'computer-vision'] | [-9.59497318e-02 -4.89425600e-01 -1.63151115e-01 4.18863334e-02
-6.83997631e-01 -2.78405309e-01 2.45066136e-01 -3.07310313e-01
-4.70911771e-01 4.31780279e-01 3.67083609e-01 3.48635733e-01
1.13653354e-01 -3.50045770e-01 -7.04851389e-01 -5.70071638e-01
-3.38701963e-01 1.33615702e-01 5.47604740e-01 -2.90687591... | [7.174097537994385, -0.6407365798950195] |
81a2b44a-097b-4274-9ac9-5152e0bd2c5f | an-efficient-keyframes-selection-based | null | null | https://aclanthology.org/2021.icon-main.29 | https://aclanthology.org/2021.icon-main.29.pdf | An Efficient Keyframes Selection Based Framework for Video Captioning | Describing a video is a challenging yet attractive task since it falls into the intersection of computer vision and natural language generation. The attention-based models have reported the best performance. However, all these models follow similar procedures, such as segmenting videos into chunks of frames or sampling... | ['Sivaji Bandyopadhyay', 'Thoudam Doren Singh', 'Salam Michael Singh', 'Loitongbam Sanayai Meetei', 'Alok Singh'] | null | null | null | null | icon-2021-12 | ['video-description'] | ['computer-vision'] | [ 3.62171799e-01 -1.41317725e-01 -3.42010707e-01 -2.11136326e-01
-7.21858442e-01 -5.18088281e-01 6.11733556e-01 1.89737111e-01
-4.28759634e-01 8.43778968e-01 3.41381937e-01 1.52925104e-01
3.73787582e-01 -4.89228696e-01 -6.58474505e-01 -7.92306244e-01
-1.35620171e-02 1.17486052e-01 4.84003156e-01 1.02304988... | [10.448274612426758, 0.5220648050308228] |
bda534f6-68f6-4f89-9a27-d0158165834f | busem-at-semeval-2017-task-4a-sentiment | null | null | https://aclanthology.org/S17-2131 | https://aclanthology.org/S17-2131.pdf | BUSEM at SemEval-2017 Task 4A Sentiment Analysis with Word Embedding and Long Short Term Memory RNN Approaches | This paper describes our approach for SemEval-2017 Task 4: Sentiment Analysis in Twitter. We have participated in Subtask A: Message Polarity Classification subtask and developed two systems. The first system uses word embeddings for feature representation and Support Vector Machine, Random Forest and Naive Bayes algor... | ['Murat Saraclar', 'Arzucan Ozgur', 'Deger Ayata'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['subjectivity-analysis'] | ['natural-language-processing'] | [ 5.15651293e-02 2.17656910e-01 -5.54199457e-01 -5.77006042e-01
-1.28503412e-01 -4.45599586e-01 9.88480091e-01 6.70048535e-01
-7.15450346e-01 7.32130706e-01 7.68694699e-01 -6.58642471e-01
3.72527748e-01 -8.54349494e-01 5.50823845e-03 -3.05535197e-01
-9.92527977e-02 5.14565587e-01 2.45159697e-02 -9.48487997... | [11.141216278076172, 7.035920143127441] |
7c0cc368-ddc3-4a2f-93b1-55eae0d32ea9 | robust-subspace-recovery-layer-for | 1904.00152 | null | https://arxiv.org/abs/1904.00152v2 | https://arxiv.org/pdf/1904.00152v2.pdf | Robust Subspace Recovery Layer for Unsupervised Anomaly Detection | We propose a neural network for unsupervised anomaly detection with a novel robust subspace recovery layer (RSR layer). This layer seeks to extract the underlying subspace from a latent representation of the given data and removes outliers that lie away from this subspace. It is used within an autoencoder. The encoder ... | ['Chieh-Hsin Lai', 'Dongmian Zou', 'Gilad Lerman'] | 2019-03-30 | null | https://openreview.net/forum?id=rylb3eBtwr | https://openreview.net/pdf?id=rylb3eBtwr | iclr-2020-1 | ['unsupervised-anomaly-detection-with-specified-5', 'unsupervised-anomaly-detection-with-specified-4', 'unsupervised-anomaly-detection-with-specified-7', 'unsupervised-anomaly-detection-with-specified-6', 'unsupervised-anomaly-detection-with-specified'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-7.80890733e-02 9.96572431e-03 1.16083892e-02 -2.11650357e-01
-7.78980017e-01 -5.03850222e-01 3.73941839e-01 -1.16283081e-01
-1.44846007e-01 1.69720918e-01 5.50570190e-01 1.30447417e-01
-1.47644579e-01 -2.46613473e-01 -9.81420159e-01 -8.85418773e-01
-1.84068158e-01 5.74445128e-01 -3.65566462e-01 1.37441367... | [7.662403106689453, 2.4048454761505127] |
af97b5e7-2bdf-45ed-a1ae-44b764e342e2 | diva-domain-invariant-variational | 1905.10427 | null | https://arxiv.org/abs/1905.10427v2 | https://arxiv.org/pdf/1905.10427v2.pdf | DIVA: Domain Invariant Variational Autoencoders | We consider the problem of domain generalization, namely, how to learn representations given data from a set of domains that generalize to data from a previously unseen domain. We propose the Domain Invariant Variational Autoencoder (DIVA), a generative model that tackles this problem by learning three independent late... | ['Christos Louizos', 'Max Welling', 'Maximilian Ilse', 'Jakub M. Tomczak'] | 2019-05-24 | null | null | null | null | ['rotated-mnist'] | ['computer-vision'] | [ 3.72468680e-01 2.33231083e-01 -7.02277618e-03 -3.99773568e-01
-4.73996490e-01 -7.85584331e-01 8.28568101e-01 5.05967438e-02
-2.01312840e-01 1.13376915e+00 3.44180316e-01 -9.48796922e-04
-2.58276105e-01 -4.98646349e-01 -7.19513834e-01 -1.02287865e+00
2.02972949e-01 1.01269007e+00 2.85894811e-01 -2.26109266... | [10.216826438903809, 2.9519152641296387] |
77de2ef2-9a5a-457c-90ea-81397f51bf29 | cluster-analysis-with-deep-embeddings-and | 2109.12714 | null | https://arxiv.org/abs/2109.12714v2 | https://arxiv.org/pdf/2109.12714v2.pdf | Cluster Analysis with Deep Embeddings and Contrastive Learning | Unsupervised disentangled representation learning is a long-standing problem in computer vision. This work proposes a novel framework for performing image clustering from deep embeddings by combining instance-level contrastive learning with a deep embedding based cluster center predictor. Our approach jointly learns re... | ['Ali Jannesari', 'John Just', 'Jansel Herrera-Gerena', 'Ramakrishnan Sundareswaran'] | 2021-09-26 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 3.16636339e-02 4.84190546e-02 -8.39832723e-02 -4.27965611e-01
-1.21978521e+00 -5.68243325e-01 9.86074150e-01 4.17921633e-01
-6.83774173e-01 1.08707257e-01 3.51604253e-01 2.53266633e-01
-3.72189671e-01 -3.29028070e-01 -5.68297088e-01 -1.11519563e+00
-2.54531950e-01 7.76182652e-01 -3.15270185e-01 2.98847765... | [9.371991157531738, 3.074007749557495] |
c1761790-3d2b-4f4c-a625-06536680d578 | synthstrip-skull-stripping-for-any-brain | 2203.09974 | null | https://arxiv.org/abs/2203.09974v2 | https://arxiv.org/pdf/2203.09974v2.pdf | SynthStrip: Skull-Stripping for Any Brain Image | The removal of non-brain signal from magnetic resonance imaging (MRI) data, known as skull-stripping, is an integral component of many neuroimage analysis streams. Despite their abundance, popular classical skull-stripping methods are usually tailored to images with specific acquisition properties, namely near-isotropi... | ['Malte Hoffmann', 'Bruce Fischl', 'Adrian V. Dalca', 'Jocelyn S. Mora', 'Andrew Hoopes'] | 2022-03-18 | null | null | null | null | ['skull-stripping'] | ['medical'] | [ 5.34402311e-01 -1.08470200e-02 9.36979651e-02 -6.08621359e-01
-9.26108539e-01 -4.26529467e-01 4.01276499e-01 -6.84885830e-02
-7.04221308e-01 5.60629666e-01 1.14564091e-01 -1.77348763e-01
-2.07812980e-01 -2.49231875e-01 -5.90206087e-01 -5.20348370e-01
-3.86158496e-01 5.97012043e-01 5.18720627e-01 -8.55234638... | [14.028482437133789, -2.3145992755889893] |
e081e86e-8818-47be-b1c4-4b3213605cbf | 190600546 | 1906.00546 | null | https://arxiv.org/abs/1906.00546v1 | https://arxiv.org/pdf/1906.00546v1.pdf | Rethinking Loss Design for Large-scale 3D Shape Retrieval | Learning discriminative shape representations is a crucial issue for large-scale 3D shape retrieval. In this paper, we propose the Collaborative Inner Product Loss (CIP Loss) to obtain ideal shape embedding that discriminative among different categories and clustered within the same class. Utilizing simple inner produc... | ['Zhaoqun Li', 'Cheng Xu', 'Biao Leng'] | 2019-06-03 | null | null | null | null | ['3d-shape-retrieval', '3d-object-retrieval'] | ['computer-vision', 'computer-vision'] | [-3.12928289e-01 -5.83628304e-02 -2.75374293e-01 -3.80456835e-01
-7.95512319e-01 -7.80484438e-01 6.72345102e-01 2.54351735e-01
-1.69532120e-01 1.33913994e-01 4.16049138e-02 -8.14417377e-02
-4.04199094e-01 -7.07976520e-01 -4.34460640e-01 -8.11648369e-01
-2.67016720e-02 5.67647040e-01 2.45946288e-01 1.05636835... | [8.152656555175781, -3.8590548038482666] |
f715ca18-f34f-4875-9b66-716e9b57bc02 | foreground-guided-facial-inpainting-with | 2105.03342 | null | https://arxiv.org/abs/2105.03342v1 | https://arxiv.org/pdf/2105.03342v1.pdf | Foreground-guided Facial Inpainting with Fidelity Preservation | Facial image inpainting, with high-fidelity preservation for image realism, is a very challenging task. This is due to the subtle texture in key facial features (component) that are not easily transferable. Many image inpainting techniques have been proposed with outstanding capabilities and high quantitative performan... | ['Moi Hoon Yap', 'Kevin Walker', 'Vincent Drouard', 'Connah Kendrick', 'Jireh Jam'] | 2021-05-07 | null | null | null | null | ['facial-inpainting', 'foreground-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.60512227e-01 2.46217057e-01 9.77680087e-02 -4.92305905e-01
-5.27648866e-01 -2.11252853e-01 4.34272051e-01 -4.97693956e-01
-2.25266039e-01 8.92160833e-01 1.16145939e-01 5.05592525e-01
1.04961619e-01 -7.16112733e-01 -8.83734584e-01 -8.11163604e-01
1.59564223e-02 -1.18162639e-01 -2.09065340e-02 -3.62076491... | [12.714282989501953, -0.1074012815952301] |
f78d2cc2-98ab-4ea5-b5fb-bb594d22fc92 | language-models-and-automated-essay-scoring | 1909.09482 | null | https://arxiv.org/abs/1909.09482v1 | https://arxiv.org/pdf/1909.09482v1.pdf | Language models and Automated Essay Scoring | In this paper, we present a new comparative study on automatic essay scoring (AES). The current state-of-the-art natural language processing (NLP) neural network architectures are used in this work to achieve above human-level accuracy on the publicly available Kaggle AES dataset. We compare two powerful language model... | ['Pedro Uria Rodriguez', 'Christopher M. Ormerod', 'Amir Jafari'] | 2019-09-18 | null | null | null | null | ['automated-essay-scoring'] | ['natural-language-processing'] | [-2.00576901e-01 -1.57219827e-01 3.83096412e-02 -2.16120332e-01
-6.28065586e-01 -4.40533787e-01 5.54381430e-01 2.28939712e-01
-5.18858254e-01 6.55990422e-01 6.81670249e-01 -7.74168670e-01
-2.35175073e-01 -8.40696514e-01 -3.30217004e-01 -2.28380308e-01
3.04522336e-01 3.17056149e-01 -1.50433242e-01 -7.00087249... | [11.273510932922363, 9.317023277282715] |
57aa923c-94a6-4faf-a9fc-24cdf33a728d | knowledge-transfer-driven-few-shot-class | 2306.10942 | null | https://arxiv.org/abs/2306.10942v1 | https://arxiv.org/pdf/2306.10942v1.pdf | Knowledge Transfer-Driven Few-Shot Class-Incremental Learning | Few-shot class-incremental learning (FSCIL) aims to continually learn new classes using a few samples while not forgetting the old classes. The key of this task is effective knowledge transfer from the base session to the incremental sessions. Despite the advance of existing FSCIL methods, the proposed knowledge transf... | ['Xueming Qian', 'Guoshuai Zhao', 'Yaxiong Wang', 'Ye Wang'] | 2023-06-19 | null | null | null | null | ['class-incremental-learning', 'few-shot-class-incremental-learning', 'incremental-learning'] | ['computer-vision', 'methodology', 'methodology'] | [ 1.41588926e-01 1.40455186e-01 -3.03827465e-01 -7.97609165e-02
-4.88876760e-01 -3.82198393e-01 5.55006683e-01 -5.74958101e-02
-4.14936543e-01 1.09231222e+00 -7.86941350e-02 6.13618568e-02
-3.87834311e-01 -8.08289409e-01 -1.01230443e+00 -1.01536465e+00
1.57318518e-01 3.99613023e-01 5.61627746e-01 -3.08719665... | [9.802639961242676, 3.4110050201416016] |
86b49603-4f23-46bb-8db5-4c73655b1dcd | privacy-preserving-representations-are-not-1 | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chelani_Privacy-Preserving_Representations_Are_Not_Enough_Recovering_Scene_Content_From_Camera_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chelani_Privacy-Preserving_Representations_Are_Not_Enough_Recovering_Scene_Content_From_Camera_CVPR_2023_paper.pdf | Privacy-Preserving Representations Are Not Enough: Recovering Scene Content From Camera Poses | Visual localization is the task of estimating the camera pose from which a given image was taken and is central to several 3D computer vision applications. With the rapid growth in the popularity of AR/VR/MR devices and cloud-based applications, privacy issues are becoming a very important aspect of the localizatio... | ['Zuzana Kukelova', 'Fredrik Kahl', 'Torsten Sattler', 'Kunal Chelani'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['visual-localization'] | ['computer-vision'] | [-3.21554802e-02 -3.84816617e-01 3.09761129e-02 -1.98715568e-01
-8.16086471e-01 -1.31948113e+00 3.27834845e-01 4.13614213e-01
-5.43586850e-01 2.80405730e-01 -4.87056732e-01 -4.95593280e-01
1.93597436e-01 -6.85216010e-01 -8.74160528e-01 -8.44619930e-01
-1.58916906e-01 3.89064789e-01 3.45443487e-01 1.30211934... | [7.59715461730957, -2.138594150543213] |
2dfa0c92-da35-48d5-9f1d-493bccde167c | automatic-code-generation-from-sketches-of | 2103.05704 | null | https://arxiv.org/abs/2103.05704v1 | https://arxiv.org/pdf/2103.05704v1.pdf | Automatic code generation from sketches of mobile applications in end-user development using Deep Learning | A common need for mobile application development by end-users or in computing education is to transform a sketch of a user interface into wireframe code using App Inventor, a popular block-based programming environment. As this task is challenging and time-consuming, we present the Sketch2aia approach that automates th... | ['Edson C. Vargas Júnior', 'Jean C. R. Hauck', 'Aldo von Wangenheim', 'Christiane Gresse von Wangenheim', 'Daniel Baulé'] | 2021-03-09 | null | null | null | null | ['component-classification'] | ['natural-language-processing'] | [ 2.14052215e-01 -1.49275929e-01 -8.65666121e-02 -4.31700982e-02
-5.14059365e-01 -7.64403522e-01 4.38683629e-01 1.14423595e-01
2.29195114e-02 -6.47810996e-02 -1.85433909e-01 -9.34745371e-01
-3.14856291e-01 -8.00606549e-01 -5.36026180e-01 2.26606689e-02
3.52290928e-01 4.99071032e-01 3.39385033e-01 -1.54923037... | [8.172926902770996, 7.209803104400635] |
61bf5435-6527-46e8-903b-e70bacd356f6 | region2vec-community-detection-on-spatial | 2210.08041 | null | https://arxiv.org/abs/2210.08041v1 | https://arxiv.org/pdf/2210.08041v1.pdf | Region2Vec: Community Detection on Spatial Networks Using Graph Embedding with Node Attributes and Spatial Interactions | Community Detection algorithms are used to detect densely connected components in complex networks and reveal underlying relationships among components. As a special type of networks, spatial networks are usually generated by the connections among geographic regions. Identifying the spatial network communities can help... | ['Song Gao', 'Wen Ye', 'Jiawei Zhu', 'Yunlei Liang'] | 2022-10-10 | null | null | null | null | ['community-detection'] | ['graphs'] | [-6.23667479e-01 -7.83996359e-02 -3.38728994e-01 3.54276178e-03
4.80118036e-01 -8.25373530e-01 8.34158957e-01 7.32927382e-01
-1.33779794e-01 1.75424471e-01 7.00826883e-01 -5.20366013e-01
-4.17085469e-01 -1.40535176e+00 -9.90900025e-02 -4.45377707e-01
-8.12416852e-01 3.25302213e-01 4.93597209e-01 -1.23272829... | [7.186811447143555, 6.014355182647705] |
aa4247a3-c0bb-411d-8bb7-32d3a0cf3e10 | joint-english-spelling-error-correction-and | null | null | https://aclanthology.org/C12-1144 | https://aclanthology.org/C12-1144.pdf | Joint English Spelling Error Correction and POS Tagging for Language Learners Writing | null | ['Mamoru Komachi', 'Tomoya Mizumoto', 'Yuji Matsumoto', 'Keisuke Sakaguchi'] | 2012-12-01 | joint-english-spelling-error-correction-and-1 | https://aclanthology.org/C12-1144 | https://aclanthology.org/C12-1144.pdf | coling-2012-12 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.377369403839111, 3.770484209060669] |
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