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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1af8ae53-17e2-4c3b-b626-72904fe8ca31 | learning-fragment-self-attention-embeddings | null | null | https://dl.acm.org/doi/10.1145/3343031.3350940 | https://dl.acm.org/doi/pdf/10.1145/3343031.3350940 | Learning fragment self-attention embeddings for image-text matching | In image-text matching task, the key to good matching quality is to capture the rich contextual dependencies between fragments of image and text. However, previous works either simply aggregate the similarity of all possible pairs of image regions and words, or take multi-step cross attention to attend to image regions... | ['Qingming Huang', 'Guoli Song', 'Shuhui Wang', 'Yiling Wu'] | 2019-10-01 | null | null | null | acmmm-2019-10 | ['text-matching'] | ['natural-language-processing'] | [ 2.29086354e-01 -3.09302777e-01 -2.97843009e-01 -4.39354897e-01
-6.98136508e-01 -3.00637811e-01 8.46779287e-01 3.26099604e-01
-4.60971028e-01 1.06815752e-02 6.83576167e-01 9.74572152e-02
2.11397186e-02 -6.53704107e-01 -8.28775883e-01 -3.88240695e-01
3.44018817e-01 2.72204038e-02 2.67104208e-01 -2.10044235... | [10.702897071838379, 1.3359330892562866] |
0ec1eb8b-73d7-40ad-92c8-57e7c6f54d5f | ludb-a-new-open-access-validation-tool-for | 1809.03393 | null | https://arxiv.org/abs/1809.03393v4 | https://arxiv.org/pdf/1809.03393v4.pdf | LUDB: a new open-access validation tool for electrocardiogram delineation algorithms | We report Lobachevsky University Database (LUDB) of ECG signals, an open tool for validating ECG delineation algorithms, that is superior to the existing publicly available data bases in several aspects. LUDB contains 200 recordings of 10-second 12-lead electrocardiograms (ECG) from different subjects, representative o... | ['Mikhail V. Ivanchenko', 'Nikolai Yu. Zolotykh', 'Grigory V. Osipov', 'Konstantin A. Kosonogov', 'Alexander V. Nikolskiy', 'Victor A. Moskalenko', 'Igor I. Yusipov', 'Alena I. Kalyakulina'] | 2018-08-30 | null | null | null | null | ['ecg-wave-delineation'] | ['medical'] | [ 1.04394503e-01 -3.94963950e-01 2.49419302e-01 -2.22325832e-01
-1.16631305e+00 -8.81901562e-01 -5.45328379e-01 4.80839729e-01
-1.98026672e-01 8.89764309e-01 -2.11341709e-01 -6.76616311e-01
-6.34058833e-01 -5.23204148e-01 -1.14628062e-01 -6.95016205e-01
-8.70128274e-01 6.89196646e-01 -2.74510026e-01 -7.60807097... | [14.323134422302246, 3.2847437858581543] |
61375d87-96d8-48ed-9977-b256c11a6b8c | self-supervised-masked-convolutional | 2209.12148 | null | https://arxiv.org/abs/2209.12148v1 | https://arxiv.org/pdf/2209.12148v1.pdf | Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection | Anomaly detection has recently gained increasing attention in the field of computer vision, likely due to its broad set of applications ranging from product fault detection on industrial production lines and impending event detection in video surveillance to finding lesions in medical scans. Regardless of the domain, a... | ['Mubarak Shah', 'Thomas B. Moeslund', 'Fahad Shahbaz Khan', 'Kamal Nasrollahi', 'Radu Tudor Ionescu', 'Nicolae-Catalin Ristea', 'Neelu Madan'] | 2022-09-25 | null | null | null | null | ['one-class-classification', 'fault-detection'] | ['miscellaneous', 'miscellaneous'] | [ 5.89602888e-01 2.07965020e-02 -1.65276174e-02 -2.25040793e-01
-3.17362398e-01 -3.08190405e-01 5.30548871e-01 1.79618984e-01
3.72627825e-02 1.50446907e-01 -3.67667109e-01 -5.05739391e-01
1.29798362e-02 -6.36316061e-01 -1.03152680e+00 -8.28737259e-01
-2.42318720e-01 5.82569614e-02 4.20436710e-01 -2.40939096... | [7.668502330780029, 1.9458937644958496] |
107f7eac-22ae-45b2-b1d3-df93ee342bb5 | towards-ethical-content-based-detection-of | 1908.11030 | null | https://arxiv.org/abs/1908.11030v1 | https://arxiv.org/pdf/1908.11030v1.pdf | Towards Ethical Content-Based Detection of Online Influence Campaigns | The detection of clandestine efforts to influence users in online communities is a challenging problem with significant active development. We demonstrate that features derived from the text of user comments are useful for identifying suspect activity, but lead to increased erroneous identifications when keywords over-... | ['Herna Viktor', 'Evan Crothers', 'Nathalie Japkowicz'] | 2019-08-29 | null | null | null | null | ['native-language-identification'] | ['natural-language-processing'] | [ 4.76527512e-01 7.13672936e-02 -4.58607227e-01 -2.39493966e-01
-9.90053952e-01 -7.66547084e-01 1.05263042e+00 3.05350631e-01
-6.88816547e-01 7.55143046e-01 5.93207657e-01 -8.47316146e-01
7.77116120e-02 -6.61587536e-01 -3.27027500e-01 -1.79603100e-02
1.38270900e-01 -1.38295749e-02 7.73705542e-02 1.59051642... | [8.583666801452637, 10.344955444335938] |
6ecba356-4680-44b5-82f0-c9118bf56c8f | neural-machine-translation-with-phrase-level | 2203.10299 | null | https://arxiv.org/abs/2203.10299v1 | https://arxiv.org/pdf/2203.10299v1.pdf | Neural Machine Translation with Phrase-Level Universal Visual Representations | Multimodal machine translation (MMT) aims to improve neural machine translation (NMT) with additional visual information, but most existing MMT methods require paired input of source sentence and image, which makes them suffer from shortage of sentence-image pairs. In this paper, we propose a phrase-level retrieval-bas... | ['Yang Feng', 'Qingkai Fang'] | 2022-03-19 | null | https://aclanthology.org/2022.acl-long.390 | https://aclanthology.org/2022.acl-long.390.pdf | acl-2022-5 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 4.22865391e-01 -1.52618706e-01 -4.62234586e-01 4.87058908e-02
-1.25294733e+00 -5.60909748e-01 4.30535316e-01 -2.82839358e-01
-3.36980999e-01 5.96209168e-01 2.95305699e-01 -3.58134925e-01
5.02072990e-01 -5.88045239e-01 -1.06996787e+00 -7.94881821e-01
7.59035528e-01 1.71958268e-01 2.73972340e-02 -2.04963133... | [11.455172538757324, 1.4782785177230835] |
f8900f90-f14e-44b3-91cd-45be3e481cd7 | usage-of-multiple-rtl-features-for-earthquake | 1905.10805 | null | https://arxiv.org/abs/1905.10805v1 | https://arxiv.org/pdf/1905.10805v1.pdf | Usage of multiple RTL features for Earthquake prediction | We construct a classification model that predicts if an earthquake with the magnitude above a threshold will take place at a given location in a time range 30-180 days from a given moment of time. A common approach is to use expert forecasts based on features like Region-Time-Length (RTL) characteristics. The proposed ... | ['I. Braslavsky', 'E. Egorov', 'P. Proskura', 'E. Burnaev', 'A. Zaytsev'] | 2019-05-26 | null | null | null | null | ['earthquake-prediction'] | ['computer-vision'] | [-4.51909840e-01 -2.39774659e-01 -9.95876268e-02 -5.40270686e-01
-7.90421009e-01 -5.55976987e-01 5.65124989e-01 9.93916273e-01
-3.96780252e-01 9.34562206e-01 3.22515845e-01 -6.26757562e-01
-3.23354185e-01 -1.22464919e+00 -4.37764049e-01 -5.55962205e-01
-6.51704013e-01 2.58670509e-01 4.06877309e-01 -3.40613961... | [6.513430595397949, 3.039111614227295] |
9b720c7a-2704-4811-893d-39b8306b77ae | self-mutual-distillation-learning-for | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Hao_Self-Mutual_Distillation_Learning_for_Continuous_Sign_Language_Recognition_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Hao_Self-Mutual_Distillation_Learning_for_Continuous_Sign_Language_Recognition_ICCV_2021_paper.pdf | Self-Mutual Distillation Learning for Continuous Sign Language Recognition | In recent years, deep learning moves video-based Continuous Sign Language Recognition (CSLR) significantly forward. Currently, a typical network combination for CSLR includes a visual module, which focuses on spatial and short-temporal information, followed by a contextual module, which focuses on long-temporal inf... | ['Xilin Chen', 'Yuecong Min', 'Aiming Hao'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['sign-language-recognition'] | ['computer-vision'] | [-1.30101219e-01 -4.10448998e-01 -2.15697393e-01 -4.03096288e-01
-3.96432847e-01 -3.90283205e-02 2.28023812e-01 -3.30330193e-01
-8.13578188e-01 4.20550823e-01 1.28369421e-01 3.79809774e-02
-1.66707009e-01 -4.50558752e-01 -5.28468907e-01 -1.06113851e+00
2.58702755e-01 -7.50626922e-02 5.73720753e-01 1.37912491... | [9.21223258972168, -6.47495698928833] |
edc8b19f-9b54-4eb1-9d5d-346305b341a6 | unifying-motion-deblurring-and-frame | 2203.12178 | null | https://arxiv.org/abs/2203.12178v2 | https://arxiv.org/pdf/2203.12178v2.pdf | Unifying Motion Deblurring and Frame Interpolation with Events | Slow shutter speed and long exposure time of frame-based cameras often cause visual blur and loss of inter-frame information, degenerating the overall quality of captured videos. To this end, we present a unified framework of event-based motion deblurring and frame interpolation for blurry video enhancement, where the ... | ['Lei Yu', 'Xiang Zhang'] | 2022-03-23 | unifying-motion-deblurring-and-frame-1 | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Unifying_Motion_Deblurring_and_Frame_Interpolation_With_Events_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Unifying_Motion_Deblurring_and_Frame_Interpolation_With_Events_CVPR_2022_paper.pdf | cvpr-2022-6 | ['video-enhancement'] | ['computer-vision'] | [ 3.52104396e-01 -6.42305791e-01 -2.41324112e-01 -3.87349784e-01
-4.45807248e-01 -3.39676917e-01 4.67128336e-01 -5.44987500e-01
-1.96281120e-01 8.36840987e-01 5.73761165e-01 -2.64813043e-02
-1.83835059e-01 -1.50428504e-01 -8.03555250e-01 -5.33150434e-01
-2.01970890e-01 -6.10984504e-01 2.78442055e-01 4.40066725... | [11.191200256347656, -2.291553497314453] |
68131d85-7e04-464e-b92b-26af14572cbf | language-to-rewards-for-robotic-skill | 2306.08647 | null | https://arxiv.org/abs/2306.08647v2 | https://arxiv.org/pdf/2306.08647v2.pdf | Language to Rewards for Robotic Skill Synthesis | Large language models (LLMs) have demonstrated exciting progress in acquiring diverse new capabilities through in-context learning, ranging from logical reasoning to code-writing. Robotics researchers have also explored using LLMs to advance the capabilities of robotic control. However, since low-level robot actions ar... | ['Fei Xia', 'Yuval Tassa', 'Jie Tan', 'Dorsa Sadigh', 'Nicolas Heess', 'Tingnan Zhang', 'Andy Zeng', 'Peng Xu', 'Ted Xiao', 'Brian Ichter', 'Jan Humplik', 'Leonard Hasenclever', 'Tom Erez', 'Hao-Tien Lewis Chiang', 'Montse Gonzalez Arenas', 'Kuang-Huei Lee', 'Sean Kirmani', 'Chuyuan Fu', 'Nimrod Gileadi', 'Wenhao Yu'] | 2023-06-14 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [-3.27491648e-02 4.64603156e-01 -3.80175114e-01 -1.27207339e-01
-5.12861550e-01 -6.61306918e-01 5.54241657e-01 -2.45262951e-01
-3.30749691e-01 4.80855465e-01 3.60384164e-03 -4.45131510e-01
-2.17049360e-01 -5.03958821e-01 -1.12910557e+00 -3.26819211e-01
-3.47416908e-01 5.99581361e-01 1.85683459e-01 -7.04181015... | [4.490677833557129, 0.8815512657165527] |
c6a6847d-3e68-4f85-abd1-06b71f8c0396 | reinterpreting-causal-discovery-as-the-task | 2305.06894 | null | https://arxiv.org/abs/2305.06894v1 | https://arxiv.org/pdf/2305.06894v1.pdf | Reinterpreting causal discovery as the task of predicting unobserved joint statistics | If $X,Y,Z$ denote sets of random variables, two different data sources may contain samples from $P_{X,Y}$ and $P_{Y,Z}$, respectively. We argue that causal discovery can help inferring properties of the `unobserved joint distributions' $P_{X,Y,Z}$ or $P_{X,Z}$. The properties may be conditional independences (as in `in... | ['Leena Chennuru Vankadara', 'Philipp M. Faller', 'Dominik Janzing'] | 2023-05-11 | null | null | null | null | ['causal-inference', 'causal-discovery', 'causal-inference'] | ['knowledge-base', 'knowledge-base', 'miscellaneous'] | [ 2.03477532e-01 5.51364779e-01 -5.65577626e-01 -4.98280942e-01
-5.64273894e-01 -6.25065625e-01 7.14266837e-01 2.75230080e-01
1.48372352e-01 1.40861881e+00 2.41035610e-01 -5.54232359e-01
-7.95122206e-01 -1.29702413e+00 -8.80171299e-01 -1.01113951e+00
-6.63467884e-01 7.06105709e-01 1.75272912e-01 2.02713758... | [7.874304294586182, 5.359012603759766] |
071081d8-3002-45d7-8062-4d04f2325d2d | the-bdcam-oes-collection-of-portuguese | null | null | https://aclanthology.org/2020.lrec-1.106 | https://aclanthology.org/2020.lrec-1.106.pdf | The BDCam\~oes Collection of Portuguese Literary Documents: a Research Resource for Digital Humanities and Language Technology | This paper presents the BDCam{\~o}es Collection of Portuguese Literary Documents, a new corpus of literary texts written in Portuguese that in its inaugural version includes close to 4 million words from over 200 complete documents from 83 authors in 14 genres, covering a time span from the 16th to the 21st century, an... | ["Ant{\\'o}nio Branco", 'Jo{\\~a}o Silva', "M{\\'a}rcia Bolrinha", 'Sara Grilo', 'Rui Vaz'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['genre-classification'] | ['computer-vision'] | [-1.41734064e-01 -1.10372998e-01 -2.79195577e-01 2.23857015e-01
-4.36144710e-01 -1.05593598e+00 9.70352113e-01 3.72191191e-01
-8.09863746e-01 1.01945758e+00 3.71518373e-01 -5.81537664e-01
-2.88248360e-01 -5.28726161e-01 -1.45232275e-01 -2.63702363e-01
4.00693178e-01 7.68298566e-01 2.76416123e-01 -6.11190379... | [10.332056045532227, 10.24225902557373] |
7542fb51-84a5-424d-a99a-3198f5be9e11 | deep-learning-based-on-chip-rapid-spectral | 2301.06321 | null | https://arxiv.org/abs/2301.06321v1 | https://arxiv.org/pdf/2301.06321v1.pdf | Deep-learning-based on-chip rapid spectral imaging with high spatial resolution | Spectral imaging extends the concept of traditional color cameras to capture images across multiple spectral channels and has broad application prospects. Conventional spectral cameras based on scanning methods suffer from low acquisition speed and large volume. On-chip computational spectral imaging based on metasurfa... | ['Fang Liu', 'Xue Feng', 'Wei zhang', 'Yidong Huang', 'Kaiyu Cui', 'Jiawei Yang'] | 2023-01-16 | null | null | null | null | ['spectral-reconstruction', 'metamerism'] | ['computer-vision', 'computer-vision'] | [ 8.92246366e-01 -6.21162355e-01 3.05890620e-01 -2.66809314e-01
-7.36045122e-01 -4.28789914e-01 1.04670428e-01 -5.47989964e-01
-4.33000892e-01 4.39295441e-01 -4.20239985e-01 -7.06839189e-02
-1.07213147e-01 -7.94146359e-01 -7.41236925e-01 -1.09825146e+00
4.17717844e-01 1.05862387e-01 4.04776275e-01 -1.43780380... | [10.242741584777832, -2.513437509536743] |
13988cae-470c-4e5d-988d-0f14cc86fba0 | the-gem-benchmark-natural-language-generation | 2102.01672 | null | https://arxiv.org/abs/2102.01672v3 | https://arxiv.org/pdf/2102.01672v3.pdf | The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics | We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this moving target, new models often still evaluate on divergent anglo-centr... | ['Simon Mille', 'Angelina McMillan-Major', 'Dhruv Kumar', 'Mihir Kale', 'Jiawei Zhou', 'Akhila Yerukola', 'Diyi Yang', 'Wei Xu', 'Nishant Subramani', 'Hendrik Strobelt', 'Marco Antonio Sobrevilla Cabezudo', 'Anastasia Shimorina', 'Samira Shaikh', 'Thibault Sellam', 'João Sedoc', 'Sashank Santhanam', 'Juan Diego Rodrigu... | 2021-02-02 | null | https://aclanthology.org/2021.gem-1.10 | https://aclanthology.org/2021.gem-1.10.pdf | acl-gem-2021-8 | ['extreme-summarization'] | ['natural-language-processing'] | [-1.33274933e-02 1.59581244e-01 -2.90350974e-01 -3.34921211e-01
-1.21107781e+00 -8.18849027e-01 1.14036465e+00 5.71397282e-02
-5.00768542e-01 1.21351361e+00 7.72106946e-01 -2.56921977e-01
1.47972479e-01 -4.76391971e-01 -3.01534534e-01 -3.86815481e-02
1.58683620e-02 8.83506298e-01 -6.09514788e-02 -5.42899191... | [11.326005935668945, 9.576085090637207] |
b914dd24-7090-421d-a756-237350cfb2cf | learning-to-prompt-for-open-vocabulary-object | 2203.14940 | null | https://arxiv.org/abs/2203.14940v1 | https://arxiv.org/pdf/2203.14940v1.pdf | Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model | Recently, vision-language pre-training shows great potential in open-vocabulary object detection, where detectors trained on base classes are devised for detecting new classes. The class text embedding is firstly generated by feeding prompts to the text encoder of a pre-trained vision-language model. It is then used as... | ['Guoqi Li', 'Yue Gao', 'Miaojing Shi', 'Zihe Zhang', 'Fangyun Wei', 'Yu Du'] | 2022-03-28 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Du_Learning_To_Prompt_for_Open-Vocabulary_Object_Detection_With_Vision-Language_Model_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Du_Learning_To_Prompt_for_Open-Vocabulary_Object_Detection_With_Vision-Language_Model_CVPR_2022_paper.pdf | cvpr-2022-1 | ['open-vocabulary-object-detection'] | ['computer-vision'] | [ 1.68134049e-01 -3.64061743e-02 -1.99876517e-01 -3.11210275e-01
-8.03402007e-01 -5.35069942e-01 7.55470634e-01 -9.00987834e-02
-5.51815748e-01 1.67345896e-01 -1.39086500e-01 -3.83447677e-01
6.60941660e-01 -6.12761021e-01 -8.40031981e-01 -6.78050101e-01
3.99390817e-01 4.75724638e-01 8.81608844e-01 -1.10168653... | [9.638030052185059, 1.4514102935791016] |
ae33c588-5153-4b9b-a9d6-14b20622741a | zero-shot-object-detection-learning-to | 1803.06049 | null | http://arxiv.org/abs/1803.06049v1 | http://arxiv.org/pdf/1803.06049v1.pdf | Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts | Current Zero-Shot Learning (ZSL) approaches are restricted to recognition of
a single dominant unseen object category in a test image. We hypothesize that
this setting is ill-suited for real-world applications where unseen objects
appear only as a part of a complex scene, warranting both the `recognition' and
`localiza... | ['Shafin Rahman', 'Salman Khan', 'Fatih Porikli'] | 2018-03-16 | null | null | null | null | ['zero-shot-object-detection', 'novel-concepts'] | ['computer-vision', 'reasoning'] | [ 3.58145863e-01 9.08485800e-02 -1.26267588e-02 -4.48472798e-01
-9.16241169e-01 -4.58805412e-01 5.84073007e-01 1.85344949e-01
-4.48334962e-01 2.47735545e-01 -1.56703055e-01 3.18720862e-02
-6.60098046e-02 -5.42610288e-01 -6.07654631e-01 -6.46571457e-01
1.56499937e-01 4.12950277e-01 3.21981966e-01 2.98417755... | [9.765237808227539, 2.094698667526245] |
bc2ce675-68d9-47e4-a85c-66ccbf42a675 | breaking-the-lower-bound-with-little | 2302.06763 | null | https://arxiv.org/abs/2302.06763v1 | https://arxiv.org/pdf/2302.06763v1.pdf | Breaking the Lower Bound with (Little) Structure: Acceleration in Non-Convex Stochastic Optimization with Heavy-Tailed Noise | We consider the stochastic optimization problem with smooth but not necessarily convex objectives in the heavy-tailed noise regime, where the stochastic gradient's noise is assumed to have bounded $p$th moment ($p\in(1,2]$). Zhang et al. (2020) is the first to prove the $\Omega(T^{\frac{1-p}{3p-2}})$ lower bound for co... | ['Zhengyuan Zhou', 'Jiawei Zhang', 'Zijian Liu'] | 2023-02-14 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 1.74871117e-01 1.11197338e-01 1.22799836e-01 5.06883338e-02
-1.30432940e+00 -6.20040655e-01 -1.60210222e-01 1.07515991e-01
-8.43957186e-01 9.70732808e-01 -6.29985571e-01 -7.16575325e-01
-7.08660543e-01 -8.11537743e-01 -8.72916102e-01 -1.26067531e+00
-5.51010907e-01 2.04171002e-01 1.81327879e-01 -1.42963201... | [6.463344097137451, 4.528484344482422] |
b9ccc75c-6f8b-4d3c-ba4d-d5df64d1a1d4 | 3d-action-recognition-from-novel-viewpoints | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Rahmani_3D_Action_Recognition_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Rahmani_3D_Action_Recognition_CVPR_2016_paper.pdf | 3D Action Recognition From Novel Viewpoints | We propose a human pose representation model that transfers human poses acquired from different unknown views to a view-invariant high-level space. The model is a deep convolutional neural network and requires a large corpus of multiview training data which is very expensive to acquire. Therefore, we propose a method t... | ['Ajmal Mian', 'Hossein Rahmani'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['3d-human-action-recognition'] | ['computer-vision'] | [ 2.43258998e-01 -1.42888948e-01 2.23230906e-02 -4.59809065e-01
-6.77271247e-01 -4.20520067e-01 5.57466686e-01 -6.52381182e-01
-2.74032921e-01 5.10169625e-01 3.42577398e-01 5.05789042e-01
1.66596249e-01 -5.88504970e-01 -1.01570427e+00 -4.81778741e-01
1.00320041e-01 6.55514896e-01 3.95399004e-01 -1.31279737... | [7.1799750328063965, -0.6504600644111633] |
a842b6aa-8a55-4a03-af88-fdbea3c01191 | analyzing-intentional-behavior-in-autonomous | 2307.01532 | null | https://arxiv.org/abs/2307.01532v1 | https://arxiv.org/pdf/2307.01532v1.pdf | Analyzing Intentional Behavior in Autonomous Agents under Uncertainty | Principled accountability for autonomous decision-making in uncertain environments requires distinguishing intentional outcomes from negligent designs from actual accidents. We propose analyzing the behavior of autonomous agents through a quantitative measure of the evidence of intentional behavior. We model an uncerta... | ['Bettina Könighofer', 'Ruzica Piskac', 'Scott J. Shapiro', 'Nicholas Shoemaker', 'Katrine Bjørner', 'Timos Antonopoulos', 'Samuel Judson', 'Filip Cano Córdoba'] | 2023-07-04 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 3.75104994e-01 8.84975672e-01 -4.18032706e-02 -3.36010695e-01
-7.07830846e-01 -5.59332490e-01 8.99302900e-01 3.35533947e-01
-5.97309649e-01 9.99710858e-01 5.31235874e-01 -8.20181489e-01
-2.75487959e-01 -8.33566070e-01 -9.55397666e-01 -4.30222660e-01
-1.12648569e-01 5.32094419e-01 2.39687741e-01 1.86245754... | [8.309354782104492, 5.85219669342041] |
91586084-5c3d-4c85-90ea-4754a85de6fa | region-of-interest-focused-mri-to-synthetic | 2203.16288 | null | https://arxiv.org/abs/2203.16288v2 | https://arxiv.org/pdf/2203.16288v2.pdf | Region of Interest focused MRI to Synthetic CT Translation using Regression and Classification Multi-task Network | In this work, we present a method for synthetic CT (sCT) generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction. We propose a loss function that favors a spatially sparse region in the image. We harness... | ['Bjoern Menze', 'Florian Wiesinger', 'Tufve Nyholm', 'Joakim Jonsson', 'Kesavadas Chandrasekharan', 'Vikas Chauhan', 'Bhairav Mehta', 'Carolin Pirkl', 'Marion I Menzel', 'Jonathan J Wyatt', 'Steven F Petit', 'Dattesh Shanbhag', 'Cristina Cozzini', 'Mikael Bylund', 'Sandeep Kaushik'] | 2022-03-30 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [ 6.48914218e-01 4.69768554e-01 1.78024635e-01 -5.18983305e-01
-1.22154665e+00 1.27699459e-02 4.41437155e-01 3.39471221e-01
-5.96662343e-01 8.63759398e-01 2.94114441e-01 -1.26424268e-01
-6.25766456e-01 -7.29760289e-01 -6.12498939e-01 -9.78591859e-01
-4.06832725e-01 7.86833286e-01 4.18923944e-01 -4.09344956... | [13.884561538696289, -2.486630916595459] |
d5dffc64-c402-4433-a13a-a2aca920415e | a-principled-approach-to-data-valuation-for | 2009.06192 | null | https://arxiv.org/abs/2009.06192v1 | https://arxiv.org/pdf/2009.06192v1.pdf | A Principled Approach to Data Valuation for Federated Learning | Federated learning (FL) is a popular technique to train machine learning (ML) models on decentralized data sources. In order to sustain long-term participation of data owners, it is important to fairly appraise each data source and compensate data owners for their contribution to the training process. The Shapley value... | ['Tianhao Wang', 'Johannes Rausch', 'Ce Zhang', 'Dawn Song', 'Ruoxi Jia'] | 2020-09-14 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 4.12141308e-02 1.95492327e-01 -5.08222938e-01 -2.84467250e-01
-8.26041341e-01 -8.12719643e-01 3.88998389e-01 3.70681256e-01
-5.81612349e-01 8.90225053e-01 7.65288994e-02 -4.76583123e-01
-4.27176625e-01 -7.33588636e-01 -7.97110796e-01 -8.34450364e-01
-3.74048322e-01 1.91228360e-01 -2.48871267e-01 2.03052275... | [5.873630046844482, 6.467391014099121] |
728988f0-326a-4979-b0ea-d7247c53be2c | adversarial-training-for-multi-channel-sign | 2008.12405 | null | https://arxiv.org/abs/2008.12405v1 | https://arxiv.org/pdf/2008.12405v1.pdf | Adversarial Training for Multi-Channel Sign Language Production | Sign Languages are rich multi-channel languages, requiring articulation of both manual (hands) and non-manual (face and body) features in a precise, intricate manner. Sign Language Production (SLP), the automatic translation from spoken to sign languages, must embody this full sign morphology to be truly understandable... | ['Ben Saunders', 'Richard Bowden', 'Necati Cihan Camgoz'] | 2020-08-27 | null | null | null | null | ['sign-language-production'] | ['natural-language-processing'] | [ 5.84747791e-01 2.77963459e-01 1.18790060e-01 -3.98474008e-01
-1.17064393e+00 -1.01414049e+00 9.52606559e-01 -1.08268893e+00
-1.83547065e-01 5.66638052e-01 5.42253673e-01 -2.24144801e-01
3.18237007e-01 -4.03379023e-01 -9.25756216e-01 -6.48313224e-01
5.91089129e-02 5.23892701e-01 -4.74953562e-01 -3.52276832... | [9.216592788696289, -6.540419101715088] |
c733eaa4-108a-4cd6-b1ff-805f6f916c16 | unrealtext-synthesizing-realistic-scene-text | 2003.10608 | null | https://arxiv.org/abs/2003.10608v6 | https://arxiv.org/pdf/2003.10608v6.pdf | UnrealText: Synthesizing Realistic Scene Text Images from the Unreal World | Synthetic data has been a critical tool for training scene text detection and recognition models. On the one hand, synthetic word images have proven to be a successful substitute for real images in training scene text recognizers. On the other hand, however, scene text detectors still heavily rely on a large amount of ... | ['Shangbang Long', 'Cong Yao'] | 2020-03-24 | unrealtext-synthesizing-realistic-scene-text-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Long_UnrealText_Synthesizing_Realistic_Scene_Text_Images_From_the_Unreal_World_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Long_UnrealText_Synthesizing_Realistic_Scene_Text_Images_From_the_Unreal_World_CVPR_2020_paper.pdf | cvpr-2020-6 | ['scene-text-detection'] | ['computer-vision'] | [ 2.78331190e-01 -4.09584612e-01 1.55171901e-01 -5.03141642e-01
-7.86171019e-01 -6.25758469e-01 8.64771724e-01 -4.15878817e-02
-3.93084913e-01 2.18585819e-01 -2.68266685e-02 -2.93260545e-01
7.69509017e-01 -7.46516109e-01 -7.97543049e-01 -5.21830916e-01
8.19543004e-01 6.32906556e-01 4.73888099e-01 -1.98993653... | [11.993362426757812, 2.207533836364746] |
d079598b-323f-4063-a76d-c54b76b51634 | can-chatgpt-defend-the-truth-automatic | 2305.13160 | null | https://arxiv.org/abs/2305.13160v1 | https://arxiv.org/pdf/2305.13160v1.pdf | Can ChatGPT Defend the Truth? Automatic Dialectical Evaluation Elicits LLMs' Deficiencies in Reasoning | We explore testing the reasoning ability of large language models (LLMs), such as ChatGPT, by engaging with them in a debate-like conversation that probes deeper into their understanding of the subject. Specifically, we formulate a new task where given a question, the LLM can generate a correct solution while the user ... | ['Huan Sun', 'Xiang Yue', 'Boshi Wang'] | 2023-05-22 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 2.28817359e-01 1.01347005e+00 3.33903432e-01 -2.82810211e-01
-1.06389511e+00 -1.02639854e+00 6.93899035e-01 4.10077423e-01
-1.14171892e-01 7.18805373e-01 2.57973671e-01 -1.19781971e+00
1.03895381e-01 -9.56834376e-01 -7.06425846e-01 -4.25346456e-02
4.98969615e-01 7.32848048e-01 1.71593487e-01 -5.12690842... | [9.415384292602539, 7.213832855224609] |
366373a5-60b5-4c60-930c-c8474c4ad415 | masc-multi-scale-affinity-with-sparse | 1902.04478 | null | http://arxiv.org/abs/1902.04478v1 | http://arxiv.org/pdf/1902.04478v1.pdf | MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation | We propose a new approach for 3D instance segmentation based on sparse
convolution and point affinity prediction, which indicates the likelihood of
two points belonging to the same instance. The proposed network, built upon
submanifold sparse convolution [3], processes a voxelized point cloud and
predicts semantic scor... | ['Yasutaka Furukawa', 'Chen Liu'] | 2019-02-12 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [-3.86174135e-02 2.81598389e-01 -1.45253539e-01 -5.19406736e-01
-6.74268544e-01 -3.18511784e-01 4.03276235e-01 1.14492029e-01
-2.09329173e-01 3.92307103e-01 -1.42977700e-01 1.25570118e-01
-1.65954828e-01 -1.03036129e+00 -1.11113429e+00 -4.92350757e-01
-2.99378335e-01 1.27391481e+00 4.60272282e-01 3.16458225... | [8.167673110961914, -3.256761312484741] |
8628b0a0-e50e-4c69-afc1-ef949be80591 | subgroup-discovery-in-unstructured-data | 2207.07781 | null | https://arxiv.org/abs/2207.07781v1 | https://arxiv.org/pdf/2207.07781v1.pdf | Subgroup Discovery in Unstructured Data | Subgroup discovery is a descriptive and exploratory data mining technique to identify subgroups in a population that exhibit interesting behavior with respect to a variable of interest. Subgroup discovery has numerous applications in knowledge discovery and hypothesis generation, yet it remains inapplicable for unstruc... | ['Martin Ester', 'Jialin Lu', 'Dev Arora', 'Ali Arab'] | 2022-07-15 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 2.22240537e-01 4.32464510e-01 -3.74886483e-01 -4.50938106e-01
-1.16828874e-01 -2.09064350e-01 3.93193394e-01 6.53264701e-01
-9.47439298e-02 8.51995945e-01 3.39493573e-01 -1.28199056e-01
-7.42198944e-01 -1.15382397e+00 -6.95582271e-01 -1.06151843e+00
-4.31792259e-01 8.97984147e-01 -6.97314367e-02 -3.80651057... | [7.849210262298584, 4.777099609375] |
56992c95-0743-4e69-ac66-a544e8a11ee8 | mac-po-multi-agent-experience-replay-via | 2302.10418 | null | https://arxiv.org/abs/2302.10418v2 | https://arxiv.org/pdf/2302.10418v2.pdf | MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization | Experience replay is crucial for off-policy reinforcement learning (RL) methods. By remembering and reusing the experiences from past different policies, experience replay significantly improves the training efficiency and stability of RL algorithms. Many decision-making problems in practice naturally involve multiple ... | ['Peng Wei', 'Guru Venkataramani', 'Tian Lan', 'Hanhan Zhou', 'Yongsheng Mei'] | 2023-02-21 | null | null | null | null | ['starcraft'] | ['playing-games'] | [-2.33047366e-01 -1.66954219e-01 -6.94143534e-01 1.10610142e-01
-8.14679921e-01 -4.83544946e-01 6.00968778e-01 3.02670926e-01
-1.01708555e+00 1.25357842e+00 1.93196297e-01 -2.89575934e-01
-2.43639767e-01 -5.44849098e-01 -7.56852090e-01 -8.92340660e-01
-5.78946173e-01 5.89239478e-01 4.34317254e-02 -4.05441761... | [3.902498483657837, 2.0250329971313477] |
18aeb6b4-ff44-4d2d-905a-ec491f2893e3 | real-time-super-resolution-system-of-4k-video | 2107.05307 | null | https://arxiv.org/abs/2107.05307v2 | https://arxiv.org/pdf/2107.05307v2.pdf | Real-Time Super-Resolution System of 4K-Video Based on Deep Learning | Video super-resolution (VSR) technology excels in reconstructing low-quality video, avoiding unpleasant blur effect caused by interpolation-based algorithms. However, vast computation complexity and memory occupation hampers the edge of deplorability and the runtime inference in real-life applications, especially for l... | ['He Li', 'Yongming Tang', 'Changjun Song', 'Chengcheng Wang', 'Yanpeng Cao'] | 2021-07-12 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 5.36593199e-02 -4.32257235e-01 -5.35107777e-02 -1.19337045e-01
-5.73786616e-01 -3.89542818e-01 2.10385069e-01 -5.68498611e-01
-4.63705391e-01 5.74214160e-01 -6.90137371e-02 -5.43667078e-01
-1.32783726e-01 -8.34382176e-01 -7.74310589e-01 -5.58381200e-01
-2.82865670e-02 -3.07962179e-01 4.12824631e-01 -3.79008532... | [11.004667282104492, -1.8960858583450317] |
b88d2e3d-9437-405c-bacc-f6e6b5f6f5dc | real-time-ground-fault-detection-for-inverter | 2304.12445 | null | https://arxiv.org/abs/2304.12445v1 | https://arxiv.org/pdf/2304.12445v1.pdf | Real-Time Ground Fault Detection for Inverter-Based Microgrid Systems | Ground fault detection in inverter-based microgrid systems is challenging, particularly in a real-time setting, as the fault current deviates slightly from the nominal value. This difficulty is reinforced when natural disturbances exhibit similar output patterns as a faulty setting does. The conventional solution of in... | ['Peyman Mohajerin Esfahani', 'Yucheng Liao', 'Jingwei Dong'] | 2023-04-24 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-1.92052443e-02 4.56635356e-02 8.00149515e-02 5.50281033e-02
-7.74424374e-01 -7.78446496e-01 1.02326035e-01 1.87167838e-01
2.04963520e-01 9.45829928e-01 -3.21662843e-01 -3.35316569e-01
-4.68339741e-01 -6.62631869e-01 -7.32075334e-01 -1.10658324e+00
-2.03794867e-01 -9.00340732e-03 -1.38653040e-01 -6.30617887... | [5.621012210845947, 2.577685594558716] |
a16f9245-a1ba-4960-9668-35feacc245cd | an-investigation-on-selecting-audio-pre | 2208.06127 | null | https://arxiv.org/abs/2208.06127v1 | https://arxiv.org/pdf/2208.06127v1.pdf | An investigation on selecting audio pre-trained models for audio captioning | Audio captioning is a task that generates description of audio based on content. Pre-trained models are widely used in audio captioning due to high complexity. Unless a comprehensive system is re-trained, it is hard to determine how well pre-trained models contribute to audio captioning system. To prevent the time cons... | ['Shengchen Li', 'Peiran Yan'] | 2022-08-12 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 2.34968677e-01 9.15898606e-02 2.71975875e-01 -3.03022921e-01
-1.06428981e+00 -5.62339127e-01 2.11501881e-01 1.77855775e-01
-1.06750511e-01 6.52422190e-01 6.00538552e-01 1.59734592e-01
1.74746998e-02 -2.23678112e-01 -7.43935108e-01 -3.93904537e-01
-2.17508927e-01 3.57446253e-01 6.45076707e-02 -1.20346583... | [15.288854598999023, 4.887353897094727] |
4fc7163d-7ab2-4e96-a6e5-bf1718a50089 | how-different-are-pre-trained-transformers | 2204.07233 | null | https://arxiv.org/abs/2204.07233v1 | https://arxiv.org/pdf/2204.07233v1.pdf | How Different are Pre-trained Transformers for Text Ranking? | In recent years, large pre-trained transformers have led to substantial gains in performance over traditional retrieval models and feedback approaches. However, these results are primarily based on the MS Marco/TREC Deep Learning Track setup, with its very particular setup, and our understanding of why and how these mo... | ['Jaap Kamps', 'David Rau'] | 2022-04-05 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [ 4.10852283e-02 -7.85228238e-02 -1.70723230e-01 -2.53934413e-01
-1.18546903e+00 -6.14684165e-01 8.80060375e-01 5.50735474e-01
-8.23814929e-01 6.91806734e-01 8.70584846e-01 -4.52426046e-01
-8.84007156e-01 -5.97391307e-01 -6.41937613e-01 -5.32918036e-01
-4.45709825e-01 8.03084314e-01 4.03962582e-01 -7.38116980... | [11.455028533935547, 7.614954948425293] |
a24abc04-5f7a-4593-82d4-37d688cb1f81 | monitoring-machine-learning-ml-based-risk | 2211.09781 | null | https://arxiv.org/abs/2211.09781v2 | https://arxiv.org/pdf/2211.09781v2.pdf | Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions | Performance monitoring of machine learning (ML)-based risk prediction models in healthcare is complicated by the issue of confounding medical interventions (CMI): when an algorithm predicts a patient to be at high risk for an adverse event, clinicians are more likely to administer prophylactic treatment and alter the v... | ['Romain Pirracchio', 'Berkman Sahiner', 'Nicholas Petrick', 'Gene Pennello', 'Alexej Gossmann', 'Jean Feng'] | 2022-11-17 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 4.46742326e-01 4.09131289e-01 -5.81804335e-01 -1.68817177e-01
-4.18595433e-01 -4.60998356e-01 1.49299875e-01 9.85268414e-01
-5.54200709e-01 7.67047524e-01 1.69349581e-01 -9.05567467e-01
-4.91259038e-01 -6.56387568e-01 -7.56075382e-01 -7.35263050e-01
-4.97704834e-01 6.92505598e-01 -1.80757985e-01 4.26448345... | [8.027194023132324, 5.379754066467285] |
b394871a-c6f2-4b31-b258-622a679b0d3e | mcmia-model-compression-against-membership | 2008.13578 | null | https://arxiv.org/abs/2008.13578v4 | https://arxiv.org/pdf/2008.13578v4.pdf | Against Membership Inference Attack: Pruning is All You Need | The large model size, high computational operations, and vulnerability against membership inference attack (MIA) have impeded deep learning or deep neural networks (DNNs) popularity, especially on mobile devices. To address the challenge, we envision that the weight pruning technique will help DNNs against MIA while re... | ['Caiwen Ding', 'Jinbo Bi', 'Hang Liu', 'Yijue Wang', 'Zigeng Wang', 'Shanglin Zhou', 'Sanguthevar Rajasekaran', 'Chenghong Wang'] | 2020-08-28 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 9.03886929e-02 3.92555207e-01 -4.58977610e-01 -1.51759967e-01
-1.35345981e-01 -5.67667186e-01 6.27200752e-02 -1.44293025e-01
-7.23154426e-01 7.47915506e-01 -3.65716070e-01 -1.10285497e+00
-1.96652070e-01 -1.14411139e+00 -9.48238969e-01 -3.94920468e-01
-1.61155552e-01 -1.74823701e-02 3.60352337e-01 1.18160598... | [5.878729343414307, 7.054177761077881] |
a87a0b5c-db9e-4b64-94f9-f1d042899c46 | distantly-supervised-road-segmentation | 1708.06118 | null | http://arxiv.org/abs/1708.06118v1 | http://arxiv.org/pdf/1708.06118v1.pdf | Distantly Supervised Road Segmentation | We present an approach for road segmentation that only requires image-level
annotations at training time. We leverage distant supervision, which allows us
to train our model using images that are different from the target domain.
Using large publicly available image databases as distant supervisors, we
develop a simple... | ['Satoshi Tsutsui', 'Tommi Kerola', 'Shunta Saito'] | 2017-08-21 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 4.31646883e-01 5.94502449e-01 -1.26710802e-01 -5.82016408e-01
-1.12794769e+00 -8.91083598e-01 5.65238416e-01 -1.11372940e-01
-6.93285108e-01 5.52803874e-01 -9.84992310e-02 -5.06429553e-01
4.42866355e-01 -8.84155869e-01 -1.09427047e+00 -2.77797759e-01
1.49906531e-01 5.31111479e-01 7.48403788e-01 2.01093704... | [9.5100736618042, 0.4623180031776428] |
bf794136-7787-4073-905d-ced95f64a8b3 | a-hybrid-morphological-disambiguation-system | null | null | https://aclanthology.org/I13-1175 | https://aclanthology.org/I13-1175.pdf | A Hybrid Morphological Disambiguation System for Turkish | null | ['Ilyas Cicekli', 'Mucahid Kutlu'] | 2013-10-01 | a-hybrid-morphological-disambiguation-system-1 | https://aclanthology.org/I13-1175 | https://aclanthology.org/I13-1175.pdf | ijcnlp-2013-10 | ['morphological-disambiguation'] | ['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... | [-7.343428611755371, 3.710782289505005] |
c8af8824-1015-4c02-a55b-dce6531a4d2a | large-scale-autonomous-flight-with-real-time | 2109.06479 | null | https://arxiv.org/abs/2109.06479v4 | https://arxiv.org/pdf/2109.06479v4.pdf | Large-scale Autonomous Flight with Real-time Semantic SLAM under Dense Forest Canopy | Semantic maps represent the environment using a set of semantically meaningful objects. This representation is storage-efficient, less ambiguous, and more informative, thus facilitating large-scale autonomy and the acquisition of actionable information in highly unstructured, GPS-denied environments. In this letter, we... | ['Vijay Kumar', 'Camillo J. Taylor', 'Roseli A. F. Romero', 'Steven W. Chen', 'Chao Qu', 'Thomas Donnelly', 'Alex Zhou', 'Yuezhan Tao', 'Fernando Cladera Ojeda', 'Guilherme V. Nardari', 'Xu Liu'] | 2021-09-14 | null | null | null | null | ['semantic-slam'] | ['computer-vision'] | [-2.09492266e-01 -4.43194695e-02 -4.15790975e-02 -2.98962355e-01
-9.68282893e-02 -1.01642597e+00 4.69233207e-02 3.26010883e-01
-1.37828052e-01 9.85472143e-01 -5.74192286e-01 -9.48678851e-02
-5.98255932e-01 -1.26824868e+00 -6.44845188e-01 -3.74066383e-01
-3.77189845e-01 1.05321038e+00 6.34514809e-01 -5.05188525... | [7.2968549728393555, -1.997840404510498] |
48c3468e-9d91-4511-9b72-ec631b121027 | introduction-to-protein-folding | 2307.02174 | null | https://arxiv.org/abs/2307.02174v2 | https://arxiv.org/pdf/2307.02174v2.pdf | Introduction to Protein Folding | While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previo... | ['Sanne Abeln', 'K. Anton Feenstra', 'Isabel Houtkamp', 'Annika Jacobsen', 'Jochem Bijlard', 'Halima Mouhib', 'Ali May', 'Erik van Dijk', 'Juami H. M. van Gils'] | 2023-07-05 | null | null | null | null | ['protein-structure-prediction', 'protein-folding'] | ['miscellaneous', 'natural-language-processing'] | [ 2.13790908e-01 -1.39869690e-01 -2.38785103e-01 -3.99577707e-01
-1.17974356e-03 -6.31459951e-01 -2.07364336e-01 4.16248888e-01
-1.36325642e-01 1.22804368e+00 -6.95173889e-02 -7.55953610e-01
2.77331620e-01 -3.36157918e-01 -7.51769066e-01 -1.29342639e+00
-3.10430467e-01 2.96352357e-01 1.32837920e-02 -5.29344201... | [4.734662055969238, 5.285408973693848] |
545eef46-42c6-45a8-935d-a9654089bae3 | faceformer-speech-driven-3d-facial-animation | 2112.05329 | null | https://arxiv.org/abs/2112.05329v4 | https://arxiv.org/pdf/2112.05329v4.pdf | FaceFormer: Speech-Driven 3D Facial Animation with Transformers | Speech-driven 3D facial animation is challenging due to the complex geometry of human faces and the limited availability of 3D audio-visual data. Prior works typically focus on learning phoneme-level features of short audio windows with limited context, occasionally resulting in inaccurate lip movements. To tackle this... | ['Taku Komura', 'Wenping Wang', 'Jun Saito', 'Zhaojiang Lin', 'Yingruo Fan'] | 2021-12-10 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Fan_FaceFormer_Speech-Driven_3D_Facial_Animation_With_Transformers_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Fan_FaceFormer_Speech-Driven_3D_Facial_Animation_With_Transformers_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-face-animation'] | ['computer-vision'] | [ 2.08523959e-01 2.40418807e-01 -3.10944077e-02 -3.47359955e-01
-8.78030539e-01 -2.07983050e-02 5.74815571e-01 -5.77320755e-01
7.98028484e-02 4.15859073e-01 6.69694304e-01 1.09103270e-01
7.89964795e-02 -3.96674573e-01 -6.87368631e-01 -6.70971930e-01
-2.21935734e-01 3.17121476e-01 -7.50942379e-02 -1.31763980... | [13.202736854553223, -0.40648630261421204] |
2268a0e0-6af8-4d4b-97c3-19ab9d325de1 | 190503445 | 1905.03445 | null | https://arxiv.org/abs/1905.03445v1 | https://arxiv.org/pdf/1905.03445v1.pdf | Two-Stage Convolutional Neural Network Architecture for Lung Nodule Detection | Early detection of lung cancer is an effective way to improve the survival rate of patients. It is a critical step to have accurate detection of lung nodules in computed tomography (CT) images for the diagnosis of lung cancer. However, due to the heterogeneity of the lung nodules and the complexity of the surrounding e... | ['Chih-Cheng Hung', 'Enmin Song', 'Xiangyang Xu', 'Tengying Liu', 'Renchao Jin', 'Hong Liu', 'Haichao Cao', 'Guangzhi Ma'] | 2019-05-09 | null | null | null | null | ['unet-segmentation', 'lung-nodule-detection'] | ['computer-vision', 'medical'] | [ 2.32721061e-01 3.19720618e-02 -8.89762193e-02 6.65318817e-02
-3.16399604e-01 1.25018835e-01 2.84054816e-01 -1.42127812e-01
-5.22585571e-01 2.92619407e-01 -2.66084105e-01 -3.42727691e-01
-1.18771471e-01 -1.08945465e+00 -2.95872182e-01 -8.87700021e-01
1.28268734e-01 4.21091527e-01 7.93585598e-01 3.05496380... | [15.353765487670898, -2.1170554161071777] |
6e51cbe5-cb12-4d95-bf45-b79852ff5096 | multi-mention-learning-for-reading | 1711.00894 | null | http://arxiv.org/abs/1711.00894v2 | http://arxiv.org/pdf/1711.00894v2.pdf | Multi-Mention Learning for Reading Comprehension with Neural Cascades | Reading comprehension is a challenging task, especially when executed across
longer or across multiple evidence documents, where the answer is likely to
reoccur. Existing neural architectures typically do not scale to the entire
evidence, and hence, resort to selecting a single passage in the document
(either via trunc... | ['Tom Kwiatkowski', 'Swabha Swayamdipta', 'Ankur P. Parikh'] | 2017-11-02 | multi-mention-learning-for-reading-1 | https://openreview.net/forum?id=HyRnez-RW | https://openreview.net/pdf?id=HyRnez-RW | iclr-2018-1 | ['triviaqa'] | ['miscellaneous'] | [ 1.11086309e-01 8.78067762e-02 -3.23502533e-02 -1.03401177e-01
-1.30734742e+00 -8.87567759e-01 5.00720978e-01 7.51937270e-01
-6.18298352e-01 5.85979879e-01 5.92206240e-01 -6.65715516e-01
-2.47196078e-01 -7.85560369e-01 -9.89276648e-01 -2.00161859e-01
2.58679479e-01 5.19428253e-01 4.00657952e-01 -2.29184642... | [11.200199127197266, 8.03711986541748] |
ece4c0b9-0f71-4acc-8bb0-efc0bd004c2e | comparative-studies-of-detecting-abusive | 1808.10245 | null | http://arxiv.org/abs/1808.10245v1 | http://arxiv.org/pdf/1808.10245v1.pdf | Comparative Studies of Detecting Abusive Language on Twitter | The context-dependent nature of online aggression makes annotating large
collections of data extremely difficult. Previously studied datasets in abusive
language detection have been insufficient in size to efficiently train deep
learning models. Recently, Hate and Abusive Speech on Twitter, a dataset much
greater in si... | ['Seunghyun Yoon', 'Younghun Lee', 'Kyomin Jung'] | 2018-08-30 | comparative-studies-of-detecting-abusive-1 | https://aclanthology.org/W18-5113 | https://aclanthology.org/W18-5113.pdf | ws-2018-10 | ['twitter-sentiment-analysis', 'abuse-detection'] | ['natural-language-processing', 'natural-language-processing'] | [-3.64216775e-01 -1.24377951e-01 -3.22955519e-01 -3.71901333e-01
-5.02880156e-01 -3.47613424e-01 6.93450987e-01 3.48792017e-01
-6.12256467e-01 7.86069930e-01 3.91027153e-01 -8.57135355e-02
-4.22138274e-02 -4.83487129e-01 -1.17890969e-01 -5.25991917e-01
-9.74146873e-02 4.64956373e-01 1.09245300e-01 -4.71123099... | [8.788386344909668, 10.545029640197754] |
a584c4d9-2f3a-48a5-b168-fefd05eeae38 | distributed-stochastic-optimization-under-a | 2301.12677 | null | https://arxiv.org/abs/2301.12677v2 | https://arxiv.org/pdf/2301.12677v2.pdf | Distributed Stochastic Optimization under a General Variance Condition | Distributed stochastic optimization has drawn great attention recently due to its effectiveness in solving large-scale machine learning problems. Though numerous algorithms have been proposed and successfully applied to general practical problems, their theoretical guarantees mainly rely on certain boundedness conditio... | ['Shi Pu', 'Xiao Li', 'Kun Huang'] | 2023-01-30 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-1.22772448e-01 -1.89093798e-01 -1.68051153e-01 -2.44145960e-01
-1.02228785e+00 -4.41222221e-01 1.36553869e-01 3.04508865e-01
-5.53671002e-01 1.01872873e+00 8.27537850e-02 8.19570869e-02
-5.88095486e-01 -4.07338887e-01 -7.22487748e-01 -1.28615165e+00
-1.85418099e-01 3.07649285e-01 -1.25392184e-01 7.88629800... | [6.3261399269104, 4.817956447601318] |
e4ecc74b-3ab0-4b56-a565-1f343a07024a | composer-creative-and-controllable-image | 2302.09778 | null | https://arxiv.org/abs/2302.09778v2 | https://arxiv.org/pdf/2302.09778v2.pdf | Composer: Creative and Controllable Image Synthesis with Composable Conditions | Recent large-scale generative models learned on big data are capable of synthesizing incredible images yet suffer from limited controllability. This work offers a new generation paradigm that allows flexible control of the output image, such as spatial layout and palette, while maintaining the synthesis quality and mod... | ['Jingren Zhou', 'Deli Zhao', 'Yujun Shen', 'Yu Liu', 'Di Chen', 'Lianghua Huang'] | 2023-02-20 | null | null | null | null | ['pose-transfer', 'virtual-try-on'] | ['computer-vision', 'computer-vision'] | [ 8.00305977e-02 6.96306750e-02 -8.82931948e-02 -3.16723399e-02
-2.12677374e-01 -7.95702755e-01 7.98913777e-01 -3.04557323e-01
1.24836817e-01 4.54358846e-01 1.69355765e-01 2.29076780e-02
-9.68310013e-02 -1.23344707e+00 -8.36531997e-01 -8.36331189e-01
3.28464061e-01 3.55471522e-01 2.23549291e-01 -4.36516762... | [11.429956436157227, -0.3955236077308655] |
0158a38d-3cd7-4145-b29e-15850dd7ed78 | dlolis-a-description-logic-based-text | 1303.5929 | null | http://arxiv.org/abs/1303.5929v1 | http://arxiv.org/pdf/1303.5929v1.pdf | DLOLIS-A: Description Logic based Text Ontology Learning | Ontology Learning has been the subject of intensive study for the past
decade. Researchers in this field have been motivated by the possibility of
automatically building a knowledge base on top of text documents so as to
support reasoning based knowledge extraction. While most works in this field
have been primarily st... | ['Rupali KaPatel', 'Ankur Padia', 'Sourish Dasgupta', 'Prasenjit Majumder', 'Kushal Shah'] | 2013-03-24 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 1.44849360e-01 8.66983771e-01 -2.33868450e-01 -6.43048942e-01
-2.82352120e-01 -3.48200232e-01 8.48599672e-01 5.09749651e-01
-4.71409112e-01 8.78512800e-01 4.86571968e-01 -8.63183379e-01
-9.01967406e-01 -1.00865805e+00 -5.41584253e-01 6.75792340e-03
3.86172719e-02 8.39004457e-01 5.58986366e-01 -6.16068244... | [9.77171802520752, 8.726953506469727] |
aed133b8-1a2d-47dd-b22f-9234428624dd | a-conglomerate-of-multiple-ocr-table | 2010.08591 | null | https://arxiv.org/abs/2010.08591v1 | https://arxiv.org/pdf/2010.08591v1.pdf | A Conglomerate of Multiple OCR Table Detection and Extraction | Information representation as tables are compact and concise method that eases searching, indexing, and storage requirements. Extracting and cloning tables from parsable documents is easier and widely used, however industry still faces challenge in detecting and extracting tables from OCR documents or images. This pape... | ['Sumit Kumar', 'Raj Ratn Pranesh', 'Smita Pallavi'] | 2020-10-16 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [ 5.18975854e-01 -5.24938881e-01 -1.43326238e-01 -1.31848127e-01
-4.08517540e-01 -1.20770359e+00 1.37985483e-01 8.26394916e-01
-1.83998123e-01 6.68160081e-01 1.79270163e-01 -4.74637091e-01
-3.18641096e-01 -8.59499753e-01 -3.51222306e-01 8.02988857e-02
3.43436688e-01 5.38333356e-01 4.24328357e-01 1.50210429... | [11.755926132202148, 2.832470655441284] |
fc7058a7-15c2-4167-8d6a-0e3d14bf4903 | multi-label-video-classification-for | 2305.17338 | null | https://arxiv.org/abs/2305.17338v1 | https://arxiv.org/pdf/2305.17338v1.pdf | Multi-label Video Classification for Underwater Ship Inspection | Today ship hull inspection including the examination of the external coating, detection of defects, and other types of external degradation such as corrosion and marine growth is conducted underwater by means of Remotely Operated Vehicles (ROVs). The inspection process consists of a manual video analysis which is a tim... | ['Martin Ludvigsen', 'Brian Elvesæter', 'Maryna Waszak', 'Ahmed Mohammed', 'Md Abulkalam Azad'] | 2023-05-27 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [ 1.89930394e-01 -3.88792902e-01 6.38927996e-01 -3.29818219e-01
-6.44329548e-01 -4.17851120e-01 -4.32487056e-02 1.80615723e-01
-6.15849376e-01 2.77646095e-01 -5.34296259e-02 -2.02998862e-01
-6.81053698e-02 -6.86323285e-01 -7.05091834e-01 -1.06105781e+00
-2.38531560e-01 -1.18087828e-01 5.86267114e-01 -2.92661369... | [10.676136016845703, -3.50374698638916] |
614e0352-2f08-483c-b7d4-976af2ffdf2b | learning-a-representation-for-cover-song | 1911.00334 | null | https://arxiv.org/abs/1911.00334v1 | https://arxiv.org/pdf/1911.00334v1.pdf | Learning a Representation for Cover Song Identification Using Convolutional Neural Network | Cover song identification represents a challenging task in the field of Music Information Retrieval (MIR) due to complex musical variations between query tracks and cover versions. Previous works typically utilize hand-crafted features and alignment algorithms for the task. More recently, further breakthroughs are achi... | ['Xiaoou Chen', 'Xiaoshuo Xu', 'Zhesong Yu', 'Deshun Yang'] | 2019-11-01 | learning-a-representation-for-cover-song-1 | null | null | arxiv-2019-11 | ['cover-song-identification'] | ['music'] | [ 4.87583369e-01 -7.02670515e-01 -3.79326791e-01 7.23320991e-02
-1.01503932e+00 -6.71615422e-01 2.65232652e-01 -2.93781370e-01
-3.63749325e-01 3.13747495e-01 1.09927215e-01 2.20836535e-01
-3.02874863e-01 -6.14316583e-01 -6.39557838e-01 -5.50515890e-01
-9.08079222e-02 2.51006275e-01 -2.60396630e-01 -3.06987166... | [15.77473258972168, 5.214552402496338] |
40ce52ac-4b2d-46c4-b570-f4815f4e3c1e | lightdefectnet-a-highly-compact-deep-anti | 2204.11765 | null | https://arxiv.org/abs/2204.11765v1 | https://arxiv.org/pdf/2204.11765v1.pdf | LightDefectNet: A Highly Compact Deep Anti-Aliased Attention Condenser Neural Network Architecture for Light Guide Plate Surface Defect Detection | Light guide plates are essential optical components widely used in a diverse range of applications ranging from medical lighting fixtures to back-lit TV displays. An essential step in the manufacturing of light guide plates is the quality inspection of defects such as scratches, bright/dark spots, and impurities. This ... | ['Alexander Wong', 'Mohammad Javad Shafiee', 'Gautam Bathla', 'Mahmoud Famouri', 'Carol Xu'] | 2022-04-25 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 4.04781073e-01 -1.58324063e-01 3.07424754e-01 2.41997223e-02
-4.52146947e-01 -8.98097977e-02 -2.46312559e-01 3.28486226e-02
-2.98875690e-01 2.03498036e-01 -8.55055749e-01 -5.77662349e-01
-3.02181751e-01 -8.32438052e-01 -6.18092537e-01 -7.59095430e-01
2.31881827e-01 2.90943235e-01 2.50390708e-01 -3.35269243... | [7.425858974456787, 1.8738305568695068] |
d0940a9b-256a-4afb-96ca-63e290a7c48e | shapepu-a-new-pu-learning-framework | 2206.02118 | null | https://arxiv.org/abs/2206.02118v1 | https://arxiv.org/pdf/2206.02118v1.pdf | ShapePU: A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentation | Cardiac segmentation is an essential step for the diagnosis of cardiovascular diseases. However, pixel-wise dense labeling is both costly and time-consuming. Scribble, as a form of sparse annotation, is more accessible than full annotations. However, it's particularly challenging to train a segmentation network with we... | ['Xiahai Zhuang', 'Ke Zhang'] | 2022-06-05 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 7.48322085e-02 1.58949912e-01 -4.03443456e-01 -3.98991138e-01
-1.10009551e+00 -6.18781924e-01 8.09599757e-02 2.48922154e-01
-3.19264174e-01 8.90147924e-01 -1.05408020e-01 -2.27913484e-01
2.08807498e-01 -4.45683867e-01 -7.24827230e-01 -1.02119172e+00
3.46645802e-01 5.94554961e-01 2.40645081e-01 5.20959198... | [14.659512519836426, -2.2042324542999268] |
0346b308-eb87-4b58-a382-e7c21a20ddb4 | submodular-maximization-under-fading-model | 1901.07708 | null | https://arxiv.org/abs/1901.07708v4 | https://arxiv.org/pdf/1901.07708v4.pdf | Cascade Submodular Maximization: Question Selection and Sequencing in Online Personality Quiz | Personality quiz is a powerful tool that enables costumer segmentation by actively asking them questions, and marketers are using it as an effective method of generating leads and increasing e-commerce sales. In this paper, we study the problem of how to select and sequence a group of quiz questions so as to optimize t... | ['Shaojie Tang', 'Jing Yuan'] | 2019-01-23 | null | null | null | null | ['question-selection'] | ['natural-language-processing'] | [ 2.46343553e-01 2.60144919e-01 -7.71060705e-01 -5.69098890e-01
-1.00043106e+00 -9.03269947e-01 -3.68952721e-01 4.24718022e-01
-4.83224839e-01 7.08851397e-01 -2.85937302e-02 -3.37377340e-01
-7.21556127e-01 -9.29282665e-01 -6.87327087e-01 -6.42787814e-01
5.50941378e-02 1.15213335e+00 1.20813213e-02 -2.22603768... | [4.652952671051025, 3.345635175704956] |
da686673-96e7-48f0-899e-5e125d692488 | mtab-matching-tabular-data-to-knowledge-graph | 1910.00246 | null | https://arxiv.org/abs/1910.00246v2 | https://arxiv.org/pdf/1910.00246v2.pdf | MTab: Matching Tabular Data to Knowledge Graph using Probability Models | This paper presents the design of our system, namely MTab, for Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2019). MTab combines the voting algorithm and the probability models to solve critical problems of the matching tasks. Results on SemTab 2019 show that MTab obtains promising perform... | ['Phuc Nguyen', 'Ryutaro Ichise', 'Natthawut Kertkeidkachorn', 'Hideaki Takeda'] | 2019-10-01 | null | null | null | null | ['table-annotation', 'table-annotation'] | ['knowledge-base', 'natural-language-processing'] | [-3.31676602e-02 3.36606622e-01 -5.57134807e-01 -4.10850585e-01
-9.18580055e-01 -6.63440228e-01 8.29050779e-01 2.75330693e-01
-6.47520497e-02 8.32176924e-01 6.64631545e-04 -4.78553981e-01
-7.52550423e-01 -1.11661744e+00 -6.33888841e-01 1.96332321e-01
2.67786443e-01 1.39788818e+00 7.35731840e-01 -4.81239319... | [9.260332107543945, 8.021889686584473] |
bb9b73d2-4a97-41f7-af7a-1345c2173918 | splitting-numerical-integration-for-matrix | 2202.06482 | null | https://arxiv.org/abs/2202.06482v1 | https://arxiv.org/pdf/2202.06482v1.pdf | Splitting numerical integration for matrix completion | Low rank matrix approximation is a popular topic in machine learning. In this paper, we propose a new algorithm for this topic by minimizing the least-squares estimation over the Riemannian manifold of fixed-rank matrices. The algorithm is an adaptation of classical gradient descent within the framework of optimization... | ['Qianqian Song'] | 2022-02-14 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-2.01974064e-01 -2.94526387e-02 1.39240980e-01 -6.43656924e-02
-8.55006039e-01 -3.96312833e-01 2.17601120e-01 -5.62865376e-01
-4.23890024e-01 5.79788744e-01 -3.27796750e-02 -1.98624849e-01
-2.69856840e-01 -3.08677733e-01 -7.01915145e-01 -8.33794534e-01
-1.02398708e-01 3.86335254e-02 -1.68058544e-01 -4.17969793... | [7.434655666351318, 4.338878631591797] |
4691ff4a-a2ee-430f-902c-89ef41ab04a2 | face-photo-sketch-recognition-using | 2108.09898 | null | https://arxiv.org/abs/2108.09898v1 | https://arxiv.org/pdf/2108.09898v1.pdf | Face Photo-Sketch Recognition Using Bidirectional Collaborative Synthesis Network | This research features a deep-learning based framework to address the problem of matching a given face sketch image against a face photo database. The problem of photo-sketch matching is challenging because 1) there is large modality gap between photo and sketch, and 2) the number of paired training samples is insuffic... | ['Juneho Yi', 'Hyunkyu Park', 'Nizam Ud Din', 'Seho Bae'] | 2021-08-23 | null | null | null | null | ['sketch-recognition'] | ['computer-vision'] | [ 4.09786224e-01 1.21612169e-01 -2.32987866e-01 -4.26647007e-01
-8.57614756e-01 -5.59875965e-01 1.06283772e+00 -7.47466743e-01
5.38647622e-02 4.33047891e-01 2.57894099e-01 -1.05860069e-01
1.63163200e-01 -7.25813746e-01 -7.48666704e-01 -6.97084129e-01
6.22012198e-01 1.96693331e-01 -2.06054077e-01 1.81862023... | [12.456687927246094, -0.00370511831715703] |
07bbf193-24b6-4922-bd95-8534e1c529e9 | end-to-end-dense-video-captioning-with | 2108.07781 | null | https://arxiv.org/abs/2108.07781v2 | https://arxiv.org/pdf/2108.07781v2.pdf | End-to-End Dense Video Captioning with Parallel Decoding | Dense video captioning aims to generate multiple associated captions with their temporal locations from the video. Previous methods follow a sophisticated "localize-then-describe" scheme, which heavily relies on numerous hand-crafted components. In this paper, we proposed a simple yet effective framework for end-to-end... | ['Ping Luo', 'Ran Cheng', 'Feng Zheng', 'Zhichao Lu', 'Ruimao Zhang', 'Teng Wang'] | 2021-08-17 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wang_End-to-End_Dense_Video_Captioning_With_Parallel_Decoding_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wang_End-to-End_Dense_Video_Captioning_With_Parallel_Decoding_ICCV_2021_paper.pdf | iccv-2021-1 | ['dense-video-captioning'] | ['computer-vision'] | [ 3.09606463e-01 1.09460235e-01 -1.88076630e-01 -2.70199150e-01
-1.09247959e+00 -4.75768536e-01 6.14538133e-01 -7.82444030e-02
-1.58987433e-01 8.34625125e-01 6.61664307e-01 -9.41602290e-02
3.46195847e-01 -3.26682895e-01 -1.11316288e+00 -5.13553381e-01
1.88021332e-01 4.83042926e-01 2.92043984e-01 -2.58394033... | [10.404016494750977, 0.6768828630447388] |
a86a119e-02b8-457e-8d81-758797502e2c | effects-of-real-life-traffic-sign-alteration | 2305.05499 | null | https://arxiv.org/abs/2305.05499v1 | https://arxiv.org/pdf/2305.05499v1.pdf | Effects of Real-Life Traffic Sign Alteration on YOLOv7- an Object Recognition Model | The advancement of Image Processing has led to the widespread use of Object Recognition (OR) models in various applications, such as airport security and mail sorting. These models have become essential in signifying the capabilities of AI and supporting vital services like national postal operations. However, the perf... | ['Edmon Begoli', 'Edward Michaud', 'Md Saif Hassan Onim', 'Shahinul Hoque', 'Farhin Farhad Riya'] | 2023-05-09 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 3.36448461e-01 -4.99590963e-01 2.47167766e-01 -2.37818837e-01
6.29831925e-02 -5.50658584e-01 5.19466639e-01 -1.05539791e-01
-4.78846639e-01 3.45675856e-01 -4.47655797e-01 -6.52187645e-01
-2.74091512e-01 -6.49314582e-01 -5.88340819e-01 -5.35188735e-01
4.62637432e-02 -1.58221796e-01 6.50317490e-01 -2.16153979... | [8.000892639160156, -0.8840978741645813] |
b0d3b7d1-7bf4-4f71-83f9-b0436db17db0 | global-consistent-point-cloud-registration | 2208.07103 | null | https://arxiv.org/abs/2208.07103v1 | https://arxiv.org/pdf/2208.07103v1.pdf | Global Consistent Point Cloud Registration Based on Lie-algebraic Cohomology | We present a novel, effective method for global point cloud registration problems by geometric topology. Based on many point cloud pairwise registration methods (e.g ICP), we focus on the problem of accumulated error for the composition of transformations along any loops. The major technical contribution of this paper ... | ['Xianfeng David Gu', 'Na lei', 'Wei Chen', 'Baowei Jiang', 'Yuxue Ren'] | 2022-08-15 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-5.25988676e-02 -2.84189999e-01 3.15873682e-01 -2.25329369e-01
-7.54828513e-01 -3.90116543e-01 4.46040034e-01 -4.59932089e-02
-3.30641955e-01 6.68531358e-01 -4.16684061e-01 -2.37649884e-02
-2.19164118e-01 -9.38578963e-01 -8.10554802e-01 -7.39796102e-01
2.08906904e-02 9.27143335e-01 3.56645763e-01 -4.45270747... | [7.777622222900391, -2.8320934772491455] |
f25851e1-6302-4c54-a495-4db2b78763a9 | knowing-depth-quality-in-advance-a-depth | 2008.04157 | null | https://arxiv.org/abs/2008.04157v1 | https://arxiv.org/pdf/2008.04157v1.pdf | Knowing Depth Quality In Advance: A Depth Quality Assessment Method For RGB-D Salient Object Detection | Previous RGB-D salient object detection (SOD) methods have widely adopted deep learning tools to automatically strike a trade-off between RGB and D (depth), whose key rationale is to take full advantage of their complementary nature, aiming for a much-improved SOD performance than that of using either of them solely. H... | ['Chenglizhao Chen', 'Xuehao Wang', 'Shuai Li', 'Hong Qin', 'Aimin Hao'] | 2020-08-07 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [-2.98489314e-02 -2.66923517e-01 3.96700986e-02 -2.93746293e-01
-9.78736877e-01 -2.10940987e-01 5.43571174e-01 3.08409274e-01
-4.31523561e-01 6.08652830e-01 -2.21368410e-02 1.48913264e-02
-2.82594353e-01 -9.65253532e-01 -2.78348118e-01 -9.54523742e-01
3.01327974e-01 1.03215367e-01 4.88740116e-01 -3.15159053... | [9.649033546447754, -0.9450000524520874] |
824abfb8-4d0a-4e2b-9013-ab78303e68a1 | improving-aspect-level-sentiment-analysis | 2005.06607 | null | https://arxiv.org/abs/2005.06607v1 | https://arxiv.org/pdf/2005.06607v1.pdf | Improving Aspect-Level Sentiment Analysis with Aspect Extraction | Aspect-based sentiment analysis (ABSA), a popular research area in NLP has two distinct parts -- aspect extraction (AE) and labeling the aspects with sentiment polarity (ALSA). Although distinct, these two tasks are highly correlated. The work primarily hypothesize that transferring knowledge from a pre-trained AE mode... | ['Louis-Philippe Morency', 'Alexander Gelbukh', 'Rishabh Bhardwaj', 'Soujanya Poria', 'Amir Hussain', 'Navonil Majumder', 'Amir Zadeh'] | 2020-05-03 | null | null | null | null | ['aspect-extraction'] | ['natural-language-processing'] | [ 2.0937046e-01 3.9925322e-01 -4.3973339e-01 -7.3242217e-01
-8.2617468e-01 -7.7790403e-01 9.0871698e-01 2.5145060e-01
-3.1573468e-01 4.7411504e-01 6.3300079e-01 -3.0155677e-01
2.5914177e-01 -8.9600968e-01 -5.2720350e-01 -4.2062277e-01
4.5017070e-01 5.5602211e-01 -1.7483285e-01 -3.6838380e-01
2.6154920e-01... | [11.424331665039062, 6.715104103088379] |
c708f98c-d8e5-4d67-9c1f-30124bc3dd2e | nautilus-a-versatile-voice-cloning-system | 2005.11004 | null | https://arxiv.org/abs/2005.11004v2 | https://arxiv.org/pdf/2005.11004v2.pdf | NAUTILUS: a Versatile Voice Cloning System | We introduce a novel speech synthesis system, called NAUTILUS, that can generate speech with a target voice either from a text input or a reference utterance of an arbitrary source speaker. By using a multi-speaker speech corpus to train all requisite encoders and decoders in the initial training stage, our system can ... | ['Hieu-Thi Luong', 'Junichi Yamagishi'] | 2020-05-22 | null | null | null | null | ['voice-cloning'] | ['speech'] | [ 2.00234458e-01 2.21898034e-01 2.91926503e-01 -3.01190257e-01
-5.29193997e-01 -4.84605551e-01 5.53656995e-01 -6.54978871e-01
-2.20055610e-01 5.97908199e-01 5.96496947e-02 -4.70645517e-01
5.27434528e-01 -4.53010827e-01 -7.10440338e-01 -8.44205916e-01
3.91014129e-01 3.94308209e-01 2.32406214e-01 -3.69309276... | [14.931519508361816, 6.626309871673584] |
0935b6aa-a2a2-4366-a6d8-e20ea8702997 | a-novel-clustering-based-algorithm-for | 2110.06996 | null | https://arxiv.org/abs/2110.06996v2 | https://arxiv.org/pdf/2110.06996v2.pdf | A Novel Clustering-Based Algorithm for Continuous and Non-invasive Cuff-Less Blood Pressure Estimation | Extensive research has been performed on continuous, non-invasive, cuffless blood pressure (BP) measurement using artificial intelligence algorithms. This approach involves extracting certain features from physiological signals like ECG, PPG, ICG, BCG, etc. as independent variables and extracting features from Arterial... | ['Elham Akhondzadeh Noughabi', 'Reza Baradaran Kazemzadeh', 'Ali Farki'] | 2021-10-13 | null | null | null | null | ['blood-pressure-estimation'] | ['medical'] | [-3.19310762e-02 -2.54413456e-01 1.02027640e-01 -5.22578061e-01
-8.90618339e-02 -1.05228402e-01 -5.26489206e-02 2.57881850e-01
-3.53462696e-01 9.96923983e-01 6.25932887e-02 -5.66268861e-01
-4.44968164e-01 -7.65917301e-01 -1.08930632e-01 -7.81431019e-01
-4.99908775e-01 3.42159897e-01 -6.80561513e-02 2.14688480... | [14.061084747314453, 2.9777305126190186] |
17f6fed3-1b61-41cf-ad42-ecc025e1ecee | a-novel-enhanced-convolution-neural-network | 2208.02953 | null | https://arxiv.org/abs/2208.02953v1 | https://arxiv.org/pdf/2208.02953v1.pdf | A Novel Enhanced Convolution Neural Network with Extreme Learning Machine: Facial Emotional Recognition in Psychology Practices | Facial emotional recognition is one of the essential tools used by recognition psychology to diagnose patients. Face and facial emotional recognition are areas where machine learning is excelling. Facial Emotion Recognition in an unconstrained environment is an open challenge for digital image processing due to differe... | ['Omar Hisham Alsadoon', 'Tarik A. Rashid', 'Ahmed Dawoud', 'P. W. C. Prasad', 'Abeer Alsadoon', 'Nitesh Banskota'] | 2022-08-05 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [-3.23399156e-01 -3.09556544e-01 1.54514477e-01 -6.38238609e-01
1.57632619e-01 1.46382272e-01 1.20895309e-03 -6.50027215e-01
-6.34977221e-01 3.64141196e-01 -3.34925473e-01 2.26688728e-01
1.36660948e-01 -2.37238139e-01 -1.12229884e-01 -8.89321864e-01
6.50191903e-02 -2.30192363e-01 -6.55190945e-01 -4.16556209... | [13.541625022888184, 1.7801958322525024] |
0c2bdddd-be82-4471-b151-51fa68d418ee | efficient-structured-inference-for-transition | null | null | https://aclanthology.org/Q16-1014 | https://aclanthology.org/Q16-1014.pdf | Efficient Structured Inference for Transition-Based Parsing with Neural Networks and Error States | Transition-based approaches based on local classification are attractive for dependency parsing due to their simplicity and speed, despite producing results slightly below the state-of-the-art. In this paper, we propose a new approach for approximate structured inference for transition-based parsing that produces score... | ['Ashish Vaswani', 'Kenji Sagae'] | 2016-01-01 | null | null | null | tacl-2016-1 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-9.10054669e-02 3.35923433e-01 -4.58227634e-01 -1.16605818e+00
-1.74236953e+00 -4.40800935e-01 9.98872891e-02 5.37620962e-01
-4.62248057e-01 9.63312387e-01 2.62227565e-01 -6.99295521e-01
2.40466923e-01 -7.65924215e-01 -7.53519058e-01 -2.94942468e-01
-5.34421066e-04 7.23713696e-01 5.24142325e-01 -6.90403506... | [10.354787826538086, 9.741865158081055] |
1082fbaf-a6a9-4d77-9342-404e9f6ade81 | looking-through-the-glass-neural-surface | 2304.08706 | null | https://arxiv.org/abs/2304.08706v1 | https://arxiv.org/pdf/2304.08706v1.pdf | Looking Through the Glass: Neural Surface Reconstruction Against High Specular Reflections | Neural implicit methods have achieved high-quality 3D object surfaces under slight specular highlights. However, high specular reflections (HSR) often appear in front of target objects when we capture them through glasses. The complex ambiguity in these scenes violates the multi-view consistency, then makes it challeng... | ['Bo Ren', 'Ming-Ming Cheng', 'Ze-Xin Yin', 'Yifan Zhu', 'Peng-Tao Jiang', 'Jiaxiong Qiu'] | 2023-04-18 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Qiu_Looking_Through_the_Glass_Neural_Surface_Reconstruction_Against_High_Specular_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Qiu_Looking_Through_the_Glass_Neural_Surface_Reconstruction_Against_High_Specular_CVPR_2023_paper.pdf | cvpr-2023-1 | ['neural-rendering'] | ['computer-vision'] | [ 5.57209432e-01 3.55548114e-02 4.71842200e-01 -2.82006174e-01
-6.40815616e-01 -2.25227892e-01 3.13349634e-01 -7.58620560e-01
4.40520883e-01 4.31953967e-01 -4.28701751e-02 3.12383659e-03
1.61172032e-01 -8.38438630e-01 -8.02870393e-01 -7.54336715e-01
3.93923193e-01 2.92269886e-01 4.92877513e-01 -2.32136205... | [9.729333877563477, -3.069593667984009] |
88b59335-d4ff-4c1a-9fdc-cdc5714a0446 | the-big-data-myth-using-diffusion-models-for | 2306.09762 | null | https://arxiv.org/abs/2306.09762v1 | https://arxiv.org/pdf/2306.09762v1.pdf | The Big Data Myth: Using Diffusion Models for Dataset Generation to Train Deep Detection Models | Despite the notable accomplishments of deep object detection models, a major challenge that persists is the requirement for extensive amounts of training data. The process of procuring such real-world data is a laborious undertaking, which has prompted researchers to explore new avenues of research, such as synthetic d... | ['Klaas Dijkstra', 'Maya Aghaei', 'Roy Voetman'] | 2023-06-16 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 5.36970854e-01 2.72140533e-01 1.20833300e-01 -2.20485300e-01
-5.29335439e-01 -7.79377282e-01 9.74721909e-01 2.39316598e-01
-4.72241461e-01 4.63627249e-01 -4.46555465e-01 -1.44383565e-01
2.66093403e-01 -1.01653671e+00 -6.29529715e-01 -7.24964261e-01
8.60734284e-02 4.12132472e-01 6.27667487e-01 -5.41711412... | [8.659360885620117, -0.9733677506446838] |
866c7535-57bc-442f-a1cb-fd50d69cc256 | designing-optimal-behavioral-experiments | 2305.07721 | null | https://arxiv.org/abs/2305.07721v1 | https://arxiv.org/pdf/2305.07721v1.pdf | Designing Optimal Behavioral Experiments Using Machine Learning | Computational models are powerful tools for understanding human cognition and behavior. They let us express our theories clearly and precisely, and offer predictions that can be subtle and often counter-intuitive. However, this same richness and ability to surprise means our scientific intuitions and traditional tools ... | ['Christopher G. Lucas', 'Michael U. Gutmann', 'Peggy Seriès', 'Neil R. Bramley', 'Steven Kleinegesse', 'Simon Valentin'] | 2023-05-12 | null | null | null | null | ['experimental-design'] | ['methodology'] | [-1.42889783e-01 -3.15335393e-01 -5.34811199e-01 -3.92513841e-01
-1.33739397e-01 -5.82936108e-01 5.12815833e-01 3.16895209e-02
-7.34590173e-01 7.04148293e-01 -6.77967668e-02 -8.00260603e-01
-5.80705523e-01 -4.00246054e-01 -5.69495499e-01 -4.44989890e-01
-1.21554077e-01 5.89713037e-01 -2.57911414e-01 -1.03312902... | [4.648215293884277, 3.071427345275879] |
c7380bb9-5f43-458a-a3bd-70063deb3fa2 | diabetic-retinopathy-detection-using-ensemble | 2106.12545 | null | https://arxiv.org/abs/2106.12545v1 | https://arxiv.org/pdf/2106.12545v1.pdf | Diabetic Retinopathy Detection using Ensemble Machine Learning | Diabetic Retinopathy (DR) is among the worlds leading vision loss causes in diabetic patients. DR is a microvascular disease that affects the eye retina, which causes vessel blockage and therefore cuts the main source of nutrition for the retina tissues. Treatment for this visual disorder is most effective when it is d... | ['Mohammad Alauthman', 'Mouhammd Alkasassbeh', 'Israa Odeh'] | 2021-06-22 | null | null | null | null | ['diabetic-retinopathy-detection'] | ['medical'] | [-8.68250907e-04 -1.48237962e-02 -3.21032666e-02 -1.58872098e-01
-3.90663505e-01 -2.20209867e-01 3.64200354e-01 2.02006236e-01
-3.23179960e-01 1.02991021e+00 5.38563579e-02 -2.49135792e-01
-4.81352806e-01 -7.25146770e-01 -4.71292548e-02 -1.02692771e+00
1.20932683e-01 2.65062660e-01 -1.66279897e-02 1.83938235... | [15.839580535888672, -3.995589017868042] |
3bd96c4d-a842-4850-bd08-2da6390fcc06 | t-sciq-teaching-multimodal-chain-of-thought | 2305.03453 | null | https://arxiv.org/abs/2305.03453v2 | https://arxiv.org/pdf/2305.03453v2.pdf | T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Large Language Model Signals for Science Question Answering | Large Language Models (LLMs) have recently demonstrated exceptional performance in various Natural Language Processing (NLP) tasks. They have also shown the ability to perform chain-of-thought (CoT) reasoning to solve complex problems. Recent studies have explored CoT reasoning in complex multimodal scenarios, such as ... | ['Heng Tao Shen', 'Hui Liu', 'Ning Liu', 'Xing Xu', 'Jiabang He', 'Yi Hu', 'Lei Wang'] | 2023-05-05 | null | null | null | null | ['science-question-answering'] | ['miscellaneous'] | [ 2.40183666e-01 1.47268698e-01 1.37295321e-01 -5.25040925e-01
-1.53187752e+00 -6.91535473e-01 4.94052529e-01 4.99819905e-01
-4.00943965e-01 3.75631034e-01 7.91727677e-02 -4.34317857e-01
-2.31125906e-01 -5.81402659e-01 -1.08261526e+00 -4.67197299e-01
5.85996807e-01 7.02531755e-01 1.34135380e-01 -4.12590623... | [11.085721015930176, 7.971562385559082] |
170f3778-d997-428e-9148-39736b7b7ef4 | probing-what-different-nlp-tasks-teach | 1904.11544 | null | https://arxiv.org/abs/1904.11544v2 | https://arxiv.org/pdf/1904.11544v2.pdf | Probing What Different NLP Tasks Teach Machines about Function Word Comprehension | We introduce a set of nine challenge tasks that test for the understanding of function words. These tasks are created by structurally mutating sentences from existing datasets to target the comprehension of specific types of function words (e.g., prepositions, wh-words). Using these probing tasks, we explore the effect... | ['Ellie Pavlick', 'Samuel R. Bowman', 'Patrick Xia', 'Alex Wang', 'Roma Patel', 'Tal Linzen', 'R. Thomas McCoy', 'Najoung Kim', 'Benjamin Van Durme', 'Adam Poliak', 'Ian Tenney', 'Alexis Ross'] | 2019-04-25 | probing-what-different-nlp-tasks-teach-1 | https://aclanthology.org/S19-1026 | https://aclanthology.org/S19-1026.pdf | semeval-2019-6 | ['ccg-supertagging'] | ['natural-language-processing'] | [ 3.37115020e-01 7.53117681e-01 -2.39178762e-01 -7.59035349e-01
-6.86218917e-01 -6.19096637e-01 7.75619209e-01 4.74265605e-01
-6.60091817e-01 6.76322937e-01 1.04169333e+00 -7.05316186e-01
2.29784518e-01 -9.40757036e-01 -1.14877844e+00 -1.62320599e-01
-6.88786134e-02 3.96475673e-01 1.10739149e-01 -5.30416191... | [10.627246856689453, 8.898904800415039] |
b51b4703-1ddf-4f84-bc5b-48b2c1dcef38 | zambezi-voice-a-multilingual-speech-corpus | 2306.04428 | null | https://arxiv.org/abs/2306.04428v2 | https://arxiv.org/pdf/2306.04428v2.pdf | Zambezi Voice: A Multilingual Speech Corpus for Zambian Languages | This work introduces Zambezi Voice, an open-source multilingual speech resource for Zambian languages. It contains two collections of datasets: unlabelled audio recordings of radio news and talk shows programs (160 hours) and labelled data (over 80 hours) consisting of read speech recorded from text sourced from public... | ['Antonios Anastasopoulos', 'Mayumbo Nyirenda', 'David Zulu', 'Martin Phiri', 'Mofya Phiri', 'Bangiwe Zulu', 'Stanly Mwape', 'Kalinda Siaminwe', 'Claytone Sikasote'] | 2023-06-07 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [-1.56809077e-01 1.64734274e-01 -3.18525970e-01 -5.39126396e-01
-1.31031311e+00 -7.62962639e-01 7.01065660e-01 -1.71882957e-01
-4.85082358e-01 5.82938313e-01 7.12515414e-01 -1.04079270e+00
3.94633502e-01 -4.21544045e-01 -6.46481216e-01 -4.59036380e-01
1.02523908e-01 8.27096701e-01 -1.33302465e-01 -2.39066079... | [14.360841751098633, 6.938343048095703] |
fb580a5b-6ef4-4c84-ae4c-5a402a8a049f | fine-grained-video-text-retrieval-with | 2003.00392 | null | https://arxiv.org/abs/2003.00392v1 | https://arxiv.org/pdf/2003.00392v1.pdf | Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning | Cross-modal retrieval between videos and texts has attracted growing attentions due to the rapid emergence of videos on the web. The current dominant approach for this problem is to learn a joint embedding space to measure cross-modal similarities. However, simple joint embeddings are insufficient to represent complica... | ['Shizhe Chen', 'Qi Wu', 'Yida Zhao', 'Qin Jin'] | 2020-03-01 | fine-grained-video-text-retrieval-with-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Fine-Grained_Video-Text_Retrieval_With_Hierarchical_Graph_Reasoning_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_Fine-Grained_Video-Text_Retrieval_With_Hierarchical_Graph_Reasoning_CVPR_2020_paper.pdf | cvpr-2020-6 | ['video-text-retrieval'] | ['computer-vision'] | [-2.19856098e-01 -4.90424693e-01 -2.97641158e-01 -1.78445339e-01
-8.77201498e-01 -6.68544471e-01 8.60105932e-01 4.73563969e-01
-1.20198347e-01 5.74768381e-03 8.44599545e-01 2.18478963e-01
-2.28889734e-01 -5.55911124e-01 -4.12446856e-01 -5.72215915e-01
9.57841948e-02 1.81008235e-01 3.05447966e-01 7.23247752... | [10.423310279846191, 1.0558546781539917] |
45099667-8816-4852-8eb8-af6e1c751059 | dm_nlp-at-semeval-2018-task-12-a-pipeline | null | null | https://aclanthology.org/S19-2156 | https://aclanthology.org/S19-2156.pdf | DM\_NLP at SemEval-2018 Task 12: A Pipeline System for Toponym Resolution | This paper describes DM-NLP{'}s system for toponym resolution task at Semeval 2019. Our system was developed for toponym detection, disambiguation and end-to-end resolution which is a pipeline of the former two. For toponym detection, we utilized the state-of-the-art sequence labeling model, namely, BiLSTM-CRF model as... | ['Chunping Ma', 'Pengjun Xie', 'Chu Liu', 'Luo Si', 'Xiaobin Wang', 'Linlin Li', 'Huafei Zheng'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['toponym-resolution'] | ['natural-language-processing'] | [-1.28287673e-01 -2.19118685e-01 -5.93273975e-02 -5.14276862e-01
-9.82115209e-01 -4.44736093e-01 6.84538662e-01 4.42959785e-01
-9.97644603e-01 8.84907782e-01 1.75947919e-01 1.43806273e-02
-7.02802613e-02 -6.79221869e-01 -3.50008637e-01 -1.67780727e-01
1.23272426e-01 1.25704467e+00 2.38147184e-01 -7.32358158... | [9.505732536315918, 9.154616355895996] |
a1b13a33-66ce-4a88-86ad-f10e7bdc4d25 | sac-gan-structure-aware-image-to-image | 2112.06596 | null | https://arxiv.org/abs/2112.06596v5 | https://arxiv.org/pdf/2112.06596v5.pdf | SAC-GAN: Structure-Aware Image Composition | We introduce an end-to-end learning framework for image-to-image composition, aiming to plausibly compose an object represented as a cropped patch from an object image into a background scene image. As our approach emphasizes more on semantic and structural coherence of the composed images, rather than their pixel-leve... | ['Ling-Xiao Zhang', 'Ali Mahdavi-Amiri', 'Lin Gao', 'Rui Ma', 'Hao Zhang', 'Hang Zhou'] | 2021-12-13 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 9.12291765e-01 3.77431393e-01 2.03874320e-01 -2.93464750e-01
-6.95233226e-01 -9.60983336e-01 7.25489736e-01 -2.80260146e-01
-7.41120204e-02 4.13516760e-01 5.65956868e-02 -4.45940159e-02
7.24637583e-02 -1.06247663e+00 -1.37946844e+00 -8.30052912e-01
3.15975726e-01 2.83313304e-01 1.81334857e-02 -1.16346262... | [11.59553050994873, -0.4958001673221588] |
dc487793-677b-4973-808c-9bb56808ac89 | ow-detr-open-world-detection-transformer | 2112.01513 | null | https://arxiv.org/abs/2112.01513v3 | https://arxiv.org/pdf/2112.01513v3.pdf | OW-DETR: Open-world Detection Transformer | Open-world object detection (OWOD) is a challenging computer vision problem, where the task is to detect a known set of object categories while simultaneously identifying unknown objects. Additionally, the model must incrementally learn new classes that become known in the next training episodes. Distinct from standard... | ['Mubarak Shah', 'Fahad Shahbaz Khan', 'Salman Khan', 'K J Joseph', 'Sanath Narayan', 'Akshita Gupta'] | 2021-12-02 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Gupta_OW-DETR_Open-World_Detection_Transformer_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Gupta_OW-DETR_Open-World_Detection_Transformer_CVPR_2022_paper.pdf | cvpr-2022-1 | ['open-world-object-detection'] | ['computer-vision'] | [ 9.53155458e-02 -1.08558431e-01 -2.05278218e-01 -2.11435571e-01
-1.11220753e+00 -6.56411409e-01 5.41899085e-01 -6.36664703e-02
-4.34047699e-01 5.48182130e-01 -1.96478933e-01 8.49270672e-02
1.78488299e-01 -3.78801048e-01 -8.68744075e-01 -5.26162505e-01
-6.76155090e-04 5.39196849e-01 7.33420968e-01 2.85003215... | [9.391746520996094, 1.421911597251892] |
a7ee5ca8-a03d-4da3-b508-e2f20eceeb25 | a-novel-motion-detection-method-resistant-to | 1612.03382 | null | http://arxiv.org/abs/1612.03382v6 | http://arxiv.org/pdf/1612.03382v6.pdf | A Novel Motion Detection Method Resistant to Severe Illumination Changes | Recently, there has been a considerable attention given to the motion
detection problem due to the explosive growth of its applications in video
analysis and surveillance systems. While the previous approaches can produce
good results, an accurate detection of motion remains a challenging task due to
the difficulties r... | ['Marius Staring', 'Jeremy Lin', 'Sahar Yousefi', 'M. T. Manzuri Shalmani'] | 2016-12-11 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 2.61213422e-01 -9.71397817e-01 -5.78269400e-02 1.75518021e-01
-3.49195898e-01 -4.68960971e-01 4.32498366e-01 -1.37485251e-01
-4.39499080e-01 3.46957088e-01 2.86957715e-02 2.38020718e-02
-1.13649212e-01 -6.53052807e-01 -4.82537746e-02 -1.23001552e+00
5.31211644e-02 -2.44657010e-01 9.11488235e-01 9.92352888... | [8.98155689239502, -0.9060744047164917] |
2644832a-9211-4dba-84f0-5c61be115332 | self-attention-networks-can-process-bounded | 2105.11115 | null | https://arxiv.org/abs/2105.11115v3 | https://arxiv.org/pdf/2105.11115v3.pdf | Self-Attention Networks Can Process Bounded Hierarchical Languages | Despite their impressive performance in NLP, self-attention networks were recently proved to be limited for processing formal languages with hierarchical structure, such as $\mathsf{Dyck}_k$, the language consisting of well-nested parentheses of $k$ types. This suggested that natural language can be approximated well w... | ['Karthik Narasimhan', 'Christos Papadimitriou', 'Binghui Peng', 'Shunyu Yao'] | 2021-05-24 | null | https://aclanthology.org/2021.acl-long.292 | https://aclanthology.org/2021.acl-long.292.pdf | acl-2021-5 | ['hard-attention'] | ['methodology'] | [-1.41617134e-01 6.13647282e-01 1.68251025e-03 -1.77194089e-01
-7.25731730e-01 -7.82466292e-01 1.92115046e-02 1.06915183e-01
-4.73282427e-01 6.78184986e-01 -1.80734284e-02 -1.10427618e+00
-1.77830637e-01 -1.26810610e+00 -1.19212949e+00 -3.63688827e-01
-5.88945329e-01 5.66590965e-01 1.31065529e-02 -1.05211049... | [9.515735626220703, 7.1205363273620605] |
ed98a428-bee4-4476-847c-7364f573bed8 | relocnet-continuous-metric-learning | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Vassileios_Balntas_RelocNet_Continous_Metric_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Vassileios_Balntas_RelocNet_Continous_Metric_ECCV_2018_paper.pdf | RelocNet: Continuous Metric Learning Relocalisation using Neural Nets | We propose a method of learning suitable convolutional representations for camera pose retrieval based on nearest neighbour matching and continuous metric learning-based feature descriptors. We introduce information from camera frusta overlaps between pairs of images to optimise our feature embedding network. T... | ['Shuda Li', 'Vassileios Balntas', 'Victor Prisacariu'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['pose-retrieval'] | ['computer-vision'] | [ 9.47270822e-03 -2.15781882e-01 -2.05699369e-01 -6.52111709e-01
-7.74080575e-01 -8.85988533e-01 9.85043228e-01 1.35440394e-01
-6.20346308e-01 2.10911874e-02 2.53942370e-01 3.02446634e-01
-5.15670717e-01 -6.45195723e-01 -8.36305678e-01 -4.03124124e-01
-1.76060259e-01 1.71462670e-01 2.97987968e-01 -2.26209313... | [7.906341075897217, -2.180464029312134] |
90327f8e-cb6e-46be-87c3-3981ee8d1db9 | iitk-at-the-finsim-task-hypernym-detection-in | 2007.11201 | null | https://arxiv.org/abs/2007.11201v1 | https://arxiv.org/pdf/2007.11201v1.pdf | IITK at the FinSim Task: Hypernym Detection in Financial Domain via Context-Free and Contextualized Word Embeddings | In this paper, we present our approaches for the FinSim 2020 shared task on "Learning Semantic Representations for the Financial Domain". The goal of this task is to classify financial terms into the most relevant hypernym (or top-level) concept in an external ontology. We leverage both context-dependent and context-in... | ['Ashutosh Modi', 'Vishal Keswani', 'Sakshi Singh'] | 2020-07-22 | null | https://aclanthology.org/2020.finnlp-1.14 | https://aclanthology.org/2020.finnlp-1.14.pdf | finnlp-coling-2020-1 | ['learning-semantic-representations'] | ['methodology'] | [-4.29067552e-01 4.42086719e-02 -2.12297261e-01 -5.84869325e-01
-3.81707460e-01 -7.58390784e-01 7.18789458e-01 5.26203573e-01
-7.86289334e-01 4.13769871e-01 5.90320289e-01 -4.27813828e-01
-3.03969651e-01 -1.08549535e+00 -2.22315609e-01 -2.86297143e-01
2.64701582e-02 6.77367270e-01 2.23955825e-01 -2.84301728... | [10.320415496826172, 8.804228782653809] |
83113458-39e4-48bf-95ae-f0ae8aa1936b | dispositionet-disentangled-pose-and-identity | 2211.05499 | null | https://arxiv.org/abs/2211.05499v1 | https://arxiv.org/pdf/2211.05499v1.pdf | DisPositioNet: Disentangled Pose and Identity in Semantic Image Manipulation | Graph representation of objects and their relations in a scene, known as a scene graph, provides a precise and discernible interface to manipulate a scene by modifying the nodes or the edges in the graph. Although existing works have shown promising results in modifying the placement and pose of objects, scene manipula... | ['Nassir Navab', 'Federico Tombari', 'Helisa Dhamo', 'Yousef Yeganeh', 'Azade Farshad'] | 2022-11-10 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 1.13363959e-01 7.31278956e-02 -3.16159315e-02 -2.67920375e-01
6.46616158e-04 -9.04352725e-01 8.04521918e-01 -7.16159195e-02
7.23650753e-02 2.86582291e-01 3.82890403e-01 3.20054024e-01
-1.95277646e-01 -7.83789992e-01 -9.55085158e-01 -8.91582370e-01
2.13700667e-01 4.94017959e-01 1.30458206e-01 -1.43427998... | [9.07448673248291, -3.1237783432006836] |
6016a9c2-73ae-4763-8826-5d9b735e0137 | smatch-standardized-and-extended-evaluation | 2305.06993 | null | https://arxiv.org/abs/2305.06993v1 | https://arxiv.org/pdf/2305.06993v1.pdf | SMATCH++: Standardized and Extended Evaluation of Semantic Graphs | The Smatch metric is a popular method for evaluating graph distances, as is necessary, for instance, to assess the performance of semantic graph parsing systems. However, we observe some issues in the metric that jeopardize meaningful evaluation. E.g., opaque pre-processing choices can affect results, and current graph... | ['Juri Opitz'] | 2023-05-11 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.53248322e-01 2.68305272e-01 -1.22801133e-01 -6.18577242e-01
-7.75023878e-01 -9.22367811e-01 1.90300673e-01 6.28642678e-01
-4.07084465e-01 4.41517800e-01 2.49658436e-01 -6.03951275e-01
-4.57819939e-01 -8.07996571e-01 -6.17945313e-01 -2.66331673e-01
-6.50484115e-02 4.73473370e-01 4.87590492e-01 -2.95085665... | [9.489320755004883, 8.140239715576172] |
ca9c52fc-a341-47ca-8f50-0b1c20d62b1c | timebankpt-a-timeml-annotated-corpus-of | null | null | https://aclanthology.org/L12-1096 | https://aclanthology.org/L12-1096.pdf | TimeBankPT: A TimeML Annotated Corpus of Portuguese | In this paper, we introduce TimeBankPT, a TimeML annotated corpus of Portuguese. It has been produced by adapting an existing resource for English, namely the data used in the first TempEval challenge. TimeBankPT is the first corpus of Portuguese with rich temporal annotations (i.e. it includes annotations not only of ... | ["Ant{\\'o}nio Branco", 'Francisco Costa'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['temporal-information-extraction'] | ['natural-language-processing'] | [-2.94290334e-02 4.76242632e-01 -2.88003802e-01 -1.17890127e-01
-3.68900746e-01 -7.83311725e-01 9.24615502e-01 6.56902194e-01
-7.72277653e-01 1.10946786e+00 5.01729369e-01 -3.38469267e-01
-2.10582450e-01 -6.01408422e-01 -2.96416581e-01 -1.50166601e-01
-2.80193895e-01 7.46262789e-01 6.76578403e-01 -2.22498074... | [9.256660461425781, 9.265345573425293] |
c2a94690-1639-4feb-9fef-aae325020da8 | towards-a-better-understanding-of-the-4 | 2305.06773 | null | https://arxiv.org/abs/2305.06773v1 | https://arxiv.org/pdf/2305.06773v1.pdf | Towards a Better Understanding of the Computer Vision Research Community in Africa | Computer vision is a broad field of study that encompasses different tasks (e.g., object detection, semantic segmentation, 3D reconstruction). Although computer vision is relevant to the African communities in various applications, yet computer vision research is under-explored in the continent and constructs only 0.06... | ['Mennatullah Siam', "Ro'ya-CV4Africa", 'Karim Gamal', 'Yvan Pimi', 'Abigail Oppong', 'Idriss Tondji', 'Mahmod Abdien', 'Houcemeddine Turki', 'Ismaila Lukman', 'Zainab Akinjobi', 'Gbetondji Dovonon', 'Eman Ehab', 'Mai Gamal', 'Abdul-Hakeem Omotayo'] | 2023-05-11 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [ 1.17816208e-02 -1.34225145e-01 -2.94913054e-01 2.47969508e-01
-6.23556316e-01 -7.28632092e-01 5.50806701e-01 1.52966335e-01
-6.99073434e-01 1.82823062e-01 2.02316761e-01 -8.82616401e-01
-1.91234469e-01 -5.73497295e-01 -6.90378070e-01 -4.89802271e-01
2.63612509e-01 3.66275042e-01 1.41607001e-01 1.01579450... | [9.559334754943848, 8.237627983093262] |
98bbfd53-6901-47d7-a973-88609f5d8139 | gesture-based-bootstrapping-for-egocentric | 1612.02889 | null | http://arxiv.org/abs/1612.02889v2 | http://arxiv.org/pdf/1612.02889v2.pdf | Gesture-based Bootstrapping for Egocentric Hand Segmentation | Accurately identifying hands in images is a key sub-task for human activity
understanding with wearable first-person point-of-view cameras. Traditional
hand segmentation approaches rely on a large corpus of manually labeled data to
generate robust hand detectors. However, these approaches still face challenges
as the a... | ['Kris M. Kitani', 'Yubo Zhang', 'Vishnu Naresh Boddeti'] | 2016-12-09 | null | null | null | null | ['hand-segmentation'] | ['computer-vision'] | [ 3.05610299e-01 -2.55464941e-01 -2.02693209e-01 -2.59859055e-01
-3.61938655e-01 -8.45595658e-01 2.86039740e-01 -2.84736007e-01
-5.01091897e-01 2.34922990e-01 -6.98335096e-02 -2.62076836e-02
2.90484011e-01 -3.15980136e-01 -6.73775017e-01 -6.98408604e-01
3.52540195e-01 4.33888197e-01 5.29826999e-01 2.43422419... | [6.65946626663208, -0.5908035039901733] |
069f2729-f866-4c0a-91bc-ed7c1baa8bd6 | decoupling-pragmatics-discriminative-decoding | null | null | https://aclanthology.org/2021.reinact-1.7 | https://aclanthology.org/2021.reinact-1.7.pdf | Decoupling Pragmatics: Discriminative Decoding for Referring Expression Generation | The shift to neural models in Referring Expression Generation (REG) has enabled more natural set-ups, but at the cost of interpretability. We argue that integrating pragmatic reasoning into the inference of context-agnostic generation models could reconcile traits of traditional and neural REG, as this offers a separat... | ['Sina Zarrieß', 'Simeon Schüz'] | null | null | null | null | reinact-2021-10 | ['referring-expression-generation'] | ['computer-vision'] | [ 3.80425453e-01 8.98773551e-01 -1.54494882e-01 -7.21105933e-01
-1.21083117e+00 -6.16307914e-01 7.75016725e-01 -7.33107179e-02
-3.27367008e-01 8.33895981e-01 1.26621473e+00 -3.03954333e-01
-7.14996010e-02 -5.28835952e-01 -5.51770687e-01 -3.49371076e-01
2.63400882e-01 5.06612897e-01 -3.17583591e-01 -4.58188504... | [10.71831226348877, 9.082273483276367] |
9d393049-8037-4da7-90a1-43503a0b485b | improving-end-to-end-slu-performance-with | 2305.08067 | null | https://arxiv.org/abs/2305.08067v1 | https://arxiv.org/pdf/2305.08067v1.pdf | Improving End-to-End SLU performance with Prosodic Attention and Distillation | Most End-to-End SLU methods depend on the pretrained ASR or language model features for intent prediction. However, other essential information in speech, such as prosody, is often ignored. Recent research has shown improved results in classifying dialogue acts by incorporating prosodic information. The margins of impr... | ['Shangeth Rajaa'] | 2023-05-14 | null | null | null | null | ['intent-classification'] | ['natural-language-processing'] | [ 1.85667947e-01 5.96678615e-01 -3.04753363e-01 -7.15557277e-01
-8.65809321e-01 -3.20352197e-01 3.63072783e-01 -2.19378695e-02
-4.29961145e-01 8.61112952e-01 1.13372481e+00 2.79298872e-02
6.12623453e-01 -3.63345981e-01 -2.25309148e-01 -5.11975288e-01
2.25549534e-01 2.43454546e-01 1.32486895e-01 -3.58204514... | [14.417622566223145, 6.989426136016846] |
88ad6cc4-8fb0-4afb-b3f7-8b39eb95036b | automated-crystal-orientation-mapping-by | 2102.09711 | null | https://arxiv.org/abs/2102.09711v2 | https://arxiv.org/pdf/2102.09711v2.pdf | Automated crystal orientation mapping by precession electron diffraction assisted four-dimensional scanning transmission electron microscopy (4D-STEM) using a scintillator based CMOS detector | The recent development of electron sensitive and pixelated detectors has attracted the use of four-dimensional scanning transmission electron microscopy (4D-STEM). Here, we present a precession electron diffraction assisted 4D-STEM technique for automated orientation mapping using diffraction spot patterns directly cap... | ['Christian H. Liebscher', 'Gerhard Dehm', 'Niels Cautaerts', 'Jiwon Jeong'] | 2021-02-19 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 7.17245579e-01 -1.82036668e-01 5.56230009e-01 -2.01077104e-01
-5.83737731e-01 -7.16632307e-02 4.61758018e-01 8.57317522e-02
-9.65555787e-01 6.36278629e-01 -2.95775503e-01 1.81097314e-01
8.06020871e-02 -7.40767300e-01 -4.09881234e-01 -1.09843314e+00
4.08551663e-01 1.04454744e+00 9.66412842e-01 -5.83976880... | [12.909872055053711, -2.7917346954345703] |
73da5a1a-9b1f-443b-a002-ac5bb6f6c7e6 | on-linear-structure-from-motion-for-light | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Johannsen_On_Linear_Structure_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Johannsen_On_Linear_Structure_ICCV_2015_paper.pdf | On Linear Structure From Motion for Light Field Cameras | We present a novel approach to relative pose estimation which is tailored to 4D light field cameras. From the relationships between scene geometry and light field structure and an analysis of the light field projection in terms of Pluecker ray coordinates, we deduce a set of linear constraints on ray space corresponden... | ['Bastian Goldluecke', 'Antonin Sulc', 'Ole Johannsen'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['point-cloud-reconstruction'] | ['computer-vision'] | [ 5.06331623e-01 -2.35729203e-01 2.50316888e-01 -4.92995501e-01
-3.72296870e-01 -8.38251531e-01 7.00117111e-01 -1.81743145e-01
-5.51496685e-01 5.42129457e-01 -1.39770731e-01 -2.48884223e-02
-2.16991186e-01 -6.70776725e-01 -7.22200513e-01 -4.09832358e-01
5.57135761e-01 1.01291668e+00 6.04666948e-01 2.40323618... | [9.456084251403809, -2.688197374343872] |
d0413599-0a03-4e11-8969-025aad31281d | creating-personalized-synthetic-voices-from | 2305.17436 | null | https://arxiv.org/abs/2305.17436v1 | https://arxiv.org/pdf/2305.17436v1.pdf | Creating Personalized Synthetic Voices from Post-Glossectomy Speech with Guided Diffusion Models | This paper is about developing personalized speech synthesis systems with recordings of mildly impaired speech. In particular, we consider consonant and vowel alterations resulted from partial glossectomy, the surgical removal of part of the tongue. The aim is to restore articulation in the synthesized speech and maxim... | ['Tan Lee', 'Guangyan Zhang', 'Yusheng Tian'] | 2023-05-27 | null | null | null | null | ['voice-conversion', 'voice-conversion', 'speech-synthesis'] | ['audio', 'speech', 'speech'] | [ 1.69713542e-01 7.69586682e-01 -3.86461079e-01 7.31049627e-02
-1.00701737e+00 -2.58238614e-01 2.67158598e-01 -4.26021606e-01
-9.24141854e-02 5.98852873e-01 1.20292258e+00 -1.27990171e-01
1.37970701e-01 -3.02820563e-01 -1.36272103e-01 -9.00927603e-01
4.08780873e-01 2.88167953e-01 -1.23975269e-01 -1.72397614... | [14.867825508117676, 6.484362602233887] |
84f671bf-d3eb-4fa8-a1d2-102432338b96 | achieving-fairness-in-multi-agent-markov | 2306.00324 | null | https://arxiv.org/abs/2306.00324v1 | https://arxiv.org/pdf/2306.00324v1.pdf | Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learning | Fairness plays a crucial role in various multi-agent systems (e.g., communication networks, financial markets, etc.). Many multi-agent dynamical interactions can be cast as Markov Decision Processes (MDPs). While existing research has focused on studying fairness in known environments, the exploration of fairness in su... | ['Ness B. Shroff', 'Arnob Ghosh', 'Peizhong Ju'] | 2023-06-01 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-3.86738628e-01 1.87799647e-01 -1.35278970e-01 -2.23983437e-01
-5.27474582e-01 -5.40161848e-01 2.14275926e-01 3.59787047e-01
-8.43842089e-01 1.42344630e+00 -2.27999404e-01 -2.95861751e-01
-5.52037895e-01 -8.40352356e-01 -5.43832779e-01 -8.21020126e-01
-2.55576730e-01 6.13001168e-01 -1.39272541e-01 -1.31943181... | [4.2667155265808105, 2.702383041381836] |
71d1eac5-42c6-4cfc-968d-99851702e0c4 | single-stage-broad-multi-instance-multi-label | 2209.02625 | null | https://arxiv.org/abs/2209.02625v2 | https://arxiv.org/pdf/2209.02625v2.pdf | Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) with Diverse Inter-Correlations and its application to medical image classification | described by multiple instances (e.g., image patches) and simultaneously associated with multiple labels. Existing MIML methods are useful in many applications but most of which suffer from relatively low accuracy and training efficiency due to several issues: i) the inter-label correlations(i.e., the probabilistic cor... | ['DeShuang Huang', 'Chi-Man Vong', 'Yanfen Gan', 'Jianhang Zhou', 'Qi Lai'] | 2022-09-06 | null | null | null | null | ['multi-label-learning'] | ['methodology'] | [ 2.62826055e-01 -2.04940096e-01 -5.21076500e-01 -5.11131883e-01
-1.14672625e+00 2.85091288e-02 2.73444384e-01 1.58887282e-01
-2.56867200e-01 6.19128585e-01 -2.22736835e-01 2.70393968e-01
-5.38069785e-01 -4.54936862e-01 -4.77630168e-01 -1.09525251e+00
9.59490091e-02 7.89621592e-01 2.79747546e-01 4.21288788... | [9.571893692016602, 4.0221476554870605] |
8f576c64-eff6-452e-8445-027f5e3a646c | a-hierarchical-transformer-with-speaker | 2012.14781 | null | https://arxiv.org/abs/2012.14781v1 | https://arxiv.org/pdf/2012.14781v1.pdf | A Hierarchical Transformer with Speaker Modeling for Emotion Recognition in Conversation | Emotion Recognition in Conversation (ERC) is a more challenging task than conventional text emotion recognition. It can be regarded as a personalized and interactive emotion recognition task, which is supposed to consider not only the semantic information of text but also the influences from speakers. The current metho... | ['Weiping Wang', 'Qingyi Si', 'Peng Fu', 'Zheng Lin', 'Jiangnan Li'] | 2020-12-29 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 5.21340445e-02 -1.86648428e-01 6.17890395e-02 -8.39312375e-01
-5.30639708e-01 -2.10117385e-01 4.62911963e-01 -2.25052685e-01
-2.78547049e-01 2.09223390e-01 7.42068291e-01 -8.78316164e-02
5.81115559e-02 -3.18710119e-01 -2.24518478e-01 -7.57528543e-01
1.32268205e-01 1.95272699e-01 4.11924571e-02 -4.51175958... | [13.034701347351074, 6.067753791809082] |
df57570b-d312-4ea0-a00e-d1ee5daa8af1 | decision-making-using-rough-set-based | 2107.12477 | null | https://arxiv.org/abs/2107.12477v1 | https://arxiv.org/pdf/2107.12477v1.pdf | Decision Making Using Rough Set based Spanning Sets for a Decision System | Rough Set based concepts of Span and Spanning Sets were recently proposed to deal with uncertainties in data. Here, this paper, presents novel concepts for generic decision-making process using Rough Set based span for a decision table. Majority of problems in Artificial Intelligence deal with decision making. This pap... | ['Nidhika Yadav'] | 2021-07-21 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 2.27592409e-01 4.05870676e-01 2.35533297e-01 -8.99151325e-01
-4.16839570e-01 -3.36522877e-01 1.00241683e-01 9.23807442e-01
5.58782220e-02 1.15650332e+00 2.05271780e-01 -2.65716702e-01
-1.17302561e+00 -1.25199723e+00 -2.08708644e-02 -1.90268010e-01
-7.00615525e-01 1.17076302e+00 -1.64137036e-01 -7.70385683... | [8.271103858947754, 4.776122570037842] |
dd2cae67-6153-4efe-9964-0b0d81530f6b | audio-dequantization-for-high-fidelity-audio | 2008.06867 | null | https://arxiv.org/abs/2008.06867v1 | https://arxiv.org/pdf/2008.06867v1.pdf | Audio Dequantization for High Fidelity Audio Generation in Flow-based Neural Vocoder | In recent works, a flow-based neural vocoder has shown significant improvement in real-time speech generation task. The sequence of invertible flow operations allows the model to convert samples from simple distribution to audio samples. However, training a continuous density model on discrete audio data can degrade mo... | ['Seong-Whan Lee', 'Hyeong-Rae Noh', 'Sang-Hoon Lee', 'Hyun-Wook Yoon'] | 2020-08-16 | null | null | null | null | ['audio-generation', 'audio-dequantization'] | ['audio', 'audio'] | [ 6.62551150e-02 -1.70021847e-01 4.53524850e-02 -9.02123526e-02
-6.57377005e-01 -2.70360559e-01 4.64021087e-01 -1.71867549e-01
-9.53650451e-04 1.09984767e+00 6.69081450e-01 -2.17855535e-03
1.59396827e-01 -8.15255284e-01 -6.29539430e-01 -6.42181993e-01
-9.80724841e-02 1.84496827e-02 1.38039201e-01 -5.59198996... | [15.511391639709473, 5.9861602783203125] |
4d66af46-bc39-43d9-804d-43186b9b4a48 | learning-to-restore-3d-face-from-in-the-wild | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhang_Learning_To_Restore_3D_Face_From_In-the-Wild_Degraded_Images_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhang_Learning_To_Restore_3D_Face_From_In-the-Wild_Degraded_Images_CVPR_2022_paper.pdf | Learning To Restore 3D Face From In-the-Wild Degraded Images | In-the-wild 3D face modelling is a challenging problem as the predicted facial geometry and texture suffer from a lack of reliable clues or priors, when the input images are degraded. To address such a problem, in this paper we propose a novel Learning to Restore (L2R) 3D face framework for unsupervised high-qualit... | ['Zhifeng Xie', 'Dongjin Huang', 'Hao Tang', 'Chengjie Wang', 'Xiaoming Huang', 'Ying Tai', 'Yanhao Ge', 'Zhenyu Zhang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['3d-face-modeling', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 3.67506355e-01 4.92407829e-01 1.41320467e-01 -3.49545777e-01
-1.00063682e+00 -1.53940514e-01 4.52068150e-01 -9.96363640e-01
3.40626448e-01 5.00380635e-01 3.14292908e-01 2.72590309e-01
-6.22267313e-02 -8.16541433e-01 -9.48503673e-01 -9.79988873e-01
3.75989944e-01 5.19744396e-01 -2.14058980e-01 -3.59232217... | [12.794160842895508, -0.1456453949213028] |
d90dbc03-8963-4639-94d5-ee94edb6d08b | adversarial-sparse-transformer-for-time | null | null | http://proceedings.neurips.cc/paper/2020/hash/c6b8c8d762da15fa8dbbdfb6baf9e260-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/c6b8c8d762da15fa8dbbdfb6baf9e260-Paper.pdf | Adversarial Sparse Transformer for Time Series Forecasting | Many approaches have been proposed for time series forecasting, in light of its significance in wide applications including business demand prediction.
However, the existing methods suffer from two key limitations. Firstly, most point prediction models only predict an exact value of each time step without flexibility,... | ['Junzhou Huang', 'Ying WEI', 'Peilin Zhao', 'Qianggang Ding', 'Xi Xiao', 'Sifan Wu'] | 2020-12-01 | null | null | null | neurips-2020-12 | ['probabilistic-time-series-forecasting'] | ['time-series'] | [ 7.55509436e-02 -1.31462410e-01 -8.46644938e-02 -4.26591486e-01
-5.42021334e-01 -2.98101127e-01 5.51825762e-01 -5.13912022e-01
3.22255492e-01 7.40863025e-01 2.34270200e-01 -2.16313660e-01
4.68726158e-02 -9.99917626e-01 -7.63025165e-01 -7.97944665e-01
1.70339286e-01 2.66435742e-01 3.47821377e-02 -2.30962917... | [6.925973892211914, 3.078063726425171] |
462939fa-dddf-4937-beef-25f47f004091 | in-out-diverse-image-outpainting-via-gan | 2104.00675 | null | https://arxiv.org/abs/2104.00675v1 | https://arxiv.org/pdf/2104.00675v1.pdf | In&Out : Diverse Image Outpainting via GAN Inversion | Image outpainting seeks for a semantically consistent extension of the input image beyond its available content. Compared to inpainting -- filling in missing pixels in a way coherent with the neighboring pixels -- outpainting can be achieved in more diverse ways since the problem is less constrained by the surrounding ... | ['Ming-Hsuan Yang', 'Sergey Tulyakov', 'Jian Ren', 'Hsin-Ying Lee', 'Chieh Hubert Lin', 'Yen-Chi Cheng'] | 2021-04-01 | null | null | null | null | ['image-outpainting'] | ['computer-vision'] | [ 8.55867922e-01 4.25379544e-01 -1.76588863e-01 4.07695770e-02
-8.23564231e-01 -6.77388608e-01 3.36737752e-01 -4.55997080e-01
2.67533455e-02 1.13370919e+00 1.51733339e-01 1.55215889e-01
3.02196771e-01 -9.35433745e-01 -1.07448137e+00 -8.92416716e-01
4.95242864e-01 1.80493221e-01 -2.35097244e-01 -1.73489109... | [11.634339332580566, -0.8556600213050842] |
a16de766-4970-4606-bd79-80565cff7aff | time-contrastive-learning-based-deep | 1905.04554 | null | https://arxiv.org/abs/1905.04554v1 | https://arxiv.org/pdf/1905.04554v1.pdf | Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification | There are a number of studies about extraction of bottleneck (BN) features from deep neural networks (DNNs)trained to discriminate speakers, pass-phrases and triphone states for improving the performance of text-dependent speaker verification (TD-SV). However, a moderate success has been achieved. A recent study [1] pr... | ['Suwon Shon', 'Zheng-Hua Tan', 'Achintya kr. Sarkar', 'James Glass', 'Hao Tang'] | 2019-05-11 | null | null | null | null | ['text-dependent-speaker-verification'] | ['speech'] | [ 9.49354023e-02 -3.33916426e-01 -2.90704221e-01 -7.69114852e-01
-8.94891918e-01 -5.18237233e-01 7.87297964e-01 -5.11931479e-02
-7.31988966e-01 4.65911895e-01 2.76240677e-01 -3.48267823e-01
-2.89715510e-02 -1.20924547e-01 -1.01942778e-01 -1.18729854e+00
-2.22069472e-01 4.19423819e-01 1.49192378e-01 -6.53060749... | [14.454752922058105, 6.197702407836914] |
8c48b853-1aa1-4e6a-9889-8b6ddd950195 | locality-aware-attention-network-with | 2208.05636 | null | https://arxiv.org/abs/2208.05636v1 | https://arxiv.org/pdf/2208.05636v1.pdf | Locality-aware Attention Network with Discriminative Dynamics Learning for Weakly Supervised Anomaly Detection | Video anomaly detection is recently formulated as a multiple instance learning task under weak supervision, in which each video is treated as a bag of snippets to be determined whether contains anomalies. Previous efforts mainly focus on the discrimination of the snippet itself without modeling the temporal dynamics, w... | ['Xiaoyu Wu', 'Yujiang Pu'] | 2022-08-11 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [-6.56105429e-02 -5.99071443e-01 -2.96163242e-02 -3.84612560e-01
-2.51773566e-01 -1.54511079e-01 4.80131686e-01 4.92827654e-01
-3.79635036e-01 2.05409646e-01 1.78537026e-01 8.69695395e-02
-1.49279490e-01 -6.06116474e-01 -8.92991841e-01 -9.30658579e-01
-5.11058688e-01 3.24422926e-01 4.36441064e-01 7.05230981... | [7.8381218910217285, 1.5920089483261108] |
c62d30d8-b3a4-4497-b622-d432ee564b55 | generative-probabilistic-novelty-detection | 1807.02588 | null | http://arxiv.org/abs/1807.02588v2 | http://arxiv.org/pdf/1807.02588v2.pdf | Generative Probabilistic Novelty Detection with Adversarial Autoencoders | Novelty detection is the problem of identifying whether a new data point is
considered to be an inlier or an outlier. We assume that training data is
available to describe only the inlier distribution. Recent approaches primarily
leverage deep encoder-decoder network architectures to compute a reconstruction
error that... | ['Donald A. Adjeroh', 'Ranya Almohsen', 'Stanislav Pidhorskyi', 'Gianfranco Doretto'] | 2018-07-06 | generative-probabilistic-novelty-detection-1 | http://papers.nips.cc/paper/7915-generative-probabilistic-novelty-detection-with-adversarial-autoencoders | http://papers.nips.cc/paper/7915-generative-probabilistic-novelty-detection-with-adversarial-autoencoders.pdf | neurips-2018-12 | ['one-class-classifier'] | ['methodology'] | [-1.76134542e-01 2.83791428e-03 2.18494167e-03 -3.50605309e-01
-9.68238771e-01 -4.67720419e-01 6.60121441e-01 2.56106555e-01
-4.02428448e-01 4.93853867e-01 2.27209374e-01 5.33341654e-02
8.82034078e-02 -6.24485254e-01 -1.31469345e+00 -7.42675066e-01
-2.01195925e-01 5.22218466e-01 -1.89800039e-02 2.22244143... | [7.694599628448486, 2.348661422729492] |
5c0200f4-79d9-447a-abfd-0f53a267bf20 | cross-lingual-transfer-with-target-language | 2306.02767 | null | https://arxiv.org/abs/2306.02767v1 | https://arxiv.org/pdf/2306.02767v1.pdf | Cross-Lingual Transfer with Target Language-Ready Task Adapters | Adapters have emerged as a modular and parameter-efficient approach to (zero-shot) cross-lingual transfer. The established MAD-X framework employs separate language and task adapters which can be arbitrarily combined to perform the transfer of any task to any target language. Subsequently, BAD-X, an extension of the MA... | ['Anna Korhonen', 'Ivan Vulić', 'Alan Ansell', 'Marinela Parović'] | 2023-06-05 | null | null | null | null | ['zero-shot-cross-lingual-transfer', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.76214960e-02 1.07488453e-01 -1.63642094e-01 -2.74938792e-01
-1.09508538e+00 -8.73101473e-01 7.98986852e-01 -2.22134963e-01
-5.41603446e-01 7.93171883e-01 2.41756335e-01 -7.31140673e-01
-1.02747999e-01 -6.77376568e-01 -7.73103237e-01 -5.03301263e-01
1.79158077e-01 7.19404519e-01 1.35575041e-01 -6.25992000... | [11.099739074707031, 9.889382362365723] |
ead1b84a-72a1-448b-8bd6-6b9bb53f79aa | graph-based-multi-ode-neural-networks-for | 2305.18687 | null | https://arxiv.org/abs/2305.18687v2 | https://arxiv.org/pdf/2305.18687v2.pdf | Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting | There is a recent surge in the development of spatio-temporal forecasting models in the transportation domain. Long-range traffic forecasting, however, remains a challenging task due to the intricate and extensive spatio-temporal correlations observed in traffic networks. Current works primarily rely on road networks w... | ['Chandan K Reddy', 'Parshin Shojaee', 'Zibo Liu'] | 2023-05-30 | null | null | null | null | ['traffic-prediction', 'spatio-temporal-forecasting'] | ['time-series', 'time-series'] | [-4.05200720e-01 -2.91448444e-01 -2.68726647e-01 -4.60621804e-01
2.37557366e-01 -2.74280280e-01 8.44997168e-01 -2.39016965e-01
2.06029013e-01 5.30256927e-01 3.88211489e-01 -6.39575183e-01
-4.82844055e-01 -9.74077404e-01 -5.70384085e-01 -4.03194070e-01
-4.65241969e-01 4.11385447e-01 6.12965345e-01 -7.77727306... | [6.498448371887207, 2.10390567779541] |
060e4e65-259d-4961-bec3-9fcf38351f1b | task-oriented-conversational-modelling-with | 2303.17695 | null | https://arxiv.org/abs/2303.17695v1 | https://arxiv.org/pdf/2303.17695v1.pdf | Task Oriented Conversational Modelling With Subjective Knowledge | Existing conversational models are handled by a database(DB) and API based systems. However, very often users' questions require information that cannot be handled by such systems. Nonetheless, answers to these questions are available in the form of customer reviews and FAQs. DSTC-11 proposes a three stage pipeline con... | ['Raja Kumar'] | 2023-03-30 | null | null | null | null | ['response-generation', 'keyword-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-2.08570510e-01 2.64698416e-01 -8.98722857e-02 -4.36284274e-01
-1.26967418e+00 -8.50182354e-01 6.37394071e-01 2.71457642e-01
-4.87218440e-01 9.18291807e-01 5.71870625e-01 -2.62755632e-01
-1.39830187e-01 -8.94532621e-01 -2.22390115e-01 -5.61492331e-02
5.90362966e-01 7.12358534e-01 4.09813523e-01 -4.75462914... | [12.233174324035645, 7.982776641845703] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.