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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
4150482c-b6c2-4fa2-bd1c-be323a0fc176 | roadtracer-automatic-extraction-of-road | 1802.03680 | null | http://arxiv.org/abs/1802.03680v2 | http://arxiv.org/pdf/1802.03680v2.pdf | RoadTracer: Automatic Extraction of Road Networks from Aerial Images | Mapping road networks is currently both expensive and labor-intensive.
High-resolution aerial imagery provides a promising avenue to automatically
infer a road network. Prior work uses convolutional neural networks (CNNs) to
detect which pixels belong to a road (segmentation), and then uses complex
post-processing heur... | ['Hari Balakrishnan', 'Songtao He', 'Sofiane Abbar', 'Favyen Bastani', 'Sanjay Chawla', 'Mohammad Alizadeh', 'David DeWitt', 'Sam Madden'] | 2018-02-11 | roadtracer-automatic-extraction-of-road-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Bastani_RoadTracer_Automatic_Extraction_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Bastani_RoadTracer_Automatic_Extraction_CVPR_2018_paper.pdf | cvpr-2018-6 | ['road-segementation'] | ['computer-vision'] | [ 4.66723651e-01 4.02890682e-01 -1.45881996e-01 -3.50400269e-01
-5.58677197e-01 -8.36664796e-01 5.39545298e-01 -6.85001686e-02
-2.38343820e-01 6.09391868e-01 -1.23517752e-01 -7.82588959e-01
-1.40551358e-01 -1.69791496e+00 -8.41320217e-01 4.92995568e-02
-2.30285376e-01 6.34088814e-01 5.37126780e-01 -1.78984761... | [8.861809730529785, -1.5171821117401123] |
25bb1a2c-4406-4385-aa7a-cbe1f131d14d | spherical-kernel-for-efficient-graph | 1909.09287 | null | https://arxiv.org/abs/1909.09287v2 | https://arxiv.org/pdf/1909.09287v2.pdf | Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds | We propose a spherical kernel for efficient graph convolution of 3D point clouds. Our metric-based kernels systematically quantize the local 3D space to identify distinctive geometric relationships in the data. Similar to the regular grid CNN kernels, the spherical kernel maintains translation-invariance and asymmetry ... | ['Huan Lei', 'Ajmal Mian', 'Naveed Akhtar'] | 2019-09-20 | null | null | null | null | ['3d-instance-segmentation-1', '3d-object-classification', '3d-part-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-4.81636047e-01 1.78000346e-01 3.50203440e-02 -3.43293071e-01
-5.45449778e-02 -5.83528817e-01 4.50071722e-01 3.87456357e-01
-1.92089632e-01 3.16196457e-02 -1.81180105e-01 -2.87435025e-01
-1.65336639e-01 -1.16726351e+00 -9.04700994e-01 -4.21661437e-01
-2.88076282e-01 4.67958540e-01 5.06291032e-01 6.90480173... | [7.971622943878174, -3.6991753578186035] |
65ca5791-0a87-45ec-94be-287f8559b042 | translating-a-math-word-problem-to-a | null | null | https://aclanthology.org/D18-1132 | https://aclanthology.org/D18-1132.pdf | Translating a Math Word Problem to a Expression Tree | Sequence-to-sequence (SEQ2SEQ) models have been successfully applied to automatic math word problem solving. Despite its simplicity, a drawback still remains: a math word problem can be correctly solved by more than one equations. This non-deterministic transduction harms the performance of maximum likelihood estimatio... | ['Xiaojiang Liu', 'Deng Cai', 'Yan Wang', 'Lei Wang', 'Dongxiang Zhang'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 2.88303494e-01 -3.03975642e-01 1.16205327e-01 -4.14614379e-01
-5.48822880e-01 -7.34965920e-01 -7.77340680e-03 1.29110053e-01
-3.33427191e-01 8.69960546e-01 1.49781415e-02 -2.44935364e-01
-4.64736253e-01 -9.24393356e-01 -3.73732209e-01 -4.80943322e-01
5.83343983e-01 3.30007255e-01 -5.80136776e-02 -6.23628259... | [9.767043113708496, 7.44555139541626] |
5daf225b-cb5e-48a8-ba6d-12b851c1648d | yolo-v3-visual-and-real-time-object-detection | 2209.12447 | null | https://arxiv.org/abs/2209.12447v1 | https://arxiv.org/pdf/2209.12447v1.pdf | YOLO v3: Visual and Real-Time Object Detection Model for Smart Surveillance Systems(3s) | Can we see it all? Do we know it All? These are questions thrown to human beings in our contemporary society to evaluate our tendency to solve problems. Recent studies have explored several models in object detection; however, most have failed to meet the demand for objectiveness and predictive accuracy, especially in ... | ['Hashim Ibrahim Bisallah', 'Ozioma Collins Oguine', 'Kanyifeechukwu Jane Oguine'] | 2022-09-26 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [ 1.19717821e-01 -2.87786901e-01 1.11592799e-01 -8.93774629e-02
-1.63389787e-01 -3.08209389e-01 4.13191348e-01 7.38463327e-02
-4.15534288e-01 3.95769596e-01 -3.18799585e-01 -3.87123764e-01
7.94357713e-03 -8.15321505e-01 -4.48955089e-01 -6.58441126e-01
-1.70541808e-01 -5.53031825e-02 7.11089492e-01 -1.61338300... | [8.6489839553833, -0.8654654622077942] |
cf65e8f8-7bb2-4519-a649-5d0a79bbb839 | learning-joint-semantic-parsers-from-disjoint | 1804.05990 | null | http://arxiv.org/abs/1804.05990v1 | http://arxiv.org/pdf/1804.05990v1.pdf | Learning Joint Semantic Parsers from Disjoint Data | We present a new approach to learning semantic parsers from multiple
datasets, even when the target semantic formalisms are drastically different,
and the underlying corpora do not overlap. We handle such "disjoint" data by
treating annotations for unobserved formalisms as latent structured variables.
Building on state... | ['Sam Thomson', 'Noah A. Smith', 'Swabha Swayamdipta', 'Hao Peng'] | 2018-04-17 | learning-joint-semantic-parsers-from-disjoint-1 | https://aclanthology.org/N18-1135 | https://aclanthology.org/N18-1135.pdf | naacl-2018-6 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [ 2.90342093e-01 8.66104305e-01 -4.44209278e-01 -7.67433524e-01
-1.36533964e+00 -1.02746034e+00 4.91741449e-01 1.56370640e-01
-2.05910280e-01 9.23732579e-01 5.13933480e-01 -2.58137107e-01
2.34317169e-01 -7.30518818e-01 -7.58706748e-01 -2.96573937e-01
3.00732851e-01 1.06105042e+00 4.42751467e-01 2.63361372... | [10.382750511169434, 9.425050735473633] |
b00cf451-28ea-42d7-a0ff-f6720e0f6f40 | ciagan-conditional-identity-anonymization | 2005.09544 | null | https://arxiv.org/abs/2005.09544v2 | https://arxiv.org/pdf/2005.09544v2.pdf | CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks | The unprecedented increase in the usage of computer vision technology in society goes hand in hand with an increased concern in data privacy. In many real-world scenarios like people tracking or action recognition, it is important to be able to process the data while taking careful consideration in protecting people's ... | ['Laura Leal-Taixé', 'Maxim Maximov', 'Ismail Elezi'] | 2020-05-19 | ciagan-conditional-identity-anonymization-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Maximov_CIAGAN_Conditional_Identity_Anonymization_Generative_Adversarial_Networks_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Maximov_CIAGAN_Conditional_Identity_Anonymization_Generative_Adversarial_Networks_CVPR_2020_paper.pdf | cvpr-2020-6 | ['face-anonymization'] | ['computer-vision'] | [ 3.78231257e-01 1.26495391e-01 8.84969458e-02 -5.03105938e-01
-4.78409320e-01 -8.20131242e-01 6.30643010e-01 -1.69912666e-01
-6.73991680e-01 6.78589344e-01 2.30102852e-01 -1.17480800e-01
3.37365836e-01 -6.52103066e-01 -7.98569739e-01 -5.66628218e-01
1.15060084e-01 2.47334778e-01 -8.84926170e-02 1.45749539... | [12.756694793701172, 0.7877780199050903] |
40c38dc5-1f7a-4705-9e43-076cf80ae162 | certifiable-robustness-for-naive-bayes | 2303.04811 | null | https://arxiv.org/abs/2303.04811v1 | https://arxiv.org/pdf/2303.04811v1.pdf | Certifiable Robustness for Naive Bayes Classifiers | Data cleaning is crucial but often laborious in most machine learning (ML) applications. However, task-agnostic data cleaning is sometimes unnecessary if certain inconsistencies in the dirty data will not affect the prediction of ML models to the test points. A test point is certifiably robust for an ML classifier if t... | ['Paraschos Koutris', 'Zhiwei Fan', 'Xiating Ouyang', 'Song Bian'] | 2023-03-08 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 2.20957428e-01 -5.92242964e-02 -1.00978516e-01 -4.23024267e-01
-1.29571640e+00 -1.01748705e+00 2.33057335e-01 7.45372474e-01
-3.08523923e-01 1.05293572e+00 -6.00244224e-01 -6.86494589e-01
-5.07904470e-01 -9.32784975e-01 -1.31720614e+00 -1.09579003e+00
-3.93352896e-01 9.33554351e-01 7.93115646e-02 3.95124406... | [5.8801751136779785, 7.363976955413818] |
83d446f1-df83-43e5-8d89-84edc71be660 | self-supervised-sentence-compression-for | 2305.07988 | null | https://arxiv.org/abs/2305.07988v1 | https://arxiv.org/pdf/2305.07988v1.pdf | Self-Supervised Sentence Compression for Meeting Summarization | The conventional summarization model often fails to capture critical information in meeting transcripts, as meeting corpus usually involves multiple parties with lengthy conversations and is stuffed with redundant and trivial content. To tackle this problem, we present SVB, an effective and efficient framework for meet... | ['Linqi Song', 'Ding Liang', 'Zhaohui Hou', 'Mingjie Zhan', 'Xinyun Zhang', 'Wei Shao', 'Han Wu', 'Haochen Tan'] | 2023-05-13 | null | null | null | null | ['sentence-compression', 'meeting-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.91480172e-01 4.79102612e-01 -3.22590292e-01 -4.59769726e-01
-1.33797908e+00 -5.10420203e-01 3.93503010e-01 8.85519862e-01
-3.94867927e-01 9.97245669e-01 9.29789305e-01 1.40527159e-01
-1.39360219e-01 -2.83522457e-01 -2.20769241e-01 -5.94201148e-01
8.03510249e-02 6.92962170e-01 1.97258994e-01 -2.47161046... | [12.595832824707031, 9.366771697998047] |
83292e12-1dc0-43b0-bfc7-6361db24ade3 | semantic-structure-enhanced-event-causality | 2305.12792 | null | https://arxiv.org/abs/2305.12792v1 | https://arxiv.org/pdf/2305.12792v1.pdf | Semantic Structure Enhanced Event Causality Identification | Event Causality Identification (ECI) aims to identify causal relations between events in unstructured texts. This is a very challenging task, because causal relations are usually expressed by implicit associations between events. Existing methods usually capture such associations by directly modeling the texts with pre... | ['Xueqi Cheng', 'Jiafeng Guo', 'Saiping Guan', 'Long Bai', 'Xiaolong Jin', 'Zixuan Li', 'Zhilei Hu'] | 2023-05-22 | null | null | null | null | ['event-causality-identification'] | ['natural-language-processing'] | [ 1.72380105e-01 8.94718692e-02 -3.57061267e-01 -4.88212079e-01
-2.69361049e-01 -2.70258099e-01 1.05090332e+00 7.60964930e-01
-3.03607762e-01 8.17611098e-01 1.01371205e+00 -1.93243176e-01
-3.50184083e-01 -1.15858054e+00 -6.54635429e-01 -3.08879584e-01
-4.85275477e-01 5.20727277e-01 5.13295412e-01 -5.47416657... | [9.087060928344727, 9.119467735290527] |
f2bfbac7-df73-4908-b022-d1cb52ebb14e | kpgt-knowledge-guided-pre-training-of-graph | 2206.03364 | null | https://arxiv.org/abs/2206.03364v1 | https://arxiv.org/pdf/2206.03364v1.pdf | KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction | Designing accurate deep learning models for molecular property prediction plays an increasingly essential role in drug and material discovery. Recently, due to the scarcity of labeled molecules, self-supervised learning methods for learning generalizable and transferable representations of molecular graphs have attract... | ['Jianyang Zeng', 'Dan Zhao', 'Han Li'] | 2022-06-02 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 3.36306095e-01 -1.82294771e-02 -6.91123784e-01 -3.35521549e-01
-4.43464816e-01 -4.01549280e-01 1.42199710e-01 6.56493366e-01
5.30284569e-02 1.14996469e+00 -3.05278748e-02 -5.34710109e-01
-2.94499844e-01 -9.66564238e-01 -8.57309878e-01 -7.71550655e-01
-1.08934723e-01 2.32930824e-01 2.51841266e-02 -1.26673311... | [5.173333168029785, 5.930690765380859] |
a9de253b-2fd0-4f0e-99b0-19d818d5edaa | few-shot-learning-for-named-entity | 1811.05468 | null | http://arxiv.org/abs/1811.05468v1 | http://arxiv.org/pdf/1811.05468v1.pdf | Few-shot Learning for Named Entity Recognition in Medical Text | Deep neural network models have recently achieved state-of-the-art
performance gains in a variety of natural language processing (NLP) tasks
(Young, Hazarika, Poria, & Cambria, 2017). However, these gains rely on the
availability of large amounts of annotated examples, without which
state-of-the-art performance is rare... | ['Alejo Nevado-Holgado', 'Maximilian Hofer', 'Paul Goldberg', 'Andrey Kormilitzin'] | 2018-11-13 | null | null | null | null | ['medical-named-entity-recognition'] | ['natural-language-processing'] | [-3.77220730e-03 1.67540580e-01 -1.24227509e-01 -4.27595109e-01
-9.21485662e-01 -4.57613319e-01 5.04037797e-01 7.00209379e-01
-1.19042337e+00 7.49999285e-01 3.86838675e-01 -3.45229447e-01
1.07295133e-01 -6.94104791e-01 -3.62498283e-01 -3.98067623e-01
-1.10498168e-01 5.51320910e-01 -2.31198557e-02 -8.07898641... | [9.663192749023438, 9.400851249694824] |
3402cafd-83e5-406d-acb4-7b353b40d735 | a-baseline-for-multi-label-image | 1811.08412 | null | https://arxiv.org/abs/1811.08412v3 | https://arxiv.org/pdf/1811.08412v3.pdf | A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks | Recent studies on multi-label image classification have focused on designing more complex architectures of deep neural networks such as the use of attention mechanisms and region proposal networks. Although performance gains have been reported, the backbone deep models of the proposed approaches and the evaluation metr... | ['Toby P. Breckon', 'Ning Jia', 'Qian Wang'] | 2018-11-20 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 2.90140331e-01 -2.19722047e-01 -6.28039300e-01 -5.38170397e-01
-6.15243554e-01 -3.63731861e-01 7.16010690e-01 2.93591768e-01
-5.03145695e-01 6.61226571e-01 -3.03874128e-02 -3.13371718e-01
-4.05641310e-02 -4.52271014e-01 -4.38565403e-01 -9.94416237e-01
2.19874755e-01 2.76544720e-01 -1.28683653e-02 -4.00678366... | [9.707778930664062, 4.163860321044922] |
346ea3b4-6196-4391-836d-5e98b47178c1 | codecmr-cross-modal-retrieval-for-function | null | null | http://proceedings.neurips.cc/paper/2020/hash/285f89b802bcb2651801455c86d78f2a-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/285f89b802bcb2651801455c86d78f2a-Paper.pdf | CodeCMR: Cross-Modal Retrieval For Function-Level Binary Source Code Matching | Binary source code matching, especially on function-level, has a critical role in the field of computer security. Given binary code only, finding the corresponding source code improves the accuracy and efficiency in reverse engineering. Given source code only, related binary code retrieval contributes to known vulnerab... | ['Shi Wu', 'Sen Nie', 'Qiyi Tang', 'Jiaqi Wang', 'Wenxin Zheng', 'Zeping Yu'] | 2020-12-01 | null | null | null | neurips-2020-12 | ['computer-security'] | ['miscellaneous'] | [ 7.39391223e-02 -4.91399646e-01 -5.70728421e-01 -1.43244281e-01
-7.81312525e-01 -9.47843671e-01 2.35611550e-03 5.63397110e-01
1.72233824e-02 5.58678396e-02 -7.95193762e-03 -7.83494592e-01
-2.17847511e-01 -1.23876023e+00 -6.04523063e-01 -3.26178013e-03
-8.38686824e-02 -3.92045617e-01 2.97648937e-01 -4.26876783... | [7.180979251861572, 7.832417011260986] |
447dfb85-b0ed-4e8c-9722-21c684b0df07 | a-data-efficient-deep-learning-framework-for | 2207.06489 | null | https://arxiv.org/abs/2207.06489v5 | https://arxiv.org/pdf/2207.06489v5.pdf | A Data-Efficient Deep Learning Framework for Segmentation and Classification of Histopathology Images | The current study of cell architecture of inflammation in histopathology images commonly performed for diagnosis and research purposes excludes a lot of information available on the biopsy slide. In autoimmune diseases, major outstanding research questions remain regarding which cell types participate in inflammation a... | ['Jacopo Cirrone', 'Pranav Singh'] | 2022-07-13 | null | null | null | null | ['classification'] | ['methodology'] | [ 2.72338122e-01 -1.34276360e-01 -3.37454110e-01 -1.51728187e-02
-8.99649620e-01 -5.99840522e-01 1.16857670e-01 6.03389919e-01
-5.33192754e-01 6.00916445e-01 1.30045429e-01 -2.70439953e-01
1.20697670e-01 -7.61651099e-01 -1.67064160e-01 -1.39511132e+00
-1.05818957e-01 8.18980694e-01 -2.96010282e-02 1.79365024... | [15.105178833007812, -3.0358633995056152] |
2768b74d-c157-4e5f-a0d2-6154ade25f7e | gammae-gamma-embeddings-for-logical-queries | 2210.15578 | null | https://arxiv.org/abs/2210.15578v2 | https://arxiv.org/pdf/2210.15578v2.pdf | GammaE: Gamma Embeddings for Logical Queries on Knowledge Graphs | Embedding knowledge graphs (KGs) for multi-hop logical reasoning is a challenging problem due to massive and complicated structures in many KGs. Recently, many promising works projected entities and queries into a geometric space to efficiently find answers. However, it remains challenging to model the negation and uni... | ['Xiaodong Lin', 'Haonan Lu', 'Yang Li', 'Peijun Qing', 'Dong Yang'] | 2022-10-27 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [-5.07426500e-01 2.31977925e-01 -5.40380716e-01 -3.66946220e-01
-4.56811965e-01 -4.16067183e-01 1.96741924e-01 3.30700815e-01
-3.07704926e-01 5.77024579e-01 2.58263946e-01 -2.78530866e-01
-6.10707641e-01 -1.45088696e+00 -8.05407047e-01 -3.47316712e-01
-1.06502231e-02 6.20071590e-01 8.94712389e-01 -1.57578662... | [9.017930030822754, 7.715435028076172] |
e46eda07-8ebf-4f0b-9f13-00aa9f8474b7 | speeding-up-the-hyperparameter-optimization | 1807.07362 | null | http://arxiv.org/abs/1807.07362v1 | http://arxiv.org/pdf/1807.07362v1.pdf | Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks | Most learning algorithms require the practitioner to manually set the values
of many hyperparameters before the learning process can begin. However, with
modern algorithms, the evaluation of a given hyperparameter setting can take a
considerable amount of time and the search space is often very
high-dimensional. We sug... | ['Nicolás Navarro-Guerrero', 'Tobias Hinz', 'Sven Magg', 'Stefan Wermter'] | 2018-07-19 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [ 1.12804413e-01 -2.23632812e-01 -1.19132578e-01 -2.64412344e-01
-8.24114501e-01 -7.24310517e-01 4.88405973e-01 1.91951662e-01
-7.19101667e-01 7.93556094e-01 -2.14330573e-02 -3.85317087e-01
-4.96658623e-01 -7.62280166e-01 -2.86609411e-01 -9.87276971e-01
1.81703940e-01 1.18462288e+00 2.23022595e-01 -3.84674817... | [6.674197196960449, 4.0192551612854] |
e4ee16f6-2447-4f86-bc46-a07c9f95851a | convolutional-neural-networks-applied-to-sky | 2005.11246 | null | https://arxiv.org/abs/2005.11246v1 | https://arxiv.org/pdf/2005.11246v1.pdf | Convolutional Neural Networks applied to sky images for short-term solar irradiance forecasting | Despite the advances in the field of solar energy, improvements of solar forecasting techniques, addressing the intermittent electricity production, remain essential for securing its future integration into a wider energy supply. A promising approach to anticipate irradiance changes consists of modeling the cloud cover... | ['Quentin Paletta', 'Joan Lasenby'] | 2020-05-22 | null | null | null | null | ['solar-irradiance-forecasting'] | ['time-series'] | [ 2.01005444e-01 -3.00641328e-01 2.59900481e-01 -4.98383075e-01
-4.27632853e-02 -8.41335237e-01 1.06982315e+00 -4.65745628e-02
-3.11252102e-02 9.73815739e-01 1.81556925e-01 -6.06052101e-01
-2.36188486e-01 -1.05554247e+00 -5.70266247e-01 -1.02713919e+00
-3.76024365e-01 -1.93630800e-01 -2.79145956e-01 -4.66921717... | [6.330924987792969, 2.766744375228882] |
d9c335ff-4bb8-438b-afcd-53d7932dc498 | dynamic-term-structure-models-with | 2305.11001 | null | https://arxiv.org/abs/2305.11001v1 | https://arxiv.org/pdf/2305.11001v1.pdf | Dynamic Term Structure Models with Nonlinearities using Gaussian Processes | The importance of unspanned macroeconomic variables for Dynamic Term Structure Models has been intensively discussed in the literature. To our best knowledge the earlier studies considered only linear interactions between the economy and the real-world dynamics of interest rates in DTSMs. We propose a generalized model... | ['Nikolaos Karouzakis', 'Konstantinos Kalogeropoulos', 'Tomasz Dubiel-Teleszynski'] | 2023-05-18 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-2.54532516e-01 1.40359119e-01 1.49794951e-01 -1.37660384e-01
-5.62123597e-01 -8.21791351e-01 1.29439783e+00 -1.58590171e-02
-4.91596669e-01 8.62941206e-01 2.90782064e-01 -8.25688243e-01
-4.04889226e-01 -9.41389263e-01 -4.13240224e-01 -9.25523281e-01
-2.86585577e-02 7.21482277e-01 -1.58701062e-01 -1.02536313... | [5.701104640960693, 3.9497885704040527] |
6667b46e-28bc-4596-83c8-614459595b3a | autotoon-automatic-geometric-warping-for-face | 2004.02377 | null | https://arxiv.org/abs/2004.02377v1 | https://arxiv.org/pdf/2004.02377v1.pdf | AutoToon: Automatic Geometric Warping for Face Cartoon Generation | Caricature, a type of exaggerated artistic portrait, amplifies the distinctive, yet nuanced traits of human faces. This task is typically left to artists, as it has proven difficult to capture subjects' unique characteristics well using automated methods. Recent development of deep end-to-end methods has achieved promi... | ['Yannick Hold-Geoffroy', 'Jingwan Lu', 'Julia Gong'] | 2020-04-06 | null | null | null | null | ['caricature'] | ['computer-vision'] | [ 3.36956561e-01 2.85900235e-01 2.08045572e-01 -6.90687120e-01
-5.26976645e-01 -7.48773754e-01 7.44594157e-01 -6.37205541e-01
5.08557335e-02 6.30637944e-01 5.65098405e-01 2.76842654e-01
1.58499762e-01 -4.49534297e-01 -5.95284164e-01 -3.25170010e-01
2.78138995e-01 3.21665138e-01 -4.76850599e-01 -5.66384852... | [12.230623245239258, -0.33452433347702026] |
bd5fc5e2-2fb3-4ba5-bafe-17ea9d42ad31 | comparative-study-of-machine-learning-models-1 | 2202.03156 | null | https://arxiv.org/abs/2202.03156v1 | https://arxiv.org/pdf/2202.03156v1.pdf | Comparative Study of Machine Learning Models for Stock Price Prediction | In this work, we apply machine learning techniques to historical stock prices to forecast future prices. To achieve this, we use recursive approaches that are appropriate for handling time series data. In particular, we apply a linear Kalman filter and different varieties of long short-term memory (LSTM) architectures ... | ['Sasha S. Yamada', 'Ogulcan E. Orsel'] | 2022-01-31 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-6.23525202e-01 -2.97209799e-01 1.21455304e-02 -1.83675930e-01
-3.98456722e-01 -9.10167098e-01 8.96282971e-01 -1.23445727e-01
-5.74092090e-01 9.40409660e-01 1.92585289e-01 -8.92770648e-01
-1.53272182e-01 -1.21357882e+00 -7.43317246e-01 -4.76723015e-01
-2.24729806e-01 2.49036446e-01 1.14676870e-01 -3.56426507... | [4.538477897644043, 4.168341159820557] |
d375592b-7ac1-4954-8106-e0fadf6fa5b3 | on-vision-features-in-multimodal-machine-1 | 2203.09173 | null | https://arxiv.org/abs/2203.09173v1 | https://arxiv.org/pdf/2203.09173v1.pdf | On Vision Features in Multimodal Machine Translation | Previous work on multimodal machine translation (MMT) has focused on the way of incorporating vision features into translation but little attention is on the quality of vision models. In this work, we investigate the impact of vision models on MMT. Given the fact that Transformer is becoming popular in computer vision,... | ['Jingbo Zhu', 'Anxiang Ma', 'Tong Xiao', 'Tao Zhou', 'Zefan Zhou', 'Chuanhao Lv', 'Bei Li'] | 2022-03-17 | null | https://aclanthology.org/2022.acl-long.438 | https://aclanthology.org/2022.acl-long.438.pdf | acl-2022-5 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 1.69398144e-01 3.00340215e-03 -1.94158882e-01 -1.63658723e-01
-8.70120108e-01 -6.10157490e-01 9.23500240e-01 -3.85115296e-01
-3.37490052e-01 4.09663230e-01 5.22712529e-01 -6.04642749e-01
5.01914203e-01 -3.77407312e-01 -9.83414114e-01 -3.72742444e-01
5.47766864e-01 3.13252926e-01 -4.69428627e-03 -2.83004075... | [11.343172073364258, 1.4568474292755127] |
6f6723b0-9736-4d4a-9e65-7f686a7257d6 | quadratic-decomposable-submodular-function-1 | 1902.10132 | null | https://arxiv.org/abs/1902.10132v4 | https://arxiv.org/pdf/1902.10132v4.pdf | Quadratic Decomposable Submodular Function Minimization: Theory and Practice (Computation and Analysis of PageRank over Hypergraphs) | We introduce a new convex optimization problem, termed quadratic decomposable submodular function minimization (QDSFM), which allows to model a number of learning tasks on graphs and hypergraphs. The problem exhibits close ties to decomposable submodular function minimization (DSFM), yet is much more challenging to sol... | ['Olgica Milenkovic', 'Niao He', 'Pan Li'] | 2019-02-26 | null | null | null | null | ['hypergraph-partitioning'] | ['graphs'] | [ 2.42834046e-01 5.34243345e-01 -6.44117296e-01 -1.03504121e-01
-1.08947241e+00 -7.63682306e-01 1.85893372e-01 1.31210297e-01
1.24317877e-01 8.87975097e-01 8.47789943e-02 -2.85234630e-01
-7.24509537e-01 -7.42959738e-01 -9.59627330e-01 -8.14414442e-01
-1.63577363e-01 1.16289675e+00 5.43664284e-02 -4.03856486... | [7.000067234039307, 5.019751071929932] |
d83d3425-9c69-43f1-861b-b31fbb4758cd | convergence-to-the-fixed-node-limit-in-deep | 2010.05316 | null | https://arxiv.org/abs/2010.05316v2 | https://arxiv.org/pdf/2010.05316v2.pdf | Convergence to the fixed-node limit in deep variational Monte Carlo | Variational quantum Monte Carlo (QMC) is an ab-initio method for solving the electronic Schr\"odinger equation that is exact in principle, but limited by the flexibility of the available ansatzes in practice. The recently introduced deep QMC approach, specifically two deep-neural-network ansatzes PauliNet and FermiNet,... | ['Frank Noé', 'Jan Hermann', 'Zeno Schätzle'] | 2020-10-11 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [-1.28597915e-01 -1.97666049e-01 1.58690706e-01 -1.04380026e-01
-1.04035842e+00 -2.02133045e-01 3.95625442e-01 -2.90446639e-01
-6.52553797e-01 1.24568117e+00 -2.72417217e-02 -4.57717061e-01
-4.65166062e-01 -8.29547524e-01 -5.43552637e-01 -1.32215333e+00
-1.53083563e-01 5.82674205e-01 -1.63223028e-01 -6.60346568... | [5.444202423095703, 5.110779285430908] |
1624e5f0-b6d5-4494-beb9-1b0203b62775 | video-action-recognition-with-attentive | 2303.09756 | null | https://arxiv.org/abs/2303.09756v1 | https://arxiv.org/pdf/2303.09756v1.pdf | Video Action Recognition with Attentive Semantic Units | Visual-Language Models (VLMs) have significantly advanced action video recognition. Supervised by the semantics of action labels, recent works adapt the visual branch of VLMs to learn video representations. Despite the effectiveness proved by these works, we believe that the potential of VLMs has yet to be fully harnes... | ['Wei Peng', 'Hao Li', 'Ruijin Liu', 'Dapeng Chen', 'Yifei Chen'] | 2023-03-17 | null | null | null | null | ['video-recognition'] | ['computer-vision'] | [ 3.41102958e-01 -1.60450473e-01 -8.24722409e-01 -2.42747590e-01
-7.22286105e-01 -3.68857980e-01 7.27122247e-01 -2.66172796e-01
-4.59580868e-01 4.59107578e-01 7.59048760e-01 3.29169571e-01
2.94248641e-01 -3.35063666e-01 -7.87438571e-01 -6.34647369e-01
-1.68584988e-01 -1.11037023e-01 5.39812624e-01 -6.18308559... | [8.641695022583008, 0.7655856609344482] |
420d32e8-f66f-4e71-9b7e-393198608224 | prediction-of-kidney-function-from-biopsy | 1702.01816 | null | http://arxiv.org/abs/1702.01816v1 | http://arxiv.org/pdf/1702.01816v1.pdf | Prediction of Kidney Function from Biopsy Images Using Convolutional Neural Networks | A Convolutional Neural Network was used to predict kidney function in
patients with chronic kidney disease from high-resolution digital pathology
scans of their kidney biopsies. Kidney biopsies were taken from participants of
the NEPTUNE study, a longitudinal cohort study whose goal is to set up
infrastructure for obse... | ['David Ledbetter', 'Long Ho', 'Kevin V Lemley'] | 2017-02-06 | null | null | null | null | ['kidney-function'] | ['medical'] | [ 1.78751007e-01 2.46338099e-02 -3.44865531e-01 -8.78742039e-01
-2.34318078e-01 -3.48380595e-01 1.09714396e-01 4.83011127e-01
-4.60856050e-01 4.92808491e-01 6.08621478e-01 -4.72110450e-01
-5.51859200e-01 -1.30764627e+00 -2.57742137e-01 -1.80588543e-01
-7.14372158e-01 1.21552122e+00 -2.71864176e-01 4.05622721... | [14.144893646240234, -2.455165386199951] |
26b82f51-af6c-4393-8aaa-e4cee2fc6c6a | 3d-shape-reconstruction-of-semi-transparent | 2304.14841 | null | https://arxiv.org/abs/2304.14841v1 | https://arxiv.org/pdf/2304.14841v1.pdf | 3D shape reconstruction of semi-transparent worms | 3D shape reconstruction typically requires identifying object features or textures in multiple images of a subject. This approach is not viable when the subject is semi-transparent and moving in and out of focus. Here we overcome these challenges by rendering a candidate shape with adaptive blurring and transparency fo... | ['David C. Hogg', 'Netta Cohen', 'Thomas Ranner', 'Omer Yuval', 'Thomas P. Ilett'] | 2023-04-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ilett_3D_Shape_Reconstruction_of_Semi-Transparent_Worms_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ilett_3D_Shape_Reconstruction_of_Semi-Transparent_Worms_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-shape-reconstruction'] | ['computer-vision'] | [ 5.99598527e-01 -2.49832287e-01 8.25934470e-01 4.28495258e-02
-2.10584193e-01 -8.82695794e-01 6.60580218e-01 4.72058691e-02
-8.35956633e-01 6.87834680e-01 -2.83032894e-01 -2.62173153e-02
1.34575590e-01 -1.79496735e-01 -9.05182540e-01 -7.87012935e-01
-3.94243568e-01 6.34039879e-01 5.51937521e-01 1.71738952... | [13.303853034973145, -3.044729232788086] |
4b6a2a6c-ad17-4bf8-8057-47cf56f8d78e | comparative-analysis-of-melodia-and-time | null | null | https://aclanthology.org/2021.smp-1.4 | https://aclanthology.org/2021.smp-1.4.pdf | Comparative Analysis of Melodia and Time-Domain Adaptive Filtering based Model for Melody Extraction from Polyphonic Music | Among the many applications of Music Information Retrieval (MIR), melody extraction is one of the most essential. It has risen to the top of the list of current research challenges in the field of MIR applications. We now need new means of defining, indexing, finding, and interacting with musical information, given the... | ['Yeshwant Singh', 'Pinki Roy', 'Anupam Biswas', 'Ranjeet Kumar'] | null | null | null | null | smp-icon-2021-12 | ['melody-extraction', 'music-information-retrieval'] | ['music', 'music'] | [ 3.96698177e-01 -5.31891286e-01 1.59458920e-01 2.37661943e-01
-9.53124881e-01 -9.09482360e-01 3.47839296e-01 2.76680738e-01
-4.58779752e-01 3.77877355e-01 2.89086908e-01 1.02633432e-01
-8.27082813e-01 -4.62731630e-01 9.59794596e-02 -6.39279842e-01
-2.04612195e-01 2.47629762e-01 2.51427352e-01 -5.26204348... | [15.979849815368652, 5.24856424331665] |
08b2488d-f5ce-4c7c-9639-d309a82e24e8 | learning-to-rank-query-graphs-for-complex | 1811.01118 | null | http://arxiv.org/abs/1811.01118v1 | http://arxiv.org/pdf/1811.01118v1.pdf | Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs | In this paper, we conduct an empirical investigation of neural query graph
ranking approaches for the task of complex question answering over knowledge
graphs. We experiment with six different ranking models and propose a novel
self-attention based slot matching model which exploits the inherent structure
of query grap... | ['Denis Lukovnikov', 'Gaurav Maheshwari', 'Priyansh Trivedi', 'Asja Fischer', 'Nilesh Chakraborty', 'Jens Lehmann'] | 2018-11-02 | null | null | null | null | ['graph-ranking'] | ['graphs'] | [ 1.30785838e-01 7.62711942e-01 -4.41871226e-01 -3.96919787e-01
-9.26275611e-01 -4.73065555e-01 5.27617753e-01 4.66632873e-01
-4.61994022e-01 8.32820833e-01 4.19990152e-01 -4.46212888e-01
-7.27264524e-01 -1.13651061e+00 -8.63870382e-01 5.38082719e-02
-5.67712402e-03 1.01655209e+00 6.36571288e-01 -8.48955214... | [10.509058952331543, 7.980247974395752] |
6b9229c2-54e1-417d-b248-baa7fde010f4 | lsvc-a-learning-based-stereo-video | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chen_LSVC_A_Learning-Based_Stereo_Video_Compression_Framework_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_LSVC_A_Learning-Based_Stereo_Video_Compression_Framework_CVPR_2022_paper.pdf | LSVC: A Learning-Based Stereo Video Compression Framework | In this work, we propose the first end-to-end optimized framework for compressing automotive stereo videos (i.e., stereo videos from autonomous driving applications) from both left and right views. Specifically, when compressing the current frame from each view, our framework reduces temporal redundancy by performi... | ['Dong Xu', 'Wei Jiang', 'Shan Liu', 'Zhihao Hu', 'Guo Lu', 'Zhenghao Chen'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['motion-compensation'] | ['computer-vision'] | [ 3.46119195e-01 -1.34215578e-01 -2.45837927e-01 -4.11829025e-01
-7.16568112e-01 -2.62290597e-01 4.61878568e-01 -5.64693868e-01
-2.63345480e-01 4.37698811e-01 3.44191223e-01 -2.97012776e-01
2.11901888e-01 -6.28874719e-01 -1.05456758e+00 -6.54010475e-01
3.11233044e-01 -1.18629821e-01 3.73150647e-01 -1.61031350... | [10.905790328979492, -1.5941109657287598] |
af750588-6747-49c6-9fcb-85d8783fcdc9 | video-coding-for-machines-a-paradigm-of | 2001.03569 | null | https://arxiv.org/abs/2001.03569v2 | https://arxiv.org/pdf/2001.03569v2.pdf | Video Coding for Machines: A Paradigm of Collaborative Compression and Intelligent Analytics | Video coding, which targets to compress and reconstruct the whole frame, and feature compression, which only preserves and transmits the most critical information, stand at two ends of the scale. That is, one is with compactness and efficiency to serve for machine vision, and the other is with full fidelity, bowing to ... | ['Ling-Yu Duan', 'Jiaying Liu', 'Wen Gao', 'Wenhan Yang', 'Tiejun Huang'] | 2020-01-10 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 5.06933093e-01 -8.18875507e-02 -2.93110579e-01 -1.16054334e-01
-3.39277864e-01 1.31346345e-01 6.22571647e-01 -1.39884979e-01
-2.86824971e-01 2.34130383e-01 3.00435215e-01 2.99152672e-01
-3.65031362e-01 -6.54211283e-01 -4.87170815e-01 -6.63038015e-01
-2.63498485e-01 -1.70693561e-01 3.39704081e-02 -2.10140646... | [11.280608177185059, -1.5477875471115112] |
c8b98265-f8c9-4c14-bfa4-aee5a171fd5c | efficient-deep-learning-models-for-land-cover | 2111.09451 | null | https://arxiv.org/abs/2111.09451v3 | https://arxiv.org/pdf/2111.09451v3.pdf | Benchmarking and scaling of deep learning models for land cover image classification | The availability of the sheer volume of Copernicus Sentinel-2 imagery has created new opportunities for exploiting deep learning (DL) methods for land use land cover (LULC) image classification. However, an extensive set of benchmark experiments is currently lacking, i.e. DL models tested on the same dataset, with a co... | ['Christos Tryfonopoulos', 'Dimitrios Michail', 'Angelos Zavras', 'Nikolaos-Ioannis Bountos', 'Ioannis Papoutsis'] | 2021-11-18 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 1.94065139e-01 -7.38205835e-02 -4.26726311e-01 -3.59038502e-01
-9.46533680e-01 -5.40552258e-01 4.51526612e-01 -1.22096390e-01
-7.59364426e-01 8.02708745e-01 -9.73384529e-02 -6.63671494e-01
8.94502029e-02 -9.97417808e-01 -9.66742456e-01 -7.85853386e-01
-5.06201804e-01 5.10933638e-01 6.22013807e-02 -3.73532861... | [9.550385475158691, -1.4520916938781738] |
1afb79b6-1770-4289-987e-35f01c424377 | u-pass-an-uncertainty-guided-deep-learning | 2306.04663 | null | https://arxiv.org/abs/2306.04663v1 | https://arxiv.org/pdf/2306.04663v1.pdf | U-PASS: an Uncertainty-guided deep learning Pipeline for Automated Sleep Staging | As machine learning becomes increasingly prevalent in critical fields such as healthcare, ensuring the safety and reliability of machine learning systems becomes paramount. A key component of reliability is the ability to estimate uncertainty, which enables the identification of areas of high and low confidence and hel... | ['Maarten De Vos', 'Mihaela van der Schaar', 'Dries Testelmans', 'Bertien Buyse', 'Nabeel Seedat', 'Elisabeth R. M. Heremans'] | 2023-06-07 | null | null | null | null | ['sleep-staging'] | ['medical'] | [ 1.51320040e-01 4.99019563e-01 -2.93279290e-01 -6.77120030e-01
-1.27217579e+00 -3.43450904e-01 1.41755641e-01 7.08268285e-01
-6.34731412e-01 8.43104303e-01 1.85537681e-01 -5.99271953e-01
-8.99063498e-02 -3.94802988e-01 -6.19410157e-01 -3.84902567e-01
-9.00992826e-02 8.47618699e-01 -7.44503457e-04 5.04888773... | [14.416688919067383, -2.017158031463623] |
ca3f67e3-3752-4509-b07c-9d0cbc6f4653 | joint-covariate-alignment-and-concept | 2208.00898 | null | https://arxiv.org/abs/2208.00898v1 | https://arxiv.org/pdf/2208.00898v1.pdf | Joint covariate-alignment and concept-alignment: a framework for domain generalization | In this paper, we propose a novel domain generalization (DG) framework based on a new upper bound to the risk on the unseen domain. Particularly, our framework proposes to jointly minimize both the covariate-shift as well as the concept-shift between the seen domains for a better performance on the unseen domain. While... | ['Shuchin Aeron', 'Matthias Scheutz', 'Prakash Ishwar', 'Boyang Lyu', 'Thuan Nguyen'] | 2022-08-01 | null | null | null | null | ['concept-alignment'] | ['computer-vision'] | [ 3.10608298e-01 4.11062315e-02 1.13352433e-01 -6.60543442e-01
-9.62784350e-01 -6.60915732e-01 6.06635332e-01 4.37223136e-01
-5.45284748e-01 6.51452661e-01 -5.21275550e-02 -7.47861713e-02
-5.30872941e-01 -6.40567541e-01 -4.96630907e-01 -8.30730319e-01
2.87362278e-01 6.65180743e-01 2.36005470e-01 -2.39669323... | [10.38026237487793, 3.161330461502075] |
a0244fa5-5873-4924-8bbb-e0037c5943bc | multi-feature-distance-metric-learning-for | 1901.03031 | null | http://arxiv.org/abs/1901.03031v1 | http://arxiv.org/pdf/1901.03031v1.pdf | Multi-feature Distance Metric Learning for Non-rigid 3D Shape Retrieval | In the past decades, feature-learning-based 3D shape retrieval approaches
have been received widespread attention in the computer graphic community.
These approaches usually explored the hand-crafted distance metric or
conventional distance metric learning methods to compute the similarity of the
single feature. The si... | ['Huibing Wang', 'Haohao Li', 'Xianping Fu'] | 2019-01-10 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [-3.39351088e-01 -7.72959173e-01 -1.13121018e-01 -3.55533540e-01
-9.49542642e-01 -5.98123014e-01 6.65686488e-01 4.20950651e-02
-1.73968360e-01 1.28845900e-01 1.30225092e-01 6.58265948e-02
-7.70081639e-01 -7.58834541e-01 -8.53344705e-03 -9.20894802e-01
2.58014381e-01 5.16107559e-01 3.31500471e-01 -7.06946701... | [8.167049407958984, -3.854463815689087] |
e1d07d69-ca85-4bb6-9527-8cf328d6cd82 | the-chai-platform-s-ai-safety-framework | 2306.02979 | null | https://arxiv.org/abs/2306.02979v1 | https://arxiv.org/pdf/2306.02979v1.pdf | The Chai Platform's AI Safety Framework | Chai empowers users to create and interact with customized chatbots, offering unique and engaging experiences. Despite the exciting prospects, the work recognizes the inherent challenges of a commitment to modern safety standards. Therefore, this paper presents the integrated AI safety principles into Chai to prioritiz... | ['William Beauchamp', 'Zongyi Liu', 'Aleksey Korshuk', 'Xiaoding Lu'] | 2023-06-05 | null | null | null | null | ['chatbot', 'chatbot'] | ['methodology', 'natural-language-processing'] | [ 1.47770792e-01 6.07447684e-01 9.70254373e-03 1.63968503e-02
-3.11951876e-01 -7.67951667e-01 8.69447410e-01 4.80368882e-02
-3.25222880e-01 6.34801090e-01 5.33185720e-01 -2.80906230e-01
-3.87430966e-01 -3.34143698e-01 -1.17581010e-01 -2.65473813e-01
1.32826433e-01 1.25100851e-01 -1.08924456e-01 -5.60675502... | [9.136704444885254, 6.394668102264404] |
acd6e0e7-c053-4648-8849-a813506a5a6a | adversarial-robustness-of-representation | 2210.00122 | null | https://arxiv.org/abs/2210.00122v1 | https://arxiv.org/pdf/2210.00122v1.pdf | Adversarial Robustness of Representation Learning for Knowledge Graphs | Knowledge graphs represent factual knowledge about the world as relationships between concepts and are critical for intelligent decision making in enterprise applications. New knowledge is inferred from the existing facts in the knowledge graphs by encoding the concepts and relations into low-dimensional feature vector... | ['Peru Bhardwaj'] | 2022-09-30 | null | null | null | null | ['data-poisoning', 'knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['adversarial', 'graphs', 'methodology'] | [ 1.21673737e-02 8.04695010e-01 -4.10994917e-01 -4.87628346e-03
1.11668192e-01 -7.80456424e-01 5.84335089e-01 5.22498786e-01
-1.88452393e-01 8.37755561e-01 5.44410013e-02 -7.43345022e-01
-4.89632398e-01 -1.43582940e+00 -9.75377142e-01 -3.74650717e-01
-2.91855186e-01 3.97403717e-01 2.81753778e-01 -5.50150216... | [6.457762718200684, 7.429534435272217] |
e9af178b-db3b-419a-849b-fe9c04672a2f | contaminated-speech-training-methods-for | 1710.03538 | null | http://arxiv.org/abs/1710.03538v1 | http://arxiv.org/pdf/1710.03538v1.pdf | Contaminated speech training methods for robust DNN-HMM distant speech recognition | Despite the significant progress made in the last years, state-of-the-art
speech recognition technologies provide a satisfactory performance only in the
close-talking condition. Robustness of distant speech recognition in adverse
acoustic conditions, on the other hand, remains a crucial open issue for future
applicatio... | ['Mirco Ravanelli', 'Maurizio Omologo'] | 2017-10-10 | null | null | null | null | ['distant-speech-recognition'] | ['speech'] | [ 3.89768124e-01 -8.07135329e-02 4.69830185e-01 -4.84245777e-01
-1.07561684e+00 -1.35848716e-01 6.43291593e-01 7.03459233e-02
-5.99297822e-01 6.16981447e-01 1.20942175e-01 -3.31086010e-01
-5.42565137e-02 -2.55942762e-01 -2.99744457e-01 -1.01742649e+00
4.64913324e-02 1.45279109e-01 5.81655383e-01 -3.74163926... | [14.845169067382812, 5.848887920379639] |
ea102079-876e-4425-a404-c139dc17c1c6 | video-driven-neural-physically-based-facial | 2202.05592 | null | https://arxiv.org/abs/2202.05592v4 | https://arxiv.org/pdf/2202.05592v4.pdf | Video-driven Neural Physically-based Facial Asset for Production | Production-level workflows for producing convincing 3D dynamic human faces have long relied on an assortment of labor-intensive tools for geometry and texture generation, motion capture and rigging, and expression synthesis. Recent neural approaches automate individual components but the corresponding latent representa... | ['Jingyi Yu', 'Lan Xu', 'Wei Yang', 'Ruixiang Cao', 'Hongyang Lin', 'Qixuan Zhang', 'Chuxiao Zeng', 'Longwen Zhang'] | 2022-02-11 | null | null | null | null | ['texture-synthesis', 'motion-retargeting'] | ['computer-vision', 'computer-vision'] | [ 1.95899442e-01 -3.40705663e-02 3.23473334e-01 -3.12171042e-01
-7.11134911e-01 -6.26567483e-01 6.97294414e-01 -7.96974540e-01
2.65523851e-01 4.63582993e-01 -3.50286551e-02 2.50539511e-01
-5.48268184e-02 -8.10063064e-01 -9.06202853e-01 -7.11008787e-01
1.90717325e-01 3.44631821e-01 -3.68132770e-01 -4.11040664... | [12.732492446899414, -0.3994024097919464] |
98e30395-7d13-4e42-ae6d-05fa87068c6f | tased-net-temporally-aggregating-spatial | 1908.05786 | null | https://arxiv.org/abs/1908.05786v1 | https://arxiv.org/pdf/1908.05786v1.pdf | TASED-Net: Temporally-Aggregating Spatial Encoder-Decoder Network for Video Saliency Detection | TASED-Net is a 3D fully-convolutional network architecture for video saliency detection. It consists of two building blocks: first, the encoder network extracts low-resolution spatiotemporal features from an input clip of several consecutive frames, and then the following prediction network decodes the encoded features... | ['Kyle Min', 'Jason J. Corso'] | 2019-08-15 | tased-net-temporally-aggregating-spatial-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Min_TASED-Net_Temporally-Aggregating_Spatial_Encoder-Decoder_Network_for_Video_Saliency_Detection_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Min_TASED-Net_Temporally-Aggregating_Spatial_Encoder-Decoder_Network_for_Video_Saliency_Detection_ICCV_2019_paper.pdf | iccv-2019-10 | ['video-saliency-detection'] | ['computer-vision'] | [ 3.92494410e-01 -8.53642151e-02 -5.25300801e-01 -2.54646480e-01
-5.03026664e-01 -3.75219658e-02 3.64663929e-01 3.77605818e-02
-2.82321751e-01 5.65225184e-01 4.97065872e-01 8.13519023e-03
2.52160847e-01 -5.28141499e-01 -1.01474822e+00 -3.03578436e-01
-4.66497689e-01 -2.74294734e-01 1.17752433e+00 -2.52066791... | [9.738151550292969, -0.2704229950904846] |
75f3ebf5-9899-4322-9442-d2cdbe2de513 | road-damage-detection-and-classification-in | 1811.04535 | null | http://arxiv.org/abs/1811.04535v1 | http://arxiv.org/pdf/1811.04535v1.pdf | Road Damage Detection And Classification In Smartphone Captured Images Using Mask R-CNN | This paper summarizes the design, experiments and results of our solution to
the Road Damage Detection and Classification Challenge held as part of the 2018
IEEE International Conference On Big Data Cup. Automatic detection and
classification of damage in roads is an essential problem for multiple
applications like mai... | ['Shashank Shekhar', 'Janpreet Singh'] | 2018-11-12 | null | null | null | null | ['road-damage-detection'] | ['computer-vision'] | [-4.66344990e-02 -1.44039914e-01 -7.06505543e-03 -3.59188259e-01
-7.83525050e-01 -2.46249527e-01 3.12594801e-01 -1.85009819e-02
-5.36588788e-01 6.42960608e-01 -3.15513581e-01 -4.32100326e-01
1.28262788e-01 -1.19482636e+00 -1.06162369e+00 -5.49640179e-01
1.63853109e-01 3.45417231e-01 6.16357923e-01 -1.48223992... | [7.431071758270264, 1.126306176185608] |
614cbf95-0335-402e-b839-531ccbf6128c | cmu-arc-factored-discriminative-semantic | null | null | https://aclanthology.org/S14-2027 | https://aclanthology.org/S14-2027.pdf | CMU: Arc-Factored, Discriminative Semantic Dependency Parsing | null | ["Brendan O{'}Connor", 'Jesse Dodge', 'Jeffrey Flanigan', 'Sam Thomson', 'Noah A. Smith', 'David Bamman', 'Chris Dyer', 'Swabha Swayamdipta', 'Nathan Schneider'] | 2014-08-01 | null | null | null | semeval-2014-8 | ['semantic-dependency-parsing'] | ['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... | [-7.333983898162842, 3.7131567001342773] |
7c35be4b-bdc5-4717-ac64-f3ee290e415c | joint-device-edge-digital-semantic | 2305.13553 | null | https://arxiv.org/abs/2305.13553v1 | https://arxiv.org/pdf/2305.13553v1.pdf | Joint Device-Edge Digital Semantic Communication with Adaptive Network Split and Learned Non-Linear Quantization | Semantic communication, an intelligent communication paradigm that aims to transmit useful information in the semantic domain, is facilitated by deep learning techniques. Although robust semantic features can be learned and transmitted in an analog fashion, it poses new challenges to hardware, protocol, and encryption.... | ['Bo Ai', 'Yuxuan Sun', 'Wei Chen', 'Lei Guo'] | 2023-05-22 | null | null | null | null | ['intelligent-communication'] | ['time-series'] | [ 7.43823886e-01 1.63087711e-01 -3.33339542e-01 -6.47401392e-01
-5.49034417e-01 -1.50871128e-01 4.33449149e-01 9.94557589e-02
-5.35601079e-01 6.41302466e-01 1.67917103e-01 -2.12386549e-01
-3.97068292e-01 -1.01830781e+00 -5.59787333e-01 -7.57847190e-01
-9.18860957e-02 -8.38689208e-02 1.50686383e-01 -8.80179107... | [11.324740409851074, -1.5889872312545776] |
7e42bf46-fc6e-4a2a-93ed-fae403ba7412 | investigating-explainability-of-generative-ai | 2202.04903 | null | https://arxiv.org/abs/2202.04903v1 | https://arxiv.org/pdf/2202.04903v1.pdf | Investigating Explainability of Generative AI for Code through Scenario-based Design | What does it mean for a generative AI model to be explainable? The emergent discipline of explainable AI (XAI) has made great strides in helping people understand discriminative models. Less attention has been paid to generative models that produce artifacts, rather than decisions, as output. Meanwhile, generative AI (... | ['Justin D. Weisz', 'Kartik Talamadupula', 'Stephanie Houde', 'Mayank Agarwal', 'Michael Muller', 'Q. Vera Liao', 'Jiao Sun'] | 2022-02-10 | null | null | null | null | ['code-translation'] | ['computer-code'] | [ 3.42655033e-01 9.39214230e-01 2.71984339e-01 -6.35819495e-01
-3.63310307e-01 -4.31957960e-01 5.64369977e-01 -2.52926201e-01
1.00590670e+00 2.18867227e-01 6.55692697e-01 -5.76858819e-01
-4.05721158e-01 -6.20409250e-01 -5.75387836e-01 1.60058618e-01
2.02455491e-01 6.22138560e-01 -6.69449389e-01 -4.47615564... | [8.094544410705566, 7.495131492614746] |
92696544-771f-40f9-82ab-f8073c11b1ad | learning-from-multi-view-representation-for | 2306.02558 | null | https://arxiv.org/abs/2306.02558v1 | https://arxiv.org/pdf/2306.02558v1.pdf | Learning from Multi-View Representation for Point-Cloud Pre-Training | A critical problem in the pre-training of 3D point clouds is leveraging massive 2D data. A fundamental challenge is to address the 2D-3D domain gap. This paper proposes a novel approach to point-cloud pre-training that enables learning 3D representations by leveraging pre-trained 2D-based networks. In particular, it av... | ['QiXing Huang', 'Youkang Kong', 'Chen Song', 'Siming Yan'] | 2023-06-05 | null | null | null | null | ['point-cloud-pre-training'] | ['computer-vision'] | [ 7.00074658e-02 2.03905553e-01 -1.42743558e-01 -5.10508001e-01
-9.12484944e-01 -6.67200267e-01 5.30029595e-01 -1.51762128e-01
-4.38834056e-02 -7.99641162e-02 -2.32055232e-01 -2.61561453e-01
-5.52433506e-02 -8.26835096e-01 -1.21195531e+00 -4.23461199e-01
4.47905511e-02 5.92025101e-01 2.13384554e-01 -5.42114340... | [8.158373832702637, -3.38466477394104] |
bcd46a45-d35e-4095-bd36-e76daa57df8f | meta-learning-for-multi-objective | 1811.03376 | null | https://arxiv.org/abs/1811.03376v2 | https://arxiv.org/pdf/1811.03376v2.pdf | Meta-Learning for Multi-objective Reinforcement Learning | Multi-objective reinforcement learning (MORL) is the generalization of standard reinforcement learning (RL) approaches to solve sequential decision making problems that consist of several, possibly conflicting, objectives. Generally, in such formulations, there is no single optimal policy which optimizes all the object... | ['Patric Jensfelt', 'Mårten Björkman', 'Ali Ghadirzadeh', 'Xi Chen'] | 2018-11-08 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 2.43034139e-02 -8.29197187e-03 -6.47975028e-01 -6.38224557e-02
-9.21117842e-01 -4.98559386e-01 2.85137385e-01 2.91215658e-01
-6.69265866e-01 1.42111146e+00 3.76267321e-02 -1.97545305e-01
-8.00288737e-01 -5.51136613e-01 -4.80970800e-01 -9.09629941e-01
-1.87667876e-01 8.85414004e-01 -2.31922537e-01 -1.27817884... | [4.310564041137695, 2.4032630920410156] |
050930d7-4390-4793-9556-2495691176a5 | topic-aware-response-generation-in-task-1 | 2212.05373 | null | https://arxiv.org/abs/2212.05373v1 | https://arxiv.org/pdf/2212.05373v1.pdf | Topic-Aware Response Generation in Task-Oriented Dialogue with Unstructured Knowledge Access | To alleviate the problem of structured databases' limited coverage, recent task-oriented dialogue systems incorporate external unstructured knowledge to guide the generation of system responses. However, these usually use word or sentence level similarities to detect the relevant knowledge context, which only partially... | ['Ignacio Iacobacci', 'Gerasimos Lampouras', 'Yue Feng'] | 2022-12-10 | null | null | null | null | ['response-generation', 'task-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.82687029e-01 7.39066899e-01 -1.48940176e-01 -4.31114614e-01
-1.36185825e+00 -5.78073025e-01 1.11705863e+00 2.20488757e-01
-5.52778184e-01 1.35611999e+00 8.73654962e-01 4.82569933e-02
-6.25401549e-03 -5.81234992e-01 -1.17405273e-01 -2.60204792e-01
4.25508678e-01 1.08127427e+00 5.49525797e-01 -9.67106938... | [12.439592361450195, 8.09168815612793] |
57bb4ba8-36f2-4160-bf35-089143bad4f4 | spatiotemporally-discriminative-video | 2303.16341 | null | https://arxiv.org/abs/2303.16341v1 | https://arxiv.org/pdf/2303.16341v1.pdf | Spatiotemporally Discriminative Video-Language Pre-Training with Text Grounding | Most of existing video-language pre-training methods focus on instance-level alignment between video clips and captions via global contrastive learning but neglect rich fine-grained local information, which is of importance to downstream tasks requiring temporal localization and semantic reasoning. In this work, we pro... | ['Liangzhe Yuan', 'Cho-Jui Hsieh', 'Ting Liu', 'Florian Schroff', 'Ming-Hsuan Yang', 'Boqing Gong', 'Long Zhao', 'Yuanhao Xiong'] | 2023-03-28 | null | null | null | null | ['video-question-answering', 'video-retrieval', 'action-recognition-in-videos', 'action-localization'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.74967015e-01 -5.81268609e-01 -8.15640986e-01 -4.18379754e-01
-1.33499789e+00 -6.56336904e-01 7.57751226e-01 2.37983987e-01
-6.17183566e-01 1.82310537e-01 6.69622183e-01 1.81327939e-01
2.97806598e-02 -7.88241476e-02 -9.09072459e-01 -4.83772516e-01
-3.48959565e-01 3.90890129e-02 5.03015339e-01 -1.01177692... | [10.041863441467285, 0.7548792958259583] |
c09b811f-6e7e-4db3-949d-7ef9b022f7a0 | student-collaboration-improves-self | 2205.05194 | null | https://arxiv.org/abs/2205.05194v3 | https://arxiv.org/pdf/2205.05194v3.pdf | Multiplexed Immunofluorescence Brain Image Analysis Using Self-Supervised Dual-Loss Adaptive Masked Autoencoder | Reliable large-scale cell detection and segmentation is the fundamental first step to understanding biological processes in the brain. The ability to phenotype cells at scale can accelerate preclinical drug evaluation and system-level brain histology studies. The impressive advances in deep learning offer a practical s... | ['Hien V. Nguyen', 'Badri Roysam', 'Dragan Maric', 'Hung Q. Vo', 'Bai Lin', 'Son T. Ly'] | 2022-05-10 | null | null | null | null | ['self-supervised-image-classification', 'cell-detection'] | ['computer-vision', 'computer-vision'] | [ 3.48963678e-01 -3.89229238e-01 9.01923031e-02 -4.79606211e-01
-7.26244926e-01 -2.25858435e-01 2.66106069e-01 4.12789553e-01
-9.52950537e-01 1.15882885e+00 -5.30504405e-01 -4.32904996e-02
3.22359622e-01 -6.41119838e-01 -6.74340665e-01 -1.22968161e+00
-1.49140075e-01 6.00908518e-01 8.82228389e-02 1.05030484... | [14.671160697937012, -3.1455698013305664] |
b1cbf940-8f70-458b-9eb0-8bbb274a3f82 | relationrs-relationship-representation | 2110.06730 | null | https://arxiv.org/abs/2110.06730v1 | https://arxiv.org/pdf/2110.06730v1.pdf | RelationRS: Relationship Representation Network for Object Detection in Aerial Images | Object detection is a basic and important task in the field of aerial image processing and has gained much attention in computer vision. However, previous aerial image object detection approaches have insufficient use of scene semantic information between different regions of large-scale aerial images. In addition, com... | ['Jihong Xiu', 'Haipeng Kuang', 'Yu Liu', 'Qingjun Li', 'Pu Huang', 'Weifeng Sun', 'Bin Li', 'Chao Sun', 'Hao Wang', 'Chongyang Liu', 'Xuefei Zhang', 'Zhiming Liu'] | 2021-10-13 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 4.47557777e-01 -5.67089975e-01 2.64328778e-01 -1.65726185e-01
7.38298055e-03 -5.17721593e-01 2.93563277e-01 1.50151804e-01
-4.43973482e-01 1.19482554e-01 -4.34627056e-01 -9.49892327e-02
-1.51686400e-01 -1.18494058e+00 -4.62928593e-01 -6.75890386e-01
-9.90196243e-02 -3.97435427e-01 9.81217802e-01 -4.54206735... | [8.755706787109375, -0.8225314021110535] |
6eaf1e6c-1a99-40a8-b187-791fe9eac565 | learning-the-human-judgment-for-the-automatic | null | null | https://aclanthology.org/2020.lrec-1.198 | https://aclanthology.org/2020.lrec-1.198.pdf | Learning the Human Judgment for the Automatic Evaluation of Chatbot | It is hard to evaluate the quality of the generated text by a generative dialogue system. Currently, dialogue evaluation relies on human judges to label the quality of the generated text. It is not a reusable mechanism that can give consistent evaluation for system developers. We believe that it is easier to get consis... | ['Sheng-Lun Chien', 'Shih-Hung Wu'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['dialogue-evaluation'] | ['natural-language-processing'] | [-1.03883468e-01 6.70600772e-01 4.49369341e-01 -6.83278620e-01
-8.90581548e-01 -7.25849926e-01 5.32011986e-01 -5.32834008e-02
-3.31151068e-01 8.61811519e-01 1.62125081e-01 -2.92032301e-01
2.87662029e-01 -6.26984537e-01 -1.57603726e-01 -2.81919777e-01
8.71125162e-01 9.40577090e-01 1.59185648e-01 -5.57786942... | [12.768404006958008, 8.1710844039917] |
bc28ee20-8770-4fc2-8bde-20346ee8f9a2 | deep-gaussian-processes-for-air-quality | 2211.10174 | null | https://arxiv.org/abs/2211.10174v1 | https://arxiv.org/pdf/2211.10174v1.pdf | Deep Gaussian Processes for Air Quality Inference | Air pollution kills around 7 million people annually, and approximately 2.4 billion people are exposed to hazardous air pollution. Accurate, fine-grained air quality (AQ) monitoring is essential to control and reduce pollution. However, AQ station deployment is sparse, and thus air quality inference for unmonitored loc... | ['Nipun Batra', 'Zeel Patel', 'Sachin Yadav', 'Saagar Parikh', 'Eshan Gujarathi', 'Aadesh Desai'] | 2022-11-18 | null | null | null | null | ['air-quality-inference'] | ['miscellaneous'] | [-1.22049868e-01 -4.84607637e-01 7.40372157e-03 1.29172742e-01
-1.19100809e+00 -4.43681151e-01 5.56333601e-01 1.82306901e-01
-1.80504218e-01 1.19182050e+00 1.31552741e-01 -5.43694317e-01
-1.71895176e-01 -1.19670141e+00 -6.52546525e-01 -1.00677240e+00
2.68062919e-01 6.81265593e-01 2.08011474e-02 3.85829002... | [6.296631336212158, 2.5730698108673096] |
75973619-0c30-475b-9bf8-dc9a626ae77a | analyzing-the-impact-of-foursquare-and | 2006.07516 | null | https://arxiv.org/abs/2006.07516v1 | https://arxiv.org/pdf/2006.07516v1.pdf | Analyzing the Impact of Foursquare and Streetlight Data with Human Demographics on Future Crime Prediction | Finding the factors contributing to criminal activities and their consequences is essential to improve quantitative crime research. To respond to this concern, we examine an extensive set of features from different perspectives and explanations. Our study aims to build data-driven models for predicting future crime occ... | ['Lucas May Petry', 'Stan Matwin', 'Fateha Khanam Bappee', 'Amilcar Soares'] | 2020-06-13 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [-1.04698218e-01 -4.42995757e-01 -2.93797165e-01 -5.04432499e-01
-5.67666411e-01 -9.25086737e-02 6.06513679e-01 5.56850553e-01
-6.47786558e-01 8.22058737e-01 8.43422294e-01 -6.25650048e-01
-3.81386369e-01 -9.47123289e-01 -1.01688527e-01 -1.47342607e-01
1.58626422e-01 -1.62504986e-01 -1.53541937e-01 -2.88370758... | [6.733029365539551, 1.9425297975540161] |
6ece59b7-c1cc-4b02-91dd-ea6e61b95230 | autoregressive-structured-prediction-with | 2210.14698 | null | https://arxiv.org/abs/2210.14698v2 | https://arxiv.org/pdf/2210.14698v2.pdf | Autoregressive Structured Prediction with Language Models | Recent years have seen a paradigm shift in NLP towards using pretrained language models ({PLM}) for a wide range of tasks. However, there are many difficult design decisions to represent structures (e.g. tagged text, coreference chains) in a way such that they can be captured by PLMs. Prior work on structured predictio... | ['Mrinmaya Sachan', 'Ryan Cotterell', 'Nicholas Monath', 'Yuchen Jiang', 'Tianyu Liu'] | 2022-10-26 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 5.36108434e-01 8.03129673e-01 -6.06252670e-01 -7.11835027e-01
-1.11316490e+00 -7.29986250e-01 7.16403067e-01 1.61548287e-01
-1.18151754e-01 8.15874755e-01 8.51255000e-01 -4.68281716e-01
-6.93370551e-02 -3.87364626e-01 -7.58995533e-01 -3.77151668e-01
-4.02063839e-02 1.18451858e+00 2.30595946e-01 -1.27858752... | [9.885522842407227, 9.087960243225098] |
5188ba54-463a-406f-be41-4476d2719c6f | pointgrow-autoregressively-learned-point | 1810.05591 | null | https://arxiv.org/abs/1810.05591v3 | https://arxiv.org/pdf/1810.05591v3.pdf | PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention | Generating 3D point clouds is challenging yet highly desired. This work presents a novel autoregressive model, PointGrow, which can generate diverse and realistic point cloud samples from scratch or conditioned on semantic contexts. This model operates recurrently, with each point sampled according to a conditional dis... | ['Yongbin Sun', 'Joshua E. Siegel', 'Yue Wang', 'Ziwei Liu', 'Sanjay E. Sarma'] | 2018-10-12 | null | null | null | null | ['point-cloud-generation', 'generating-3d-point-clouds'] | ['computer-vision', 'computer-vision'] | [ 1.08618978e-02 5.98230958e-02 7.23122135e-02 -5.27541041e-01
-8.96418214e-01 -3.60375792e-01 9.61306393e-01 -7.09980801e-02
3.01819265e-01 5.24559379e-01 1.00542726e-02 3.93396989e-02
1.43918827e-01 -1.19375432e+00 -1.24467599e+00 -7.11671293e-01
-3.79849859e-02 1.11120737e+00 -2.37689272e-01 -1.44542038... | [8.861173629760742, -3.669236183166504] |
cf925dd4-c88f-460b-a24b-e746b5b87cf3 | learning-event-guided-high-dynamic-range | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_Learning_Event_Guided_High_Dynamic_Range_Video_Reconstruction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Learning_Event_Guided_High_Dynamic_Range_Video_Reconstruction_CVPR_2023_paper.pdf | Learning Event Guided High Dynamic Range Video Reconstruction | Limited by the trade-off between frame rate and exposure time when capturing moving scenes with conventional cameras, frame based HDR video reconstruction suffers from scene-dependent exposure ratio balancing and ghosting artifacts. Event cameras provide an alternative visual representation with a much higher dynam... | ['Boxin Shi', 'Imari Sato', 'Jinxiu Liang', 'Jin Han', 'Yixin Yang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-reconstruction'] | ['computer-vision'] | [ 3.39775503e-01 -6.27705336e-01 -1.88611627e-01 -1.06568381e-01
-8.07231486e-01 -2.50689864e-01 5.24828672e-01 -4.21886802e-01
-8.81514251e-02 6.72694385e-01 6.15896225e-01 2.36941800e-01
-7.99324885e-02 -4.50100482e-01 -4.40702260e-01 -1.13555789e+00
8.35614949e-02 -4.44352984e-01 1.27067819e-01 -1.00333676... | [10.757333755493164, -2.114896774291992] |
d28f58d2-6add-49c0-93ad-d7f12ec5b831 | envisioning-a-next-generation-extended | 2306.16541 | null | https://arxiv.org/abs/2306.16541v1 | https://arxiv.org/pdf/2306.16541v1.pdf | Envisioning a Next Generation Extended Reality Conferencing System with Efficient Photorealistic Human Rendering | Meeting online is becoming the new normal. Creating an immersive experience for online meetings is a necessity towards more diverse and seamless environments. Efficient photorealistic rendering of human 3D dynamics is the core of immersive meetings. Current popular applications achieve real-time conferencing but fall s... | ['Heather Yu', 'Liang Peng', 'Xiyun Song', 'Masood Mortazavi', 'Zhangsihao Yang', 'Letian Zhang', 'Chuanyue Shen'] | 2023-06-28 | null | null | null | null | ['neural-rendering', 'human-dynamics'] | ['computer-vision', 'computer-vision'] | [ 4.31180224e-02 7.71050155e-02 8.20736468e-01 -2.46995434e-01
-7.76272774e-01 -4.46467310e-01 7.20355511e-01 -5.34249485e-01
2.12895244e-01 4.34249610e-01 4.91324037e-01 -2.84373939e-01
1.83135822e-01 -8.78951490e-01 -5.12242496e-01 -4.01259989e-01
-3.69277835e-01 2.80637681e-01 -1.21929958e-01 -8.70540738... | [12.98461627960205, -0.5002389550209045] |
61bde8bd-bfc1-4bf7-8852-f18062e500d0 | self-replicating-machines-in-continuous-space | cs/0304022 | null | https://arxiv.org/abs/cs/0304022v1 | https://arxiv.org/pdf/cs/0304022v1.pdf | Self-Replicating Machines in Continuous Space with Virtual Physics | JohnnyVon is an implementation of self-replicating machines in continuous two-dimensional space. Two types of particles drift about in a virtual liquid. The particles are automata with discrete internal states but continuous external relationships. Their internal states are governed by finite state machines but their e... | ['Robert Ewaschuk', 'Peter Turney', 'Arnold Smith'] | 2003-04-15 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 2.90042460e-01 2.06316233e-01 8.30619857e-02 3.63550872e-01
5.16592860e-01 -1.02193999e+00 9.66760099e-01 -1.49460807e-01
-3.07964414e-01 8.41080785e-01 -8.12221784e-03 -3.12444180e-01
3.06623757e-01 -1.38545990e+00 -7.74556458e-01 -1.13702083e+00
-2.02593610e-01 6.05314910e-01 6.09879792e-01 -4.55386102... | [5.624760150909424, 4.181692600250244] |
544a982a-d56e-4ed7-a12c-cee0806f8c66 | l1-gp-l1-adaptive-control-with-bayesian | null | null | https://openreview.net/forum?id=TZhplO4Q3YM | https://openreview.net/pdf?id=TZhplO4Q3YM | L1-GP: L1 Adaptive Control with Bayesian Learning | We present L1-GP, an architecture based on L1 adaptive control and Gaussian Process Regression (GPR) for safe simultaneous control and learning. On one hand, the L1 adaptive control provides stability and transient performance guarantees, which allows for GPR to efficiently and safely learn the uncertain dynamics. On t... | ['Evangelos Theodorou', 'Naira Hovakimyan', 'Andrew Patterson', 'Pan Zhao', 'Aditya Gahlawat'] | 2020-06-08 | null | null | null | l4dc-2020-6 | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-3.88220400e-01 3.59261751e-01 -4.35609341e-01 2.50412107e-01
-7.48707294e-01 -3.57103676e-01 2.88981259e-01 1.19437709e-01
2.35859267e-02 1.02746522e+00 -2.04680189e-01 -2.70288199e-01
-6.33633018e-01 -4.92991120e-01 -6.31270409e-01 -1.21426201e+00
-9.76194143e-02 -1.49126992e-01 -8.23893026e-02 1.83529437... | [5.04996919631958, 2.4289093017578125] |
e46ec5a0-6dcd-4eda-b254-de07ea1971e4 | an-ensemble-deep-learning-based-cyber-attack | 2005.00936 | null | https://arxiv.org/abs/2005.00936v1 | https://arxiv.org/pdf/2005.00936v1.pdf | An Ensemble Deep Learning-based Cyber-Attack Detection in Industrial Control System | The integration of communication networks and the Internet of Things (IoT) in Industrial Control Systems (ICSs) increases their vulnerability towards cyber-attacks, causing devastating outcomes. Traditional Intrusion Detection Systems (IDSs), which are mainly developed to support Information Technology (IT) systems, co... | ['Reza M. Parizi', 'Ali Dehghantanha', 'Hadis Karimipour', 'Abdulrahman Al-Abassi'] | 2020-05-02 | null | null | null | null | ['cyber-attack-detection'] | ['miscellaneous'] | [ 1.83010042e-01 -2.06454545e-01 -2.63572097e-01 -3.61657739e-01
-2.55820919e-02 -3.91543746e-01 5.09819567e-01 1.82943761e-01
1.83000609e-01 6.85186923e-01 -3.53826374e-01 -7.11083829e-01
-4.35062081e-01 -1.23269892e+00 -1.98318258e-01 -6.35297656e-01
2.45870531e-01 6.37279928e-01 3.47560830e-02 1.04348687... | [5.229930877685547, 7.204278945922852] |
e941372b-6bd6-405d-bff0-395dca6ecd60 | frequency-selective-mesh-to-mesh-resampling | 2203.09224 | null | https://arxiv.org/abs/2203.09224v2 | https://arxiv.org/pdf/2203.09224v2.pdf | Frequency-Selective Mesh-to-Mesh Resampling for Color Upsampling of Point Clouds | With the increased use of virtual and augmented reality applications, the importance of point cloud data rises. High-quality capturing of point clouds is still expensive and thus, the need for point cloud super-resolution or point cloud upsampling techniques emerges. In this paper, we propose an interpolation scheme fo... | ['André Kaup', 'Andreas Spruck', 'Viktoria Heimann'] | 2022-03-17 | null | null | null | null | ['point-cloud-super-resolution'] | ['computer-vision'] | [ 2.33160108e-01 -4.40705746e-01 4.70197648e-01 5.24952449e-02
-7.59225667e-01 -8.99479389e-02 5.73740602e-01 3.63359004e-02
-9.81322080e-02 7.79794216e-01 -3.50479722e-01 -9.20010656e-02
1.76768228e-01 -1.19438803e+00 -7.07602561e-01 -5.40054440e-01
-9.97446626e-02 6.85944557e-01 4.93268132e-01 -3.00428092... | [8.700267791748047, -2.9897780418395996] |
ca9b5e87-8895-4701-bf00-012c63f88f25 | time-domain-audio-source-separation-based-on | 2001.10190 | null | https://arxiv.org/abs/2001.10190v1 | https://arxiv.org/pdf/2001.10190v1.pdf | Time-Domain Audio Source Separation Based on Wave-U-Net Combined with Discrete Wavelet Transform | We propose a time-domain audio source separation method using down-sampling (DS) and up-sampling (US) layers based on a discrete wavelet transform (DWT). The proposed method is based on one of the state-of-the-art deep neural networks, Wave-U-Net, which successively down-samples and up-samples feature maps. We find tha... | ['Tomohiko Nakamura', 'Hiroshi Saruwatari'] | 2020-01-28 | null | null | null | null | ['audio-source-separation', 'music-source-separation'] | ['audio', 'music'] | [ 1.75095752e-01 -3.68175298e-01 1.14557311e-01 7.53905326e-02
-6.47509098e-01 -2.77138263e-01 2.35907927e-01 -1.06989995e-01
-1.02697343e-01 4.94473189e-01 4.39325392e-01 1.00076333e-01
-5.11274278e-01 -7.41784751e-01 -4.36070442e-01 -8.78004789e-01
-2.05454811e-01 -4.07510519e-01 3.56405199e-01 -2.41986662... | [15.356598854064941, 5.654824733734131] |
ee6e409f-60cc-4fb7-909f-4d6dd146f691 | efficient-twitter-sentiment-classification | 1701.03051 | null | http://arxiv.org/abs/1701.03051v1 | http://arxiv.org/pdf/1701.03051v1.pdf | Efficient Twitter Sentiment Classification using Subjective Distant Supervision | As microblogging services like Twitter are becoming more and more influential
in today's globalised world, its facets like sentiment analysis are being
extensively studied. We are no longer constrained by our own opinion. Others
opinions and sentiments play a huge role in shaping our perspective. In this
paper, we buil... | ['Naveen Reddy Chedeti', 'Chinmay Chandak', 'Manish Singh', 'Tapan Sahni'] | 2017-01-11 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-7.01153800e-02 -1.25021681e-01 -4.44021165e-01 -7.05252588e-01
-5.45911372e-01 -4.99564826e-01 6.82884276e-01 5.47408700e-01
-9.07891631e-01 7.95693219e-01 3.32148641e-01 -3.34645599e-01
3.30528319e-01 -9.54725325e-01 -3.22892398e-01 -6.22656107e-01
1.43092439e-01 3.47008914e-01 4.88521665e-01 -7.49995172... | [11.083715438842773, 6.953710079193115] |
a935b0b7-ca90-40a8-b46d-0470bd9303d6 | revise-self-supervised-speech-resynthesis-1 | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Hsu_ReVISE_Self-Supervised_Speech_Resynthesis_With_Visual_Input_for_Universal_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hsu_ReVISE_Self-Supervised_Speech_Resynthesis_With_Visual_Input_for_Universal_and_CVPR_2023_paper.pdf | ReVISE: Self-Supervised Speech Resynthesis With Visual Input for Universal and Generalized Speech Regeneration | Prior works on improving speech quality with visual input typically study each type of auditory distortion separately (e.g., separation, inpainting, video-to-speech) and present tailored algorithms. This paper proposes to unify these subjects and study Generalized Speech Regeneration, where the goal is not to recon... | ['Yossi Adi', 'Jacob Donley', 'Bowen Shi', 'Tal Remez', 'Wei-Ning Hsu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-synchronization', 'text-to-speech-synthesis', 'speech-synthesis', 'visual-speech-recognition', 'audio-visual-speech-recognition'] | ['computer-vision', 'speech', 'speech', 'speech', 'speech'] | [ 2.29637817e-01 -1.73360661e-01 2.08575614e-02 9.76500139e-02
-1.23399770e+00 -4.41310406e-01 3.76892000e-01 -4.80547160e-01
2.45767310e-02 5.59034526e-01 6.81546807e-01 -4.24320161e-01
3.21546972e-01 -1.33681640e-01 -8.98543775e-01 -7.72614062e-01
3.21467668e-01 -1.52638718e-01 3.08383517e-02 -1.68070942... | [14.580291748046875, 5.406525611877441] |
e06414ca-e640-4afb-ba17-40ba71afc1f6 | cross-align-modeling-deep-cross-lingual | 2210.04141 | null | https://arxiv.org/abs/2210.04141v1 | https://arxiv.org/pdf/2210.04141v1.pdf | Cross-Align: Modeling Deep Cross-lingual Interactions for Word Alignment | Word alignment which aims to extract lexicon translation equivalents between source and target sentences, serves as a fundamental tool for natural language processing. Recent studies in this area have yielded substantial improvements by generating alignments from contextualized embeddings of the pre-trained multilingua... | ['Jie zhou', 'Jinan Xu', 'Yufeng Chen', 'Fandong Meng', 'Zhen Yang', 'Siyu Lai'] | 2022-10-09 | null | null | null | null | ['word-alignment'] | ['natural-language-processing'] | [ 1.95611343e-01 -1.60938390e-02 -2.46954218e-01 -5.04291236e-01
-9.74308729e-01 -3.61867398e-01 6.56294286e-01 -2.14186776e-02
-5.96864223e-01 6.24534190e-01 4.74821389e-01 -6.37762666e-01
6.10077620e-01 -6.38416111e-01 -9.23296332e-01 -4.14425671e-01
5.58991313e-01 5.65692544e-01 -3.13632399e-01 -4.91275281... | [11.59030532836914, 10.210981369018555] |
5d40dd76-5b3b-48b1-833f-f82a03b675a4 | deeppruner-learning-efficient-stereo-matching | 1909.05845 | null | https://arxiv.org/abs/1909.05845v1 | https://arxiv.org/pdf/1909.05845v1.pdf | DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch | Our goal is to significantly speed up the runtime of current state-of-the-art stereo algorithms to enable real-time inference. Towards this goal, we developed a differentiable PatchMatch module that allows us to discard most disparities without requiring full cost volume evaluation. We then exploit this representation ... | ['Wei-Chiu Ma', 'Raquel Urtasun', 'Shivam Duggal', 'Shenlong Wang', 'Rui Hu'] | 2019-09-12 | deeppruner-learning-efficient-stereo-matching-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Duggal_DeepPruner_Learning_Efficient_Stereo_Matching_via_Differentiable_PatchMatch_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Duggal_DeepPruner_Learning_Efficient_Stereo_Matching_via_Differentiable_PatchMatch_ICCV_2019_paper.pdf | iccv-2019-10 | ['stereo-matching'] | ['computer-vision'] | [ 3.09040397e-01 -3.78800593e-02 -4.35758289e-03 -5.52912176e-01
-1.01189554e+00 -4.56147552e-01 5.63110411e-01 4.00563441e-02
-7.45014250e-01 5.68818152e-01 1.13949832e-03 -4.03191924e-01
2.95270979e-01 -9.04181600e-01 -9.36824679e-01 -3.38533908e-01
-4.00949754e-02 5.74791968e-01 6.94701612e-01 2.08026052... | [8.747462272644043, -2.361281633377075] |
70d465bc-f50a-44a0-bdab-a4d194315d1c | leveraging-summary-guidance-on-medical-report | 2302.04001 | null | https://arxiv.org/abs/2302.04001v1 | https://arxiv.org/pdf/2302.04001v1.pdf | Leveraging Summary Guidance on Medical Report Summarization | This study presents three deidentified large medical text datasets, named DISCHARGE, ECHO and RADIOLOGY, which contain 50K, 16K and 378K pairs of report and summary that are derived from MIMIC-III, respectively. We implement convincing baselines of automated abstractive summarization on the proposed datasets with pre-t... | ['Wensheng Zhang', 'Yuanyuan Wu', 'Xuebing Yang', 'Yunqi Zhu'] | 2023-02-08 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 1.82948351e-01 8.41538191e-01 -1.18797667e-01 -3.84734035e-01
-1.59037054e+00 -2.67692059e-01 5.50108075e-01 7.23548412e-01
-3.89963508e-01 1.13032019e+00 1.60320818e+00 -1.69305816e-01
-2.82624543e-01 -2.53269881e-01 -6.79812193e-01 -3.90054405e-01
-3.28193426e-01 5.87004185e-01 -2.57331222e-01 2.14729607... | [12.24787425994873, 9.35527229309082] |
4b488f79-b632-473f-a2de-27448059d762 | dctd-deep-conditional-target-densities-for | 1909.12297 | null | https://arxiv.org/abs/1909.12297v4 | https://arxiv.org/pdf/1909.12297v4.pdf | Energy-Based Models for Deep Probabilistic Regression | While deep learning-based classification is generally tackled using standardized approaches, a wide variety of techniques are employed for regression. In computer vision, one particularly popular such technique is that of confidence-based regression, which entails predicting a confidence value for each input-target pai... | ['Thomas B. Schön', 'Goutam Bhat', 'Fredrik K. Gustafsson', 'Martin Danelljan'] | 2019-09-26 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3472_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123650324.pdf | eccv-2020-8 | ['head-pose-estimation'] | ['computer-vision'] | [-9.68602598e-02 -4.13381495e-02 -3.66794527e-01 -5.27396262e-01
-1.26403284e+00 -2.01181784e-01 6.59379423e-01 1.69456065e-01
-6.98270738e-01 8.41590226e-01 -7.43129775e-02 -1.07868701e-01
1.16446979e-01 -4.71471518e-01 -9.37354863e-01 -8.86624694e-01
-2.51811184e-02 4.92420554e-01 2.38809690e-01 2.85185784... | [8.635522842407227, 1.9925332069396973] |
07b0dc7e-ee43-417d-b222-823cd29fa7cb | table-to-text-generation-with-effective | 1909.02304 | null | https://arxiv.org/abs/1909.02304v1 | https://arxiv.org/pdf/1909.02304v1.pdf | Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time) | Although Seq2Seq models for table-to-text generation have achieved remarkable progress, modeling table representation in one dimension is inadequate. This is because (1) the table consists of multiple rows and columns, which means that encoding a table should not depend only on one dimensional sequence or set of record... | ['Xiaocheng Feng', 'Ting Liu', 'Heng Gong', 'Bing Qin'] | 2019-09-05 | table-to-text-generation-with-effective-1 | https://aclanthology.org/D19-1310 | https://aclanthology.org/D19-1310.pdf | ijcnlp-2019-11 | ['table-to-text-generation'] | ['natural-language-processing'] | [ 2.70165019e-02 -2.21459344e-01 -3.44126135e-01 -5.43177351e-02
-7.48928845e-01 -9.91724491e-01 6.13468170e-01 5.01878619e-01
-3.11891109e-01 1.12341261e+00 9.34627891e-01 -2.63926178e-01
1.14968434e-01 -1.08751011e+00 -7.39874959e-01 -4.41617250e-01
3.16801593e-02 5.46480417e-01 1.82612821e-01 -7.52714515... | [11.653059005737305, 8.821075439453125] |
4a1d88fb-f5fc-4cb2-ac2e-78e12f2b0807 | alf-a-fitness-based-artificial-life-form-for | 2104.08252 | null | https://arxiv.org/abs/2104.08252v1 | https://arxiv.org/pdf/2104.08252v1.pdf | ALF -- A Fitness-Based Artificial Life Form for Evolving Large-Scale Neural Networks | Machine Learning (ML) is becoming increasingly important in daily life. In this context, Artificial Neural Networks (ANNs) are a popular approach within ML methods to realize an artificial intelligence. Usually, the topology of ANNs is predetermined. However, there are problems where it is difficult to find a suitable ... | ['Rolf Drechsler', 'Mirco Bockholt', 'Marcel Merten', 'Rune Krauss'] | 2021-04-16 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 1.93936765e-01 -2.40238085e-01 1.31242186e-01 -1.79343373e-01
1.77428439e-01 -2.21243545e-01 9.97167230e-02 1.76092461e-01
-4.19872493e-01 1.05552471e+00 -5.13959587e-01 1.02377869e-01
-4.04221833e-01 -1.08218670e+00 -6.60623848e-01 -9.71046984e-01
1.56310707e-01 5.75434744e-01 1.94033951e-01 -3.31830174... | [8.1262845993042, 3.3763229846954346] |
63a96e30-0a7f-41e7-a28d-03451a8bca9e | unsupervised-domain-expansion-for-visual | 2104.00233 | null | https://arxiv.org/abs/2104.00233v1 | https://arxiv.org/pdf/2104.00233v1.pdf | Unsupervised Domain Expansion for Visual Categorization | Expanding visual categorization into a novel domain without the need of extra annotation has been a long-term interest for multimedia intelligence. Previously, this challenge has been approached by unsupervised domain adaptation (UDA). Given labeled data from a source domain and unlabeled data from a target domain, UDA... | ['Xirong Li', 'Gang Yang', 'Dayong Ding', 'Kaibin Tian', 'Jie Wang'] | 2021-04-01 | null | null | null | null | ['unsupervised-domain-expansion'] | ['methodology'] | [ 1.31528601e-01 -6.98474795e-02 -4.57211047e-01 -3.76795948e-01
-6.60768986e-01 -7.95097828e-01 6.65717959e-01 -1.81560174e-01
-4.35076475e-01 6.82881296e-01 1.95184037e-01 -2.09090993e-01
1.94380224e-01 -7.50055611e-01 -5.57495952e-01 -5.13901412e-01
3.59295398e-01 6.35710835e-01 4.46010649e-01 -1.64691001... | [10.217103004455566, 2.745321273803711] |
f60c61a2-5d79-40c0-ba86-e4e445d5aa1a | pose-guided-feature-alignment-for-occluded | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Miao_Pose-Guided_Feature_Alignment_for_Occluded_Person_Re-Identification_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Miao_Pose-Guided_Feature_Alignment_for_Occluded_Person_Re-Identification_ICCV_2019_paper.pdf | Pose-Guided Feature Alignment for Occluded Person Re-Identification | Persons are often occluded by various obstacles in person retrieval scenarios. Previous person re-identification (re-id) methods, either overlook this issue or resolve it based on an extreme assumption. To alleviate the occlusion problem, we propose to detect the occluded regions, and explicitly exclude those regions d... | [' Yi Yang', ' Yuhang Ding', ' Ping Liu', ' Yu Wu', 'Jiaxu Miao'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['person-retrieval'] | ['computer-vision'] | [-1.10052414e-01 -2.26460278e-01 -1.58455953e-01 -2.99344957e-01
-7.64472723e-01 -2.78957814e-01 6.35850728e-01 -1.02815196e-01
-3.48821372e-01 5.89822114e-01 7.43989289e-01 4.12058264e-01
-2.60469764e-01 -5.86535156e-01 -2.47468442e-01 -4.93308127e-01
3.24441969e-01 8.84772778e-01 -2.22324178e-01 -1.07878624... | [14.67551326751709, 0.9054436087608337] |
db27b1fa-7782-406a-af88-c5b8fcb3192b | stereobj-1m-large-scale-stereo-image-dataset | 2109.10115 | null | https://arxiv.org/abs/2109.10115v3 | https://arxiv.org/pdf/2109.10115v3.pdf | StereOBJ-1M: Large-scale Stereo Image Dataset for 6D Object Pose Estimation | We present a large-scale stereo RGB image object pose estimation dataset named the $\textbf{StereOBJ-1M}$ dataset. The dataset is designed to address challenging cases such as object transparency, translucency, and specular reflection, in addition to the common challenges of occlusion, symmetry, and variations in illum... | ['Kris M. Kitani', 'Shun Iwase', 'Xingyu Liu'] | 2021-09-21 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Liu_StereOBJ-1M_Large-Scale_Stereo_Image_Dataset_for_6D_Object_Pose_Estimation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Liu_StereOBJ-1M_Large-Scale_Stereo_Image_Dataset_for_6D_Object_Pose_Estimation_ICCV_2021_paper.pdf | iccv-2021-1 | ['transparent-objects'] | ['computer-vision'] | [-3.72544639e-02 -1.85485497e-01 2.15772599e-01 -5.63010991e-01
-9.35040653e-01 -7.17899799e-01 2.59422839e-01 -5.16568661e-01
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1.11652754e-01 -2.29741096e-01 -1.24767220e+00 -4.63216394e-01
1.60482347e-01 7.77102053e-01 3.64870101e-01 4.34657857... | [7.106992721557617, -2.250148296356201] |
51379499-547a-4146-b81e-ed03199be4b3 | geomae-masked-geometric-target-prediction-for | 2305.08808 | null | https://arxiv.org/abs/2305.08808v1 | https://arxiv.org/pdf/2305.08808v1.pdf | GeoMAE: Masked Geometric Target Prediction for Self-supervised Point Cloud Pre-Training | This paper tries to address a fundamental question in point cloud self-supervised learning: what is a good signal we should leverage to learn features from point clouds without annotations? To answer that, we introduce a point cloud representation learning framework, based on geometric feature reconstruction. In contra... | ['Hang Zhao', 'Yue Wang', 'Haoxi Ran', 'Xiaoyu Tian'] | 2023-05-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tian_GeoMAE_Masked_Geometric_Target_Prediction_for_Self-Supervised_Point_Cloud_Pre-Training_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tian_GeoMAE_Masked_Geometric_Target_Prediction_for_Self-Supervised_Point_Cloud_Pre-Training_CVPR_2023_paper.pdf | cvpr-2023-1 | ['point-cloud-pre-training'] | ['computer-vision'] | [ 7.04953894e-02 3.19241017e-01 -9.03735682e-02 -3.09281379e-01
-1.13600385e+00 -5.94672263e-01 6.89909518e-01 1.64530694e-01
-2.56351948e-01 2.14829043e-01 -1.44166410e-01 -2.46537670e-01
1.67102918e-01 -8.71294975e-01 -1.55804181e+00 -5.98581553e-01
-9.75528732e-02 8.78114581e-01 4.41035002e-01 1.47975996... | [7.993978023529053, -3.347223997116089] |
4740ce11-b2fa-4c5a-b379-bd8641cc2d12 | traffic-accident-benchmark-for-causality | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/312_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520528.pdf | Traffic Accident Benchmark for Causality Recognition | We propose a brand new benchmark for analyzing causality in traffic accident videos by decomposing an accident into a pair of events, cause and effect. We collect videos containing traffic accident scenes and annotate cause and effect events for each accident with their temporal intervals and semantic labels; such anno... | ['Bohyung Han', 'Tackgeun You'] | null | null | null | null | eccv-2020-8 | ['accident-anticipation'] | ['computer-vision'] | [ 2.66809106e-01 1.42792761e-01 -4.16248709e-01 -5.42199016e-01
-3.96728754e-01 -7.03179181e-01 5.77087343e-01 4.89004016e-01
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-3.18016142e-01 -6.47791564e-01 -7.64020026e-01 -5.85604668e-01
-2.60558337e-01 1.19919084e-01 3.99110764e-01 6.06137291... | [8.301508903503418, 0.6275436282157898] |
075cba77-a19b-431c-88b0-9e3c2dd9c7ae | gaitmixer-skeleton-based-gait-representation | 2210.15491 | null | https://arxiv.org/abs/2210.15491v2 | https://arxiv.org/pdf/2210.15491v2.pdf | GaitMixer: Skeleton-based Gait Representation Learning via Wide-spectrum Multi-axial Mixer | Most existing gait recognition methods are appearance-based, which rely on the silhouettes extracted from the video data of human walking activities. The less-investigated skeleton-based gait recognition methods directly learn the gait dynamics from 2D/3D human skeleton sequences, which are theoretically more robust so... | ['Chen Chen', 'Minwoo Lee', 'Pu Wang', 'Ayman Ali', 'Ekkasit Pinyoanuntapong'] | 2022-10-27 | null | null | null | null | ['gait-recognition', 'multiview-gait-recognition'] | ['computer-vision', 'computer-vision'] | [-1.06658630e-01 -6.31879508e-01 -1.55999094e-01 -2.18019988e-02
-4.19119388e-01 -2.23246329e-02 3.07072461e-01 -4.24028277e-01
-3.10695201e-01 4.33829784e-01 2.62273282e-01 5.29084086e-01
1.65812433e-01 -5.09739101e-01 -4.14957225e-01 -9.44911957e-01
-3.37807685e-01 2.38191262e-01 3.40382755e-01 -1.86888158... | [14.283947944641113, 1.4256477355957031] |
c1be1f96-d265-4795-b1c3-ee7b07fe972b | graphical-contrastive-losses-for-scene-graph | 1903.02728 | null | https://arxiv.org/abs/1903.02728v5 | https://arxiv.org/pdf/1903.02728v5.pdf | Graphical Contrastive Losses for Scene Graph Parsing | Most scene graph parsers use a two-stage pipeline to detect visual relationships: the first stage detects entities, and the second predicts the predicate for each entity pair using a softmax distribution. We find that such pipelines, trained with only a cross entropy loss over predicate classes, suffer from two common ... | ['Andrew Tao', 'Kevin J. Shih', 'Ahmed Elgammal', 'Ji Zhang', 'Bryan Catanzaro'] | 2019-03-07 | graphical-contrastive-losses-for-scene-graph-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_Graphical_Contrastive_Losses_for_Scene_Graph_Parsing_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Graphical_Contrastive_Losses_for_Scene_Graph_Parsing_CVPR_2019_paper.pdf | cvpr-2019-6 | ['visual-relationship-detection'] | ['computer-vision'] | [ 6.03501141e-01 4.58476901e-01 2.89980266e-02 -5.46776056e-01
-7.85369873e-01 -8.78969789e-01 5.09735405e-01 6.41246438e-01
-2.26432174e-01 3.99588108e-01 -1.89431071e-01 -2.76051223e-01
9.82650649e-03 -6.84832454e-01 -1.14633036e+00 -3.11996460e-01
-1.89753637e-01 5.56217432e-01 2.21615463e-01 2.99597889... | [10.3607177734375, 1.6689082384109497] |
d8e62d2f-f4b5-48ba-a2f9-734f16de1393 | investigating-the-effect-of-auxiliary | null | null | https://aclanthology.org/2020.acl-main.206 | https://aclanthology.org/2020.acl-main.206.pdf | Investigating the effect of auxiliary objectives for the automated grading of learner English speech transcriptions | We address the task of automatically grading the language proficiency of spontaneous speech based on textual features from automatic speech recognition transcripts. Motivated by recent advances in multi-task learning, we develop neural networks trained in a multi-task fashion that learn to predict the proficiency level... | ['Paula Buttery', 'Helen Yannakoudakis', 'Hannah Craighead', 'Andrew Caines'] | 2020-07-01 | null | null | null | acl-2020-6 | ['native-language-identification'] | ['natural-language-processing'] | [ 5.20316124e-01 2.75605619e-01 -2.35275179e-01 -7.39888191e-01
-1.77928734e+00 -7.59040594e-01 4.43171948e-01 1.26286194e-01
-6.51514947e-01 5.53437173e-01 9.87034798e-01 -8.02286863e-01
5.26273698e-02 -4.59596664e-01 -5.53638637e-01 -1.16185084e-01
3.76008809e-01 5.73476434e-01 8.04689303e-02 -2.41246000... | [14.142291069030762, 6.9161696434021] |
8bace296-5ae6-4adb-be1c-ca639e930708 | invariant-representation-learning-for | 2011.12379 | null | https://arxiv.org/abs/2011.12379v2 | https://arxiv.org/pdf/2011.12379v2.pdf | Invariant Representation Learning for Treatment Effect Estimation | The defining challenge for causal inference from observational data is the presence of `confounders', covariates that affect both treatment assignment and the outcome. To address this challenge, practitioners collect and adjust for the covariates, hoping that they adequately correct for confounding. However, including ... | ['David Blei', 'Victor Veitch', 'Claudia Shi'] | 2020-11-24 | null | null | null | null | ['causal-identification'] | ['reasoning'] | [ 3.84932458e-01 3.03740203e-01 -9.25495386e-01 -5.55305779e-01
-9.67902958e-01 -5.84402382e-01 3.68094057e-01 4.26573187e-01
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-4.44180399e-01 4.89409059e-01 -3.46960098e-01 1.81799993... | [8.015498161315918, 5.359692573547363] |
fe49f64d-85f0-4e64-8d28-c53cad841525 | efficient-uncertainty-estimation-with | 2303.08599 | null | https://arxiv.org/abs/2303.08599v1 | https://arxiv.org/pdf/2303.08599v1.pdf | Efficient Uncertainty Estimation with Gaussian Process for Reliable Dialog Response Retrieval | Deep neural networks have achieved remarkable performance in retrieval-based dialogue systems, but they are shown to be ill calibrated. Though basic calibration methods like Monte Carlo Dropout and Ensemble can calibrate well, these methods are time-consuming in the training or inference stages. To tackle these challen... | ['Jing Xiao', 'Ning Cheng', 'Jianzong Wang', 'Zhitao Li', 'Tong Ye'] | 2023-03-15 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [-2.83127934e-01 1.03452116e-01 2.80060899e-02 -8.18741798e-01
-1.50378323e+00 -4.11042571e-01 6.39110267e-01 -2.05105096e-01
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-2.05145329e-01 -4.72874254e-01 -6.06959820e-01 -4.98160154e-01
2.19413295e-01 1.36696637e+00 6.08741157e-02 -2.56883532... | [12.02637767791748, 8.003334045410156] |
d05fe0e7-b71e-41ef-add4-74f971d57c33 | generated-graph-detection | 2306.07758 | null | https://arxiv.org/abs/2306.07758v1 | https://arxiv.org/pdf/2306.07758v1.pdf | Generated Graph Detection | Graph generative models become increasingly effective for data distribution approximation and data augmentation. While they have aroused public concerns about their malicious misuses or misinformation broadcasts, just as what Deepfake visual and auditory media has been delivering to society. Hence it is essential to re... | ['Yang Zhang', 'Yun Shen', 'Michael Backes', 'Xinlei He', 'Ning Yu', 'Zhikun Zhang', 'Yihan Ma'] | 2023-06-13 | null | null | null | null | ['face-swapping', 'misinformation'] | ['computer-vision', 'miscellaneous'] | [ 2.44388029e-01 6.18627489e-01 2.65981387e-02 -2.08075251e-03
-3.41112226e-01 -6.82671487e-01 1.03039002e+00 3.95127505e-01
-8.94313902e-02 6.09666467e-01 -5.24686165e-02 -4.74160671e-01
-1.47866264e-01 -9.32413459e-01 -4.83948827e-01 -6.41808689e-01
-4.67463434e-01 5.61818719e-01 3.13690156e-01 -4.08846438... | [5.967221736907959, 7.416622638702393] |
84758e87-c86a-46af-8099-c31b98d6bd50 | biomechanics-guided-facial-action-unit | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Cui_Biomechanics-Guided_Facial_Action_Unit_Detection_Through_Force_Modeling_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cui_Biomechanics-Guided_Facial_Action_Unit_Detection_Through_Force_Modeling_CVPR_2023_paper.pdf | Biomechanics-Guided Facial Action Unit Detection Through Force Modeling | Existing AU detection algorithms are mainly based on appearance information extracted from 2D images, and well-established facial biomechanics that governs 3D facial skin deformation is rarely considered. In this paper, we propose a biomechanics-guided AU detection approach, where facial muscle activation forces ar... | ['Qiang Ji', 'Kartik Talamadupula', 'Tian Gao', 'Chenyi Kuang', 'Zijun Cui'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 1.54606938e-01 2.85862803e-01 -3.15821081e-01 1.26163736e-02
-2.30465814e-01 -1.89165100e-01 2.08825156e-01 -3.07924300e-01
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6.66215867e-02 -1.08111851e-01 8.25470686e-02 -2.45581344... | [12.92601203918457, -0.10472511500120163] |
d2e7a1eb-679b-4b58-890d-2c2c5b0fac22 | deep-video-inpainting | 1905.01639 | null | https://arxiv.org/abs/1905.01639v1 | https://arxiv.org/pdf/1905.01639v1.pdf | Deep Video Inpainting | Video inpainting aims to fill spatio-temporal holes with plausible content in a video. Despite tremendous progress of deep neural networks for image inpainting, it is challenging to extend these methods to the video domain due to the additional time dimension. In this work, we propose a novel deep network architecture ... | ['Joon-Young Lee', 'Dahun Kim', 'Sanghyun Woo', 'In So Kweon'] | 2019-05-05 | deep-video-inpainting-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Kim_Deep_Video_Inpainting_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Kim_Deep_Video_Inpainting_CVPR_2019_paper.pdf | cvpr-2019-6 | ['video-denoising', 'video-to-video-synthesis', 'video-inpainting'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.48767906e-01 5.46744503e-02 -1.11909159e-01 -1.73186809e-01
-7.31044352e-01 -2.88721085e-01 3.46813232e-01 -3.19319457e-01
-2.01046064e-01 8.08773518e-01 3.99447292e-01 -6.37242869e-02
3.53732198e-01 -5.69742203e-01 -1.23061919e+00 -2.39075392e-01
2.54386008e-01 2.42396723e-02 1.68931440e-01 -4.12729234... | [10.790971755981445, -1.2066869735717773] |
ad11365c-5d91-4a4f-bdf9-c23d1d62d0a2 | derivation-of-document-vectors-from | null | null | https://aclanthology.org/E17-2073 | https://aclanthology.org/E17-2073.pdf | Derivation of Document Vectors from Adaptation of LSTM Language Model | In many natural language processing (NLP) tasks, a document is commonly modeled as a bag of words using the term frequency-inverse document frequency (TF-IDF) vector. One major shortcoming of the frequency-based TF-IDF feature vector is that it ignores word orders that carry syntactic and semantic relationships among t... | ['Wei Li', 'Brian Mak'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['genre-classification'] | ['computer-vision'] | [ 1.98195428e-01 -3.83580208e-01 -4.08999681e-01 -6.32813156e-01
-2.65621901e-01 -4.91979390e-01 9.16272223e-01 4.10365164e-01
-5.83593011e-01 6.19678497e-01 7.77054369e-01 -4.91843432e-01
-1.88894019e-01 -6.63799524e-01 -4.06196445e-01 -6.09188318e-01
-1.64931342e-01 2.51174837e-01 -1.86041877e-01 -4.17078994... | [11.047313690185547, 7.696857452392578] |
2ef752ed-1c6f-4bd4-a6d4-e6dff9a51cd7 | filtered-cophy-unsupervised-learning-of-1 | 2202.00368 | null | https://arxiv.org/abs/2202.00368v1 | https://arxiv.org/pdf/2202.00368v1.pdf | Filtered-CoPhy: Unsupervised Learning of Counterfactual Physics in Pixel Space | Learning causal relationships in high-dimensional data (images, videos) is a hard task, as they are often defined on low dimensional manifolds and must be extracted from complex signals dominated by appearance, lighting, textures and also spurious correlations in the data. We present a method for learning counterfactua... | ['Christian Wolf', 'Greg Mori', 'Madiha Nadri', 'Natalia Neverova', 'Fabien Baradel', 'Steeven Janny'] | 2022-02-01 | filtered-cophy-unsupervised-learning-of | https://openreview.net/forum?id=1L0C5ROtFp | https://openreview.net/pdf?id=1L0C5ROtFp | iclr-2022-4 | ['video-prediction'] | ['computer-vision'] | [ 4.72063214e-01 4.87483032e-02 -1.70448437e-01 -5.26651815e-02
-3.15349519e-01 -3.97368014e-01 1.42881238e+00 1.12856500e-01
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-4.51022029e-01 -5.98712504e-01 -1.25322282e+00 -9.86308575e-01
-5.15463948e-01 2.60099798e-01 -1.31528527e-01 1.43198907... | [8.600893020629883, 0.4662756621837616] |
da4361cf-71ce-4563-9a1e-46284f347e2e | the-methodius-corpus-of-rhetorical-discourse | null | null | https://aclanthology.org/L16-1273 | https://aclanthology.org/L16-1273.pdf | The Methodius Corpus of Rhetorical Discourse Structures and Generated Texts | Using the Methodius Natural Language Generation (NLG) System, we have created a corpus which consists of a collection of generated texts which describe ancient Greek artefacts. Each text is linked to two representations created as part of the NLG process. The first is a content plan, which uses rhetorical relations to ... | ['Amy Isard'] | 2016-05-01 | the-methodius-corpus-of-rhetorical-discourse-1 | https://aclanthology.org/L16-1273 | https://aclanthology.org/L16-1273.pdf | lrec-2016-5 | ['referring-expression-generation'] | ['computer-vision'] | [ 3.09856087e-01 1.00547934e+00 1.19815528e-01 -2.70550609e-01
-7.08409548e-01 -6.05174363e-01 1.13828635e+00 4.18142974e-01
5.96185699e-02 1.06040204e+00 9.89556551e-01 -5.04105449e-01
7.74357542e-02 -1.14591038e+00 -4.90129322e-01 -2.76743144e-01
1.44537121e-01 7.95324802e-01 3.51100713e-01 -6.76017940... | [11.282164573669434, 9.20444393157959] |
a100ab68-2173-4998-bc00-1c90b6ce12d1 | projection-based-point-convolution-for | 2202.01991 | null | https://arxiv.org/abs/2202.01991v1 | https://arxiv.org/pdf/2202.01991v1.pdf | Projection-based Point Convolution for Efficient Point Cloud Segmentation | Understanding point cloud has recently gained huge interests following the development of 3D scanning devices and the accumulation of large-scale 3D data. Most point cloud processing algorithms can be classified as either point-based or voxel-based methods, both of which have severe limitations in processing time or me... | ['Junmo Kim', 'Chanho Lee', 'Eojindl Yi', 'JuYoung Yang', 'Pyunghwan Ahn'] | 2022-02-04 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [-2.93747067e-01 -2.23898396e-01 1.15607202e-01 -3.41862857e-01
-3.17321658e-01 -2.34111473e-01 5.74125946e-01 2.88365185e-01
-5.15325844e-01 1.45623773e-01 -5.43538690e-01 -5.22273660e-01
-1.05840296e-01 -1.33981884e+00 -9.77408111e-01 -3.71304512e-01
-8.56039375e-02 6.04189694e-01 7.05338895e-01 6.01098593... | [7.920266151428223, -3.500889778137207] |
15bace52-8c42-42c9-b312-dc1128ea9806 | placing-historical-facts-on-a-timeline-a | 2206.14089 | null | https://arxiv.org/abs/2206.14089v1 | https://arxiv.org/pdf/2206.14089v1.pdf | Placing (Historical) Facts on a Timeline: A Classification cum Coref Resolution Approach | A timeline provides one of the most effective ways to visualize the important historical facts that occurred over a period of time, presenting the insights that may not be so apparent from reading the equivalent information in textual form. By leveraging generative adversarial learning for important sentence classifica... | ['Animesh Mukherjee', 'Aditya Basu', 'Altaf Ahmad', 'Sayantan Adak'] | 2022-06-28 | null | null | null | null | ['sentence-classification'] | ['natural-language-processing'] | [ 4.79470402e-01 4.31028873e-01 -2.94146668e-02 -3.29162568e-01
-1.18234992e+00 -9.23708558e-01 1.25477469e+00 7.96216249e-01
-3.82344872e-01 1.04327476e+00 1.23201692e+00 -5.07466257e-01
-1.12300083e-01 -8.78710270e-01 -5.25139987e-01 -3.50980997e-01
-2.82455772e-01 5.01697004e-01 -3.82582843e-02 -5.95633924... | [11.114189147949219, 8.919440269470215] |
a07e3ecc-f1e9-42f5-90ca-32b6d1848856 | crosswoz-a-large-scale-chinese-cross-domain | 2002.11893 | null | https://arxiv.org/abs/2002.11893v2 | https://arxiv.org/pdf/2002.11893v2.pdf | CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset | To advance multi-domain (cross-domain) dialogue modeling as well as alleviate the shortage of Chinese task-oriented datasets, we propose CrossWOZ, the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restauran... | ['Qi Zhu', 'Minlie Huang', 'Xiaoyan Zhu', 'Zheng Zhang', 'Kaili Huang'] | 2020-02-27 | crosswoz-a-large-scale-chinese-cross-domain-1 | https://aclanthology.org/2020.tacl-1.19 | https://aclanthology.org/2020.tacl-1.19.pdf | tacl-2020-1 | ['user-simulation'] | ['natural-language-processing'] | [-4.71519589e-01 4.26871777e-01 -1.32653549e-01 -5.91402769e-01
-6.09035075e-01 -8.72996151e-01 8.65224063e-01 8.79043117e-02
-3.40832144e-01 1.06312287e+00 7.01496363e-01 -5.59422493e-01
2.55888760e-01 -5.10422289e-01 1.43205076e-01 -1.66471928e-01
-8.02715048e-02 1.26567256e+00 4.81994539e-01 -1.14096582... | [12.839547157287598, 7.990377426147461] |
d19477cd-2c5c-4809-88d8-4350dc7e73e3 | visual-context-driven-audio-feature | 2207.06020 | null | https://arxiv.org/abs/2207.06020v1 | https://arxiv.org/pdf/2207.06020v1.pdf | Visual Context-driven Audio Feature Enhancement for Robust End-to-End Audio-Visual Speech Recognition | This paper focuses on designing a noise-robust end-to-end Audio-Visual Speech Recognition (AVSR) system. To this end, we propose Visual Context-driven Audio Feature Enhancement module (V-CAFE) to enhance the input noisy audio speech with a help of audio-visual correspondence. The proposed V-CAFE is designed to capture ... | ['Yong Man Ro', 'Daehun Yoo', 'Minsu Kim', 'Joanna Hong'] | 2022-07-13 | null | null | null | null | ['noisy-speech-recognition', 'audio-visual-speech-recognition'] | ['speech', 'speech'] | [ 3.80151153e-01 -4.06340003e-01 4.29614902e-01 -3.42405409e-01
-1.16832113e+00 -3.04693073e-01 5.21693289e-01 -1.60070017e-01
-3.14873189e-01 2.53447890e-01 6.57868385e-01 -1.21836536e-01
8.31275284e-02 -2.24292904e-01 -4.48139548e-01 -7.89702356e-01
3.95048231e-01 -4.34181780e-01 7.49735087e-02 -1.59267813... | [14.484912872314453, 5.298834323883057] |
bd0d8d71-305d-43c0-b6b5-a6d74ec9102c | real-time-neural-radiance-caching-for-path | 2106.12372 | null | https://arxiv.org/abs/2106.12372v2 | https://arxiv.org/pdf/2106.12372v2.pdf | Real-time Neural Radiance Caching for Path Tracing | We present a real-time neural radiance caching method for path-traced global illumination. Our system is designed to handle fully dynamic scenes, and makes no assumptions about the lighting, geometry, and materials. The data-driven nature of our approach sidesteps many difficulties of caching algorithms, such as locati... | ['Alexander Keller', 'Jan Novák', 'Fabrice Rousselle', 'Thomas Müller'] | 2021-06-23 | null | null | null | null | ['neural-radiance-caching'] | ['computer-vision'] | [ 5.74045070e-02 -5.93663275e-01 3.33038419e-01 -5.39371789e-01
-8.55857909e-01 -3.00242096e-01 2.89688736e-01 -1.45770788e-01
-7.22164094e-01 4.67746049e-01 -1.99635644e-02 -5.33064425e-01
1.84053779e-01 -1.03220522e+00 -1.02585948e+00 -7.21037447e-01
-2.39503875e-01 1.51176199e-01 4.41456020e-01 -2.07507744... | [9.968094825744629, -2.425234079360962] |
9cc27d57-e6a8-4076-b23f-21dd7334489b | upb-at-semeval-2022-task-5-enhancing-uniter | 2205.14769 | null | https://arxiv.org/abs/2205.14769v1 | https://arxiv.org/pdf/2205.14769v1.pdf | UPB at SemEval-2022 Task 5: Enhancing UNITER with Image Sentiment and Graph Convolutional Networks for Multimedia Automatic Misogyny Identification | In recent times, the detection of hate-speech, offensive, or abusive language in online media has become an important topic in NLP research due to the exponential growth of social media and the propagation of such messages, as well as their impact. Misogyny detection, even though it plays an important part in hate-spee... | ['Dumitru-Clementin Cercel', 'Mihai Dascalu', 'Andrei Paraschiv'] | 2022-05-29 | null | https://aclanthology.org/2022.semeval-1.85 | https://aclanthology.org/2022.semeval-1.85.pdf | semeval-naacl-2022-7 | ['abusive-language'] | ['natural-language-processing'] | [ 7.88261443e-02 2.86085531e-02 4.68308702e-02 1.91821918e-01
-7.07280338e-01 -8.02796245e-01 1.01948750e+00 5.23249447e-01
-4.43204045e-01 3.66498739e-01 3.08164239e-01 -1.36007100e-01
4.41042721e-01 -2.61813164e-01 -5.21161020e-01 -4.71274793e-01
1.40569717e-01 1.69083849e-01 4.54403497e-02 -3.25826466... | [8.619780540466309, 10.616959571838379] |
592d2894-69ca-4835-a577-481ee6bac0b7 | security-and-privacy-problems-in-voice | 2304.09486 | null | https://arxiv.org/abs/2304.09486v1 | https://arxiv.org/pdf/2304.09486v1.pdf | Security and Privacy Problems in Voice Assistant Applications: A Survey | Voice assistant applications have become omniscient nowadays. Two models that provide the two most important functions for real-life applications (i.e., Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR) models and Speaker Identification (SI) models. According to recent studies, security and ... | ['Jun Zhang', 'Hossein Ghodosi', 'Mostafa Rahimi Azghadi', 'Lei Pan', 'Chao Chen', 'Jingjin Li'] | 2023-04-19 | null | null | null | null | ['speaker-identification'] | ['speech'] | [-1.15477182e-01 -5.25510646e-02 -2.15801582e-01 -4.66700315e-01
-2.08202273e-01 -7.04457939e-01 5.50118268e-01 -3.18730623e-01
-4.84781027e-01 8.12157452e-01 8.11029822e-02 -5.60989976e-01
-1.68607801e-01 -5.39636314e-01 2.20910043e-01 -6.90721035e-01
3.91137302e-01 1.36991248e-01 1.56260207e-01 -1.55888721... | [13.98326587677002, 5.828022480010986] |
42a58852-45de-464c-a843-2f80ed266f03 | minimal-time-deadbeat-consensus-and | 2304.06224 | null | https://arxiv.org/abs/2304.06224v1 | https://arxiv.org/pdf/2304.06224v1.pdf | Minimal-time Deadbeat Consensus and Individual Disagreement Degree Prediction for High-order Linear Multi-agent Systems | In this paper, a Hankel matrix-based fully distributed algorithm is proposed to address a minimal-time deadbeat consensus prediction problem for discrete-time high-order multi-agent systems (MASs). Therein, each agent can predict the consensus value with the minimum number of observable historical outputs of its own. A... | ['Wei Ren', 'Zhe Hu', 'Bowen Xu', 'Hai-Tao Zhang', 'Fu-Long Hu'] | 2023-04-13 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [-0.28346643 0.19660294 0.09108175 0.12764938 -0.6314055 -0.6618075
0.4836923 0.22105238 -0.04145888 1.1182323 -0.514189 -0.09952964
-0.53072894 -0.31305656 -0.06455656 -1.1977823 -0.196339 0.59214365
-0.11559355 -0.13954239 0.02307089 0.12401674 -0.7906868 -0.53324854
1.1140726 1.1573117 0.0... | [5.1746416091918945, 2.5893378257751465] |
50c58701-1608-4442-bc81-c8ffd6faea70 | spontaneous-emotion-recognition-from-facial | 2012.06973 | null | https://arxiv.org/abs/2012.06973v1 | https://arxiv.org/pdf/2012.06973v1.pdf | Spontaneous Emotion Recognition from Facial Thermal Images | One of the key research areas in computer vision addressed by a vast number of publications is the processing and understanding of images containing human faces. The most often addressed tasks include face detection, facial landmark localization, face recognition and facial expression analysis. Other, more specialized ... | ['Chirag Kyal'] | 2020-12-13 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [ 3.72366846e-01 -2.02883810e-01 1.34632021e-01 -7.65059114e-01
-2.77327359e-01 -4.82800335e-01 3.56370836e-01 -1.04295544e-01
-4.86505687e-01 4.70876038e-01 -5.06533384e-01 7.45285451e-02
-7.81826023e-03 -1.58587471e-01 -2.16413230e-01 -7.34737217e-01
-1.84419319e-01 2.47905463e-01 -2.50839442e-01 -1.63955048... | [13.284937858581543, 0.9757174253463745] |
5626d6dd-0a83-4b3a-9b46-87d99a862a3a | self-sustaining-ultra-wideband-positioning | 2212.04896 | null | https://arxiv.org/abs/2212.04896v2 | https://arxiv.org/pdf/2212.04896v2.pdf | Self-sustaining Ultra-wideband Positioning System for Event-driven Indoor Localization | Smart and unobtrusive mobile sensor nodes that accurately track their own position have the potential to augment data collection with location-based functions. To attain this vision of unobtrusiveness, the sensor nodes must have a compact form factor and operate over long periods without battery recharging or replaceme... | ['Luca Benini', 'Michele Magno', 'Philipp Mayer'] | 2022-12-09 | null | null | null | null | ['motion-detection', 'indoor-localization'] | ['computer-vision', 'computer-vision'] | [ 0.12350588 0.22407174 0.08826767 -0.2392431 -1.0324303 -0.63650477
-0.13581045 0.38517007 -0.6494533 1.0734566 -0.52385014 -0.4711321
-0.4290177 -0.8512843 -0.4846285 -0.9699974 -0.42159528 -0.11763798
-0.18712902 0.17587526 -0.15399969 0.28897923 -1.4125953 -0.98158634
0.79390234 1.6989744 0.3... | [6.329766750335693, 1.0629806518554688] |
9439d745-f282-443d-81c9-e87468a27eba | vcsum-a-versatile-chinese-meeting | 2305.05280 | null | https://arxiv.org/abs/2305.05280v2 | https://arxiv.org/pdf/2305.05280v2.pdf | VCSUM: A Versatile Chinese Meeting Summarization Dataset | Compared to news and chat summarization, the development of meeting summarization is hugely decelerated by the limited data. To this end, we introduce a versatile Chinese meeting summarization dataset, dubbed VCSum, consisting of 239 real-life meetings, with a total duration of over 230 hours. We claim our dataset is v... | ['Linqi Song', 'Ding Liang', 'Zhaohui Hou', 'Haochen Tan', 'Mingjie Zhan', 'Han Wu'] | 2023-05-09 | null | null | null | null | ['meeting-summarization'] | ['natural-language-processing'] | [ 1.85249344e-01 4.94322896e-01 -1.26923233e-01 -2.14126766e-01
-1.48440778e+00 -7.45993137e-01 7.67137229e-01 5.56757867e-01
1.26978848e-02 1.13649631e+00 1.18306422e+00 3.75797376e-02
2.00438708e-01 -3.59463990e-01 -3.52456838e-01 -3.00789595e-01
8.76509845e-02 3.53975475e-01 1.45214900e-01 -1.74039319... | [12.649751663208008, 9.424224853515625] |
ee506a66-c2f7-4e2d-a36e-fa201a732581 | time-aware-dynamic-graph-embedding-for | 2207.00594 | null | https://arxiv.org/abs/2207.00594v2 | https://arxiv.org/pdf/2207.00594v2.pdf | Time-aware Dynamic Graph Embedding for Asynchronous Structural Evolution | Dynamic graphs refer to graphs whose structure dynamically changes over time. Despite the benefits of learning vertex representations (i.e., embeddings) for dynamic graphs, existing works merely view a dynamic graph as a sequence of changes within the vertex connections, neglecting the crucial asynchronous nature of su... | ['Lei Chen', 'Xiaofang Zhou', 'Quoc Viet Hung Nguyen', 'Tong Chen', 'Jiannong Cao', 'Hongzhi Yin', 'Yu Yang'] | 2022-07-01 | null | null | null | null | ['graph-mining', 'dynamic-graph-embedding'] | ['graphs', 'graphs'] | [-1.45904496e-01 1.88425452e-01 -4.37411457e-01 -1.52947262e-01
3.38575035e-01 -9.28130388e-01 7.62593567e-01 6.84677780e-01
-3.13788168e-02 3.60040039e-01 1.84327513e-01 -3.54014874e-01
-2.56533414e-01 -1.29396236e+00 -6.42399967e-01 -7.53244042e-01
-6.53121114e-01 5.71388721e-01 4.23883080e-01 -1.57992661... | [7.191611289978027, 6.079714298248291] |
d2f4461b-b375-4837-928b-289df69b0306 | c-4-net-contextual-compression-and | 2110.11887 | null | https://arxiv.org/abs/2110.11887v1 | https://arxiv.org/pdf/2110.11887v1.pdf | C$^{4}$Net: Contextual Compression and Complementary Combination Network for Salient Object Detection | Deep learning solutions of the salient object detection problem have achieved great results in recent years. The majority of these models are based on encoders and decoders, with a different multi-feature combination. In this paper, we show that feature concatenation works better than other combination methods like mul... | ['Hazarapet Tunanyan'] | 2021-10-22 | null | null | null | null | ['salient-object-detection'] | ['computer-vision'] | [ 2.06600130e-02 8.61225277e-03 -2.61488110e-01 -4.11390364e-01
-5.01857877e-01 2.82847703e-01 4.93142456e-01 1.44968256e-01
-4.10657108e-01 5.65096796e-01 3.97753179e-01 2.95026153e-01
4.43073809e-02 -9.43449438e-01 -7.56520331e-01 -5.46112418e-01
-3.78203273e-01 -3.58895093e-01 8.98561895e-01 -2.23541662... | [9.613761901855469, -0.4832178056240082] |
587bb871-8b19-401e-8603-a3c207f582f7 | pipeline-for-3d-reconstruction-of-the-human | 2111.05409 | null | https://arxiv.org/abs/2111.05409v1 | https://arxiv.org/pdf/2111.05409v1.pdf | Pipeline for 3D reconstruction of the human body from AR/VR headset mounted egocentric cameras | In this paper, we propose a novel pipeline for the 3D reconstruction of the full body from egocentric viewpoints. 3-D reconstruction of the human body from egocentric viewpoints is a challenging task as the view is skewed and the body parts farther from the cameras are occluded. One such example is the view from camera... | ['Vanita Jain', 'Kshitij Sidana', 'Shivam Grover'] | 2021-11-09 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 6.12809062e-02 6.94028795e-01 2.82026172e-01 -3.70918989e-01
-1.84774280e-01 -3.40741932e-01 3.98228705e-01 -7.40120411e-01
-4.52401163e-03 4.40636605e-01 4.06564504e-01 2.55777121e-01
5.33831239e-01 -8.18473160e-01 -8.04189026e-01 -2.37376988e-01
1.68386564e-01 9.88155842e-01 3.92472237e-01 -4.12269592... | [7.192683696746826, -1.0668765306472778] |
f15563d9-f2b3-40a7-9e1d-64a78a6fe4f6 | crowdsourcing-lung-nodules-detection-and | 1809.06402 | null | http://arxiv.org/abs/1809.06402v1 | http://arxiv.org/pdf/1809.06402v1.pdf | Crowdsourcing Lung Nodules Detection and Annotation | We present crowdsourcing as an additional modality to aid radiologists in the
diagnosis of lung cancer from clinical chest computed tomography (CT) scans.
More specifically, a complete workflow is introduced which can help maximize
the sensitivity of lung nodule detection by utilizing the collective
intelligence of the... | ['Saeed Boorboor', 'Ji Hwan Park', 'Arie Kaufman', 'Saad Nadeem', 'Kevin Baker'] | 2018-09-17 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 1.72448054e-01 5.30368447e-01 -1.38705596e-01 -3.95254344e-02
-1.23685539e+00 -8.25032592e-01 -1.15051856e-02 2.27192968e-01
-5.48600554e-01 4.13451910e-01 -2.02098954e-02 -6.31372631e-01
2.19882667e-01 -6.43612981e-01 -5.69814026e-01 -7.34001935e-01
-5.90212569e-02 9.97728229e-01 9.23397720e-01 1.56163782... | [15.376221656799316, -2.1434240341186523] |
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