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989e6108-a003-4129-a9a6-e4b7cae24c0d | self-supervised-sparse-representation-for | null | null | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136730727.pdf | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136730727.pdf | Self-supervised Sparse Representation for Video Anomaly Detection | Video anomaly detection (VAD) aims at localizing unexpected actions or activities in a video sequence. Existing mainstream VAD techniques are based on either the one-class formulation, which assumes all training data are normal, or weakly-supervised, which requires only video-level normal/anomaly labels. To establish a... | ['Tyng-Luh Liu', 'Chiou-Shann Fuh', 'Ding-Jie Chen', 'He-Yen Hsieh*', 'Jhih-Ciang Wu*'] | 2022-10-23 | null | null | null | eccv-2022-2022-10 | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [-6.37345575e-03 -4.66013908e-01 -3.69565129e-01 -3.18889976e-01
-5.83904147e-01 -2.93919623e-01 5.61469853e-01 9.85635146e-02
7.10393339e-02 3.32961828e-02 3.49369347e-01 -1.31651148e-01
2.71299601e-01 -5.01379967e-01 -6.36883199e-01 -5.91224730e-01
-4.06559646e-01 1.80350468e-01 9.11271274e-02 -3.55582610... | [7.853052139282227, 1.6024186611175537] |
73078336-be6e-4196-a928-b207f0ef2d59 | arbitrary-oriented-object-detection-with | 2003.05597 | null | https://arxiv.org/abs/2003.05597v4 | https://arxiv.org/pdf/2003.05597v4.pdf | On the Arbitrary-Oriented Object Detection: Classification based Approaches Revisited | Arbitrary-oriented object detection has been a building block for rotation sensitive tasks. We first show that the boundary problem suffered in existing dominant regression-based rotation detectors, is caused by angular periodicity or corner ordering, according to the parameterization protocol. We also show that the ro... | ['Xue Yang', 'Junchi Yan'] | 2020-03-12 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/666_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530664.pdf | eccv-2020-8 | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 1.15020059e-01 -2.35146247e-02 -1.21238448e-01 -4.86549407e-01
-3.91577482e-01 -5.57665706e-01 2.93350875e-01 -2.99757987e-01
9.87182558e-03 3.59334797e-01 1.19851520e-02 -2.50742584e-01
-1.52912617e-01 -7.46459603e-01 -5.23910582e-01 -9.91213620e-01
-1.57652184e-01 2.71113724e-01 2.80523717e-01 -3.23983788... | [8.686772346496582, -0.8193923234939575] |
73c3664d-c5f2-45fe-8882-4e79e95f93f4 | analyzing-large-receptive-field-convolutional | 1910.07047 | null | https://arxiv.org/abs/1910.07047v1 | https://arxiv.org/pdf/1910.07047v1.pdf | Analyzing Large Receptive Field Convolutional Networks for Distant Speech Recognition | Despite significant efforts over the last few years to build a robust automatic speech recognition (ASR) system for different acoustic settings, the performance of the current state-of-the-art technologies significantly degrades in noisy reverberant environments. Convolutional Neural Networks (CNNs) have been successfu... | ['Vinay Kothapally', 'Soheil Khorram', 'John H. L. Hansen', 'Salar Jafarlou'] | 2019-10-15 | null | null | null | null | ['distant-speech-recognition'] | ['speech'] | [ 6.73955753e-02 -1.42888382e-01 6.11878276e-01 -4.17368144e-01
-7.60141194e-01 -2.67081559e-01 6.46006107e-01 -4.18613106e-01
-4.58195448e-01 4.67098683e-01 4.20582831e-01 -7.49521315e-01
-3.58968861e-02 -3.32039446e-01 -5.93044102e-01 -7.37024784e-01
2.85289790e-02 -1.78272247e-01 2.65661031e-01 -6.65229440... | [14.722054481506348, 6.0901641845703125] |
1b11497f-ae02-4d09-8428-5914b949c160 | learning-video-salient-object-detection | 2204.02008 | null | https://arxiv.org/abs/2204.02008v1 | https://arxiv.org/pdf/2204.02008v1.pdf | Learning Video Salient Object Detection Progressively from Unlabeled Videos | Recent deep learning-based video salient object detection (VSOD) has achieved some breakthrough, but these methods rely on expensive annotated videos with pixel-wise annotations, weak annotations, or part of the pixel-wise annotations. In this paper, based on the similarities and the differences between VSOD and image ... | ['Peng Chen', 'Ronghua Liang', 'Weihua Gong', 'Wentian Ni', 'Haoran Liang', 'Binwei Xu'] | 2022-04-05 | null | null | null | null | ['video-salient-object-detection'] | ['computer-vision'] | [ 1.48729265e-01 1.48817571e-02 -5.34061730e-01 -7.98362941e-02
-4.16280061e-01 -1.66253045e-01 3.04665565e-01 6.71264753e-02
-4.48104322e-01 6.76910996e-01 4.19131666e-01 2.55288184e-01
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5.87573610e-02 -2.91753769e-01 1.30618405e+00 -1.22594737... | [9.663679122924805, -0.32023248076438904] |
3cffd820-9d19-4a97-98a8-0027638d8920 | automatic-parallel-corpus-creation-for-hindi | 1901.08625 | null | http://arxiv.org/abs/1901.08625v1 | http://arxiv.org/pdf/1901.08625v1.pdf | Automatic Parallel Corpus Creation for Hindi-English News Translation Task | The parallel corpus for multilingual NLP tasks, deep learning applications
like Statistical Machine Translation Systems is very important. The parallel
corpus of Hindi-English language pair available for news translation task till
date is of very limited size as per the requirement of the systems are
concerned. In this... | ['Rakesh Chandra Balabantaray', 'Priyankit Acharya', 'Aditya Kumar Pathak', 'Dilpreet Kaur'] | 2019-01-24 | null | null | null | null | ['multilingual-nlp'] | ['natural-language-processing'] | [-2.19380766e-01 2.78473292e-02 1.94508266e-02 -1.22669026e-01
-1.12909615e+00 -5.36611438e-01 1.05799961e+00 -2.73399483e-02
-6.70275927e-01 1.35822463e+00 5.45956016e-01 -6.40700042e-01
4.01902169e-01 -7.45677054e-01 -7.47116864e-01 -2.71982938e-01
7.31316283e-02 1.18557847e+00 -1.16106227e-01 -6.83612943... | [11.485491752624512, 10.449694633483887] |
6a9cc9ea-b257-4347-91c1-de3188a4ccc6 | evolvehypergraph-group-aware-dynamic | 2208.05470 | null | https://arxiv.org/abs/2208.05470v1 | https://arxiv.org/pdf/2208.05470v1.pdf | EvolveHypergraph: Group-Aware Dynamic Relational Reasoning for Trajectory Prediction | While the modeling of pair-wise relations has been widely studied in multi-agent interacting systems, its ability to capture higher-level and larger-scale group-wise activities is limited. In this paper, we propose a group-aware relational reasoning approach (named EvolveHypergraph) with explicit inference of the under... | ['Mykel J. Kochenderfer', 'Victoria Dax', 'Hengbo Ma', 'Jinkyoo Park', 'Chuanbo Hua', 'Jiachen Li'] | 2022-08-10 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [-2.29996353e-01 4.94457901e-01 -1.47995695e-01 -2.12605804e-01
2.34587744e-01 -3.15201402e-01 7.31677175e-01 5.32675087e-01
1.91093180e-02 8.23206604e-01 2.01580435e-01 -2.54606847e-02
-6.65071189e-01 -1.17631173e+00 -7.57421434e-01 -5.67627788e-01
-6.30148113e-01 1.10784566e+00 5.43866456e-01 -4.85692143... | [5.925436973571777, 0.8793867826461792] |
99f3ef8b-b4b9-4866-9d61-2c629d1cc6b7 | deep-phase-correlation-for-end-to-end | 2008.09474 | null | https://arxiv.org/abs/2008.09474v4 | https://arxiv.org/pdf/2008.09474v4.pdf | Deep Phase Correlation for End-to-End Heterogeneous Sensor Measurements Matching | The crucial step for localization is to match the current observation to the map. When the two sensor modalities are significantly different, matching becomes challenging. In this paper, we present an end-to-end deep phase correlation network (DPCN) to match heterogeneous sensor measurements. In DPCN, the primary compo... | ['Xuecheng Xu', 'Rong Xiong', 'Yue Wang', 'Zexi Chen'] | 2020-08-21 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 9.34726149e-02 -6.33346289e-02 -4.45699878e-02 -4.40935612e-01
-9.68466938e-01 -5.44740736e-01 4.24191982e-01 -1.44143641e-01
-2.28082538e-01 5.82510352e-01 3.12663685e-03 1.30050123e-01
-4.67693090e-01 -6.54419720e-01 -7.23949909e-01 -8.24527502e-01
-1.83582515e-01 2.96462953e-01 2.10552007e-01 -1.52935028... | [7.527985095977783, -2.0464086532592773] |
f6e2f409-ab44-435b-9bb8-cccdc74f671c | towards-unified-human-parsing-and-pose | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Dong_Towards_Unified_Human_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Dong_Towards_Unified_Human_2014_CVPR_paper.pdf | Towards Unified Human Parsing and Pose Estimation | We study the problem of human body configuration analysis, more specifically, human parsing and human pose estimation. These two tasks, i.e. identifying the semantic regions and body joints respectively over the human body image, are intrinsically highly correlated. However, previous works generally solve these two pro... | ['Xiaohui Shen', 'Qiang Chen', 'Jianchao Yang', 'Jian Dong', 'Shuicheng Yan'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['human-parsing'] | ['computer-vision'] | [ 2.40254402e-01 8.18986371e-02 -2.86712013e-02 -2.43882760e-01
-7.50782132e-01 -4.51380581e-01 2.83025950e-01 -1.76085830e-01
-1.86873987e-01 3.30489010e-01 2.02605218e-01 3.57493073e-01
-7.99051449e-02 -5.44588208e-01 -5.58356166e-01 -6.77182496e-01
1.12157010e-01 5.70558131e-01 3.16922814e-01 -1.68982267... | [7.564724445343018, -0.4780471622943878] |
7a09f85d-50b5-48c7-ac70-d19033d1ed90 | extractive-and-abstractive-summarization | null | null | https://aclanthology.org/2022.fnp-1.8 | https://aclanthology.org/2022.fnp-1.8.pdf | Extractive and Abstractive Summarization Methods for Financial Narrative Summarization in English, Spanish and Greek | This paper describes the three summarization systems submitted to the Financial Narrative Summarization Shared Task (FNS-2022). We developed a task-specific extractive summarization method for the reports in English. It was based on a sequence classification task whose objective was to find the sentence where the summa... | ['Álvaro Barbero Jiménez', 'David Betancur', 'Alba Segurado', 'Alejandro Vaca'] | null | null | null | null | fnp-lrec-2022-6 | ['extractive-summarization'] | ['natural-language-processing'] | [ 4.23252672e-01 6.16621017e-01 -1.53028473e-01 -8.92684385e-02
-1.49525630e+00 -6.51630938e-01 8.60231221e-01 3.58707458e-01
-3.47729772e-01 1.34042025e+00 1.24376762e+00 -1.16293430e-01
3.61422807e-01 -2.96849996e-01 -5.55358887e-01 -3.08899581e-01
2.16353759e-01 2.53838271e-01 -8.93604606e-02 -2.52606362... | [12.412590980529785, 9.541230201721191] |
c8aa0007-0922-4df1-ab0f-759ccdb9a6ce | ingeotec-at-semeval-2017-task-4-a-b4msa | null | null | https://aclanthology.org/S17-2130 | https://aclanthology.org/S17-2130.pdf | INGEOTEC at SemEval 2017 Task 4: A B4MSA Ensemble based on Genetic Programming for Twitter Sentiment Analysis | This paper describes the system used in SemEval-2017 Task 4 (Subtask A): Message Polarity Classification for both English and Arabic languages. Our proposed system is an ensemble of two layers, the first one uses our generic framework for multilingual polarity classification (B4MSA) and the second layer combines all th... | ["Sabino a-Jim{\\'e}nez", 'Mir', 'Eric Sadit Tellez', 'Mario Graff', 'Daniela Moctezuma'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-6.65482283e-02 4.51654434e-01 -2.08943471e-01 -3.75568956e-01
-8.46039534e-01 -7.27275908e-01 1.14569819e+00 5.12191117e-01
-4.40654755e-01 1.10492039e+00 1.81455538e-01 -4.33076859e-01
7.09979853e-04 -5.42485416e-01 -5.08141339e-01 -5.71168423e-01
5.07108271e-02 7.59970486e-01 3.00525159e-01 -9.89275515... | [11.168465614318848, 6.9236907958984375] |
abf43e44-f47a-486c-aaf8-39161d3f5475 | towards-more-realistic-membership-inference | 2306.12983 | null | https://arxiv.org/abs/2306.12983v1 | https://arxiv.org/pdf/2306.12983v1.pdf | Towards More Realistic Membership Inference Attacks on Large Diffusion Models | Generative diffusion models, including Stable Diffusion and Midjourney, can generate visually appealing, diverse, and high-resolution images for various applications. These models are trained on billions of internet-sourced images, raising significant concerns about the potential unauthorized use of copyright-protected... | ['Paweł Morawiecki', 'Tomasz Trzciński', 'Przemysław Rokita', 'Stanisław Pawlak', 'Antoni Kowalczuk', 'Jan Dubiński'] | 2023-06-22 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [ 3.67096782e-01 6.11856170e-02 2.00053789e-02 -8.57647136e-02
-6.55244350e-01 -1.22627032e+00 1.06627917e+00 -1.25183538e-01
-3.21693808e-01 4.42814112e-01 -4.66953516e-02 -7.85154939e-01
4.29294966e-02 -8.27949405e-01 -6.75946891e-01 -6.18001580e-01
-1.53664231e-01 9.76369530e-02 2.92972982e-01 -5.09216636... | [12.439229965209961, 1.1276533603668213] |
0a51245e-ac3b-469f-809f-5fef495d9679 | mianet-aggregating-unbiased-instance-and-1 | 2305.13864 | null | https://arxiv.org/abs/2305.13864v1 | https://arxiv.org/pdf/2305.13864v1.pdf | MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation | Existing few-shot segmentation methods are based on the meta-learning strategy and extract instance knowledge from a support set and then apply the knowledge to segment target objects in a query set. However, the extracted knowledge is insufficient to cope with the variable intra-class differences since the knowledge i... | ['Tianlin Huang', 'Yuan Feng', 'Qiong Chen', 'Yong Yang'] | 2023-05-23 | mianet-aggregating-unbiased-instance-and | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_MIANet_Aggregating_Unbiased_Instance_and_General_Information_for_Few-Shot_Semantic_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_MIANet_Aggregating_Unbiased_Instance_and_General_Information_for_Few-Shot_Semantic_CVPR_2023_paper.pdf | cvpr-2023-1 | ['few-shot-image-segmentation', 'general-knowledge'] | ['computer-vision', 'miscellaneous'] | [ 4.47646230e-01 2.69965902e-02 -5.25592387e-01 -6.25945687e-01
-1.17121029e+00 -2.35966101e-01 2.77791023e-01 1.41633689e-01
-5.29543817e-01 4.84237522e-01 -1.64651141e-01 2.92055994e-01
8.32407456e-03 -8.80092382e-01 -7.76773274e-01 -7.22067833e-01
3.22280526e-01 4.26946253e-01 6.13698304e-01 -2.27205157... | [9.580294609069824, 1.3711696863174438] |
33467a7f-3790-466d-9666-d4287416ecf1 | experiences-of-adapting-multimodal-machine | null | null | https://aclanthology.org/2021.mmtlrl-1.7 | https://aclanthology.org/2021.mmtlrl-1.7.pdf | Experiences of Adapting Multimodal Machine Translation Techniques for Hindi | Multimodal Neural Machine Translation (MNMT) is an interesting task in natural language processing (NLP) where we use visual modalities along with a source sentence to aid the source to target translation process. Recently, there has been a lot of works in MNMT frameworks to boost the performance of standalone Machine ... | ['Asif Ekbal', 'Dibyanayan Bandyopadhyay', 'Baban Gain'] | null | null | null | null | mmtlrl-ranlp-2021-9 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 2.82912225e-01 -2.69593388e-01 -3.24766874e-01 -2.49264061e-01
-1.06057858e+00 -8.28731894e-01 9.54616010e-01 -3.85788769e-01
-5.34205616e-01 1.10090566e+00 3.10613036e-01 -7.41384447e-01
5.89460552e-01 -4.68703926e-01 -7.75736749e-01 -3.59188437e-01
5.13667226e-01 8.06832135e-01 -1.27085224e-01 -5.42165756... | [11.485397338867188, 1.5389994382858276] |
84bbd099-57b2-4d41-b15a-5e907a53aeda | deep-convolutional-generative-adversarial-2 | 2005.04188 | null | https://arxiv.org/abs/2005.04188v1 | https://arxiv.org/pdf/2005.04188v1.pdf | Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images | Sufficient high-quality traffic data are a crucial component of various Intelligent Transportation System (ITS) applications and research related to congestion prediction, speed prediction, incident detection, and other traffic operation tasks. Nonetheless, missing traffic data are a common issue in sensor data which i... | ['Anuj Sharma', 'Pranamesh Chakraborty', 'Tongge Huang'] | 2020-05-05 | null | null | null | null | ['traffic-data-imputation'] | ['time-series'] | [ 5.00503838e-01 -4.27686125e-01 3.93759161e-02 -4.15011048e-01
-8.29352975e-01 1.16439521e-01 4.27509010e-01 -3.35923105e-01
-4.23329920e-02 1.27202630e+00 1.57213062e-01 -4.22397196e-01
-2.02035278e-01 -1.12538207e+00 -7.39028573e-01 -8.22102427e-01
3.09194088e-01 4.08928543e-01 -1.53136803e-02 -3.05783689... | [6.508443832397461, 2.040390729904175] |
5968fbf3-bcbe-4b04-b587-5fe6b265e3c5 | query-your-model-with-definitions-in-framenet | 2212.02036 | null | https://arxiv.org/abs/2212.02036v1 | https://arxiv.org/pdf/2212.02036v1.pdf | Query Your Model with Definitions in FrameNet: An Effective Method for Frame Semantic Role Labeling | Frame Semantic Role Labeling (FSRL) identifies arguments and labels them with frame semantic roles defined in FrameNet. Previous researches tend to divide FSRL into argument identification and role classification. Such methods usually model role classification as naive multi-class classification and treat arguments ind... | ['Baobao Chang', 'Yiming Wang', 'Ce Zheng'] | 2022-12-05 | null | null | null | null | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 3.86110216e-01 4.08593029e-01 -6.05578125e-01 -4.84461606e-01
-6.04454815e-01 -8.87467027e-01 9.22714829e-01 5.14395475e-01
-4.69171792e-01 8.31262410e-01 6.48223877e-01 -1.38572082e-01
-1.08094789e-01 -8.67258072e-01 -5.03660560e-01 -4.21801388e-01
4.72652316e-01 4.66876149e-01 8.56655180e-01 -4.92000312... | [10.158230781555176, 9.199433326721191] |
4fb542be-6d9a-4392-9442-81f813af6199 | one-size-does-not-fit-all-the-case-for | 2205.02564 | null | https://arxiv.org/abs/2205.02564v1 | https://arxiv.org/pdf/2205.02564v1.pdf | One Size Does Not Fit All: The Case for Personalised Word Complexity Models | Complex Word Identification (CWI) aims to detect words within a text that a reader may find difficult to understand. It has been shown that CWI systems can improve text simplification, readability prediction and vocabulary acquisition modelling. However, the difficulty of a word is a highly idiosyncratic notion that de... | ['Manuel Tragut', 'Sian Gooding'] | 2022-05-05 | null | https://aclanthology.org/2022.findings-naacl.27 | https://aclanthology.org/2022.findings-naacl.27.pdf | findings-naacl-2022-7 | ['complex-word-identification'] | ['natural-language-processing'] | [ 3.29001337e-01 3.64434719e-01 -3.96164954e-01 -1.66013137e-01
-5.48967838e-01 -5.76446652e-01 6.80380821e-01 1.09358156e+00
-7.13505805e-01 1.93390623e-01 5.86609483e-01 -2.94845730e-01
-4.16625738e-01 -5.41773736e-01 -1.84904635e-01 2.02695206e-01
3.20217311e-01 5.91992259e-01 -2.23968383e-02 -4.48079526... | [10.83163833618164, 10.377511978149414] |
11c9b13b-ec79-4264-b4e5-bf5b396b096a | anyface-free-style-text-to-face-synthesis-and | 2203.15334 | null | https://arxiv.org/abs/2203.15334v1 | https://arxiv.org/pdf/2203.15334v1.pdf | AnyFace: Free-style Text-to-Face Synthesis and Manipulation | Existing text-to-image synthesis methods generally are only applicable to words in the training dataset. However, human faces are so variable to be described with limited words. So this paper proposes the first free-style text-to-face method namely AnyFace enabling much wider open world applications such as metaverse, ... | ['Zhenan Sun', 'Min Ren', 'Muyi Sun', 'Qi Li', 'Qiyao Deng', 'Jianxin Sun'] | 2022-03-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Sun_AnyFace_Free-Style_Text-To-Face_Synthesis_and_Manipulation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Sun_AnyFace_Free-Style_Text-To-Face_Synthesis_and_Manipulation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['text-to-face-generation'] | ['computer-vision'] | [ 1.55630782e-01 -4.44106124e-02 -1.13123832e-02 -6.39544368e-01
-8.70148361e-01 -3.81417841e-01 8.05460036e-01 -8.91843617e-01
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4.39843148e-01 4.11079675e-01 -6.20990276e-01 -2.44899020... | [12.60246753692627, -0.13444356620311737] |
ab431c23-9415-4d9e-9385-a3db2edd82b8 | dependency-aware-self-training-for-entity | 2211.16101 | null | https://arxiv.org/abs/2211.16101v1 | https://arxiv.org/pdf/2211.16101v1.pdf | Dependency-aware Self-training for Entity Alignment | Entity Alignment (EA), which aims to detect entity mappings (i.e. equivalent entity pairs) in different Knowledge Graphs (KGs), is critical for KG fusion. Neural EA methods dominate current EA research but still suffer from their reliance on labelled mappings. To solve this problem, a few works have explored boosting t... | ['Guido Zuccon', 'Wen Hua', 'Tiancheng Lan', 'Bing Liu'] | 2022-11-29 | null | null | null | null | ['entity-alignment', 'entity-alignment'] | ['knowledge-base', 'natural-language-processing'] | [ 1.69720620e-01 5.61464667e-01 -4.02678877e-01 -3.08417916e-01
-3.68761897e-01 -4.44483399e-01 4.78435993e-01 3.08329612e-01
-5.64912915e-01 8.64745796e-01 1.53789833e-01 -2.42753550e-01
-1.93889260e-01 -1.07668996e+00 -9.62560713e-01 -4.34087396e-01
-1.43091129e-02 3.95572066e-01 4.49262619e-01 -4.52348232... | [9.242093086242676, 8.459824562072754] |
8acbedb4-00d5-4d72-b33a-a0c25da75e94 | tokenization-and-the-noiseless-channel | 2306.16842 | null | https://arxiv.org/abs/2306.16842v1 | https://arxiv.org/pdf/2306.16842v1.pdf | Tokenization and the Noiseless Channel | Subword tokenization is a key part of many NLP pipelines. However, little is known about why some tokenizer and hyperparameter combinations lead to better downstream model performance than others. We propose that good tokenizers lead to \emph{efficient} channel usage, where the channel is the means by which some input ... | ['Ryan Cotterell', 'Mrinmaya Sachan', 'Li Du', 'Juan Luis Gastaldi', 'Clara Meister', 'Vilém Zouhar'] | 2023-06-29 | null | null | null | null | ['machine-translation'] | ['natural-language-processing'] | [ 1.08361796e-01 4.24630582e-01 -5.76992810e-01 -2.27025136e-01
-1.02011287e+00 -7.17027545e-01 4.01905119e-01 4.08525646e-01
-8.68679821e-01 6.32722735e-01 5.74783385e-01 -5.58815241e-01
-1.13112688e-01 -6.66310430e-01 -5.65399289e-01 -6.73616946e-01
-3.35829370e-02 5.50806701e-01 -2.97689080e-01 -9.67580900... | [11.275471687316895, 9.323846817016602] |
a1214523-222e-439f-b742-99a01980d354 | segmentation-of-blood-vessels-optic-disc | 2207.04345 | null | https://arxiv.org/abs/2207.04345v1 | https://arxiv.org/pdf/2207.04345v1.pdf | Segmentation of Blood Vessels, Optic Disc Localization, Detection of Exudates and Diabetic Retinopathy Diagnosis from Digital Fundus Images | Diabetic Retinopathy (DR) is a complication of long-standing, unchecked diabetes and one of the leading causes of blindness in the world. This paper focuses on improved and robust methods to extract some of the features of DR, viz. Blood Vessels and Exudates. Blood vessels are segmented using multiple morphological and... | ['Anindya Sen', 'Ankit Bhattacharya', 'Sayantan Mukherjee', 'Soham Basu'] | 2022-07-09 | null | null | null | null | ['template-matching', 'contour-detection', 'diabetic-retinopathy-detection'] | ['computer-vision', 'computer-vision', 'medical'] | [-1.34713784e-01 -1.86121296e-02 8.29636902e-02 -4.07057494e-01
-1.56503811e-01 -3.67120355e-01 1.02682747e-02 -5.95512167e-02
-3.29940528e-01 7.03084886e-01 1.51059628e-01 -4.71656442e-01
-4.70463037e-02 -8.10872674e-01 8.97511467e-03 -8.32940280e-01
-1.07761115e-01 1.24196358e-01 1.78309858e-01 3.29110831... | [15.833322525024414, -3.995621919631958] |
db5c377b-6bf4-4329-8d98-6d2309d0bbe9 | pancreas-segmentation-via-spatial-context | 1903.00832 | null | https://arxiv.org/abs/1903.00832v3 | https://arxiv.org/pdf/1903.00832v3.pdf | A Model-Driven Stack-Based Fully Convolutional Network for Pancreas Segmentation | The irregular geometry and high inter-slice variability in computerized tomography (CT) scans of the human pancreas make an accurate segmentation of this crucial organ a challenging task for existing data-driven deep learning methods. To address this problem, we present a novel model-driven stack-based fully convolutio... | ['Xiaohua Qian', 'Xiaozhu Lin', 'Jun Li', 'Hao Li'] | 2019-03-03 | null | null | null | null | ['pancreas-segmentation'] | ['medical'] | [-2.89028343e-02 -6.29655260e-04 -3.63983959e-02 -7.73961008e-01
-8.91468763e-01 -3.05011928e-01 1.85313478e-01 4.05843556e-01
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-5.34362674e-01 2.98211366e-01 5.63208997e-01 1.09887354... | [14.491666793823242, -2.7013087272644043] |
2aae29fe-2140-459f-87cd-f28d96ec5c13 | who-should-i-engage-with-at-what-time-a | 2301.08399 | null | https://arxiv.org/abs/2301.08399v1 | https://arxiv.org/pdf/2301.08399v1.pdf | Who Should I Engage with At What Time? A Missing Event Aware Temporal Graph Neural Network | Temporal graph neural network has recently received significant attention due to its wide application scenarios, such as bioinformatics, knowledge graphs, and social networks. There are some temporal graph neural networks that achieve remarkable results. However, these works focus on future event prediction and are per... | ['Zhongjie Wang', 'Xiaofei Xu', 'Zhiying Tu', 'Mingyi Liu'] | 2023-01-20 | null | null | null | null | ['point-processes'] | ['methodology'] | [-1.19906152e-03 4.37133573e-02 -4.08427656e-01 -2.55375415e-01
2.31301650e-01 -2.62424767e-01 5.34287214e-01 7.23411739e-01
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-3.40739965e-01 -1.32636011e+00 -5.76126218e-01 -3.54360372e-01
-7.77074814e-01 5.69888711e-01 5.05530238e-01 -2.57796869... | [7.241791248321533, 5.781636714935303] |
43e45d2e-1072-43de-aced-ccc129acc78d | enforcenet-monocular-camera-localization-in | 1907.07160 | null | https://arxiv.org/abs/1907.07160v1 | https://arxiv.org/pdf/1907.07160v1.pdf | EnforceNet: Monocular Camera Localization in Large Scale Indoor Sparse LiDAR Point Cloud | Pose estimation is a fundamental building block for robotic applications such as autonomous vehicles, UAV, and large scale augmented reality. It is also a prohibitive factor for those applications to be in mass production, since the state-of-the-art, centimeter-level pose estimation often requires long mapping procedur... | ['Yu Chen', 'Guan Wang'] | 2019-07-16 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [-3.82992066e-03 3.39973122e-02 -2.89421737e-01 -5.64675391e-01
-4.98804122e-01 -2.82009095e-01 2.17629388e-01 -1.13007441e-01
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-1.75601795e-01 -1.09624755e+00 -1.03546727e+00 -1.52470589e-01
-1.67589679e-01 8.09107304e-01 2.50255942e-01 -1.79399639... | [7.580437183380127, -2.138148546218872] |
4adc754d-b7c4-4992-9eff-327ef3da2f23 | a-survey-and-an-extensive-evaluation-of | 2007.07663 | null | https://arxiv.org/abs/2007.07663v2 | https://arxiv.org/pdf/2007.07663v2.pdf | A survey and an extensive evaluation of popular audio declipping methods | Dynamic range limitations in signal processing often lead to clipping, or saturation, in signals. The task of audio declipping is estimating the original audio signal, given its clipped measurements, and has attracted much interest in recent years. Audio declipping algorithms often make assumptions about the underlying... | ['Pavel Záviška', 'Pavel Rajmic', 'Lucas Rencker', 'Alexey Ozerov'] | 2020-07-15 | null | null | null | null | ['audio-declipping'] | ['audio'] | [ 5.58567882e-01 -1.26397341e-01 9.56956595e-02 -8.16164538e-02
-1.17285991e+00 -7.06300974e-01 -6.79137483e-02 -4.87802885e-02
-8.57897848e-03 6.21519506e-01 5.41939020e-01 1.17841907e-01
-3.83693904e-01 3.14846113e-02 -5.32909870e-01 -4.90619987e-01
-5.10504246e-01 -3.10592204e-01 -1.13103546e-01 1.46465197... | [15.475996971130371, 5.55682373046875] |
42fd7872-3795-4874-b96e-9941991e2294 | densely-connected-multidilated-convolutional | 2011.11844 | null | https://arxiv.org/abs/2011.11844v2 | https://arxiv.org/pdf/2011.11844v2.pdf | Densely connected multidilated convolutional networks for dense prediction tasks | Tasks that involve high-resolution dense prediction require a modeling of both local and global patterns in a large input field. Although the local and global structures often depend on each other and their simultaneous modeling is important, many convolutional neural network (CNN)-based approaches interchange represen... | ['Yuki Mitsufuji', 'Naoya Takahashi'] | 2020-11-21 | null | null | null | null | ['audio-source-separation', 'music-source-separation'] | ['audio', 'music'] | [-7.33610913e-02 -1.45113856e-01 1.80857927e-01 -3.66188198e-01
-5.88145971e-01 -2.31712237e-01 3.10550302e-01 -1.19791023e-01
-4.78894979e-01 4.14790452e-01 3.09447676e-01 1.63427860e-01
-6.40866384e-02 -1.00367308e+00 -7.40545928e-01 -5.27380228e-01
2.30803844e-02 7.25426152e-02 6.39658093e-01 -2.77504772... | [15.47749137878418, 5.375308513641357] |
7f3620dc-d6db-4eb7-b19a-13c3e4808c0d | modeling-exemplification-in-long-form | null | null | https://openreview.net/forum?id=QXSIhKXBaFP | https://openreview.net/pdf?id=QXSIhKXBaFP | Modeling Exemplification in Long-form Question Answering via Retrieval | Exemplification is a process by which writers explain or clarify a concept by providing an example. While common in all forms of writing, exemplification is particularly useful in the task of long-form question answering (LFQA), where a complicated answer can be made more understandable through simple examples. In this... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['long-form-question-answering'] | ['natural-language-processing'] | [ 9.29335803e-02 6.23549402e-01 1.23096913e-01 -5.02512753e-01
-1.35935128e+00 -9.40417290e-01 1.21730590e+00 4.54713374e-01
-2.69265026e-01 1.18200135e+00 3.64928961e-01 -4.71839666e-01
-3.41863573e-01 -6.26734197e-01 -7.18566716e-01 6.35262281e-02
5.25418699e-01 1.30236232e+00 2.46009350e-01 -8.66774142... | [11.37888240814209, 8.087306022644043] |
4d98f2a6-d50b-4097-b7b3-f647027a6b83 | categorizing-items-with-short-and-noisy | 2110.11431 | null | https://arxiv.org/abs/2110.11431v1 | https://arxiv.org/pdf/2110.11431v1.pdf | Categorizing Items with Short and Noisy Descriptions using Ensembled Transferred Embeddings | Item categorization is a machine learning task which aims at classifying e-commerce items, typically represented by textual attributes, to their most suitable category from a predefined set of categories. An accurate item categorization system is essential for improving both the user experience and the operational proc... | ['Erez Shmueli', 'Yonatan Hadar'] | 2021-10-21 | null | null | null | null | ['embeddings-evaluation'] | ['natural-language-processing'] | [-7.13539496e-02 -4.54918057e-01 -4.34444070e-01 -6.35817230e-01
-6.55406535e-01 -9.08159733e-01 2.70641923e-01 5.90676725e-01
-4.96320009e-01 4.47066277e-01 1.72085181e-01 -2.71394402e-01
-1.84850350e-01 -8.60108316e-01 -4.01322633e-01 -3.46340120e-01
1.59616411e-01 7.29992867e-01 -1.58317342e-01 -1.01195760... | [10.017016410827637, 6.223222255706787] |
b495142e-67f1-4db3-bfa3-c68e41a385ec | feature-extraction-of-hyperspectral-images | null | null | https://doi.org/10.1109/TGRS.2013.2275613 | https://doi.org/10.1109/TGRS.2013.2275613 | Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering | Feature extraction is known to be an effective way in both reducing computational complexity and increasing accuracy of hyperspectral image classification. In this paper, a simple yet quite powerful feature extraction method based on image fusion and recursive filtering (IFRF) is proposed. First, the hyperspectral imag... | ['Jón Atli Benediktsson', 'Shutao Li', 'Xudong Kang'] | 2013-09-16 | null | null | null | ieee-transactions-on-geoscience-and-remote-17 | ['few-shot-image-classification'] | ['computer-vision'] | [ 8.89470041e-01 -8.06171894e-01 1.26709923e-01 -1.99444398e-01
-2.68081784e-01 -4.11487013e-01 2.90819734e-01 2.43709758e-02
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-6.15464449e-01 -1.07030499e+00 1.05089411e-01 -1.27443075e+00
3.91087145e-01 -3.11488599e-01 2.43209712e-02 -1.11952228... | [9.816518783569336, -1.7810112237930298] |
9396a6cd-ca36-442c-81d0-e48431da68f6 | edge-augmented-graph-transformers-global-self | 2108.03348 | null | https://arxiv.org/abs/2108.03348v3 | https://arxiv.org/pdf/2108.03348v3.pdf | Global Self-Attention as a Replacement for Graph Convolution | We propose an extension to the transformer neural network architecture for general-purpose graph learning by adding a dedicated pathway for pairwise structural information, called edge channels. The resultant framework - which we call Edge-augmented Graph Transformer (EGT) - can directly accept, process and output stru... | ['Dharmashankar Subramanian', 'Mohammed J. Zaki', 'Md Shamim Hussain'] | 2021-08-07 | null | null | null | null | ['graph-property-prediction', 'graph-regression'] | ['graphs', 'graphs'] | [ 1.82943001e-01 2.61809796e-01 -2.19229698e-01 -1.82902217e-01
-7.90720657e-02 -4.02513921e-01 5.06208837e-01 5.51841557e-01
-3.73238266e-01 8.63632739e-01 -4.74302471e-03 -7.86669254e-01
7.10915998e-02 -1.24633503e+00 -1.28356886e+00 -1.06474113e+00
-4.91713196e-01 5.34400582e-01 1.66624069e-01 -5.21950364... | [6.796009063720703, 6.217291831970215] |
d0fc8ada-6690-4b8a-a039-09c4cda223e2 | assessing-pattern-recognition-performance-of | 2012.10355 | null | https://arxiv.org/abs/2012.10355v2 | https://arxiv.org/pdf/2012.10355v2.pdf | Assessing Pattern Recognition Performance of Neuronal Cultures through Accurate Simulation | Previous work has shown that it is possible to train neuronal cultures on Multi-Electrode Arrays (MEAs), to recognize very simple patterns. However, this work was mainly focused to demonstrate that it is possible to induce plasticity in cultures, rather than performing a rigorous assessment of their pattern recognition... | ['Giuseppe Amato', 'Federico Cremisi', 'Tommaso Pizzorusso', 'Guido Marco Cicchini', 'Claudio Gennaro', 'Fabrizio Falchi', 'Raffaele Mazziotti', 'Gabriele Lagani'] | 2020-12-18 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 3.22666019e-01 -3.16312201e-02 7.20508277e-01 2.30499029e-01
-1.49281114e-01 -5.60009420e-01 6.70124412e-01 -1.74258664e-01
-4.93602037e-01 1.02520525e+00 -2.89394379e-01 -2.64073461e-01
3.30445357e-02 -6.74134612e-01 -1.04730701e+00 -9.19828415e-01
-5.94431311e-02 2.55123526e-01 4.50117409e-01 1.70040265... | [8.033114433288574, 2.890683889389038] |
b4cfc41f-2c01-4746-b4e4-77197369123b | adjacent-level-feature-cross-fusion-with-3d | 2302.05109 | null | https://arxiv.org/abs/2302.05109v1 | https://arxiv.org/pdf/2302.05109v1.pdf | Adjacent-level Feature Cross-Fusion with 3D CNN for Remote Sensing Image Change Detection | Deep learning-based change detection using remote sensing images has received increasing attention in recent years. However, how to effectively extract and fuse the deep features of bi-temporal images to improve the accuracy of change detection is still a challenge. To address that, a novel adjacent-level feature fusio... | ['Yao Qin', 'Jianwei Fan', 'Guangyang Lei', 'Liang Zhou', 'Mengmeng Wang', 'Yuanxin Ye'] | 2023-02-10 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 4.56182212e-02 -8.64788473e-01 5.68574190e-01 -4.61696148e-01
-2.98651636e-01 -8.35240856e-02 5.83037019e-01 1.44429401e-01
-4.51468915e-01 5.09674728e-01 2.25270092e-01 -1.73963636e-01
-4.52432007e-01 -1.18596005e+00 -3.57129514e-01 -9.62464452e-01
-3.19888622e-01 -5.49437940e-01 3.34252238e-01 -4.47579443... | [9.75543212890625, -1.323255181312561] |
af957e55-c45e-43e0-845a-7c596f8bfef5 | automatic-evaluation-of-turn-taking-cues-in | 2305.17971 | null | https://arxiv.org/abs/2305.17971v1 | https://arxiv.org/pdf/2305.17971v1.pdf | Automatic Evaluation of Turn-taking Cues in Conversational Speech Synthesis | Turn-taking is a fundamental aspect of human communication where speakers convey their intention to either hold, or yield, their turn through prosodic cues. Using the recently proposed Voice Activity Projection model, we propose an automatic evaluation approach to measure these aspects for conversational speech synthes... | ['Gabriel Skantze', 'Joakim Gustafson', 'Éva Székely', 'Siyang Wang', 'Erik Ekstedt'] | 2023-05-29 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [ 1.94893375e-01 4.31890517e-01 2.65854765e-02 -4.91279781e-01
-9.03823674e-01 -7.43365645e-01 8.20439816e-01 -2.09740713e-01
1.51165977e-01 7.77954578e-01 8.15161586e-01 -4.76113647e-01
1.70614854e-01 -3.85151863e-01 -2.11067259e-01 -4.43394393e-01
2.85364985e-01 2.64429361e-01 2.77195662e-01 -7.21952856... | [14.748151779174805, 6.6167778968811035] |
0a9d8279-e42c-4c62-a4af-68f477ca43df | range-nullspace-video-frame-interpolation | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yu_Range-Nullspace_Video_Frame_Interpolation_With_Focalized_Motion_Estimation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yu_Range-Nullspace_Video_Frame_Interpolation_With_Focalized_Motion_Estimation_CVPR_2023_paper.pdf | Range-Nullspace Video Frame Interpolation With Focalized Motion Estimation | Continuous-time video frame interpolation is a fundamental technique in computer vision for its flexibility in synthesizing motion trajectories and novel video frames at arbitrary intermediate time steps. Yet, how to infer accurate intermediate motion and synthesize high-quality video frames are two critical challe... | ['Shunqing Ren', 'Jimmy S. Ren', 'Xijun Chen', 'Dongqing Zou', 'Yu Zhang', 'ZHIYANG YU'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-frame-interpolation'] | ['computer-vision'] | [ 2.84181535e-01 -4.52698141e-01 -2.67871141e-01 -1.48553913e-02
-8.13509226e-01 -5.99703968e-01 4.74794894e-01 -7.52590001e-01
-2.43479177e-01 9.50975120e-01 3.05119276e-01 -1.77982047e-01
-2.21910805e-01 -2.00509921e-01 -8.93380404e-01 -7.44199872e-01
-5.20667844e-02 8.13438371e-02 1.75004035e-01 2.11470798... | [10.646709442138672, -1.4642970561981201] |
85a3915b-4fd4-44bd-a349-640a7294bfa2 | neural-shuffle-exchange-networks-sequence-1 | null | null | http://papers.nips.cc/paper/8889-neural-shuffle-exchange-networks-sequence-processing-in-on-log-n-time | http://papers.nips.cc/paper/8889-neural-shuffle-exchange-networks-sequence-processing-in-on-log-n-time.pdf | Neural Shuffle-Exchange Networks - Sequence Processing in O(n log n) Time | A key requirement in sequence to sequence processing is the modeling of long range dependencies. To this end, a vast majority of the state-of-the-art models use attention mechanism which is of O(n^2) complexity that leads to slow execution for long sequences.
We introduce a new Shuffle-Exchange neural network model f... | ['Agris Šostaks', 'Emīls Ozoliņš', 'Karlis Freivalds'] | 2019-12-01 | null | null | null | neurips-2019-12 | ['lambada'] | ['natural-language-processing'] | [ 5.40522039e-01 -3.19419414e-01 -5.38741378e-03 -4.18166548e-01
-8.18235695e-01 -8.19215596e-01 2.63451010e-01 3.94873708e-01
-7.87672579e-01 5.92358470e-01 3.16662490e-01 -7.75308311e-01
5.79424761e-02 -7.57206321e-01 -1.01072574e+00 -3.40978324e-01
-2.71861374e-01 7.53169179e-01 3.70790005e-01 -7.10845053... | [10.841506004333496, 7.24830436706543] |
ea5eb57a-dfa1-4bfd-9d48-33de977c9ea6 | programmable-spectral-filter-arrays-for | 2109.14450 | null | https://arxiv.org/abs/2109.14450v2 | https://arxiv.org/pdf/2109.14450v2.pdf | Programmable Spectral Filter Arrays using Phase Spatial Light Modulator | Spatially varying spectral modulation can be implemented using a liquid crystal spatial light modulator (SLM) since it provides an array of liquid crystal cells, each of which can be purposed to act as a programmable spectral filter array. However, such an optical setup suffers from strong optical aberrations due to th... | ['Tuo Zhuang', 'Ryuichi Tadano', 'Vijay Rengarajan', 'Vishwanath Saragadam', 'Jun Murayama', 'Hideki Oyaizu', 'Aswin C. Sankaranarayanan'] | 2021-09-29 | null | null | null | null | ['material-classification'] | ['computer-vision'] | [ 1.15386021e+00 -3.78972322e-01 2.15675503e-01 -2.48425757e-03
-2.53055930e-01 -4.99289334e-01 4.09332454e-01 -5.75983346e-01
-3.26448590e-01 7.32747018e-01 -1.93802521e-01 -3.31389695e-01
-5.45266807e-01 -8.68020475e-01 -6.57915294e-01 -1.18170536e+00
1.79760069e-01 2.57536471e-01 2.12609768e-01 -2.87846141... | [10.256566047668457, -2.477782964706421] |
f26c1110-df91-471c-bab7-7ee2015be881 | evaluating-the-robustness-of-machine-reading | 2304.03145 | null | https://arxiv.org/abs/2304.03145v1 | https://arxiv.org/pdf/2304.03145v1.pdf | Evaluating the Robustness of Machine Reading Comprehension Models to Low Resource Entity Renaming | Question answering (QA) models have shown compelling results in the task of Machine Reading Comprehension (MRC). Recently these systems have proved to perform better than humans on held-out test sets of datasets e.g. SQuAD, but their robustness is not guaranteed. The QA model's brittleness is exposed when evaluated on ... | ['Tunde Oluwaseyi Ajayi', 'Clemencia Siro'] | 2023-04-06 | null | null | null | null | ['reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.75524086e-01 5.37032485e-01 4.96349692e-01 -3.70247781e-01
-9.68652368e-01 -9.63442564e-01 6.78902149e-01 3.03591251e-01
-5.43020546e-01 1.21742094e+00 3.35101783e-01 -4.28838551e-01
-4.92743142e-02 -8.49190652e-01 -1.12909949e+00 -1.63030505e-01
-3.67593467e-02 5.48713744e-01 3.75152439e-01 -6.39973402... | [11.022895812988281, 8.043771743774414] |
200e90c0-6f10-40c7-aafc-e3fd38b24b7e | instant-visual-odometry-initialization-for | 2107.14659 | null | https://arxiv.org/abs/2107.14659v1 | https://arxiv.org/pdf/2107.14659v1.pdf | Instant Visual Odometry Initialization for Mobile AR | Mobile AR applications benefit from fast initialization to display world-locked effects instantly. However, standard visual odometry or SLAM algorithms require motion parallax to initialize (see Figure 1) and, therefore, suffer from delayed initialization. In this paper, we present a 6-DoF monocular visual odometry tha... | ['Luc Oth', 'Christian Forster', 'Jesús Briales', 'Michael Burri', 'Alejo Concha'] | 2021-07-30 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-5.59281670e-02 6.23068772e-02 -1.30750373e-01 -3.42533179e-02
-4.25541192e-01 -9.32561576e-01 7.64515936e-01 -5.48538603e-02
-5.16727149e-01 6.93722546e-01 -1.83482319e-01 -3.25339079e-01
7.09894150e-02 -5.16062021e-01 -7.97237754e-01 -4.33929175e-01
-1.19739719e-01 7.01167047e-01 1.58060506e-01 -2.99722970... | [7.424172878265381, -2.1401848793029785] |
345472c8-72cd-4ba3-9c65-dbc3cbc335a5 | non-convex-approaches-for-low-rank-tensor | 2303.12721 | null | https://arxiv.org/abs/2303.12721v1 | https://arxiv.org/pdf/2303.12721v1.pdf | Non-convex approaches for low-rank tensor completion under tubal sampling | Tensor completion is an important problem in modern data analysis. In this work, we investigate a specific sampling strategy, referred to as tubal sampling. We propose two novel non-convex tensor completion frameworks that are easy to implement, named tensor $L_1$-$L_2$ (TL12) and tensor completion via CUR (TCCUR). We ... | ['Yifei Lou', 'HanQin Cai', 'Longxiu Huang', 'Zheng Tan'] | 2023-03-17 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [-1.71486780e-01 -3.88516754e-01 3.01467180e-02 -6.67069852e-02
-9.04791415e-01 -4.86332357e-01 3.64597291e-01 -2.63660401e-01
-3.38465065e-01 7.23409355e-01 1.67701736e-01 -2.01306850e-01
-2.47038156e-01 -4.56306458e-01 -6.58519089e-01 -5.50480306e-01
-2.48876780e-01 1.63373098e-01 -8.00050721e-02 -2.88701326... | [7.369930267333984, 4.514855861663818] |
5730d1b9-a348-45a6-b022-9066e2d78021 | openicl-an-open-source-framework-for-in | 2303.02913 | null | https://arxiv.org/abs/2303.02913v1 | https://arxiv.org/pdf/2303.02913v1.pdf | OpenICL: An Open-Source Framework for In-context Learning | In recent years, In-context Learning (ICL) has gained increasing attention and emerged as the new paradigm for large language model (LLM) evaluation. Unlike traditional fine-tuning methods, ICL instead adapts the pre-trained models to unseen tasks without any parameter updates. However, the implementation of ICL is sop... | ['Zhiyong Wu', 'Yu Qiao', 'Jingjing Xu', 'Jiangtao Feng', 'Jiacheng Ye', 'Yaoxiang Wang', 'Zhenyu Wu'] | 2023-03-06 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [-8.23930800e-02 -4.20264274e-01 -3.92771661e-01 -5.79552531e-01
-1.37131476e+00 -9.68309224e-01 5.47180057e-01 1.60185784e-01
-5.19792855e-01 5.58231592e-01 1.18683912e-01 -5.13817012e-01
3.45631763e-02 -3.69938821e-01 -6.44867122e-01 -2.26065442e-01
5.53051889e-01 8.18247855e-01 1.70003757e-01 -5.98222613... | [11.031598091125488, 8.687082290649414] |
3f60001c-45ac-498e-ae63-02c3a2c69038 | multi-turn-response-selection-for-chatbots | null | null | https://aclanthology.org/P18-1103 | https://aclanthology.org/P18-1103.pdf | Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network | Human generates responses relying on semantic and functional dependencies, including coreference relation, among dialogue elements and their context. In this paper, we investigate matching a response with its multi-turn context using dependency information based entirely on attention. Our solution is inspired by the re... | ['dianhai yu', 'daxiang dong', 'Ying Chen', 'Yi Liu', 'Hua Wu', 'Xiangyang Zhou', 'Lu Li', 'Wayne Xin Zhao'] | 2018-07-01 | null | null | null | acl-2018-7 | ['conversational-response-selection'] | ['natural-language-processing'] | [ 4.67607111e-01 2.31246784e-01 -3.39427859e-01 -5.67099869e-01
-1.20596051e+00 -6.97235227e-01 6.82195425e-01 1.00010909e-01
-5.41146398e-01 8.31817448e-01 7.54513681e-01 -3.45455825e-01
1.26346529e-01 -5.90808749e-01 -6.32927060e-01 -1.58053800e-01
6.38193309e-01 7.78020263e-01 3.03378403e-01 -6.76313519... | [12.406606674194336, 7.940566062927246] |
78428275-bfe0-465c-ad90-20f1f0acdd47 | fednilm-applying-federated-learning-to-nilm | 2106.07751 | null | https://arxiv.org/abs/2106.07751v1 | https://arxiv.org/pdf/2106.07751v1.pdf | FedNILM: Applying Federated Learning to NILM Applications at the Edge | Non-intrusive load monitoring (NILM) helps disaggregate the household's main electricity consumption to energy usages of individual appliances, thus greatly cutting down the cost in fine-grained household load monitoring. To address the arisen privacy concern in NILM applications, federated learning (FL) could be lever... | ['Jiadong Lou', 'Xudong Wang', 'Yi Wang', 'Qianyi Huang', 'Guoming Tang', 'Yu Zhang'] | 2021-06-07 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 6.62787259e-02 -1.34491697e-02 -4.34079647e-01 -3.50984126e-01
-9.00656521e-01 -4.18448657e-01 2.50991076e-01 1.90748610e-02
1.63805112e-01 5.88497698e-01 2.05178931e-01 -4.23707485e-01
-1.38135195e-01 -1.06344950e+00 -6.18017614e-01 -6.85856760e-01
-1.14683464e-01 4.59236085e-01 -5.67456603e-01 4.06735867... | [5.876384735107422, 2.81178879737854] |
88ca41a2-661a-427c-9ba0-0e4f52d76985 | reconstruction-of-fragmented-trajectories-of | 2110.10428 | null | https://arxiv.org/abs/2110.10428v1 | https://arxiv.org/pdf/2110.10428v1.pdf | Reconstruction of Fragmented Trajectories of Collective Motion using Hadamard Deep Autoencoders | Learning dynamics of collectively moving agents such as fish or humans is an active field in research. Due to natural phenomena such as occlusion and change of illumination, the multi-object methods tracking such dynamics might lose track of the agents where that might result fragmentation in the constructed trajectori... | ['Anura P. Jayasumana', 'Randy Paffenroth', 'Yonggi Park', 'Kelum Gajamannage'] | 2021-10-20 | null | null | null | null | ['low-rank-matrix-completion'] | ['methodology'] | [-3.87061507e-01 -1.55533418e-01 5.11073291e-01 -1.01016350e-02
-4.11723256e-02 -5.47036469e-01 8.30222785e-01 -6.03229143e-02
-6.33408546e-01 4.94242668e-01 5.49858034e-01 1.43718734e-01
-4.25897509e-01 -7.47109354e-01 -1.10245061e+00 -1.11419237e+00
-6.81067348e-01 8.39183092e-01 3.17690223e-02 1.88361201... | [5.935495376586914, 0.7603923678398132] |
0831caac-aa84-4bea-8afe-532039912453 | prototypical-model-with-novel-information | 2112.03134 | null | https://arxiv.org/abs/2112.03134v1 | https://arxiv.org/pdf/2112.03134v1.pdf | Prototypical Model with Novel Information-theoretic Loss Function for Generalized Zero Shot Learning | Generalized zero shot learning (GZSL) is still a technical challenge of deep learning as it has to recognize both source and target classes without data from target classes. To preserve the semantic relation between source and target classes when only trained with data from source classes, we address the quantification... | ['Huiwen Yang', 'Meiying Zhang', 'Feng Chen', 'Zhan Xiong', 'Hanchu Shen', 'Chunlin Ji'] | 2021-12-06 | null | null | null | null | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 2.28858158e-01 5.22967637e-01 -1.73499525e-01 -3.01158130e-01
-8.10513496e-01 -3.92756999e-01 8.51025581e-01 -1.31591529e-01
-1.73092753e-01 7.37726152e-01 7.17486292e-02 1.38763815e-01
-6.01854503e-01 -1.24125957e+00 -9.74518001e-01 -9.97067750e-01
1.38694137e-01 5.93045354e-01 2.13836916e-02 -4.09590565... | [9.941956520080566, 2.6592588424682617] |
0ddc4746-94d4-4c25-9ca4-776e0907aa63 | is-translation-helpful-an-empirical-analysis | 2305.12480 | null | https://arxiv.org/abs/2305.12480v1 | https://arxiv.org/pdf/2305.12480v1.pdf | Is Translation Helpful? An Empirical Analysis of Cross-Lingual Transfer in Low-Resource Dialog Generation | Cross-lingual transfer is important for developing high-quality chatbots in multiple languages due to the strongly imbalanced distribution of language resources. A typical approach is to leverage off-the-shelf machine translation (MT) systems to utilize either the training corpus or developed models from high-resource ... | ['Xiaoyu Shen', 'Shuai Yu', 'Lei Shen'] | 2023-05-21 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [-4.00037691e-02 1.51109353e-01 8.19590464e-02 -2.94958800e-01
-1.05788791e+00 -8.55729580e-01 7.05519080e-01 -4.63520974e-01
-6.19016528e-01 1.14446259e+00 4.71732020e-01 -3.99044633e-01
5.64883530e-01 -4.69374150e-01 -3.80080789e-01 -3.96614999e-01
5.17320037e-01 8.50446045e-01 8.81707668e-03 -7.48850703... | [12.507572174072266, 8.43808650970459] |
0617ad2e-d2f0-41d9-8677-75498e2f472a | weakly-supervised-spatial-context-networks | 1704.02998 | null | http://arxiv.org/abs/1704.02998v2 | http://arxiv.org/pdf/1704.02998v2.pdf | Weakly-Supervised Spatial Context Networks | We explore the power of spatial context as a self-supervisory signal for
learning visual representations. In particular, we propose spatial context
networks that learn to predict a representation of one image patch from another
image patch, within the same image, conditioned on their real-valued relative
spatial offset... | ['Larry S. Davis', 'Zuxuan Wu', 'Leonid Sigal'] | 2017-04-10 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 4.27120298e-01 2.50712246e-01 -3.20392370e-01 -4.98772979e-01
-4.98901010e-01 -4.15625423e-01 8.81953776e-01 3.05247545e-01
-3.90374541e-01 5.68632007e-01 3.43347490e-01 -3.04942638e-01
-5.61236516e-02 -8.28644216e-01 -1.20883811e+00 -6.00293875e-01
-2.49680057e-01 9.34771821e-03 3.31556231e-01 -2.72208489... | [9.875931739807129, 1.5740197896957397] |
c56ac986-e7f7-4747-85e1-761f4338ff56 | bioimageloader-easy-handling-of-bioimage | 2303.02158 | null | https://arxiv.org/abs/2303.02158v1 | https://arxiv.org/pdf/2303.02158v1.pdf | BioImageLoader: Easy Handling of Bioimage Datasets for Machine Learning | BioImageLoader (BIL) is a python library that handles bioimage datasets for machine learning applications, easing simple workflows and enabling complex ones. BIL attempts to wrap the numerous and varied bioimages datasets in unified interfaces, to easily concatenate, perform image augmentation, and batch-load them. By ... | ['Anatole Chessel', 'Emmanuel Beaurepaire', 'Xingjian Zhang', 'Seongbin Lim'] | 2023-03-02 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 3.28518420e-01 -2.48300254e-01 8.47717673e-02 -5.96867681e-01
-5.48107862e-01 -7.41044819e-01 7.18045473e-01 3.36108774e-01
-8.48423421e-01 6.95841730e-01 -8.14768448e-02 -5.71450651e-01
1.72355995e-01 -3.76974016e-01 -7.25599706e-01 -8.60971391e-01
-2.73874223e-01 6.70510769e-01 2.84142733e-01 2.88206991... | [14.776898384094238, -2.997745990753174] |
1afe5642-f563-42f6-8a15-c524af8cabf0 | ivt-an-end-to-end-instance-guided-video | 2208.03431 | null | https://arxiv.org/abs/2208.03431v1 | https://arxiv.org/pdf/2208.03431v1.pdf | IVT: An End-to-End Instance-guided Video Transformer for 3D Pose Estimation | Video 3D human pose estimation aims to localize the 3D coordinates of human joints from videos. Recent transformer-based approaches focus on capturing the spatiotemporal information from sequential 2D poses, which cannot model the contextual depth feature effectively since the visual depth features are lost in the step... | ['Dongmei Fu', 'Jian Wang', 'Qiansheng Yang', 'Zhongwei Qiu'] | 2022-08-06 | null | null | null | null | ['3d-pose-estimation', '3d-multi-person-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-2.87209481e-01 -1.36254475e-01 -2.85124123e-01 -2.00211719e-01
-8.42785835e-01 -1.33105367e-01 4.12560523e-01 -3.58436942e-01
-3.53549302e-01 2.87099034e-01 4.48547214e-01 5.02969861e-01
2.17372805e-01 -3.49817097e-01 -9.47284579e-01 -6.43185675e-01
-1.16017900e-01 6.07777476e-01 2.33967483e-01 -5.95494732... | [7.162216663360596, -0.7350761890411377] |
a35f1f34-3da8-43ec-87a5-55459134d96e | mixgen-a-new-multi-modal-data-augmentation | 2206.08358 | null | https://arxiv.org/abs/2206.08358v3 | https://arxiv.org/pdf/2206.08358v3.pdf | MixGen: A New Multi-Modal Data Augmentation | Data augmentation is a necessity to enhance data efficiency in deep learning. For vision-language pre-training, data is only augmented either for images or for text in previous works. In this paper, we present MixGen: a joint data augmentation for vision-language representation learning to further improve data efficien... | ['Mu Li', 'Bo Li', 'Wanqian Zhang', 'Aston Zhang', 'Srikar Appalaraju', 'Yi Zhu', 'Xiaoshuai Hao'] | 2022-06-16 | null | null | null | null | ['visual-reasoning', 'visual-reasoning', 'visual-entailment'] | ['computer-vision', 'reasoning', 'reasoning'] | [ 3.35816815e-02 8.65343437e-02 1.61496729e-01 -2.96516269e-01
-8.09694707e-01 -8.09569538e-01 1.09149289e+00 1.14761837e-01
-7.96823621e-01 2.49073878e-01 2.42927432e-01 -5.91665268e-01
4.92009521e-01 -5.92438161e-01 -9.81369197e-01 -5.93728982e-02
4.78160679e-01 4.65548366e-01 1.92080408e-01 -5.39332867... | [10.908561706542969, 1.562545895576477] |
ac076472-a1eb-40d9-9f20-4d76a4fc63a3 | a-multi-task-mean-teacher-for-semi-supervised | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Chen_A_Multi-Task_Mean_Teacher_for_Semi-Supervised_Shadow_Detection_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_A_Multi-Task_Mean_Teacher_for_Semi-Supervised_Shadow_Detection_CVPR_2020_paper.pdf | A Multi-Task Mean Teacher for Semi-Supervised Shadow Detection | Existing shadow detection methods suffer from an intrinsic limitation in relying on limited labeled datasets, and they may produce poor results in some complicated situations. To boost the shadow detection performance, this paper presents a multi-task mean teacher model for semi-supervised shadow detection by leveragin... | [' Pheng-Ann Heng', ' Wei Feng', ' Song Wang', ' Liang Wan', ' Lei Zhu', 'Zhihao Chen'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['shadow-detection'] | ['computer-vision'] | [ 3.12726557e-01 3.72021556e-01 -2.77557343e-01 -6.89797103e-01
-8.30524981e-01 -1.40779942e-01 3.42053920e-01 -5.10114670e-01
-2.03818291e-01 9.37516689e-01 -1.18998280e-02 -5.13537765e-01
5.27601719e-01 -2.41703764e-01 -6.63129151e-01 -1.18135417e+00
3.71693760e-01 2.08847523e-01 1.09065831e+00 4.77581978... | [10.850373268127441, -4.1140055656433105] |
0a261c5a-14ca-4b0d-a75e-ce3af4fd18f4 | bridge-prompt-towards-ordinal-action | 2203.14104 | null | https://arxiv.org/abs/2203.14104v1 | https://arxiv.org/pdf/2203.14104v1.pdf | Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos | Action recognition models have shown a promising capability to classify human actions in short video clips. In a real scenario, multiple correlated human actions commonly occur in particular orders, forming semantically meaningful human activities. Conventional action recognition approaches focus on analyzing single ac... | ['Jiwen Lu', 'Jie zhou', 'Jianjiang Feng', 'Zhilan Hu', 'Yueqi Duan', 'Lei Chen', 'Muheng Li'] | 2022-03-26 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Bridge-Prompt_Towards_Ordinal_Action_Understanding_in_Instructional_Videos_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Bridge-Prompt_Towards_Ordinal_Action_Understanding_in_Instructional_Videos_CVPR_2022_paper.pdf | cvpr-2022-1 | ['action-understanding'] | ['computer-vision'] | [ 3.25909495e-01 -1.80108875e-01 -5.55988491e-01 -5.09122491e-01
-5.98618031e-01 -5.09007692e-01 7.21286297e-01 -1.95096787e-02
-2.77608037e-01 4.10164267e-01 7.32997060e-01 2.37878710e-02
-1.13285162e-01 -2.92827785e-01 -8.07523310e-01 -6.33692503e-01
6.38135746e-02 -4.37302068e-02 4.09705967e-01 7.27116913... | [8.54498291015625, 0.6338307857513428] |
97c8ea4b-726d-4743-aa42-32e51bd5ddff | faceless-person-recognition-privacy | 1607.08438 | null | http://arxiv.org/abs/1607.08438v1 | http://arxiv.org/pdf/1607.08438v1.pdf | Faceless Person Recognition; Privacy Implications in Social Media | As we shift more of our lives into the virtual domain, the volume of data
shared on the web keeps increasing and presents a threat to our privacy. This
works contributes to the understanding of privacy implications of such data
sharing by analysing how well people are recognisable in social media data. To
facilitate a ... | ['Rodrigo Benenson', 'Seong Joon Oh', 'Mario Fritz', 'Bernt Schiele'] | 2016-07-28 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 1.90891981e-01 2.18137249e-01 7.37253278e-02 -5.66505015e-01
-1.21286847e-01 -8.95378053e-01 4.22604173e-01 1.67914152e-01
-6.59014404e-01 6.68919802e-01 2.84626365e-01 5.71981929e-02
3.20790648e-01 -7.78781533e-01 -6.59031212e-01 -3.46599430e-01
-2.32727200e-01 1.19341187e-01 1.29240632e-01 1.92099307... | [12.794586181640625, 0.7971318364143372] |
b3c7c92d-1f82-4470-9f75-288b7332c70b | kg-sp-knowledge-guided-simple-primitives-for | 2205.06784 | null | https://arxiv.org/abs/2205.06784v1 | https://arxiv.org/pdf/2205.06784v1.pdf | KG-SP: Knowledge Guided Simple Primitives for Open World Compositional Zero-Shot Learning | The goal of open-world compositional zero-shot learning (OW-CZSL) is to recognize compositions of state and objects in images, given only a subset of them during training and no prior on the unseen compositions. In this setting, models operate on a huge output space, containing all possible state-object compositions. W... | ['Zeynep Akata', 'Massimiliano Mancini', 'Shyamgopal Karthik'] | 2022-05-13 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Karthik_KG-SP_Knowledge_Guided_Simple_Primitives_for_Open_World_Compositional_Zero-Shot_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Karthik_KG-SP_Knowledge_Guided_Simple_Primitives_for_Open_World_Compositional_Zero-Shot_CVPR_2022_paper.pdf | cvpr-2022-1 | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 3.05860877e-01 3.91425818e-01 -3.87556255e-01 -8.30274522e-02
-6.25830054e-01 -5.54529488e-01 8.83905768e-01 -9.18281153e-02
-2.01158613e-01 3.43676865e-01 1.58548519e-01 7.09717572e-02
1.77295759e-01 -6.91059530e-01 -1.01720536e+00 -7.79968262e-01
2.32103541e-01 8.71143341e-01 4.25276995e-01 1.42012849... | [10.264893531799316, 2.2346768379211426] |
79b4f440-7134-452c-86cc-36be417b22ed | improving-complex-knowledge-base-question | 2212.13036 | null | https://arxiv.org/abs/2212.13036v1 | https://arxiv.org/pdf/2212.13036v1.pdf | Improving Complex Knowledge Base Question Answering via Question-to-Action and Question-to-Question Alignment | Complex knowledge base question answering can be achieved by converting questions into sequences of predefined actions. However, there is a significant semantic and structural gap between natural language and action sequences, which makes this conversion difficult. In this paper, we introduce an alignment-enhanced comp... | ['Weiming Lu', 'Xiaoxia Cheng', 'Yechun Tang'] | 2022-12-26 | null | null | null | null | ['knowledge-base-question-answering', 'question-rewriting'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.53074253e-01 1.81782544e-02 -6.60614595e-02 -5.47651470e-01
-1.36169434e+00 -8.06064725e-01 5.10862231e-01 -8.16241875e-02
-3.61041397e-01 5.95579743e-01 5.64512372e-01 -3.73831362e-01
-1.87258676e-01 -7.78957665e-01 -6.33019865e-01 -1.64086118e-01
6.54505789e-01 3.73793691e-01 6.14709020e-01 -5.17423630... | [11.455384254455566, 7.9579997062683105] |
11d80b97-6df2-4716-890e-a09ab4febbef | beyond-semantic-to-instance-segmentation | 2109.09477 | null | https://arxiv.org/abs/2109.09477v3 | https://arxiv.org/pdf/2109.09477v3.pdf | Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-Refinement | Weakly-supervised instance segmentation (WSIS) has been considered as a more challenging task than weakly-supervised semantic segmentation (WSSS). Compared to WSSS, WSIS requires instance-wise localization, which is difficult to extract from image-level labels. To tackle the problem, most WSIS approaches use off-the-sh... | ['Junmo Kim', 'Chaeeun Rhee', 'Youngjoon Yoo', 'Beomyoung Kim'] | 2021-09-20 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Kim_Beyond_Semantic_to_Instance_Segmentation_Weakly-Supervised_Instance_Segmentation_via_Semantic_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Kim_Beyond_Semantic_to_Instance_Segmentation_Weakly-Supervised_Instance_Segmentation_via_Semantic_CVPR_2022_paper.pdf | cvpr-2022-1 | ['weakly-supervised-instance-segmentation', 'point-supervised-instance-segmentation', 'image-level-supervised-instance-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.40967286e-01 2.82261431e-01 -3.17207217e-01 -5.68623781e-01
-7.99668670e-01 -5.01839936e-01 3.05592179e-01 1.28382882e-02
-5.99146783e-01 8.44210863e-01 -5.17369509e-01 -1.86970979e-02
-3.40172346e-03 -4.93846864e-01 -9.98089373e-01 -9.50428128e-01
3.74311090e-01 5.29821873e-01 8.07948291e-01 1.99353695... | [9.522485733032227, 0.9647348523139954] |
3cae823c-4d3d-4f46-9e2b-4b59861cdd7a | artificial-error-generation-with-machine | 1707.05236 | null | http://arxiv.org/abs/1707.05236v1 | http://arxiv.org/pdf/1707.05236v1.pdf | Artificial Error Generation with Machine Translation and Syntactic Patterns | Shortage of available training data is holding back progress in the area of
automated error detection. This paper investigates two alternative methods for
artificially generating writing errors, in order to create additional
resources. We propose treating error generation as a machine translation task,
where grammatica... | ['Zheng Yuan', 'Ted Briscoe', 'Mariano Felice', 'Marek Rei'] | 2017-07-17 | artificial-error-generation-with-machine-1 | https://aclanthology.org/W17-5032 | https://aclanthology.org/W17-5032.pdf | ws-2017-9 | ['grammatical-error-detection'] | ['natural-language-processing'] | [ 6.38300776e-01 3.77451748e-01 1.69864327e-01 -4.19394881e-01
-7.32474089e-01 -4.66770738e-01 4.72682178e-01 3.31797928e-01
-5.59264898e-01 1.16875267e+00 2.45378092e-01 -7.05648601e-01
3.93261522e-01 -6.33908033e-01 -9.53377962e-01 2.99006313e-01
5.33882022e-01 5.64461708e-01 -1.18904293e-01 -3.35059255... | [11.147491455078125, 10.567237854003906] |
b745ce22-0b0a-4de7-9ea1-686b8f1368cc | learning-to-rank-with-bert-in-tf-ranking | 2004.08476 | null | https://arxiv.org/abs/2004.08476v3 | https://arxiv.org/pdf/2004.08476v3.pdf | Learning-to-Rank with BERT in TF-Ranking | This paper describes a machine learning algorithm for document (re)ranking, in which queries and documents are firstly encoded using BERT [1], and on top of that a learning-to-rank (LTR) model constructed with TF-Ranking (TFR) [2] is applied to further optimize the ranking performance. This approach is proved to be eff... | ['Xuanhui Wang', 'Marc Najork', 'Shuguang Han', 'Mike Bendersky'] | 2020-04-17 | null | null | null | null | ['passage-re-ranking'] | ['natural-language-processing'] | [-2.32025638e-01 -1.66222766e-01 -4.14521575e-01 -4.61402088e-01
-1.82234514e+00 -7.14135408e-01 1.31538868e+00 6.00424290e-01
-5.69607854e-01 8.91052246e-01 5.84236503e-01 -5.49381152e-02
-5.88079214e-01 -5.90466976e-01 -6.51871562e-01 -1.33011907e-01
-6.89632177e-01 9.67600524e-01 4.39936042e-01 -7.80496001... | [11.502025604248047, 7.611375331878662] |
c34040ad-c2d5-41df-930d-c33697f36fa9 | a-multilingual-wikified-data-set-of | null | null | https://aclanthology.org/L18-1073 | https://aclanthology.org/L18-1073.pdf | A Multilingual Wikified Data Set of Educational Material | null | ['Antal Van den Bosch', 'Maria Stasimioti', 'Valia Kordoni', 'Thanasis Naskos', 'Maja Popovic', 'Katia Lida Kermanidis', 'Hugo de Vos', 'Eirini Takoulidou', 'Markus Egg', 'Panayota Georgakopoulou', 'Menno van Zaanen', 'Iris Hendrickx', 'Vilelmini Sosoni'] | 2018-05-01 | a-multilingual-wikified-data-set-of-1 | https://aclanthology.org/L18-1073 | https://aclanthology.org/L18-1073.pdf | lrec-2018-5 | ['cross-lingual-semantic-textual-similarity'] | ['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... | [-7.465922832489014, 3.6727030277252197] |
197e3e61-a5ca-4355-9a0b-e20ddd1e36cf | scope-structural-continuity-preservation-for | 2304.14572 | null | https://arxiv.org/abs/2304.14572v1 | https://arxiv.org/pdf/2304.14572v1.pdf | SCOPE: Structural Continuity Preservation for Medical Image Segmentation | Although the preservation of shape continuity and physiological anatomy is a natural assumption in the segmentation of medical images, it is often neglected by deep learning methods that mostly aim for the statistical modeling of input data as pixels rather than interconnected structures. In biological structures, howe... | ['Nassir Navab', 'Ehsan Adeli', 'Yongjian Tang', 'Rui Xiao', 'Amr Abu-zer', 'Goktug Guevercin', 'Azade Farshad', 'Yousef Yeganeh'] | 2023-04-28 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [ 3.90691273e-02 4.90805507e-01 -1.64764315e-01 -4.81144845e-01
1.33717537e-01 -6.57545507e-01 3.17505419e-01 6.07781231e-01
-1.09536916e-01 5.74427843e-01 1.23280272e-01 -5.37498951e-01
-7.51710311e-02 -9.17913079e-01 -6.16960168e-01 -4.73887950e-01
-4.20496948e-02 5.42060435e-01 4.45606738e-01 7.35579953... | [14.335618019104004, -2.8060457706451416] |
640cbb74-30a3-4141-b60f-b84557cbb296 | physics-informed-machine-learning-for-the | 2008.08162 | null | https://arxiv.org/abs/2008.08162v1 | https://arxiv.org/pdf/2008.08162v1.pdf | Physics-informed machine learning for the COVID-19 pandemic: Adherence to social distancing and short-term predictions for eight countries | The spread of COVID-19 during the initial phase of the first half of 2020 was curtailed to a larger or lesser extent through measures of social distancing imposed by most countries. In this work, we link directly, through machine learning techniques, infection data at a country level to a single number that signifies s... | ['G. P. Tsironis', 'G. D. Barmparis'] | 2020-08-18 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-3.67309421e-01 9.77478325e-02 -1.72746718e-01 2.75031328e-01
1.42132610e-01 -3.66564304e-01 1.10888565e+00 2.97603697e-01
-5.55823267e-01 9.23115551e-01 2.27153137e-01 -5.62502086e-01
-4.45355326e-01 -1.10115600e+00 -3.70193362e-01 -1.03729773e+00
-4.19304818e-01 1.06441593e+00 9.37341377e-02 -7.35902548... | [5.992849826812744, 4.382608890533447] |
58893bca-fa17-4f8d-8701-01695d9876d2 | the-limited-integrator-model-regulator-and | 2304.13161 | null | https://arxiv.org/abs/2304.13161v1 | https://arxiv.org/pdf/2304.13161v1.pdf | The Limited Integrator Model Regulator And its Use in Vehicle Steering Control | Unexpected yaw disturbances like braking on unilaterally icy road, side wind forces and tire rupture are very difficult to handle by the driver of a road vehicle, due to his/her large panic reaction period ranging between 0.5 to 2 seconds. Automatic driver assist systems provide counteracting yaw moments during this dr... | ['Levent Guvenc', 'Bilin Aksun-Guvenc'] | 2023-04-25 | null | null | null | null | ['steering-control'] | ['computer-vision'] | [-4.51544635e-02 8.43625784e-01 -2.71688044e-01 -1.17413513e-01
2.17164576e-01 -6.16524756e-01 6.92647219e-01 -5.38654745e-01
-1.29800305e-01 6.88395143e-01 -3.55487391e-02 -7.77940214e-01
-1.91984758e-01 -1.50561318e-01 -9.72387344e-02 -8.19553852e-01
6.55919015e-01 1.06772907e-01 3.29215825e-01 -9.22048986... | [5.469031810760498, 1.919940710067749] |
af6d10e1-84dd-4b9f-9ef1-024127c5872f | bayesian-learning-of-feature-spaces-for | 2209.03028 | null | https://arxiv.org/abs/2209.03028v1 | https://arxiv.org/pdf/2209.03028v1.pdf | Bayesian learning of feature spaces for multitasks problems | This paper presents a Bayesian framework to construct non-linear, parsimonious, shallow models for multitask regression. The proposed framework relies on the fact that Random Fourier Features (RFFs) enables the approximation of an RBF kernel by an extreme learning machine whose hidden layer is formed by RFFs. The main ... | ['Emilio Parrado-Hernández', 'Vanessa Gómez-Verdejo', 'Ascensión Gallardo-Antolín', 'Carlos Sevilla-Salcedo'] | 2022-09-07 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [-1.33768380e-01 2.60542810e-01 -1.29492775e-01 -4.18897182e-01
-5.28947353e-01 -5.75974211e-02 7.13734031e-01 -4.83046323e-01
-1.13581613e-01 6.33041561e-01 3.09531391e-02 5.00670224e-02
-8.90779614e-01 -4.38363612e-01 -4.02773917e-01 -1.12006676e+00
6.31800964e-02 4.19139534e-01 -2.23209970e-02 1.03306167... | [7.485313415527344, 4.03417444229126] |
899c2201-64a1-439e-a3df-e72000c06c68 | mirror-mirror-on-the-wall-whos-got-the | 1805.11589 | null | http://arxiv.org/abs/1805.11589v2 | http://arxiv.org/pdf/1805.11589v2.pdf | Mirror, Mirror, on the Wall, Who's Got the Clearest Image of Them All? - A Tailored Approach to Single Image Reflection Removal | Removing reflection artefacts from a single image is a problem of both
theoretical and practical interest, which still presents challenges because of
the massively ill-posed nature of the problem. In this work, we propose a
technique based on a novel optimisation problem. Firstly, we introduce a simple
user interaction... | ['Dong-Dong Chen', 'Sabine Süsstrunk', 'Carola-Bibiane Schönlieb', 'Angelica I. Aviles-Rivero', 'Qingnan Fan', 'Georg Maierhofer', 'Daniel Heydecker'] | 2018-05-29 | null | null | null | null | ['reflection-removal'] | ['computer-vision'] | [ 5.85272133e-01 4.67961282e-02 6.45419717e-01 1.18536189e-01
-8.12679946e-01 -9.07938927e-02 6.12714648e-01 7.97340274e-02
-5.06031692e-01 6.53175831e-01 1.99689697e-02 -1.75500046e-02
-3.57443631e-01 -6.42957211e-01 -4.08099473e-01 -9.07138169e-01
-3.34210023e-02 -2.71059632e-01 3.48230124e-01 -3.02143693... | [10.813300132751465, -2.650067090988159] |
2ded49e2-193d-4d3a-9209-5691c271687e | cross-lingual-training-of-dense-retrievers | null | null | https://aclanthology.org/2021.mrl-1.24 | https://aclanthology.org/2021.mrl-1.24.pdf | Cross-Lingual Training of Dense Retrievers for Document Retrieval | Dense retrieval has shown great success for passage ranking in English. However, its effectiveness for non-English languages remains unexplored due to limitation in training resources. In this work, we explore different transfer techniques for document ranking from English annotations to non-English languages. Our expe... | ['Jimmy Lin', 'He Bai', 'Rui Zhang', 'Peng Shi'] | null | null | null | null | emnlp-mrl-2021-11 | ['document-ranking', 'passage-ranking'] | ['natural-language-processing', 'natural-language-processing'] | [-2.07501173e-01 -2.72195309e-01 -6.61344707e-01 -5.62924445e-02
-1.83806992e+00 -6.87083423e-01 9.38722968e-01 2.17112582e-02
-9.61249590e-01 1.20691335e+00 7.45426834e-01 -4.16035056e-01
3.04192510e-02 -7.58663356e-01 -7.01006055e-01 -6.03117887e-03
1.11377306e-01 8.68426204e-01 5.24759352e-01 -7.51138270... | [11.399682998657227, 9.728362083435059] |
ee57be7e-36a4-448e-ae98-913dd4b2b3e5 | emotion-recognition-by-fusing-time | 2010.14102 | null | https://arxiv.org/abs/2010.14102v2 | https://arxiv.org/pdf/2010.14102v2.pdf | Emotion recognition by fusing time synchronous and time asynchronous representations | In this paper, a novel two-branch neural network model structure is proposed for multimodal emotion recognition, which consists of a time synchronous branch (TSB) and a time asynchronous branch (TAB). To capture correlations between each word and its acoustic realisation, the TSB combines speech and text modalities at ... | ['Philip C. Woodland', 'Chao Zhang', 'Wen Wu'] | 2020-10-27 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 2.05103800e-01 -3.84992622e-02 1.77569330e-01 -7.24308491e-01
-1.02157891e+00 -4.22362417e-01 6.88977420e-01 9.47897285e-02
-6.05940044e-01 1.04998879e-01 1.83936879e-01 -1.39361188e-01
2.34765008e-01 -1.49950460e-01 -2.96587557e-01 -7.63657510e-01
-6.05739616e-02 1.44495562e-01 -3.84206064e-02 -2.26716682... | [13.574153900146484, 5.720552444458008] |
095764c7-abce-4bf5-926e-bb65ede6d503 | robust-one-class-classification-with-signed | 2303.01978 | null | https://arxiv.org/abs/2303.01978v1 | https://arxiv.org/pdf/2303.01978v1.pdf | Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks | We propose a new method, dubbed One Class Signed Distance Function (OCSDF), to perform One Class Classification (OCC) by provably learning the Signed Distance Function (SDF) to the boundary of the support of any distribution. The distance to the support can be interpreted as a normality score, and its approximation usi... | ['Andres Troya-Galvis', 'Quentin Vincenot', 'Mathieu Serrurier', 'Guillaume Coiffier', 'Thibaut Boissin', 'Paul Novello', 'Louis Bethune'] | 2023-01-26 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 2.61947691e-01 4.66446996e-01 -2.65244961e-01 -4.51231688e-01
-1.27784753e+00 -1.13737297e+00 7.56787598e-01 -2.03332044e-02
-3.05459917e-01 8.15560699e-01 -1.74270704e-01 -4.25897956e-01
-2.47451812e-01 -7.84842730e-01 -1.33263469e+00 -7.09230542e-01
-4.61342961e-01 3.54250461e-01 7.73288831e-02 3.79651897... | [5.672607898712158, 7.799917221069336] |
09eb8c11-6d0f-4640-b187-62985e3f449b | large-car-following-data-based-on-lyft-level | 2305.18921 | null | https://arxiv.org/abs/2305.18921v1 | https://arxiv.org/pdf/2305.18921v1.pdf | Large Car-following Data Based on Lyft level-5 Open Dataset: Following Autonomous Vehicles vs. Human-driven Vehicles | Car-Following (CF), as a fundamental driving behaviour, has significant influences on the safety and efficiency of traffic flow. Investigating how human drivers react differently when following autonomous vs. human-driven vehicles (HV) is thus critical for mixed traffic flow. Research in this field can be expedited wit... | ['J. W. C. van Lint', 'Simeon C. Calvert', 'Victor L. Knoop', 'Yiru Jiao', 'Guopeng Li'] | 2023-05-30 | null | null | null | null | ['autonomous-vehicles', 'motion-planning'] | ['computer-vision', 'robots'] | [-2.45499909e-01 -2.57252991e-01 -3.32377106e-01 -4.34572041e-01
-6.98931396e-01 -4.70117509e-01 8.73955429e-01 2.46630996e-01
-5.43734550e-01 5.03801346e-01 1.51839688e-01 -8.22149754e-01
-3.38831514e-01 -9.59821641e-01 -6.76072776e-01 -8.66199195e-01
-8.80661309e-02 2.09389791e-01 5.61211586e-01 -5.62242866... | [5.838973045349121, 1.1605075597763062] |
8b727d84-a3dd-49d4-9ce2-69c10d59c3d3 | spatio-temporal-relation-modeling-for-few | 2112.05132 | null | https://arxiv.org/abs/2112.05132v2 | https://arxiv.org/pdf/2112.05132v2.pdf | Spatio-temporal Relation Modeling for Few-shot Action Recognition | We propose a novel few-shot action recognition framework, STRM, which enhances class-specific feature discriminability while simultaneously learning higher-order temporal representations. The focus of our approach is a novel spatio-temporal enrichment module that aggregates spatial and temporal contexts with dedicated ... | ['Bernard Ghanem', 'Fahad Shahbaz Khan', 'Rao Muhammad Anwer', 'Salman Khan', 'Sanath Narayan', 'Anirudh Thatipelli'] | 2021-12-09 | spatio-temporal-relation-modeling-for-few-1 | http://openaccess.thecvf.com//content/CVPR2022/html/Thatipelli_Spatio-Temporal_Relation_Modeling_for_Few-Shot_Action_Recognition_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Thatipelli_Spatio-Temporal_Relation_Modeling_for_Few-Shot_Action_Recognition_CVPR_2022_paper.pdf | cvpr-2022-1 | ['few-shot-action-recognition'] | ['computer-vision'] | [ 4.27381158e-01 -5.10725975e-01 -6.52513683e-01 -3.12363237e-01
-1.00771368e+00 -1.78076670e-01 6.92368865e-01 2.64148116e-01
-3.12431753e-01 4.18381304e-01 4.19512779e-01 4.40966755e-01
-3.27111125e-01 -5.73119879e-01 -4.77010161e-01 -6.77391648e-01
-2.71371156e-01 -9.51273888e-02 8.21310639e-01 -1.70660526... | [8.436676979064941, 0.789359986782074] |
801e9b00-f915-4a43-85a3-b520d83d33ce | the-role-of-packaging-sites-in-efficient-and | 1502.05029 | null | http://arxiv.org/abs/1502.05029v1 | http://arxiv.org/pdf/1502.05029v1.pdf | The role of packaging sites in efficient and specific virus assembly | During the lifecycle of many single-stranded RNA viruses, including many
human pathogens, a protein shell called the capsid spontaneously assembles
around the viral genome. Understanding the mechanisms by which capsid proteins
selectively assemble around the viral RNA amidst diverse host RNAs is a key
question in virol... | [] | 2015-02-17 | null | null | null | null | ['genome-understanding', 'virology'] | ['medical', 'miscellaneous'] | [ 2.45096400e-01 -3.64126354e-01 -4.64159325e-02 -1.39327357e-02
-2.80670226e-01 -1.52118945e+00 6.19353175e-01 1.63981155e-01
-4.16191906e-01 9.36837494e-01 3.70912135e-01 -6.87166154e-01
2.69537836e-01 -6.40649021e-01 -6.87569618e-01 -1.15933740e+00
-2.04535022e-01 1.02064013e+00 2.13142529e-01 3.91599312... | [4.78074836730957, 5.22767448425293] |
2567b5f5-bc41-4c6a-9cac-e056a4446fd3 | visual-goal-step-inference-using-wikihow | 2104.05845 | null | https://arxiv.org/abs/2104.05845v2 | https://arxiv.org/pdf/2104.05845v2.pdf | Visual Goal-Step Inference using wikiHow | Understanding what sequence of steps are needed to complete a goal can help artificial intelligence systems reason about human activities. Past work in NLP has examined the task of goal-step inference for text. We introduce the visual analogue. We propose the Visual Goal-Step Inference (VGSI) task, where a model is giv... | ['Chris Callison-Burch', 'Mark Yatskar', 'Li Zhang', 'Qing Lyu', 'Artemis Panagopoulou', 'Yue Yang'] | 2021-04-12 | null | https://aclanthology.org/2021.emnlp-main.165 | https://aclanthology.org/2021.emnlp-main.165.pdf | emnlp-2021-11 | ['vgsi'] | ['computer-vision'] | [ 4.35266435e-01 6.23757422e-01 -1.23520002e-01 -3.24057519e-01
-8.46459270e-01 -6.05146945e-01 1.13030183e+00 1.77375183e-01
-3.99917871e-01 7.74259031e-01 7.10886359e-01 -4.10861403e-01
1.10082507e-01 -6.35347486e-01 -8.67984295e-01 -2.26881325e-01
1.20361038e-02 8.88976574e-01 3.45268212e-02 -2.55284786... | [10.670262336730957, 1.662308931350708] |
3b10d11d-d048-442d-9417-33b03d3c8e51 | s4rl-surprisingly-simple-self-supervision-for | 2103.06326 | null | https://arxiv.org/abs/2103.06326v2 | https://arxiv.org/pdf/2103.06326v2.pdf | S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning | Offline reinforcement learning proposes to learn policies from large collected datasets without interacting with the physical environment. These algorithms have made it possible to learn useful skills from data that can then be deployed in the environment in real-world settings where interactions may be costly or dange... | ['Animesh Garg', 'Ajay Mandlekar', 'Samarth Sinha'] | 2021-03-10 | null | null | null | null | ['d4rl'] | ['robots'] | [-4.09630202e-02 2.85119653e-01 -3.66793513e-01 -1.82549968e-01
-4.86817628e-01 -6.64992750e-01 5.73263049e-01 2.16491669e-01
-8.17026019e-01 1.10803735e+00 -1.64823115e-01 -5.72598755e-01
-3.46257836e-01 -6.67348266e-01 -1.34912062e+00 -5.70332110e-01
-5.90684354e-01 8.10122013e-01 4.24197555e-01 -7.01063216... | [4.21461820602417, 1.5904086828231812] |
23cee43a-d586-4c2b-a65b-12885a29fdc0 | stable-training-of-autoencoders-for | 2109.13748 | null | https://arxiv.org/abs/2109.13748v3 | https://arxiv.org/pdf/2109.13748v3.pdf | Improving Autoencoder Training Performance for Hyperspectral Unmixing with Network Reinitialisation | Neural networks, in particular autoencoders, are one of the most promising solutions for unmixing hyperspectral data, i.e. reconstructing the spectra of observed substances (endmembers) and their relative mixing fractions (abundances), which is needed for effective hyperspectral analysis and classification. However, as... | ['Krisztián Búza', 'Bartosz Grabowski', 'Michał Cholewa', 'Michał Romaszewski', 'Przemysław Głomb', 'Kamil Książek'] | 2021-09-28 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 3.90390247e-01 -3.94632995e-01 2.86528051e-01 -9.42491218e-02
2.40492254e-01 -5.86625457e-01 5.59891284e-01 7.61258006e-02
-4.98527527e-01 8.98424804e-01 3.05080991e-02 -2.54366606e-01
-2.82898694e-01 -8.85986328e-01 -6.99301124e-01 -1.27933323e+00
1.55941263e-01 3.52028042e-01 -4.40552622e-01 -2.12352797... | [10.076043128967285, -2.0221869945526123] |
829d391d-4331-4d28-9a0f-3a5d56593a2e | motion-estimation-for-fisheye-video-sequences | 2212.01164 | null | https://arxiv.org/abs/2212.01164v1 | https://arxiv.org/pdf/2212.01164v1.pdf | Motion estimation for fisheye video sequences combining perspective projection with camera calibration information | Fisheye cameras prove a convenient means in surveillance and automotive applications as they provide a very wide field of view for capturing their surroundings. Contrary to typical rectilinear imagery, however, fisheye video sequences follow a different mapping from the world coordinates to the image plane which is not... | ['André Kaup', 'Michel Bätz', 'Andrea Eichenseer'] | 2022-12-02 | null | null | null | null | ['camera-calibration', 'motion-compensation'] | ['computer-vision', 'computer-vision'] | [ 2.31632710e-01 -3.23323160e-01 6.56705424e-02 -3.57616395e-01
-1.66905612e-01 -6.77721560e-01 6.16430402e-01 -6.64703369e-01
-4.43282664e-01 5.27160943e-01 -9.08976570e-02 -4.06260431e-01
1.65851578e-01 -6.63020849e-01 -8.13353121e-01 -7.75639415e-01
1.50674373e-01 -2.47795343e-01 8.40183079e-01 -2.16561258... | [8.865074157714844, -2.4264702796936035] |
561449cf-c89d-44ca-a64c-b7297f3b35e6 | improving-breast-cancer-detection-using | 1808.08273 | null | http://arxiv.org/abs/1808.08273v1 | http://arxiv.org/pdf/1808.08273v1.pdf | Improving Breast Cancer Detection using Symmetry Information with Deep Learning | Convolutional Neural Networks (CNN) have had a huge success in many areas of
computer vision and medical image analysis. However, there is still an immense
potential for performance improvement in mammogram breast cancer detection
Computer-Aided Detection (CAD) systems by integrating all the information that
the radiol... | ['Yeman Brhane Hagos', 'Albert Gubern Merida', 'Jonas Teuwen'] | 2018-08-17 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 2.39804953e-01 2.77867168e-01 -2.97818840e-01 -4.00594324e-01
-8.91576171e-01 -1.43417254e-01 3.18618298e-01 6.58459663e-01
-6.78295255e-01 4.38233137e-01 1.37487769e-01 -8.29775095e-01
-2.50447363e-01 -8.32580447e-01 -7.31606901e-01 -6.31547630e-01
-3.11533839e-01 3.04622091e-02 4.22857136e-01 -8.87245163... | [15.222908020019531, -2.5156972408294678] |
59f7853e-513e-433e-a0d2-6064ab21a956 | 190600389 | 1906.00389 | null | https://arxiv.org/abs/1906.00389v4 | https://arxiv.org/pdf/1906.00389v4.pdf | Disparate Vulnerability to Membership Inference Attacks | A membership inference attack (MIA) against a machine-learning model enables an attacker to determine whether a given data record was part of the model's training data or not. In this paper, we provide an in-depth study of the phenomenon of disparate vulnerability against MIAs: unequal success rate of MIAs against diff... | ['Mohammad Yaghini', 'Bogdan Kulynych', 'Carmela Troncoso', 'Michael Veale', 'Giovanni Cherubin'] | 2019-06-02 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [-5.21663837e-02 -8.89799185e-03 -2.65440285e-01 -3.84230435e-01
-1.03044724e+00 -1.19669068e+00 3.86722118e-01 2.88077325e-01
-3.33446145e-01 8.01249623e-01 -1.24080509e-01 -9.00883555e-01
-2.08076119e-01 -1.09910536e+00 -7.37159193e-01 -5.06136954e-01
-3.77897948e-01 2.77225703e-01 -1.61199212e-01 1.80995136... | [5.975590705871582, 7.0305328369140625] |
b28b0074-4a23-4b9c-8fc6-2199abc77e8b | xiaomingbot-a-multilingual-robot-news-1 | 2007.08005 | null | https://arxiv.org/abs/2007.08005v1 | https://arxiv.org/pdf/2007.08005v1.pdf | Xiaomingbot: A Multilingual Robot News Reporter | This paper proposes the building of Xiaomingbot, an intelligent, multilingual and multimodal software robot equipped with four integral capabilities: news generation, news translation, news reading and avatar animation. Its system summarizes Chinese news that it automatically generates from data tables. Next, it transl... | ['Yu-Ping Wang', 'Lei LI', 'Xijin Zhang', 'Songcheng Jiang', 'Runxin Xu', 'Yuxuan Wang', 'Li Chen', 'Jun Cao', 'Hao Zhou', 'Xiang Yin', 'Jiaze Chen', 'Ying Zeng', 'Mingxuan Wang'] | 2020-07-12 | xiaomingbot-a-multilingual-robot-news | https://aclanthology.org/2020.acl-demos.1 | https://aclanthology.org/2020.acl-demos.1.pdf | acl-2020-6 | ['news-generation', 'voice-cloning'] | ['natural-language-processing', 'speech'] | [-3.21921617e-01 7.30246305e-01 -4.76646990e-01 1.97000541e-02
-8.00632358e-01 -7.42061079e-01 1.19906294e+00 -2.09030703e-01
-1.83055624e-01 1.09793162e+00 7.37965167e-01 -2.79861182e-01
6.23483598e-01 -6.87101662e-01 -8.85063350e-01 -1.31266296e-01
3.65758002e-01 8.40595961e-01 -1.37579679e-01 -7.86898553... | [12.401538848876953, 8.490050315856934] |
99c139d8-c373-4bc2-a8ac-1164a30457c1 | superglue-learning-feature-matching-with | 1911.11763 | null | https://arxiv.org/abs/1911.11763v2 | https://arxiv.org/pdf/1911.11763v2.pdf | SuperGlue: Learning Feature Matching with Graph Neural Networks | This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points. Assignments are estimated by solving a differentiable optimal transport problem, whose costs are predicted by a graph neural network. We introduce a flexible c... | ['Daniel DeTone', 'Paul-Edouard Sarlin', 'Tomasz Malisiewicz', 'Andrew Rabinovich'] | 2019-11-26 | superglue-learning-feature-matching-with-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Sarlin_SuperGlue_Learning_Feature_Matching_With_Graph_Neural_Networks_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Sarlin_SuperGlue_Learning_Feature_Matching_With_Graph_Neural_Networks_CVPR_2020_paper.pdf | cvpr-2020-6 | ['image-matching'] | ['computer-vision'] | [-1.16961934e-01 1.67668372e-01 -1.01806454e-01 -6.73089385e-01
-6.87760830e-01 -5.95969498e-01 5.17728090e-01 2.29436964e-01
-5.32025337e-01 5.03671288e-01 1.29762217e-01 -1.96231768e-01
-7.53572732e-02 -8.25916350e-01 -1.18686759e+00 -2.35394970e-01
-3.21054041e-01 9.78098035e-01 1.69721887e-01 -7.28477761... | [7.628195762634277, -2.1943531036376953] |
b9c64ccc-93c5-4bcd-8349-1890c9b6f733 | enhanced-masked-image-modeling-for-analysis | 2306.10623 | null | https://arxiv.org/abs/2306.10623v1 | https://arxiv.org/pdf/2306.10623v1.pdf | Enhanced Masked Image Modeling for Analysis of Dental Panoramic Radiographs | The computer-assisted radiologic informative report has received increasing research attention to facilitate diagnosis and treatment planning for dental care providers. However, manual interpretation of dental images is limited, expensive, and time-consuming. Another barrier in dental imaging is the limited number of a... | ['Longin Jan Latecki', 'Amani Almalki'] | 2023-06-18 | null | null | null | null | ['self-supervised-learning'] | ['computer-vision'] | [ 4.78504241e-01 8.04771483e-01 -6.15628719e-01 -7.76002407e-01
-1.46220684e+00 3.28856289e-01 1.08100593e-01 2.30568960e-01
-4.03615981e-01 3.21095288e-01 1.18596099e-01 -2.15375468e-01
2.95454320e-02 -7.59843290e-01 -7.51732349e-01 -7.48706043e-01
1.24799788e-01 6.49496913e-01 3.31992358e-01 2.02088639... | [13.761332511901855, -2.2351796627044678] |
8e0227a2-cd51-4887-9298-12b9c64755b2 | investigating-the-challenges-of-temporal | null | null | https://aclanthology.org/W18-5607 | https://aclanthology.org/W18-5607.pdf | Investigating the Challenges of Temporal Relation Extraction from Clinical Text | Temporal reasoning remains as an unsolved task for Natural Language Processing (NLP), particularly demonstrated in the clinical domain. The complexity of temporal representation in language is evident as results of the 2016 Clinical TempEval challenge indicate: the current state-of-the-art systems perform well in solvi... | ['Diana Galvan', 'Naoaki Okazaki', 'Kentaro Inui', 'Koji Matsuda'] | 2018-10-01 | null | null | null | ws-2018-10 | ['temporal-relation-extraction', 'temporal-information-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.31583855e-01 6.95154607e-01 -5.59787333e-01 -2.77173609e-01
-8.86029303e-01 -4.11439031e-01 6.95046544e-01 7.80973196e-01
-7.24085152e-01 7.29561388e-01 7.30734229e-01 -6.93427980e-01
-6.13533556e-01 -3.23549569e-01 -1.83710769e-01 -2.88370252e-01
-7.44105756e-01 7.02388227e-01 1.35149032e-01 -4.28699195... | [8.499953269958496, 9.015286445617676] |
a49eec92-3206-446c-9e82-97adda90db09 | integrating-prior-knowledge-in-post-hoc | 2204.11634 | null | https://arxiv.org/abs/2204.11634v1 | https://arxiv.org/pdf/2204.11634v1.pdf | Integrating Prior Knowledge in Post-hoc Explanations | In the field of eXplainable Artificial Intelligence (XAI), post-hoc interpretability methods aim at explaining to a user the predictions of a trained decision model. Integrating prior knowledge into such interpretability methods aims at improving the explanation understandability and allowing for personalised explanati... | ['Marcin Detyniecki', 'Christophe Marsala', 'Marie-Jeanne Lesot', 'Thibault Laugel', 'Adulam Jeyasothy'] | 2022-04-25 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 5.95682681e-01 1.15657806e+00 -1.67453393e-01 -6.98358297e-01
-1.51625663e-01 -4.00152206e-01 8.06047857e-01 2.76024908e-01
-2.03447819e-01 9.91283596e-01 2.81824857e-01 -6.01215720e-01
-8.47590625e-01 -5.81123650e-01 -7.15175748e-01 -2.69198507e-01
5.10274880e-02 1.02241981e+00 -3.83413374e-01 7.46595114... | [8.754082679748535, 5.710731029510498] |
c06d7a15-dc9d-4975-b90c-7c3c2e9b65f4 | randomized-quantization-is-all-you-need-for | 2306.11913 | null | https://arxiv.org/abs/2306.11913v1 | https://arxiv.org/pdf/2306.11913v1.pdf | Randomized Quantization is All You Need for Differential Privacy in Federated Learning | Federated learning (FL) is a common and practical framework for learning a machine model in a decentralized fashion. A primary motivation behind this decentralized approach is data privacy, ensuring that the learner never sees the data of each local source itself. Federated learning then comes with two majors challenge... | ['Jacob Abernethy', 'Juba Ziani', 'Zihao Hu', 'Yeojoon Youn'] | 2023-06-20 | null | null | null | null | ['quantization'] | ['methodology'] | [-3.95690426e-02 5.71397990e-02 -3.36804062e-01 -2.58573830e-01
-1.34024787e+00 -1.06400383e+00 5.18922448e-01 2.99026221e-01
-5.25779724e-01 8.35019827e-01 9.05191228e-02 -4.41038400e-01
-1.63275987e-01 -8.98048818e-01 -9.65392768e-01 -9.42374647e-01
-1.88461915e-01 2.91738272e-01 -1.35835648e-01 1.80204019... | [5.8773674964904785, 6.6325860023498535] |
e581edb3-bba9-45f1-825a-03be135f7421 | goal-oriented-next-best-activity | 2205.03219 | null | https://arxiv.org/abs/2205.03219v1 | https://arxiv.org/pdf/2205.03219v1.pdf | Goal-Oriented Next Best Activity Recommendation using Reinforcement Learning | Recommending a sequence of activities for an ongoing case requires that the recommendations conform to the underlying business process and meet the performance goal of either completion time or process outcome. Existing work on next activity prediction can predict the future activity but cannot provide guarantees of th... | ['Sampath Dechu', 'Renuka Sindhgatta', 'Avani Gupta', 'Prerna Agarwal'] | 2022-05-06 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 3.06664318e-01 1.78282350e-01 -7.10297525e-01 -5.57328820e-01
-2.46864617e-01 -2.25428149e-01 7.01092720e-01 2.04737633e-02
-7.01615065e-02 7.47508883e-01 8.47208679e-01 -2.12874845e-01
-8.40456665e-01 -9.27548409e-01 -5.59458733e-01 -3.29617411e-01
-3.05211335e-01 5.26073575e-01 1.11378888e-02 -1.75067633... | [7.922903537750244, 0.7013313174247742] |
2403bba3-5b0d-4af2-8218-93af990e7817 | impulsive-noise-removal-from-color-images | 1707.03126 | null | http://arxiv.org/abs/1707.03126v1 | http://arxiv.org/pdf/1707.03126v1.pdf | Impulsive noise removal from color images with morphological filtering | This paper deals with impulse noise removal from color images. The proposed
noise removal algorithm employs a novel approach with morphological filtering
for color image denoising; that is, detection of corrupted pixels and removal
of the detected noise by means of morphological filtering. With the help of
computer sim... | ['Alexey Ruchay', 'Vitaly Kober'] | 2017-07-11 | null | null | null | null | ['color-image-denoising'] | ['computer-vision'] | [ 6.04074478e-01 -8.24233234e-01 8.56613994e-01 9.82378200e-02
-1.35629445e-01 -3.58596414e-01 1.51680782e-01 -3.44567448e-02
-8.97486687e-01 5.66454947e-01 3.33419405e-02 -3.71368706e-01
-9.46381614e-02 -8.44557226e-01 2.88266148e-02 -1.02533698e+00
2.91777045e-01 -4.54599112e-01 2.52408713e-01 -3.09614748... | [11.226486206054688, -2.5073060989379883] |
2d849643-ff2c-4f7d-bc8f-3bf6a87ac542 | otfs-a-mathematical-foundation-for | 2302.08696 | null | https://arxiv.org/abs/2302.08696v1 | https://arxiv.org/pdf/2302.08696v1.pdf | OTFS -- A Mathematical Foundation for Communication and Radar Sensing in the Delay-Doppler Domain | Orthogonal time frequency space (OTFS) is a framework for communication and active sensing that processes signals in the delay-Doppler (DD) domain. This paper explores three key features of the OTFS framework, and explains their value to applications. The first feature is a compact and sparse DD domain parameterization... | ['Robert Calderbank', 'Ananthanarayanan Chockalingam', 'Ronny Hadani', 'Saif Khan Mohammed'] | 2023-02-17 | null | null | null | null | ['self-driving-cars'] | ['computer-vision'] | [ 5.72751164e-01 -1.03188969e-01 -3.33963215e-01 2.10894734e-01
-1.15676090e-01 -5.10433853e-01 6.84225380e-01 -2.73003995e-01
-4.26276147e-01 8.12818646e-01 -1.91832989e-01 -3.13479334e-01
-4.50708240e-01 -9.47923303e-01 -1.51747793e-01 -1.22566175e+00
-8.09760690e-01 1.21283438e-02 2.30884835e-01 -3.20480287... | [6.4268479347229, 1.246323823928833] |
6077e26f-858d-4e29-b984-386c08be6aa3 | elucidation-of-molecular-targets-of-bioactive | 1506.02008 | null | http://arxiv.org/abs/1506.02008v1 | http://arxiv.org/pdf/1506.02008v1.pdf | Elucidation of molecular targets of bioactive principles of black cumin relevant to its anti-tumour functionality - An Insilico target fishing approach | Black cumin (Nigella sativa) is a spice having medicinal properties with
pungent and bitter odour. It is used since thousands of years to treat various
ailments, including cancer mainly in South Asia and Middle Eastern regions.
Substantial evidence in multiple research studies emphasizes about the
therapeutic importanc... | [] | 2015-02-20 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 4.91355807e-01 4.32469733e-02 -4.75407988e-01 3.43707889e-01
-2.33722433e-01 -8.92418146e-01 5.97552896e-01 8.25613022e-01
-2.78466105e-01 1.28889894e+00 4.89393435e-02 -3.83815646e-01
-7.49297142e-02 -6.01765692e-01 -2.33157098e-01 -1.11024261e+00
-4.93518114e-01 1.86865926e-01 7.95904454e-03 -4.81382549... | [4.660658836364746, 5.111394882202148] |
af2b6750-104c-49f9-87e8-afa87a847262 | sdd-fiqa-unsupervised-face-image-quality | 2103.05977 | null | https://arxiv.org/abs/2103.05977v1 | https://arxiv.org/pdf/2103.05977v1.pdf | SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance | In recent years, Face Image Quality Assessment (FIQA) has become an indispensable part of the face recognition system to guarantee the stability and reliability of recognition performance in an unconstrained scenario. For this purpose, the FIQA method should consider both the intrinsic property and the recognizability ... | ['Yuan-Gen Wang', 'Liujuan Cao', 'Yong Li', 'Jilin Li', 'Shaoxin Li', 'Yuge Huang', 'Ruixin Zhang', 'Xingyu Chen', 'Fu-Zhao Ou'] | 2021-03-10 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Ou_SDD-FIQA_Unsupervised_Face_Image_Quality_Assessment_With_Similarity_Distribution_Distance_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Ou_SDD-FIQA_Unsupervised_Face_Image_Quality_Assessment_With_Similarity_Distribution_Distance_CVPR_2021_paper.pdf | cvpr-2021-1 | ['face-image-quality', 'face-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 1.02492087e-01 -2.35103458e-01 3.15285325e-02 -8.88393164e-01
-7.37251341e-01 -1.26089454e-01 4.41806346e-01 -2.72708952e-01
1.24344155e-02 3.41913819e-01 -1.90904364e-03 1.59819931e-01
-4.67242718e-01 -8.19691420e-01 -3.23944896e-01 -9.63685215e-01
1.65357247e-01 2.75887370e-01 -2.19776213e-01 6.63771182... | [13.089505195617676, 0.6631797552108765] |
7c8a8a5d-d40c-4f79-a74c-31ba435f57c7 | prototype-based-domain-generalization | 2204.07358 | null | https://arxiv.org/abs/2204.07358v1 | https://arxiv.org/pdf/2204.07358v1.pdf | Prototype-based Domain Generalization Framework for Subject-Independent Brain-Computer Interfaces | Brain-computer interface (BCI) is challenging to use in practice due to the inter/intra-subject variability of electroencephalography (EEG). The BCI system, in general, necessitates a calibration technique to obtain subject/session-specific data in order to tune the model each time the system is utilized. This issue is... | ['Seong-Whan Lee', 'Ji-Hoon Jeong', 'Dong-Kyun Han', 'Serkan Musellim'] | 2022-04-15 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 4.98414040e-01 -8.99852663e-02 5.78799069e-01 -5.14524579e-01
-4.85482335e-01 -4.98025596e-01 2.77323902e-01 -2.57553041e-01
-5.12162685e-01 1.16916978e+00 -2.85796821e-01 1.93763852e-01
-6.20549440e-01 -5.16172767e-01 -6.80851400e-01 -8.82388413e-01
-3.87286842e-02 5.76403499e-01 8.26024041e-02 -1.80238456... | [13.124414443969727, 3.456477642059326] |
952558be-792c-456b-a874-a4a32a880749 | finxabsa-explainable-finance-through-aspect | 2303.02563 | null | https://arxiv.org/abs/2303.02563v3 | https://arxiv.org/pdf/2303.02563v3.pdf | FinXABSA: Explainable Finance through Aspect-Based Sentiment Analysis | This paper presents a novel approach for explainability in financial analysis by utilizing the Pearson correlation coefficient to establish a relationship between aspect-based sentiment analysis and stock prices. The proposed methodology involves constructing an aspect list from financial news articles and analyzing se... | ['Erik Cambria', 'Gianmarco Mengaldo', 'Ranjan Satapathy', 'Wihan van der Heever', 'Keane Ong'] | 2023-03-05 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [-5.59576452e-01 -1.67801574e-01 -5.75278223e-01 -4.00002092e-01
-1.31220862e-01 -1.04258931e+00 4.96433645e-01 6.05685413e-01
-1.61348179e-01 5.18253505e-01 4.87047255e-01 -5.74717820e-01
-2.02146366e-01 -1.10879052e+00 -2.20003560e-01 -5.02294660e-01
6.87570572e-02 7.33208731e-02 -2.33285740e-01 -5.73049366... | [4.4908366203308105, 4.335035800933838] |
56911c8f-6e95-47b0-b377-cd2927d974a4 | semantically-aware-mask-cyclegan-for | 2306.06577 | null | https://arxiv.org/abs/2306.06577v1 | https://arxiv.org/pdf/2306.06577v1.pdf | Semantically-aware Mask CycleGAN for Translating Artistic Portraits to Photo-realistic Visualizations | Image-to-image translation (I2I) is defined as a computer vision task where the aim is to transfer images in a source domain to a target domain with minimal loss or alteration of the content representations. Major progress has been made since I2I was proposed with the invention of a variety of revolutionary generative ... | ['Zhuohao Yin'] | 2023-06-11 | null | null | null | null | ['image-to-image-translation', 'image-to-image-translation'] | ['computer-vision', 'miscellaneous'] | [ 7.20883906e-01 3.86274099e-01 1.88328400e-01 -2.98557252e-01
-2.47904792e-01 -6.06646359e-01 8.12767029e-01 -8.16933632e-01
9.23542604e-02 8.99789989e-01 3.64790857e-02 1.44137189e-01
3.61379683e-01 -9.03798342e-01 -6.27956808e-01 -7.32371390e-01
5.27428269e-01 5.47180831e-01 9.34268627e-03 -3.46267909... | [11.83109188079834, -0.3908349871635437] |
93c1dcc0-5f34-4a6c-bb09-5665e8038a15 | global-stock-market-prediction-based-on-stock | 1902.10948 | null | http://arxiv.org/abs/1902.10948v1 | http://arxiv.org/pdf/1902.10948v1.pdf | Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network | We applied Deep Q-Network with a Convolutional Neural Network function
approximator, which takes stock chart images as input, for making global stock
market predictions. Our model not only yields profit in the stock market of the
country where it was trained but generally yields profit in global stock
markets. We train... | ['Yookyung Koh', 'Raehyun Kim', 'Jaewoo Kang', 'Jinho Lee'] | 2019-02-28 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-9.96260047e-01 -8.19110200e-02 -3.30599248e-01 1.77967444e-01
-1.21153116e-01 -8.97405505e-01 5.82780004e-01 -3.51246625e-01
-3.28964174e-01 9.94576454e-01 8.47953781e-02 -8.93764317e-01
3.29355687e-01 -1.49704361e+00 -7.25502968e-01 -3.36276203e-01
-2.61476815e-01 4.28636819e-01 2.10218187e-02 -6.26033783... | [4.456366062164307, 4.217419147491455] |
992ff40f-432d-4e8d-b000-411d48776f8f | dali-dynamically-adjusted-label-importance | 2301.12077 | null | https://arxiv.org/abs/2301.12077v2 | https://arxiv.org/pdf/2301.12077v2.pdf | ALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learning | Noisy partial label learning (noisy PLL) is an important branch of weakly supervised learning. Unlike PLL where the ground-truth label must conceal in the candidate label set, noisy PLL relaxes this constraint and allows the ground-truth label may not be in the candidate label set. To address this challenging problem, ... | ['JianHua Tao', 'Bin Liu', 'Lei Feng', 'Zheng Lian', 'Mingyu Xu'] | 2023-01-28 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 3.31913650e-01 2.77222842e-01 -5.64482033e-01 -4.30791527e-01
-1.38344228e+00 -7.37851560e-01 1.25575900e-01 2.23818749e-01
-4.21092182e-01 9.78647530e-01 -3.60782713e-01 -3.42402346e-02
8.69056582e-02 -5.37832737e-01 -9.39100444e-01 -1.00158727e+00
1.51971981e-01 2.95705467e-01 3.06431800e-01 4.18217689... | [9.436103820800781, 3.944321632385254] |
2237aaeb-0f05-43e8-9b1b-973c412644de | transformers-in-3d-point-clouds-a-survey | 2205.07417 | null | https://arxiv.org/abs/2205.07417v2 | https://arxiv.org/pdf/2205.07417v2.pdf | Transformers in 3D Point Clouds: A Survey | Transformers have been at the heart of the Natural Language Processing (NLP) and Computer Vision (CV) revolutions. The significant success in NLP and CV inspired exploring the use of Transformers in point cloud processing. However, how do Transformers cope with the irregularity and unordered nature of point clouds? How... | ['Linlin Xu', 'Kyle Gao', 'Jonathan Li', 'Mingqiang Wei', 'Qian Xie', 'Dening Lu'] | 2022-05-16 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [-1.34233953e-02 -4.08169687e-01 2.01127559e-01 -2.13743210e-01
-5.15245855e-01 -6.31064355e-01 5.02969444e-01 1.58375889e-01
-1.01428293e-01 1.48068285e-02 -3.98564696e-01 -5.78525662e-01
-3.01671743e-01 -1.04874182e+00 -5.04598439e-01 -4.90937859e-01
-4.59533595e-02 8.05151880e-01 3.76714617e-01 -2.30644539... | [7.976195812225342, -3.4697670936584473] |
d0456c99-ca51-4034-bdb5-ed23dea44dbb | sleepeegnet-automated-sleep-stage-scoring | 1903.02108 | null | http://arxiv.org/abs/1903.02108v1 | http://arxiv.org/pdf/1903.02108v1.pdf | SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach | Electroencephalogram (EEG) is a common base signal used to monitor brain
activity and diagnose sleep disorders. Manual sleep stage scoring is a
time-consuming task for sleep experts and is limited by inter-rater
reliability. In this paper, we propose an automatic sleep stage annotation
method called SleepEEGNet using a... | ['U. Rajendra Acharya', 'Fatemeh Afghah', 'Sajad Mousavi'] | 2019-03-05 | null | null | null | null | ['sleep-stage-detection'] | ['medical'] | [ 6.20366223e-02 -3.26586366e-01 1.69968963e-01 -5.77576637e-01
-5.93066454e-01 -2.32247025e-01 -1.48577064e-01 7.95680732e-02
-7.08355904e-01 9.40486014e-01 -6.06110580e-02 2.85166744e-02
-1.18348628e-01 -1.85991243e-01 -2.41336808e-01 -6.73572183e-01
-2.46506438e-01 7.90986046e-02 5.77310063e-02 7.70376697... | [13.503805160522461, 3.5023579597473145] |
3111c885-361e-4bc5-9662-b48bdcb92bbd | unsupervised-cnn-based-co-saliency-detection | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Kuang-Jui_Hsu_Unsupervised_CNN-based_co-saliency_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Kuang-Jui_Hsu_Unsupervised_CNN-based_co-saliency_ECCV_2018_paper.pdf | Unsupervised CNN-based Co-Saliency Detection with Graphical Optimization | In this paper, we address co-saliency detection in a set of images jointly covering objects of a specific class by an unsupervised convolutional neural network (CNN). Our method does not require any additional training data in the form of object masks. We decompose co-saliency detection into two sub-tasks, single-image... | ['Yung-Yu Chuang', 'Yen-Yu Lin', 'Chung-Chi Tsai', 'Kuang-Jui Hsu', 'Xiaoning Qian'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['co-saliency-detection'] | ['computer-vision'] | [ 8.17348301e-01 5.03036976e-01 -2.05233052e-01 -3.78167152e-01
-7.35726953e-01 -2.01825172e-01 6.15722477e-01 1.65619925e-01
-2.31672212e-01 7.67731965e-01 -1.86368264e-02 2.19183356e-01
-4.22595738e-04 -5.67994952e-01 -1.08714712e+00 -6.11937106e-01
1.62853897e-01 -3.92829515e-02 8.33817661e-01 -9.69359502... | [9.807169914245605, -0.30722007155418396] |
ff92b164-bf80-44ab-85a4-4c2b302c156f | simulspeech-end-to-end-simultaneous-speech-to | null | null | https://aclanthology.org/2020.acl-main.350 | https://aclanthology.org/2020.acl-main.350.pdf | SimulSpeech: End-to-End Simultaneous Speech to Text Translation | In this work, we develop SimulSpeech, an end-to-end simultaneous speech to text translation system which translates speech in source language to text in target language concurrently. SimulSpeech consists of a speech encoder, a speech segmenter and a text decoder, where 1) the segmenter builds upon the encoder and lever... | ['Tie-Yan Liu', 'Tao Qin', 'Yi Ren', 'Jinglin Liu', 'Chen Zhang', 'Zhou Zhao', 'Xu Tan'] | 2020-07-01 | null | null | null | acl-2020-6 | ['speech-to-text-translation', 'simultaneous-speech-to-text-translation'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.66933054e-01 1.81079403e-01 -3.01179260e-01 -2.47750074e-01
-1.37665439e+00 -6.55281305e-01 4.31580007e-01 -3.63828152e-01
-5.64524651e-01 3.80746752e-01 1.34828612e-01 -1.23675799e+00
5.26199579e-01 -2.27317661e-01 -1.05279505e+00 -5.17497599e-01
3.99072737e-01 7.22487390e-01 3.58317867e-02 -1.18698478... | [14.487174987792969, 7.1487812995910645] |
5795f6be-a96b-4e7c-9dc5-c6df72bb78a8 | can-large-language-models-be-an-alternative | 2305.01937 | null | https://arxiv.org/abs/2305.01937v1 | https://arxiv.org/pdf/2305.01937v1.pdf | Can Large Language Models Be an Alternative to Human Evaluations? | Human evaluation is indispensable and inevitable for assessing the quality of texts generated by machine learning models or written by humans. However, human evaluation is very difficult to reproduce and its quality is notoriously unstable, hindering fair comparisons among different natural language processing (NLP) mo... | ['Hung-Yi Lee', 'Cheng-Han Chiang'] | 2023-05-03 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 3.24090689e-01 4.34160084e-01 1.93203270e-01 -3.63630831e-01
-1.13534260e+00 -9.95085001e-01 9.64841187e-01 2.77673900e-01
-7.71757185e-01 8.96157801e-01 4.43374753e-01 -3.09584200e-01
6.29799888e-02 -5.99444270e-01 -6.87726676e-01 -1.07526846e-01
4.72293139e-01 8.20138752e-01 1.15616377e-02 -3.19145620... | [11.742654800415039, 8.829221725463867] |
ace2454e-d1c0-42d8-875d-5b5d0e8df6cc | down-sampled-epsilon-lexicase-selection-for | 2302.04301 | null | https://arxiv.org/abs/2302.04301v1 | https://arxiv.org/pdf/2302.04301v1.pdf | Down-Sampled Epsilon-Lexicase Selection for Real-World Symbolic Regression Problems | Epsilon-lexicase selection is a parent selection method in genetic programming that has been successfully applied to symbolic regression problems. Recently, the combination of random subsampling with lexicase selection significantly improved performance in other genetic programming domains such as program synthesis. Ho... | ['Franz Rothlauf', 'Dominik Sobania', 'Alina Geiger'] | 2023-02-08 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 5.04937053e-01 7.95805454e-02 -2.56116390e-01 -7.22743049e-02
-3.15123647e-01 -3.06644827e-01 2.68184572e-01 3.96033585e-01
-2.32128412e-01 1.21256840e+00 -2.73654431e-01 -2.35377327e-01
-6.33345783e-01 -1.11183739e+00 -6.34563088e-01 -7.10037410e-01
-1.84213102e-01 6.77950442e-01 3.76322776e-01 -5.71421504... | [8.026570320129395, 7.204242706298828] |
4e7d81a3-e648-455a-a6e3-4c84044dc13b | wisdom-of-the-contexts-active-ensemble | 2101.11560 | null | https://arxiv.org/abs/2101.11560v4 | https://arxiv.org/pdf/2101.11560v4.pdf | Wisdom of the Contexts: Active Ensemble Learning for Contextual Anomaly Detection | In contextual anomaly detection, an object is only considered anomalous within a specific context. Most existing methods for CAD use a single context based on a set of user-specified contextual features. However, identifying the right context can be very challenging in practice, especially in datasets, with a large num... | ['Onur Dikmen', 'Mohamed-Rafik Bouguelia', 'Slawomir Nowaczyk', 'Ece Calikus'] | 2021-01-27 | null | null | null | null | ['contextual-anomaly-detection'] | ['miscellaneous'] | [ 3.20799142e-01 -3.54842544e-01 -1.01462193e-01 -5.94369590e-01
-6.91047549e-01 -4.69250351e-01 5.24904072e-01 6.79184437e-01
-3.77803296e-03 3.94565850e-01 1.32068262e-01 -4.58236665e-01
-2.46558398e-01 -7.77773857e-01 -4.60160792e-01 -6.92467690e-01
-4.94927727e-03 2.02233464e-01 6.72367036e-01 5.40309474... | [7.614894390106201, 2.5221176147460938] |
832ef3a9-77c3-448d-8285-f240344066ff | pasts-progress-aware-spatio-temporal | 2305.11918 | null | https://arxiv.org/abs/2305.11918v1 | https://arxiv.org/pdf/2305.11918v1.pdf | PASTS: Progress-Aware Spatio-Temporal Transformer Speaker For Vision-and-Language Navigation | Vision-and-language navigation (VLN) is a crucial but challenging cross-modal navigation task. One powerful technique to enhance the generalization performance in VLN is the use of an independent speaker model to provide pseudo instructions for data augmentation. However, current speaker models based on Long-Short Term... | ['Qijun Chen', 'Huiyi Chen', 'Qingqing Yan', 'Shu Li', 'Zongtao He', 'Chengju Liu', 'Liuyi Wang'] | 2023-05-19 | null | null | null | null | ['vision-and-language-navigation'] | ['robots'] | [ 2.26844206e-01 -2.94319242e-01 -1.12694524e-01 -7.27479517e-01
-8.29803348e-01 -9.44130719e-02 6.99641168e-01 -2.64575452e-01
-5.63137949e-01 3.74370456e-01 5.53042948e-01 -5.67008972e-01
1.83378980e-01 -4.67761010e-01 -1.03332782e+00 -5.86196363e-01
2.90875942e-01 -2.08156332e-02 2.64213175e-01 -3.84781420... | [14.238630294799805, 5.0958685874938965] |
e927c27b-bcfe-4bf7-a64c-50b7aad33a5b | local-shape-spectrum-analysis-for-3d-facial | 1705.06900 | null | http://arxiv.org/abs/1705.06900v1 | http://arxiv.org/pdf/1705.06900v1.pdf | Local Shape Spectrum Analysis for 3D Facial Expression Recognition | We investigate the problem of facial expression recognition using 3D data.
Building from one of the most successful frameworks for facial analysis using
exclusively 3D geometry, we extend the analysis from a curve-based
representation into a spectral representation, which allows a complete
description of the underlying... | ['Federico M. Sukno', 'Dmytro Derkach'] | 2017-05-19 | null | null | null | null | ['3d-facial-expression-recognition'] | ['computer-vision'] | [ 2.94425428e-01 3.97321992e-02 9.41672027e-02 -3.93980682e-01
-4.00634110e-01 -5.49558163e-01 6.48667097e-01 3.52607667e-02
-2.63462663e-01 3.32631379e-01 -7.12077990e-02 1.24224953e-01
-2.30388522e-01 -7.71146953e-01 -3.95319611e-01 -1.06877398e+00
-1.00196406e-01 1.29590511e-01 7.74030760e-02 -4.17045683... | [12.864557266235352, 0.7841561436653137] |
12063b47-29fb-4c97-9bec-cc1de1a11ec0 | cross-document-event-coreference-search-task | 2210.12654 | null | https://arxiv.org/abs/2210.12654v1 | https://arxiv.org/pdf/2210.12654v1.pdf | Cross-document Event Coreference Search: Task, Dataset and Modeling | The task of Cross-document Coreference Resolution has been traditionally formulated as requiring to identify all coreference links across a given set of documents. We propose an appealing, and often more applicable, complementary set up for the task - Cross-document Coreference Search, focusing in this paper on event c... | ['Ido Dagan', 'Avi Caciularu', 'Alon Eirew'] | 2022-10-23 | null | null | null | null | ['cross-document-coreference-resolution', 'passage-retrieval', 'coreference-resolution', 'open-domain-question-answering'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.04403824e-01 4.08051640e-01 -2.86163300e-01 -1.94774255e-01
-1.57201493e+00 -9.51116025e-01 9.55962241e-01 5.60583055e-01
-7.49636889e-01 7.34382272e-01 1.09073162e+00 -2.20762119e-02
-5.35877943e-01 -5.53785324e-01 -7.62152493e-01 -3.82859290e-01
1.11675575e-01 1.06287944e+00 4.08906072e-01 -4.54635262... | [9.278921127319336, 9.542388916015625] |
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