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5f181d65-ae02-4235-b67a-8015612d9b15 | dependency-induction-through-the-lens-of | 2109.09790 | null | https://arxiv.org/abs/2109.09790v1 | https://arxiv.org/pdf/2109.09790v1.pdf | Dependency Induction Through the Lens of Visual Perception | Most previous work on grammar induction focuses on learning phrasal or dependency structure purely from text. However, because the signal provided by text alone is limited, recently introduced visually grounded syntax models make use of multimodal information leading to improved performance in constituency grammar indu... | ['Graham Neubig', 'Yonatan Bisk', 'Xinyu Wang', 'Junxian He', 'Hao Zhu', 'Shruti Rijhwani', 'Ruisi Su'] | 2021-09-20 | null | https://aclanthology.org/2021.conll-1.2 | https://aclanthology.org/2021.conll-1.2.pdf | conll-emnlp-2021-11 | ['constituency-grammar-induction', 'constituency-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.02798367e-02 6.98367000e-01 -1.78203613e-01 -2.76172340e-01
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3.91196683e-02 6.87886477e-01 1.78057715e-01 -3.22788656... | [10.653831481933594, 1.531087875366211] |
5e784b2c-df81-49d7-b1d1-1e9bfbf40192 | uncertainty-aware-temporal-self-learning-uats | 2104.03840 | null | https://arxiv.org/abs/2104.03840v1 | https://arxiv.org/pdf/2104.03840v1.pdf | Uncertainty-Aware Temporal Self-Learning (UATS): Semi-Supervised Learning for Segmentation of Prostate Zones and Beyond | Various convolutional neural network (CNN) based concepts have been introduced for the prostate's automatic segmentation and its coarse subdivision into transition zone (TZ) and peripheral zone (PZ). However, when targeting a fine-grained segmentation of TZ, PZ, distal prostatic urethra (DPU) and the anterior fibromusc... | ['Marko Rak', 'Christian Hansen', 'Sebastian Stober', 'Martin Schostak', 'Daniel Schindele', 'Suhita Ghosh', 'Anneke Meyer'] | 2021-04-08 | null | null | null | null | ['prostate-zones-segmentation', 'skin-lesion-segmentation'] | ['computer-vision', 'medical'] | [ 4.32305306e-01 5.50623953e-01 -5.07244349e-01 -3.90367389e-01
-8.59025478e-01 -6.91642463e-01 5.49691856e-01 3.86996955e-01
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-1.19868137e-01 6.05514348e-01 1.93789035e-01 3.02858740... | [14.610100746154785, -2.320460796356201] |
f6ff249a-ad06-4f0d-b262-b435a08cd84d | predicting-camera-viewpoint-improves-cross | 2004.03143 | null | https://arxiv.org/abs/2004.03143v1 | https://arxiv.org/pdf/2004.03143v1.pdf | Predicting Camera Viewpoint Improves Cross-dataset Generalization for 3D Human Pose Estimation | Monocular estimation of 3d human pose has attracted increased attention with the availability of large ground-truth motion capture datasets. However, the diversity of training data available is limited and it is not clear to what extent methods generalize outside the specific datasets they are trained on. In this work ... | ['Daeyun Shin', 'Charless C. Fowlkes', 'Zhe Wang'] | 2020-04-07 | null | null | null | null | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [ 3.42871808e-02 -4.50867712e-01 -3.42852831e-01 -5.83260298e-01
-6.18832111e-01 -7.80170918e-01 6.49552703e-01 -2.96402425e-01
-5.86610317e-01 6.25063479e-01 3.89464676e-01 1.64200529e-01
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9.57187265e-02 4.90219116e-01 6.91421777e-02 1.52246460... | [7.077831268310547, -1.005632758140564] |
017f6fce-d855-4462-b0e3-e4fd43f59082 | learning-a-spatio-temporal-embedding-for-1 | 1912.08969 | null | https://arxiv.org/abs/1912.08969v1 | https://arxiv.org/pdf/1912.08969v1.pdf | Learning a Spatio-Temporal Embedding for Video Instance Segmentation | We present a novel embedding approach for video instance segmentation. Our method learns a spatio-temporal embedding integrating cues from appearance, motion, and geometry; a 3D causal convolutional network models motion, and a monocular self-supervised depth loss models geometry. In this embedding space, video-pixels ... | ['Anthony Hu', 'Alex Kendall', 'Roberto Cipolla'] | 2019-12-19 | null | https://openreview.net/forum?id=HyxTJxrtvr | https://openreview.net/pdf?id=HyxTJxrtvr | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 1.39044933e-02 6.70193210e-02 -4.43842143e-01 -2.97296643e-01
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-5.50397813e-01 5.37164867e-01 8.71092975e-01 3.44360977... | [8.911947250366211, -0.6820238828659058] |
e049835a-f87f-4f50-8e6d-faffa55494fd | dense-dual-path-network-for-real-time | 2010.10778 | null | https://arxiv.org/abs/2010.10778v1 | https://arxiv.org/pdf/2010.10778v1.pdf | Dense Dual-Path Network for Real-time Semantic Segmentation | Semantic segmentation has achieved remarkable results with high computational cost and a large number of parameters. However, real-world applications require efficient inference speed on embedded devices. Most previous works address the challenge by reducing depth, width and layer capacity of network, which leads to po... | ['Feilin Liu', 'Junqiao Zhao', 'Yan Wu', 'Xinneng Yang'] | 2020-10-21 | null | null | null | null | ['2048'] | ['playing-games'] | [ 1.83881417e-01 -5.87517582e-02 -1.63812473e-01 -3.77690285e-01
-4.03240889e-01 -2.70443320e-01 1.15084648e-02 -2.39129409e-01
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-8.96071196e-02 -1.23026335e+00 -4.57568735e-01 -6.24790549e-01
6.38115630e-02 2.61573583e-01 1.08061230e+00 3.39398645... | [9.275223731994629, -0.4829396605491638] |
e72d4670-5c72-4c57-9e10-1fcfba0d89ff | limited-angle-tomographic-reconstruction-of | 2007.10734 | null | https://arxiv.org/abs/2007.10734v1 | https://arxiv.org/pdf/2007.10734v1.pdf | Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning | Limited-angle tomography of strongly scattering quasi-transparent objects is a challenging, highly ill-posed problem with practical implications in medical and biological imaging, manufacturing, automation, and environmental and food security. Regularizing priors are necessary to reduce artifacts by improving the condi... | ['George Barbastathis', 'Alexandre Goy', 'Iksung Kang'] | 2020-07-21 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 5.46223164e-01 -4.34344746e-02 6.39668345e-01 -4.53470916e-01
-4.20976043e-01 -1.27880014e-02 3.75840962e-01 -6.54524446e-01
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-3.64629596e-01 -4.12647069e-01 -5.33881903e-01 -1.36410582e+00
9.18885395e-02 3.59627426e-01 2.60461774e-02 -1.48739487... | [12.471506118774414, -2.590125322341919] |
5d95b031-f7fe-49a0-a5e7-6325ad5d392e | decamouflage-a-framework-to-detect-image | 2010.03735 | null | https://arxiv.org/abs/2010.03735v1 | https://arxiv.org/pdf/2010.03735v1.pdf | Decamouflage: A Framework to Detect Image-Scaling Attacks on Convolutional Neural Networks | As an essential processing step in computer vision applications, image resizing or scaling, more specifically downsampling, has to be applied before feeding a normally large image into a convolutional neural network (CNN) model because CNN models typically take small fixed-size images as inputs. However, image scaling ... | ['Surya Nepal', 'Hyoungshick Kim', 'Muhammad Ejaz Ahmed', 'Yifeng Zheng', 'Yansong Gao', 'Alsharif Abuadbba', 'Bedeuro Kim'] | 2020-10-08 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 6.58058763e-01 -1.97448328e-01 2.72058696e-01 -5.81536256e-02
-2.59920090e-01 -1.01611114e+00 5.15976846e-01 1.19092762e-01
-6.83651686e-01 2.11541176e-01 -5.06446421e-01 -9.34260905e-01
4.21020865e-01 -9.56491709e-01 -9.97881591e-01 -6.29199445e-01
-1.27369717e-01 -4.77904201e-01 5.97094774e-01 -1.14574516... | [5.466084003448486, 7.863165855407715] |
e6fb52b0-e1f5-4d23-9d4b-3a69a9ae4465 | few-shot-document-level-relation-extraction | 2205.02048 | null | https://arxiv.org/abs/2205.02048v2 | https://arxiv.org/pdf/2205.02048v2.pdf | Few-Shot Document-Level Relation Extraction | We present FREDo, a few-shot document-level relation extraction (FSDLRE) benchmark. As opposed to existing benchmarks which are built on sentence-level relation extraction corpora, we argue that document-level corpora provide more realism, particularly regarding none-of-the-above (NOTA) distributions. Therefore, we pro... | ['Michael Färber', 'Nicholas Popovic'] | 2022-05-04 | null | https://aclanthology.org/2022.naacl-main.421 | https://aclanthology.org/2022.naacl-main.421.pdf | naacl-2022-7 | ['few-shot-relation-classification', 'document-level-relation-extraction', 'relation-classification', 'few-shot-relation-classification'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.30154610e-01 3.24374437e-01 -6.88563824e-01 -3.68845671e-01
-1.16489828e+00 -6.73006892e-01 9.46206689e-01 4.16057885e-01
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-7.88866282e-02 6.98890626e-01 3.26463699e-01 -4.58412290... | [9.604154586791992, 8.710772514343262] |
8df42c26-9b12-4536-b3e6-38cd33d1351b | group-fairness-in-adaptive-submodular | 2207.03364 | null | https://arxiv.org/abs/2207.03364v2 | https://arxiv.org/pdf/2207.03364v2.pdf | Group Equality in Adaptive Submodular Maximization | In this paper, we study the classic submodular maximization problem subject to a group equality constraint under both non-adaptive and adaptive settings. It has been shown that the utility function of many machine learning applications, including data summarization, influence maximization in social networks, and person... | ['Jing Yuan', 'Shaojie Tang'] | 2022-07-07 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 2.23901674e-01 4.25758511e-01 -6.92500591e-01 -3.38136822e-01
-2.86729038e-01 -7.50094414e-01 3.44959795e-02 3.53729635e-01
-8.85402635e-02 8.90923500e-01 2.21374378e-01 -9.23312157e-02
-6.69236898e-01 -9.14127111e-01 -6.08286202e-01 -7.21581578e-01
-2.01005042e-01 5.38139045e-01 -1.68791816e-01 -2.96058327... | [6.552906513214111, 4.940930366516113] |
d965e9e9-d6fe-4f9d-9a17-c8996f5f8e36 | what-does-transformer-learn-about-source-code | 2207.08466 | null | https://arxiv.org/abs/2207.08466v1 | https://arxiv.org/pdf/2207.08466v1.pdf | What does Transformer learn about source code? | In the field of source code processing, the transformer-based representation models have shown great powerfulness and have achieved state-of-the-art (SOTA) performance in many tasks. Although the transformer models process the sequential source code, pieces of evidence show that they may capture the structural informat... | ['Zhi Jin', 'Ge Li', 'Kechi Zhang'] | 2022-07-18 | null | null | null | null | ['variable-misuse'] | ['computer-code'] | [ 1.69117033e-01 5.08309305e-01 -4.49872166e-01 -2.81561673e-01
-4.69465792e-01 -5.38514316e-01 3.99001956e-01 3.99445236e-01
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-1.19752875e-02 -9.18703675e-01 -7.99222052e-01 -1.81369156e-01
-1.02526002e-01 -2.16936186e-01 3.64306688e-01 -3.58403862... | [7.520246505737305, 7.900884628295898] |
ddee7367-5bed-475a-8794-d741de25d327 | exploring-effective-mask-sampling-modeling | 2306.05704 | null | https://arxiv.org/abs/2306.05704v1 | https://arxiv.org/pdf/2306.05704v1.pdf | Exploring Effective Mask Sampling Modeling for Neural Image Compression | Image compression aims to reduce the information redundancy in images. Most existing neural image compression methods rely on side information from hyperprior or context models to eliminate spatial redundancy, but rarely address the channel redundancy. Inspired by the mask sampling modeling in recent self-supervised le... | ['Qi Tian', 'Yanfeng Wang', 'Houqiang Li', 'Wengang Zhou', 'Wenlong Lyu', 'Shanxin Yuan', 'Mingming Zhao', 'Lin Liu'] | 2023-06-09 | null | null | null | null | ['image-compression'] | ['computer-vision'] | [ 7.63250768e-01 -3.82957235e-02 -4.01680917e-01 -2.49612153e-01
-5.25616407e-01 1.52064845e-01 3.56017351e-01 -5.79237053e-03
-6.81207120e-01 4.84860182e-01 1.91330880e-01 -5.13905883e-01
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7.87449256e-02 -4.84576859e-02 1.73193961e-01 1.28989741... | [11.366003036499023, -1.568457841873169] |
d7b34abf-e51e-487e-b013-00a7bbfbda73 | slice-transformer-and-self-supervised | 2301.08957 | null | https://arxiv.org/abs/2301.08957v1 | https://arxiv.org/pdf/2301.08957v1.pdf | Slice Transformer and Self-supervised Learning for 6DoF Localization in 3D Point Cloud Maps | Precise localization is critical for autonomous vehicles. We present a self-supervised learning method that employs Transformers for the first time for the task of outdoor localization using LiDAR data. We propose a pre-text task that reorganizes the slices of a $360^\circ$ LiDAR scan to leverage its axial properties. ... | ['Ajmal Mian', 'Michael Wise', 'Saeed Anwar', 'Naveed Akhtar', 'Muhammad Ibrahim'] | 2023-01-21 | null | null | null | null | ['outdoor-localization'] | ['robots'] | [-6.29298389e-02 2.03397814e-02 -1.56311944e-01 -9.01604354e-01
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-3.66891623e-01 7.07902551e-01 1.99397445e-01 4.10239846... | [7.834353446960449, -2.160329580307007] |
560d0ff7-5d88-4afa-bfae-136a45030788 | discrepancy-modeling-framework-learning | 2203.05164 | null | https://arxiv.org/abs/2203.05164v1 | https://arxiv.org/pdf/2203.05164v1.pdf | Discrepancy Modeling Framework: Learning missing physics, modeling systematic residuals, and disambiguating between deterministic and random effects | Physics-based and first-principles models pervade the engineering and physical sciences, allowing for the ability to model the dynamics of complex systems with a prescribed accuracy. The approximations used in deriving governing equations often result in discrepancies between the model and sensor-based measurements of ... | ['J. Nathan Kutz', 'Katherine M. Steele', 'Megan R. Ebers'] | 2022-03-10 | null | null | null | null | ['gpr', 'model-discovery', 'gpr'] | ['computer-vision', 'miscellaneous', 'miscellaneous'] | [ 3.78449827e-01 -4.18074936e-01 1.75901279e-01 1.81767732e-01
-6.43929660e-01 -5.85003078e-01 8.51568997e-01 -4.28864323e-02
9.57265422e-02 7.02683151e-01 -1.29569858e-01 -3.36924881e-01
-6.71252668e-01 -5.51214218e-01 -4.84787077e-01 -9.73525345e-01
-5.08305952e-02 5.43999672e-01 -6.02590255e-02 -3.06077659... | [6.526763439178467, 3.4962761402130127] |
8ffd3db1-af60-4a50-a8e5-051f0fd7ee50 | lexical-simplification-with-neural-ranking | null | null | https://aclanthology.org/E17-2006 | https://aclanthology.org/E17-2006.pdf | Lexical Simplification with Neural Ranking | We present a new Lexical Simplification approach that exploits Neural Networks to learn substitutions from the Newsela corpus - a large set of professionally produced simplifications. We extract candidate substitutions by combining the Newsela corpus with a retrofitted context-aware word embeddings model and rank them ... | ['Lucia Specia', 'Gustavo Paetzold'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['complex-word-identification'] | ['natural-language-processing'] | [ 3.09099942e-01 4.22828257e-01 -6.69586837e-01 -4.36710328e-01
-9.03984487e-01 -7.80229867e-02 5.48308551e-01 1.67867348e-01
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-3.41764726e-02 -5.30057967e-01 -6.64354742e-01 1.01073697e-01
2.97175676e-01 8.38990450e-01 -2.20204532e-01 -7.96278417... | [11.020440101623535, 10.389841079711914] |
8406cd3a-462c-4c9e-bd06-b1e1ac82cc78 | model-reference-gaussian-process-regression-1 | 2303.09828 | null | https://arxiv.org/abs/2303.09828v1 | https://arxiv.org/pdf/2303.09828v1.pdf | Model Reference Gaussian Process Regression: Data-Driven State Feedback Controller | This paper proposes a data-driven state feedback controller that enables reference tracking for nonlinear discrete-time systems. The controller is designed based on the identified inverse model of the system and a given reference model, assuming that the identification of the inverse model is carried out using only the... | ['Hyungbo Shim', 'Hamin Chang', 'Hyuntae Kim'] | 2023-03-17 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 2.65636474e-01 2.62228489e-01 -3.41810316e-01 4.66923982e-01
-2.21886352e-01 -4.52830166e-01 4.17020649e-01 -2.76529752e-02
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-5.08162200e-01 -5.31090081e-01 -7.60884047e-01 -9.00976539e-01
2.72278696e-01 2.58451398e-03 -9.86051261e-02 -1.26658902... | [5.279411792755127, 2.4993343353271484] |
bcf9afe8-ed92-4cb4-a68e-1930989c170c | unified-pose-sequence-modeling | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Foo_Unified_Pose_Sequence_Modeling_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Foo_Unified_Pose_Sequence_Modeling_CVPR_2023_paper.pdf | Unified Pose Sequence Modeling | We propose a Unified Pose Sequence Modeling approach to unify heterogeneous human behavior understanding tasks based on pose data, e.g., action recognition, 3D pose estimation and 3D early action prediction. A major obstacle is that different pose-based tasks require different output data formats. Specifically, the... | ['Jun Liu', 'Qiuhong Ke', 'Hossein Rahmani', 'Tianjiao Li', 'Lin Geng Foo'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-pose-estimation', 'action-recognition-in-videos'] | ['computer-vision', 'computer-vision'] | [ 2.57010102e-01 -3.55087787e-01 -3.41772228e-01 -5.23143351e-01
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3.40086192e-01 4.47357833e-01 3.51389021e-01 -2.11166963... | [7.597548007965088, -0.11203334480524063] |
94743619-f210-4501-9ee6-7993ce6029f9 | erqa-edge-restoration-quality-assessment-for | 2110.09992 | null | https://arxiv.org/abs/2110.09992v2 | https://arxiv.org/pdf/2110.09992v2.pdf | ERQA: Edge-Restoration Quality Assessment for Video Super-Resolution | Despite the growing popularity of video super-resolution (VSR), there is still no good way to assess the quality of the restored details in upscaled frames. Some SR methods may produce the wrong digit or an entirely different face. Whether a method's results are trustworthy depends on how well it restores truthful deta... | ['Dmitry Vatolin', 'Anastasia Antsiferova', 'Eugene Lyapustin', 'Anastasia Kirillova'] | 2021-10-19 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 3.22415769e-01 -1.88718751e-01 2.45418884e-02 -3.79135340e-01
-1.08519053e+00 -3.38860512e-01 4.08672065e-01 -3.70426506e-01
9.85446386e-03 1.08606100e+00 5.08842528e-01 9.06046256e-02
1.54190198e-01 -9.67629850e-01 -7.24442840e-01 -6.91930652e-01
1.45412632e-03 -1.75917223e-01 4.10254836e-01 -3.47178340... | [11.106524467468262, -2.084967851638794] |
0034ef11-5d9b-47ad-a3a9-762dc1249e1e | dense-graph-convolutional-neural-networks-on | 2106.15778 | null | https://arxiv.org/abs/2106.15778v1 | https://arxiv.org/pdf/2106.15778v1.pdf | Dense Graph Convolutional Neural Networks on 3D Meshes for 3D Object Segmentation and Classification | This paper presents new designs of graph convolutional neural networks (GCNs) on 3D meshes for 3D object segmentation and classification. We use the faces of the mesh as basic processing units and represent a 3D mesh as a graph where each node corresponds to a face. To enhance the descriptive power of the graph, we int... | ['Wenming Tang Guoping Qiu'] | 2021-06-30 | null | null | null | null | ['3d-object-classification'] | ['computer-vision'] | [-1.91221327e-01 3.94124746e-01 -1.81755185e-01 -3.15081418e-01
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2.56222486e-02 -1.14437532e+00 -8.81227434e-01 -3.26003641e-01
-6.37961268e-01 4.67089623e-01 4.35724765e-01 9.03056711... | [8.021856307983398, -3.683809280395508] |
76cdabd0-6dd6-4704-8a39-5301b997f3eb | pico-newton-mechanical-forces-promote-neurite | 1810.08362 | null | http://arxiv.org/abs/1810.08362v1 | http://arxiv.org/pdf/1810.08362v1.pdf | Pico-Newton mechanical forces promote neurite growth | Investigations over half a century have indicated that mechanical forces
induce neurite growth - with neurites elongating at a rate of
0.1-0.3{\mu}mh^{-1} per pico-Newton (pN) of applied force - when mechanical
tension exceeds a threshold, with this being identified as 400-1000 pN for
neurites of PC12 cells. Here we de... | [] | 2018-10-19 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [ 4.98527110e-01 2.69986957e-01 -3.37429732e-01 2.12454230e-01
-3.14678997e-01 -5.17454028e-01 3.60051632e-01 1.38343871e-01
-9.91159379e-01 1.34699786e+00 -6.54066503e-02 -4.89455223e-01
2.60803610e-01 -6.08411312e-01 -6.92482769e-01 -1.07356024e+00
7.42130429e-02 1.76040009e-01 6.27116978e-01 -2.07446545... | [7.984625339508057, 2.7861456871032715] |
e6f4f089-eeff-46ef-b8b8-24bf2bb7d4c6 | spatial-aggregation-of-holistically-nested | 1606.07830 | null | http://arxiv.org/abs/1606.07830v1 | http://arxiv.org/pdf/1606.07830v1.pdf | Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation | Accurate automatic organ segmentation is an important yet challenging problem
for medical image analysis. The pancreas is an abdominal organ with very high
anatomical variability. This inhibits traditional segmentation methods from
achieving high accuracies, especially compared to other organs such as the
liver, heart ... | ['Andrew Sohn', 'Amal Farag', 'Le Lu', 'Holger R. Roth', 'Ronald M. Summers'] | 2016-06-24 | null | null | null | null | ['pancreas-segmentation', 'automated-pancreas-segmentation'] | ['medical', 'medical'] | [ 1.79567456e-01 3.65001440e-01 -2.67535180e-01 -3.26103181e-01
-9.67353463e-01 -6.80515885e-01 2.52392948e-01 6.27078831e-01
-3.06243479e-01 7.23804832e-01 -5.99316228e-03 -2.00557008e-01
-1.05880402e-01 -5.61256051e-01 -6.12816632e-01 -8.40864122e-01
-4.99234825e-01 5.39432704e-01 2.52879918e-01 5.55907845... | [14.501473426818848, -2.6914544105529785] |
b14c41a6-ee1a-4f0a-adc2-57955548bbe1 | data-free-knowledge-distillation-via-feature | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yu_Data-Free_Knowledge_Distillation_via_Feature_Exchange_and_Activation_Region_Constraint_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yu_Data-Free_Knowledge_Distillation_via_Feature_Exchange_and_Activation_Region_Constraint_CVPR_2023_paper.pdf | Data-Free Knowledge Distillation via Feature Exchange and Activation Region Constraint | Despite the tremendous progress on data-free knowledge distillation (DFKD) based on synthetic data generation, there are still limitations in diverse and efficient data synthesis. It is naive to expect that a simple combination of generative network-based data synthesis and data augmentation will solve these issues... | ['Shuqiang Jiang', 'Hu Han', 'Jiachen Chen', 'Shikang Yu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [-2.00169146e-01 7.60156959e-02 3.90494317e-02 -2.78710306e-01
-3.11098903e-01 -4.32312727e-01 4.68748420e-01 -6.35125279e-01
-3.61486286e-01 1.04791451e+00 -1.37790050e-02 -3.57190609e-01
-1.35794342e-01 -9.97180104e-01 -8.38260770e-01 -8.91096830e-01
3.28234673e-01 3.03619951e-01 1.82567015e-01 -1.96753353... | [9.489862442016602, 3.267852544784546] |
dd558bc1-8b0a-422c-a416-2582c962b1fb | deep-offline-reinforcement-learning-for-real | 2302.07549 | null | https://arxiv.org/abs/2302.07549v2 | https://arxiv.org/pdf/2302.07549v2.pdf | Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications | There is increasing interest in data-driven approaches for recommending optimal treatment strategies in many chronic disease management and critical care applications. Reinforcement learning methods are well-suited to this sequential decision-making problem, but must be trained and evaluated exclusively on retrospectiv... | ['Pavitra Krishnaswamy', 'Yong Mong Bee', 'Yu En Chan', 'Priscilla Ong', 'Supriyo Ghosh', 'Milashini Nambiar'] | 2023-02-15 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [ 3.49999636e-01 1.61942735e-01 -8.29854429e-01 -2.62095630e-01
-9.05495286e-01 -3.48591685e-01 3.00562140e-02 6.91867411e-01
-5.22749960e-01 1.23090422e+00 1.63246080e-01 -6.82836354e-01
-5.79190910e-01 -4.53746617e-01 -5.24956584e-01 -7.33883202e-01
-3.66693586e-01 8.87672722e-01 -2.99224824e-01 -1.08844682... | [4.021296977996826, 2.725306272506714] |
bb0605f1-33c7-4c17-a15c-c646eec9be92 | optimal-bandwidth-selection-for-denclue | 2307.03206 | null | https://arxiv.org/abs/2307.03206v1 | https://arxiv.org/pdf/2307.03206v1.pdf | Optimal Bandwidth Selection for DENCLUE | In modern day industry, clustering algorithms are daily routines of algorithm engineers. Although clustering algorithms experienced rapid growth before 2010. Innovation related to the research topic has stagnated after deep learning became the de facto industrial standard for machine learning applications. In 2007, a d... | ['Hao Wang'] | 2023-07-06 | null | null | null | null | ['clustering'] | ['methodology'] | [-6.62120581e-01 -5.75945079e-01 -2.65227020e-01 -2.94162422e-01
-2.30999857e-01 -3.56109321e-01 2.14590341e-01 3.44747789e-02
-4.62823212e-01 5.18137455e-01 -2.28652388e-01 -1.94508553e-01
-3.53258997e-01 -6.39831662e-01 -1.36433974e-01 -1.10006452e+00
-7.29587376e-02 1.06623328e+00 -1.03171892e-01 4.17407781... | [9.044240951538086, 3.374284029006958] |
6aa30bb7-2b5b-4ed6-92c3-6e5e47645176 | building-bilingual-and-code-switched-voice | 2104.10832 | null | https://arxiv.org/abs/2104.10832v1 | https://arxiv.org/pdf/2104.10832v1.pdf | Building Bilingual and Code-Switched Voice Conversion with Limited Training Data Using Embedding Consistency Loss | Building cross-lingual voice conversion (VC) systems for multiple speakers and multiple languages has been a challenging task for a long time. This paper describes a parallel non-autoregressive network to achieve bilingual and code-switched voice conversion for multiple speakers when there are only mono-lingual corpora... | ['Ming Li', 'Mingyang Xu', 'Huahua Cui', 'Shanshan Liang', 'Xiaoyi Qin', 'Haozhe Zhang', 'Yaogen Yang'] | 2021-04-22 | null | null | null | null | ['voice-cloning'] | ['speech'] | [-3.27716880e-02 -8.91452376e-03 1.41100250e-02 -4.09712344e-01
-1.23312151e+00 -6.67510450e-01 4.82366085e-01 -6.77707553e-01
-1.71976760e-01 4.85132635e-01 5.68578839e-01 -5.18745959e-01
5.67977786e-01 -4.31489408e-01 -6.64911151e-01 -3.97109360e-01
4.33554381e-01 2.49663427e-01 -2.25046650e-01 -4.68051791... | [14.822967529296875, 6.633591651916504] |
1f2fe447-420e-4709-852a-2b43d5b97ec6 | multi-scale-octave-convolutions-for-robust | 1910.14443 | null | https://arxiv.org/abs/1910.14443v1 | https://arxiv.org/pdf/1910.14443v1.pdf | Multi-scale Octave Convolutions for Robust Speech Recognition | We propose a multi-scale octave convolution layer to learn robust speech representations efficiently. Octave convolutions were introduced by Chen et al [1] in the computer vision field to reduce the spatial redundancy of the feature maps by decomposing the output of a convolutional layer into feature maps at two differ... | ['Joanna Rownicka', 'Steve Renals', 'Peter Bell'] | 2019-10-31 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 8.87581483e-02 3.59848589e-01 7.71123409e-01 -3.45645607e-01
-8.67611229e-01 -5.69723010e-01 6.27945721e-01 1.39458841e-02
-6.89406276e-01 4.20209408e-01 5.79902530e-01 -6.27064332e-02
-3.01322520e-01 -7.06513584e-01 -7.68544793e-01 -7.97632456e-01
1.08288117e-01 -3.76273572e-01 2.08870098e-01 -2.52981454... | [15.00778579711914, 5.924849033355713] |
3e8efeb8-75d8-4f75-a430-8dc52e7b7c85 | ixa-pipeline-efficient-and-ready-to-use | null | null | https://aclanthology.org/L14-1605 | https://aclanthology.org/L14-1605.pdf | IXA pipeline: Efficient and Ready to Use Multilingual NLP tools | IXA pipeline is a modular set of Natural Language Processing tools (or pipes) which provide easy access to NLP technology. It offers robust and efficient linguistic annotation to both researchers and non-NLP experts with the aim of lowering the barriers of using NLP technology either for research purposes or for small ... | ['Rodrigo Agerri', 'Josu Bermudez', 'German Rigau'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['multilingual-nlp'] | ['natural-language-processing'] | [-1.59388840e-01 5.29187441e-01 -3.95583838e-01 -3.84699345e-01
-7.35168159e-01 -1.20383525e+00 5.12513578e-01 5.44684708e-01
-4.61503655e-01 6.51401281e-01 2.58122921e-01 -6.59605920e-01
-1.55035079e-01 -2.94016480e-01 -3.21642071e-01 -5.85957356e-02
9.62808356e-02 8.23007047e-01 2.42819682e-01 -1.90778732... | [10.006394386291504, 9.618925094604492] |
00db50b8-7301-4c93-82e6-586daf5a402e | one-shot-learning-of-stochastic-differential | 2209.12086 | null | https://arxiv.org/abs/2209.12086v3 | https://arxiv.org/pdf/2209.12086v3.pdf | One-Shot Learning of Stochastic Differential Equations with Data Adapted Kernels | We consider the problem of learning Stochastic Differential Equations of the form $dX_t = f(X_t)dt+\sigma(X_t)dW_t $ from one sample trajectory. This problem is more challenging than learning deterministic dynamical systems because one sample trajectory only provides indirect information on the unknown functions $f$, $... | ['Peyman Tavallali', 'Houman Owhadi', 'Giulia Livieri', 'Boumediene Hamzi', 'Matthieu Darcy'] | 2022-09-24 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [-3.82544816e-01 -1.02267183e-01 3.96887541e-01 -4.42080162e-02
-7.07300782e-01 -5.11314631e-01 4.64459687e-01 5.07759340e-02
-4.35598314e-01 1.11895657e+00 -4.44142610e-01 -1.63837358e-01
-6.14457965e-01 -8.58843565e-01 -7.68045664e-01 -1.12792981e+00
-5.21676779e-01 5.42176127e-01 2.42544059e-02 1.03727996... | [6.5942912101745605, 3.5929245948791504] |
31744e15-b930-4c6a-a8f2-1d5b37f91798 | local-contrast-and-global-contextual | 2301.12093 | null | https://arxiv.org/abs/2301.12093v3 | https://arxiv.org/pdf/2301.12093v3.pdf | Local Contrast and Global Contextual Information Make Infrared Small Object Salient Again | Infrared small object detection (ISOS) aims to segment small objects only covered with several pixels from clutter background in infrared images. It's of great challenge due to: 1) small objects lack of sufficient intensity, shape and texture information; 2) small objects are easily lost in the process where detection ... | ['Peiwen Pan', 'Huan Wang', 'Chenyi Wang'] | 2023-01-28 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 3.95810992e-01 -3.01018089e-01 -3.94292586e-02 -1.64064854e-01
-2.76705265e-01 -2.94988692e-01 1.08538762e-01 -2.95281798e-01
-4.59032595e-01 3.40657681e-01 -1.74771324e-01 -3.83310914e-01
1.74003720e-01 -8.46227884e-01 -7.25900948e-01 -8.98830175e-01
2.65386879e-01 -2.99997002e-01 7.94793725e-01 -2.33671471... | [9.008063316345215, -0.8820396661758423] |
ac72c8d0-7045-452e-9161-1918e16ff112 | a-cnn-based-blind-denoising-method-for-1 | 2003.06986 | null | https://arxiv.org/abs/2003.06986v1 | https://arxiv.org/pdf/2003.06986v1.pdf | A CNN-Based Blind Denoising Method for Endoscopic Images | The quality of images captured by wireless capsule endoscopy (WCE) is key for doctors to diagnose diseases of gastrointestinal (GI) tract. However, there exist many low-quality endoscopic images due to the limited illumination and complex environment in GI tract. After an enhancement process, the severe noise become an... | ['Xiang Xie', 'Shaofeng Zou', 'Xuyang Wang', 'Zhihua Wang', 'Mingzhu Long', 'Guolin Li'] | 2020-03-16 | a-cnn-based-blind-denoising-method-for | null | null | conference-2019-12 | ['blind-image-quality-assessment'] | ['computer-vision'] | [-1.12457626e-01 -4.30073768e-01 3.41458142e-01 -9.72004086e-02
-6.59747183e-01 -3.62098455e-01 -1.29996106e-01 -3.06365907e-01
-6.39717638e-01 3.49992096e-01 2.90013671e-01 -1.21961348e-01
-7.12179095e-02 -7.32371271e-01 -6.73769712e-01 -7.85692453e-01
-1.02404639e-01 -3.83761913e-01 2.87539303e-01 -9.04112011... | [13.555490493774414, -2.576749324798584] |
c46b56b1-1fcd-416f-b026-3cf5d901987d | shift-reduce-task-oriented-semantic-parsing | 2210.11984 | null | https://arxiv.org/abs/2210.11984v1 | https://arxiv.org/pdf/2210.11984v1.pdf | Shift-Reduce Task-Oriented Semantic Parsing with Stack-Transformers | Intelligent voice assistants, such as Apple Siri and Amazon Alexa, are widely used nowadays. These task-oriented dialog systems require a semantic parsing module in order to process user utterances and understand the action to be performed. This semantic parsing component was initially implemented by rule-based or stat... | ['Daniel Fernández-González'] | 2022-10-21 | null | null | null | null | ['constituency-parsing', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.88995016e-01 3.63594353e-01 -8.99212528e-03 -7.38347471e-01
-9.91431236e-01 -9.78334725e-01 7.55954742e-01 -3.03513873e-02
-4.17242646e-01 4.49050367e-01 3.65669399e-01 -7.60571480e-01
1.60105079e-01 -7.16211855e-01 -3.76303643e-01 -5.53968959e-02
4.12259519e-01 1.15448594e+00 6.79555595e-01 -5.97539127... | [12.399529457092285, 7.793672561645508] |
340d3427-49ed-43f0-8027-01d78fa5b05c | icar-bridging-image-classification-and-image | 2204.10760 | null | https://arxiv.org/abs/2204.10760v1 | https://arxiv.org/pdf/2204.10760v1.pdf | iCAR: Bridging Image Classification and Image-text Alignment for Visual Recognition | Image classification, which classifies images by pre-defined categories, has been the dominant approach to visual representation learning over the last decade. Visual learning through image-text alignment, however, has emerged to show promising performance, especially for zero-shot recognition. We believe that these tw... | ['Baining Guo', 'Han Hu', 'Zhenda Xie', 'Zhuliang Yao', 'Zheng Zhang', 'Yue Cao', 'Yixuan Wei'] | 2022-04-22 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 7.19644308e-01 -1.37785375e-01 -6.32779002e-01 -5.27337015e-01
-9.75976050e-01 -5.44832289e-01 7.80837357e-01 1.23517387e-01
-5.54450929e-01 2.83376724e-01 1.81484565e-01 -3.74142945e-01
3.50910984e-02 -4.18468922e-01 -7.26990700e-01 -7.41851687e-01
5.76040685e-01 4.69318777e-01 2.98625201e-01 -1.30919926... | [9.995326042175293, 2.2856695652008057] |
fc02cfc2-378d-4576-a9fb-b057e9a906a9 | color-constancy-convolutional-autoencoder | 1906.01340 | null | https://arxiv.org/abs/1906.01340v1 | https://arxiv.org/pdf/1906.01340v1.pdf | Color Constancy Convolutional Autoencoder | In this paper, we study the importance of pre-training for the generalization capability in the color constancy problem. We propose two novel approaches based on convolutional autoencoders: an unsupervised pre-training algorithm using a fine-tuned encoder and a semi-supervised pre-training algorithm using a novel compo... | ['Alexandros Iosifidis', 'Moncef Gabbouj', 'Jenni Raitoharju', 'Jarno Nikkanen', 'Firas Laakom'] | 2019-06-04 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 2.10413039e-01 -4.55847621e-01 2.23301183e-02 -6.51605904e-01
-3.06928158e-01 -2.71238714e-01 3.54721218e-01 -3.17563176e-01
-6.69435918e-01 5.69118261e-01 -3.84510666e-01 -2.00034231e-01
-2.73192823e-01 -6.36369467e-01 -8.39440703e-01 -7.02550173e-01
1.38343632e-01 1.64675385e-01 6.34256452e-02 -3.34130704... | [10.404394149780273, -2.5136237144470215] |
71649a60-2319-4463-99b6-28c5a3caa348 | thoracic-cartilage-ultrasound-ct-registration | 2307.03800 | null | https://arxiv.org/abs/2307.03800v1 | https://arxiv.org/pdf/2307.03800v1.pdf | Thoracic Cartilage Ultrasound-CT Registration using Dense Skeleton Graph | Autonomous ultrasound (US) imaging has gained increased interest recently, and it has been seen as a potential solution to overcome the limitations of free-hand US examinations, such as inter-operator variations. However, it is still challenging to accurately map planned paths from a generic atlas to individual patient... | ['Nassir Navab', 'Xuesong Li', 'Chenyang Li', 'Zhongliang Jiang'] | 2023-07-07 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 2.50492841e-01 2.78780192e-01 3.59918952e-01 -8.27172175e-02
-1.05361056e+00 -6.80823326e-01 1.36582211e-01 3.03099304e-01
-3.39034379e-01 2.29526535e-01 -1.95522651e-01 -1.34802282e-01
-5.06039917e-01 -7.03071773e-01 -5.06793022e-01 -9.05989289e-01
-5.44004381e-01 7.07426071e-01 4.90218848e-01 1.83795020... | [13.88483715057373, -2.766556978225708] |
fad0174c-3d64-4f57-9f73-f4d1c2d6e0bd | noise-robust-speech-recognition-with-10 | 2203.15321 | null | https://arxiv.org/abs/2203.15321v1 | https://arxiv.org/pdf/2203.15321v1.pdf | Noise-robust Speech Recognition with 10 Minutes Unparalleled In-domain Data | Noise-robust speech recognition systems require large amounts of training data including noisy speech data and corresponding transcripts to achieve state-of-the-art performances in face of various practical environments. However, such plenty of in-domain data is not always available in the real-life world. In this pape... | ['Eng Siong Chng', 'Shashank Shirol', 'Yuchen Hu', 'Nana Hou', 'Chen Chen'] | 2022-03-29 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 4.36023593e-01 -9.20544565e-02 5.93319952e-01 -4.67379719e-01
-1.45031607e+00 -3.73050243e-01 4.39236134e-01 -7.46130645e-01
-3.25507432e-01 6.78913176e-01 2.87602186e-01 -5.96324265e-01
4.15803283e-01 -5.47431707e-01 -5.89080930e-01 -9.52074409e-01
3.64113986e-01 1.25245452e-02 -1.75071925e-01 -4.73212868... | [14.847700119018555, 6.183080196380615] |
f1c01313-8370-45d6-be9a-0e4310baabd4 | deep-learning-for-laboratory-earthquake | 2203.13313 | null | https://arxiv.org/abs/2203.13313v3 | https://arxiv.org/pdf/2203.13313v3.pdf | Deep learning for laboratory earthquake prediction and autoregressive forecasting of fault zone stress | Earthquake forecasting and prediction have long and in some cases sordid histories but recent work has rekindled interest based on advances in early warning, hazard assessment for induced seismicity and successful prediction of laboratory earthquakes. In the lab, frictional stick-slip events provide an analog for earth... | ['Chris Marone', 'Luca Franco', 'Fabio Galasso', 'Elisa Tinti', 'Laura Laurenti'] | 2022-03-24 | null | null | null | null | ['earthquake-prediction'] | ['computer-vision'] | [-6.67671934e-02 -3.80465865e-01 2.21923023e-01 -6.94791675e-02
-9.94466782e-01 -3.47319186e-01 5.32259405e-01 -1.26733810e-01
-2.30006009e-01 4.27893937e-01 4.64281857e-01 -5.04897356e-01
-1.52151808e-01 -1.09906876e+00 -7.96898901e-01 -9.67067361e-01
-8.08063626e-01 2.25050196e-01 4.27728683e-01 -5.86255074... | [6.81418514251709, 2.757838249206543] |
5676771b-a2e3-4b72-978d-ec1180b553a6 | reactive-perturbation-defocusing-for-textual | 2305.04067 | null | https://arxiv.org/abs/2305.04067v1 | https://arxiv.org/pdf/2305.04067v1.pdf | Reactive Perturbation Defocusing for Textual Adversarial Defense | Recent studies have shown that large pre-trained language models are vulnerable to adversarial attacks. Existing methods attempt to reconstruct the adversarial examples. However, these methods usually have limited performance in defense against adversarial examples, while also negatively impacting the performance on na... | ['Ke Li', 'Heng Yang'] | 2023-05-06 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 2.19569936e-01 1.66727811e-01 2.57011622e-01 4.26412709e-02
-9.78943944e-01 -1.22635210e+00 7.33241558e-01 -9.64229107e-02
-2.78395325e-01 7.20923543e-01 7.66847208e-02 -2.87915498e-01
4.50001121e-01 -8.90930831e-01 -9.32941496e-01 -6.92420602e-01
-8.61090422e-02 2.99277455e-01 3.20999205e-01 -3.61924052... | [5.796001434326172, 7.887788772583008] |
450d872d-26e2-4739-88b8-3212643ce97f | musical-prosody-driven-emotion-classification | 2106.02556 | null | https://arxiv.org/abs/2106.02556v2 | https://arxiv.org/pdf/2106.02556v2.pdf | Musical Prosody-Driven Emotion Classification: Interpreting Vocalists Portrayal of Emotions Through Machine Learning | The task of classifying emotions within a musical track has received widespread attention within the Music Information Retrieval (MIR) community. Music emotion recognition has traditionally relied on the use of acoustic features, verbal features, and metadata-based filtering. The role of musical prosody remains under-e... | ['Gil Weinberg', 'Richard Savery', 'Brian Model', 'Nicholas Farris'] | 2021-06-04 | null | null | null | null | ['music-emotion-recognition', 'music-information-retrieval'] | ['music', 'music'] | [ 2.49871179e-01 -3.66091698e-01 -1.53642267e-01 -1.69279426e-01
-8.49482894e-01 -8.85938406e-01 3.59096706e-01 1.93860486e-01
-4.79413986e-01 3.47920150e-01 4.28736776e-01 2.67343074e-01
-3.55380088e-01 -4.57194775e-01 -1.11811638e-01 -3.60797971e-01
-6.07383363e-02 1.64274216e-01 -1.95530236e-01 -8.04666355... | [15.903080940246582, 5.22848653793335] |
728da66a-6a0c-4f81-b4df-6c4756e3d8e4 | continual-semantic-segmentation-via-repulsion | 2103.06342 | null | https://arxiv.org/abs/2103.06342v3 | https://arxiv.org/pdf/2103.06342v3.pdf | Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations | Deep neural networks suffer from the major limitation of catastrophic forgetting old tasks when learning new ones. In this paper we focus on class incremental continual learning in semantic segmentation, where new categories are made available over time while previous training data is not retained. The proposed continu... | ['Pietro Zanuttigh', 'Umberto Michieli'] | 2021-03-10 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Michieli_Continual_Semantic_Segmentation_via_Repulsion-Attraction_of_Sparse_and_Disentangled_Latent_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Michieli_Continual_Semantic_Segmentation_via_Repulsion-Attraction_of_Sparse_and_Disentangled_Latent_CVPR_2021_paper.pdf | cvpr-2021-1 | ['overlapped-10-1', 'disjoint-15-5', 'disjoint-10-1', 'disjoint-15-1', 'overlapped-15-5', 'overlapped-15-1', 'continual-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 5.84960282e-01 2.30922908e-01 -5.76806553e-02 -4.72476065e-01
-2.15376541e-01 -4.64267522e-01 6.68665290e-01 3.46863031e-01
-8.39449883e-01 8.30215156e-01 4.71572019e-02 1.29207015e-01
-1.95678294e-01 -7.37057149e-01 -7.73933351e-01 -8.36133838e-01
6.49393350e-02 6.22002244e-01 6.65651381e-01 3.06849658... | [9.388188362121582, 2.193549633026123] |
9ad864e0-6956-4a37-9a99-5d930a783db2 | video-segmentation-learning-using-cascade | 2212.10570 | null | https://arxiv.org/abs/2212.10570v1 | https://arxiv.org/pdf/2212.10570v1.pdf | Video Segmentation Learning Using Cascade Residual Convolutional Neural Network | Video segmentation consists of a frame-by-frame selection process of meaningful areas related to foreground moving objects. Some applications include traffic monitoring, human tracking, action recognition, efficient video surveillance, and anomaly detection. In these applications, it is not rare to face challenges such... | ['João P. Papa', 'Danilo Colombo', 'Rafael G. Pires', 'Daniel F. S. Santos'] | 2022-12-20 | null | null | null | null | ['change-detection', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.73853773e-01 -4.28814471e-01 7.24456906e-02 -1.91614598e-01
-3.81704360e-01 -3.50575447e-01 5.63644826e-01 -1.43755898e-02
-5.96762955e-01 8.26339602e-01 -5.33643186e-01 -3.87040317e-01
-3.58681343e-02 -7.81030774e-01 -7.75510609e-01 -9.43773687e-01
-2.94447273e-01 1.76638067e-01 8.50363076e-01 2.37977598... | [8.88774299621582, -0.6792480945587158] |
adcf9984-e7df-485f-a75f-0882ea857545 | which-cnns-and-training-settings-to-choose | 2111.08320 | null | https://arxiv.org/abs/2111.08320v1 | https://arxiv.org/pdf/2111.08320v1.pdf | Which CNNs and Training Settings to Choose for Action Unit Detection? A Study Based on a Large-Scale Dataset | In this paper we explore the influence of some frequently used Convolutional Neural Networks (CNNs), training settings, and training set structures, on Action Unit (AU) detection. Specifically, we first compare 10 different shallow and deep CNNs in AU detection. Second, we investigate how the different training setting... | ['Mohammad Mavadati', 'Mohamed Ashraf', 'Ahmed Ghoneim', 'Mina Bishay'] | 2021-11-16 | null | null | null | null | ['action-unit-detection'] | ['computer-vision'] | [ 2.55004436e-01 -1.91964507e-01 -5.10022640e-02 -7.94278607e-02
-1.91583291e-01 -5.33037364e-01 4.84940320e-01 -1.58067256e-01
-9.00149882e-01 2.35556260e-01 3.59838337e-01 -1.26786768e-01
3.23729306e-01 -5.33032417e-01 -7.72730529e-01 -6.46543503e-01
-1.85682282e-01 -2.45596245e-01 5.40510595e-01 -2.88436767... | [8.812386512756348, 0.40799322724342346] |
8dde0331-5da2-471c-973b-c8246392fb82 | credit-card-fraud-detection-using-enhanced | 2303.06514 | null | https://arxiv.org/abs/2303.06514v1 | https://arxiv.org/pdf/2303.06514v1.pdf | Credit Card Fraud Detection Using Enhanced Random Forest Classifier for Imbalanced Data | The credit card has become the most popular payment method for both online and offline transactions. The necessity to create a fraud detection algorithm to precisely identify and stop fraudulent activity arises as a result of both the development of technology and the rise in fraud cases. This paper implements the rand... | ['Huthaifa I. Ashqar', 'AlsharifHasan Mohamad Aburbeian'] | 2023-03-11 | null | null | null | null | ['fraud-detection'] | ['miscellaneous'] | [-1.94885880e-01 -2.42614895e-01 -2.46096551e-01 -4.30873662e-01
3.81442793e-02 -2.64451832e-01 9.78409275e-02 2.57187188e-01
-4.84551817e-01 1.17057216e+00 -8.16285610e-02 -2.53631979e-01
4.35240120e-02 -1.10183144e+00 -1.80961758e-01 -4.78966892e-01
1.81255504e-01 6.68455362e-01 7.38850757e-02 -1.30044445... | [8.029208183288574, 4.954714775085449] |
a4f83944-46bf-4a2a-9cba-bc50eec1e31d | semi-supervised-multi-view-concept | 2307.00924 | null | https://arxiv.org/abs/2307.00924v1 | https://arxiv.org/pdf/2307.00924v1.pdf | Semi-supervised multi-view concept decomposition | Concept Factorization (CF), as a novel paradigm of representation learning, has demonstrated superior performance in multi-view clustering tasks. It overcomes limitations such as the non-negativity constraint imposed by traditional matrix factorization methods and leverages kernel methods to learn latent representation... | ['Qibin Zhao', 'Guoxu Zhou', 'Qi Jiang'] | 2023-07-03 | null | null | null | null | ['clustering'] | ['methodology'] | [-2.01388210e-01 -5.82115710e-01 -5.18947065e-01 -2.15044677e-01
-5.74805796e-01 -6.76393688e-01 4.70562249e-01 3.06585040e-02
-1.67043936e-02 3.74131054e-01 6.02332771e-01 -7.90929869e-02
-3.55765074e-01 -5.17250717e-01 -1.37929156e-01 -9.54351425e-01
3.01888645e-01 2.83547580e-01 -2.31657445e-01 1.71476975... | [8.355842590332031, 4.555904865264893] |
744fe29a-96d8-401b-bee3-b9642ccc124d | pointpillars-fast-encoders-for-object | 1812.05784 | null | https://arxiv.org/abs/1812.05784v2 | https://arxiv.org/pdf/1812.05784v2.pdf | PointPillars: Fast Encoders for Object Detection from Point Clouds | Object detection in point clouds is an important aspect of many robotics applications such as autonomous driving. In this paper we consider the problem of encoding a point cloud into a format appropriate for a downstream detection pipeline. Recent literature suggests two types of encoders; fixed encoders tend to be fas... | ['Jiong Yang', 'Alex H. Lang', 'Sourabh Vora', 'Holger Caesar', 'Lubing Zhou', 'Oscar Beijbom'] | 2018-12-14 | pointpillars-fast-encoders-for-object-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Lang_PointPillars_Fast_Encoders_for_Object_Detection_From_Point_Clouds_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Lang_PointPillars_Fast_Encoders_for_Object_Detection_From_Point_Clouds_CVPR_2019_paper.pdf | cvpr-2019-6 | ['birds-eye-view-object-detection', 'robust-3d-object-detection'] | ['computer-vision', 'computer-vision'] | [ 3.25170495e-02 -1.13208927e-01 4.77544256e-02 -3.25160682e-01
-6.37247384e-01 -6.16065145e-01 8.16667199e-01 4.55902934e-01
-6.13324463e-01 -5.20940451e-03 -4.31912512e-01 -4.51693296e-01
1.10228047e-01 -9.53441203e-01 -1.28634202e+00 -2.95091480e-01
-2.72391796e-01 5.33116281e-01 8.30857337e-01 -4.31526184... | [7.7370805740356445, -2.5724122524261475] |
51ff14b1-294c-4a0a-90fc-7ba378d8aeae | scale-and-context-aware-convolutional-non | 1911.07183 | null | https://arxiv.org/abs/1911.07183v1 | https://arxiv.org/pdf/1911.07183v1.pdf | Scale- and Context-Aware Convolutional Non-intrusive Load Monitoring | Non-intrusive load monitoring addresses the challenging task of decomposing the aggregate signal of a household's electricity consumption into appliance-level data without installing dedicated meters. By detecting load malfunction and recommending energy reduction programs, cost-effective non-intrusive load monitoring ... | ['Jinliang He', 'Yu Zhang', 'Jun Hu', 'Qin Wang', 'Kunjin Chen', 'Hang Fan'] | 2019-11-17 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 1.95014566e-01 -1.56582251e-01 -1.50498301e-01 -7.58660436e-01
-5.81843555e-01 -5.52891970e-01 3.04506004e-01 -1.66765926e-03
-4.53774966e-02 6.88315034e-01 2.57445514e-01 -2.35227436e-01
2.85882708e-02 -1.09468222e+00 -4.77137297e-01 -9.26514864e-01
-1.13848515e-01 -6.29844293e-02 -2.50477821e-01 -1.51882907... | [16.06595802307129, 7.579995155334473] |
88cfab5e-dff3-4508-a0c7-f7ee56b44253 | variational-dynamic-mixtures-1 | 2010.10403 | null | https://arxiv.org/abs/2010.10403v2 | https://arxiv.org/pdf/2010.10403v2.pdf | Variational Dynamic Mixtures | Deep probabilistic time series forecasting models have become an integral part of machine learning. While several powerful generative models have been proposed, we provide evidence that their associated inference models are oftentimes too limited and cause the generative model to predict mode-averaged dynamics. Modeave... | ['Maja Rudolph', 'Stephan Mandt', 'Chen Qiu'] | 2020-10-20 | variational-dynamic-mixtures | https://openreview.net/forum?id=aAY23UgDBv0 | https://openreview.net/pdf?id=aAY23UgDBv0 | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [-1.74788892e-01 -2.14139391e-02 -3.50292295e-01 -2.76826590e-01
-9.70364273e-01 -6.06167078e-01 1.24551833e+00 -4.75198507e-01
2.03431800e-01 8.62679064e-01 6.01308465e-01 -1.33689702e-01
-1.19175725e-02 -7.65046656e-01 -8.46318305e-01 -7.09995627e-01
-1.89438313e-02 1.01605213e+00 1.50672600e-01 2.35188436... | [6.965216159820557, 3.4142494201660156] |
6bb3667a-52bf-4fec-8527-56ee849cf77c | photoscene-photorealistic-material-and-1 | 2207.00757 | null | https://arxiv.org/abs/2207.00757v1 | https://arxiv.org/pdf/2207.00757v1.pdf | PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes | Most indoor 3D scene reconstruction methods focus on recovering 3D geometry and scene layout. In this work, we go beyond this to propose PhotoScene, a framework that takes input image(s) of a scene along with approximately aligned CAD geometry (either reconstructed automatically or manually specified) and builds a phot... | ['Manmohan Chandraker', 'Kalyan Sunkavalli', 'Miloš Hašan', 'Zexiang Xu', 'Rui Zhu', 'Yannick Hold-Geoffroy', 'Zhengqin Li', 'Yu-Ying Yeh'] | 2022-07-02 | photoscene-photorealistic-material-and | http://openaccess.thecvf.com//content/CVPR2022/html/Yeh_PhotoScene_Photorealistic_Material_and_Lighting_Transfer_for_Indoor_Scenes_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yeh_PhotoScene_Photorealistic_Material_and_Lighting_Transfer_for_Indoor_Scenes_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 5.35988629e-01 -5.81366122e-02 4.79505122e-01 -3.00358385e-01
-2.48076603e-01 -9.38553631e-01 6.26298189e-01 -2.58747905e-01
2.60459900e-01 2.82719493e-01 1.59558743e-01 -3.49772930e-01
4.90428172e-02 -1.06005192e+00 -8.81836295e-01 -7.06007481e-02
3.73875022e-01 5.98881602e-01 2.59645283e-01 -1.36008710... | [9.340038299560547, -3.106302499771118] |
1d65b04a-a384-4c00-86bd-a8ea10cb493b | automated-icd-coding-using-extreme-multi | 2212.05857 | null | https://arxiv.org/abs/2212.05857v2 | https://arxiv.org/pdf/2212.05857v2.pdf | Automated ICD Coding using Extreme Multi-label Long Text Transformer-based Models | Background: Encouraged by the success of pretrained Transformer models in many natural language processing tasks, their use for International Classification of Diseases (ICD) coding tasks is now actively being explored. In this study, we investigate three types of Transformer-based models, aiming to address the extreme... | ['Louisa Jorm', 'Vicki Bennett', 'Anthony Nguyen', 'Oscar Perez-Concha', 'Leibo Liu'] | 2022-12-12 | null | null | null | null | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 4.24453616e-01 2.46877596e-01 -3.44205886e-01 -3.80890742e-02
-1.01273882e+00 -1.60969600e-01 4.63818163e-01 3.91146421e-01
-4.62135464e-01 4.30985034e-01 2.61262238e-01 -5.77969253e-01
-7.50693828e-02 -4.20874089e-01 -2.42135927e-01 -4.14830208e-01
1.76509500e-01 1.10980523e+00 1.30834430e-02 -1.15846405... | [8.119330406188965, 6.74227237701416] |
f1a9a17f-9c88-44b5-8c1c-fdd72ae564d1 | delving-into-robust-object-detection-from | 1908.03856 | null | https://arxiv.org/abs/1908.03856v2 | https://arxiv.org/pdf/1908.03856v2.pdf | Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach | Object detection from images captured by Unmanned Aerial Vehicles (UAVs) is becoming increasingly useful. Despite the great success of the generic object detection methods trained on ground-to-ground images, a huge performance drop is observed when they are directly applied to images captured by UAVs. The unsatisfactor... | ['Zhen-Yu Wu', 'Heesung Kwon', 'Zhangyang Wang', 'Karthik Suresh', 'Hongyu Xu', 'Priya Narayanan'] | 2019-08-11 | delving-into-robust-object-detection-from-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Wu_Delving_Into_Robust_Object_Detection_From_Unmanned_Aerial_Vehicles_A_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wu_Delving_Into_Robust_Object_Detection_From_Unmanned_Aerial_Vehicles_A_ICCV_2019_paper.pdf | iccv-2019-10 | ['robust-object-detection'] | ['computer-vision'] | [ 3.06057632e-01 -5.67410767e-01 3.29369865e-02 1.61097690e-01
-5.11757851e-01 -1.13026571e+00 6.93225801e-01 -2.92183697e-01
-2.99890757e-01 6.47624254e-01 -4.43883717e-01 -1.69215217e-01
-2.41120979e-01 -7.87835658e-01 -7.42856383e-01 -8.58011961e-01
-4.54780310e-01 -1.41941980e-01 2.26291865e-01 -4.29620057... | [8.454263687133789, -1.1239458322525024] |
b19eb882-b92a-4d43-ba19-9e614bb0db65 | r2cnn-multi-dimensional-attention-based | 1811.07126 | null | https://arxiv.org/abs/1811.07126v4 | https://arxiv.org/pdf/1811.07126v4.pdf | SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects | Object detection has been a building block in computer vision. Though considerable progress has been made, there still exist challenges for objects with small size, arbitrary direction, and dense distribution. Apart from natural images, such issues are especially pronounced for aerial images of great importance. This p... | ['Kun fu', 'Tengfei Zhang', 'Zhi Guo', 'Yue Zhang', 'Sun Xian', 'Xue Yang', 'Junchi Yan', 'Jirui Yang'] | 2018-11-17 | scrdet-towards-more-robust-detection-for | http://openaccess.thecvf.com/content_ICCV_2019/html/Yang_SCRDet_Towards_More_Robust_Detection_for_Small_Cluttered_and_Rotated_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Yang_SCRDet_Towards_More_Robust_Detection_for_Small_Cluttered_and_Rotated_ICCV_2019_paper.pdf | iccv-2019-10 | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 2.90339768e-01 -2.92637736e-01 6.36234581e-02 -2.89153308e-01
-6.15974486e-01 -3.47213596e-01 3.23613495e-01 -2.06892282e-01
-4.27339226e-01 2.49025047e-01 -6.32602349e-02 -1.99437842e-01
-4.04166691e-02 -5.46850026e-01 -6.06580377e-01 -1.13409626e+00
-4.76347916e-02 -2.09576637e-01 3.15601051e-01 -1.29759640... | [8.858527183532715, -0.7903229594230652] |
6ef0fefb-9050-4e90-89d0-43f81e279ec4 | deep-structure-inference-network-for-facial | 1803.05873 | null | http://arxiv.org/abs/1803.05873v2 | http://arxiv.org/pdf/1803.05873v2.pdf | Deep Structure Inference Network for Facial Action Unit Recognition | Facial expressions are combinations of basic components called Action Units
(AU). Recognizing AUs is key for developing general facial expression analysis.
In recent years, most efforts in automatic AU recognition have been dedicated
to learning combinations of local features and to exploiting correlations
between Acti... | ['Meysam Madadi', 'Ciprian A. Corneanu', 'Sergio Escalera'] | 2018-03-15 | deep-structure-inference-network-for-facial-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Ciprian_Corneanu_Deep_Structure_Inference_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Ciprian_Corneanu_Deep_Structure_Inference_ECCV_2018_paper.pdf | eccv-2018-9 | ['facial-action-unit-detection'] | ['computer-vision'] | [ 3.38900149e-01 1.44771367e-01 -2.11234510e-01 -7.32710242e-01
-6.03968561e-01 -7.63227940e-02 6.86264813e-01 -7.12238103e-02
-5.06467760e-01 5.75390160e-01 -2.99050082e-02 1.27751678e-01
2.91041076e-01 -6.48516953e-01 -6.15467250e-01 -6.27381444e-01
-4.30804253e-01 1.43209904e-01 -1.19430505e-01 -2.80479670... | [13.589658737182617, 1.6919435262680054] |
e439561e-cdc6-4c8d-984f-2605fad0626b | mluke-the-power-of-entity-representations-in | 2110.08151 | null | https://arxiv.org/abs/2110.08151v3 | https://arxiv.org/pdf/2110.08151v3.pdf | mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models | Recent studies have shown that multilingual pretrained language models can be effectively improved with cross-lingual alignment information from Wikipedia entities. However, existing methods only exploit entity information in pretraining and do not explicitly use entities in downstream tasks. In this study, we explore ... | ['Yoshimasa Tsuruoka', 'Ikuya Yamada', 'Ryokan Ri'] | 2021-10-15 | null | https://aclanthology.org/2022.acl-long.505 | https://aclanthology.org/2022.acl-long.505.pdf | acl-2022-5 | ['cross-lingual-question-answering'] | ['natural-language-processing'] | [-5.03395319e-01 6.44496605e-02 -4.09761131e-01 -4.74287570e-01
-1.22282505e+00 -9.46618021e-01 5.83538353e-01 2.68251866e-01
-1.00908589e+00 7.86671877e-01 6.97467625e-01 -6.05213642e-01
3.47210914e-01 -7.59091616e-01 -1.05517578e+00 -3.33156213e-02
4.85275090e-02 3.99285734e-01 -2.71454006e-01 -4.54585850... | [10.701156616210938, 9.793149948120117] |
5fc0d95d-ff51-4e31-8e0b-9d3bf97506f9 | rovist-learning-robust-metrics-for-visual | null | null | https://openreview.net/forum?id=bwIOahh3kHO | https://openreview.net/pdf?id=bwIOahh3kHO | RoViST: Learning Robust Metrics for Visual Storytelling | Visual storytelling (VST) is the task of generating a story paragraph that describes a given image sequence. Most existing storytelling approaches have evaluated their models using traditional natural language generation metrics like BLEU or CIDEr. However, such metrics based on $n$-gram matching tend to have poor corr... | ['Anonymous'] | 2021-12-17 | null | null | null | acl-arr-december-2022-12 | ['visual-storytelling'] | ['natural-language-processing'] | [ 1.35055363e-01 3.48009735e-01 3.12672965e-02 -2.41016701e-01
-7.04607844e-01 -5.90175152e-01 1.32812715e+00 6.29716456e-01
-1.32993162e-01 8.32594752e-01 7.50765681e-01 -1.85130283e-01
-1.22323580e-01 -8.72700632e-01 -6.23740554e-01 -3.03236991e-01
1.87691540e-01 4.75694060e-01 4.81219888e-01 -2.88864732... | [11.765352249145508, 8.800399780273438] |
2c961f10-bd89-4118-ae66-e24aa14fdb8c | rethinking-text-attribute-transfer-a-lexical | 1909.12335 | null | https://arxiv.org/abs/1909.12335v1 | https://arxiv.org/pdf/1909.12335v1.pdf | Rethinking Text Attribute Transfer: A Lexical Analysis | Text attribute transfer is modifying certain linguistic attributes (e.g. sentiment, style, authorship, etc.) of a sentence and transforming them from one type to another. In this paper, we aim to analyze and interpret what is changed during the transfer process. We start from the observation that in many existing model... | ['Lei LI', 'Yao Fu', 'Hao Zhou', 'Jiaze Chen'] | 2019-09-26 | rethinking-text-attribute-transfer-a-lexical-1 | https://aclanthology.org/W19-8604 | https://aclanthology.org/W19-8604.pdf | ws-2019-10 | ['text-attribute-transfer', 'lexical-analysis'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.98687726e-01 1.60117764e-02 -3.60892743e-01 -7.78993368e-01
-3.39961052e-01 -8.60269010e-01 6.88430429e-01 6.25294387e-01
-4.61081833e-01 7.40107656e-01 5.34563065e-01 -3.14216197e-01
-2.48418637e-02 -8.61118376e-01 -7.50805914e-01 -4.78831679e-01
5.12764692e-01 3.13530862e-01 2.63771582e-02 -6.23557925... | [11.434992790222168, 9.350085258483887] |
53337736-a2eb-4562-aa66-7b8129dcb181 | afrisenti-a-twitter-sentiment-analysis | 2302.08956 | null | https://arxiv.org/abs/2302.08956v4 | https://arxiv.org/pdf/2302.08956v4.pdf | AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages | Africa is home to over 2000 languages from over six language families and has the highest linguistic diversity among all continents. This includes 75 languages with at least one million speakers each. Yet, there is little NLP research conducted on African languages. Crucial in enabling such research is the availability... | ['Davis David', 'Saif M. Mohammad', 'Steven Arthur', 'Bernard Opoku', 'Hagos Tesfahun Gebremichael', 'Sisay Adugna Chala', 'Hailu Beshada Balcha', 'Wendimu Baye Messelle', 'Tadesse Belay', 'Samuel Rutunda', 'Tajuddeen Gwadabe', 'Falalu Ibrahim', 'Bello Shehu Bello', 'Salomey Osei', 'Felermino Dário Mário António Ali', ... | 2023-02-17 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-4.69981849e-01 -1.61472186e-01 -2.72710264e-01 -6.27023995e-01
-6.42762840e-01 -8.96565735e-01 8.31052601e-01 2.93398410e-01
-6.03275716e-01 9.84712780e-01 3.94094348e-01 -4.97426808e-01
3.33490461e-01 -6.61055148e-01 -1.85949773e-01 -3.90538424e-01
-1.19313918e-01 8.82689238e-01 -3.82524908e-01 -9.27229285... | [11.176841735839844, 7.040548324584961] |
3c353805-8809-4c8e-ab37-f5f8a8382c37 | along-the-time-timeline-traced-embedding-for | null | null | https://dl.acm.org/doi/abs/10.1145/3511808.3557233 | https://dl.acm.org/doi/pdf/10.1145/3511808.3557233 | Along the Time: Timeline-traced Embedding for Temporal Knowledge Graph Completion | Recent years have witnessed remarkable progress on knowledge graph embedding (KGE) methods to learn the representations of entities and relations in static knowledge graphs (SKGs). However, knowledge changes over time. In order to represent the facts happening in a specific time, temporal knowledge graph (TKG) embeddin... | ['Qing He.', 'Yongjun Xu', 'Fuzhen Zhuang', 'Xiang Ao', 'Zhao Zhang', 'Fuwei Zhang'] | 2022-10-17 | null | null | null | the-31st-acm-international-conference-on | ['graph-embedding', 'link-prediction', 'knowledge-graph-embedding', 'knowledge-graph-completion', 'temporal-knowledge-graph-completion'] | ['graphs', 'graphs', 'graphs', 'knowledge-base', 'knowledge-base'] | [-5.81909239e-01 -1.34362087e-01 -4.82051551e-01 -2.61789560e-01
5.05420044e-02 -4.70116556e-01 6.34828091e-01 3.63801450e-01
-3.27591211e-01 4.35724944e-01 2.07598537e-01 -3.07073712e-01
-4.55822706e-01 -9.81726825e-01 -5.93214154e-01 -3.94915789e-01
-5.04051328e-01 2.15543821e-01 1.78514034e-01 -3.31076145... | [8.578661918640137, 7.897542476654053] |
7b10f00b-e72a-41ae-b883-65d659fd20cb | polyresponse-a-rank-based-approach-to-task | 1909.01296 | null | https://arxiv.org/abs/1909.01296v1 | https://arxiv.org/pdf/1909.01296v1.pdf | PolyResponse: A Rank-based Approach to Task-Oriented Dialogue with Application in Restaurant Search and Booking | We present PolyResponse, a conversational search engine that supports task-oriented dialogue. It is a retrieval-based approach that bypasses the complex multi-component design of traditional task-oriented dialogue systems and the use of explicit semantics in the form of task-specific ontologies. The PolyResponse engine... | ['Pei-Hao Su', 'Nikola Mrkšić', 'Tsung-Hsien Wen', 'Paweł Budzianowski', 'Iñigo Casanueva', 'Ivan Vulić', 'Matthew Henderson', 'Georgios Spithourakis', 'Daniela Gerz', 'Sam Coope'] | 2019-09-03 | polyresponse-a-rank-based-approach-to-task-1 | https://aclanthology.org/D19-3031 | https://aclanthology.org/D19-3031.pdf | ijcnlp-2019-11 | ['conversational-search'] | ['natural-language-processing'] | [-1.05752863e-01 4.25993294e-01 -1.73139483e-01 -4.55612510e-01
-9.78882253e-01 -9.08262491e-01 9.94897068e-01 3.58827263e-01
-6.55170262e-01 9.84984696e-01 1.01575565e+00 -2.89155632e-01
-4.25187260e-01 -6.28546536e-01 1.36180878e-01 2.80070510e-02
1.24320045e-01 1.25415182e+00 5.97010911e-01 -1.19365251... | [12.506185531616211, 7.876317024230957] |
2ba91e82-7d2b-4bf5-9933-7012de01fc76 | cc-3dt-panoramic-3d-object-tracking-via-cross | 2212.01247 | null | https://arxiv.org/abs/2212.01247v1 | https://arxiv.org/pdf/2212.01247v1.pdf | CC-3DT: Panoramic 3D Object Tracking via Cross-Camera Fusion | To track the 3D locations and trajectories of the other traffic participants at any given time, modern autonomous vehicles are equipped with multiple cameras that cover the vehicle's full surroundings. Yet, camera-based 3D object tracking methods prioritize optimizing the single-camera setup and resort to post-hoc fusi... | ['Fisher Yu', 'Min Sun', 'Suryansh Kumar', 'Yung-Hsu Yang', 'Tobias Fischer'] | 2022-12-02 | null | null | null | null | ['3d-object-tracking'] | ['computer-vision'] | [-3.69151115e-01 -5.87218404e-01 -2.71329373e-01 -3.55718993e-02
-7.29505181e-01 -1.03182471e+00 7.86369026e-01 -6.17234223e-02
-4.80344057e-01 9.34333280e-02 -2.86109924e-01 -2.03458637e-01
1.81777686e-01 -2.94248760e-01 -8.95569384e-01 -5.46831310e-01
1.66669562e-01 6.95567667e-01 1.08154488e+00 1.21774532... | [6.622315883636475, -2.1414432525634766] |
9c1588a7-655f-4ba0-973e-b169aa0a2075 | argument-mining-using-bert-and-self-attention | 2302.13906 | null | https://arxiv.org/abs/2302.13906v1 | https://arxiv.org/pdf/2302.13906v1.pdf | Argument Mining using BERT and Self-Attention based Embeddings | Argument mining automatically identifies and extracts the structure of inference and reasoning conveyed in natural language arguments. To the best of our knowledge, most of the state-of-the-art works in this field have focused on using tree-like structures and linguistic modeling. But, these approaches are not able to ... | ['Anurag Goel', 'Pranav Bhatnagar', 'Pranjal Srivastava'] | 2023-02-27 | null | null | null | null | ['argument-mining'] | ['natural-language-processing'] | [-1.50436386e-02 1.02998638e+00 -5.76359570e-01 -1.38456732e-01
-3.96576747e-02 -5.62431872e-01 1.06208730e+00 1.16968453e+00
-1.72903568e-01 8.50681067e-01 8.97913992e-01 -1.13036394e+00
-4.33385521e-01 -1.05085576e+00 -5.40721714e-01 7.44890422e-02
-1.35323480e-01 4.84290212e-01 4.25980121e-01 -7.36306667... | [9.544236183166504, 9.600581169128418] |
eda2268c-21b0-4a90-b97c-c6808c9edc6e | analyzing-the-use-of-influence-functions-for | 2210.13281 | null | https://arxiv.org/abs/2210.13281v1 | https://arxiv.org/pdf/2210.13281v1.pdf | Analyzing the Use of Influence Functions for Instance-Specific Data Filtering in Neural Machine Translation | Customer feedback can be an important signal for improving commercial machine translation systems. One solution for fixing specific translation errors is to remove the related erroneous training instances followed by re-training of the machine translation system, which we refer to as instance-specific data filtering. I... | ['Felix Hieber', 'Eva Hasler', 'Tsz Kin Lam'] | 2022-10-24 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 8.82022858e-01 2.35371351e-01 -5.45063257e-01 -6.33394122e-01
-1.17664850e+00 -5.20008504e-01 6.81777418e-01 6.76075146e-02
-3.69938463e-01 1.00483477e+00 1.33112416e-01 -6.78896070e-01
-5.78174368e-02 -3.60158354e-01 -1.19092119e+00 -4.89599884e-01
4.07693893e-01 7.30622292e-01 -1.71711549e-01 -3.78039241... | [11.620944023132324, 10.091196060180664] |
cf9d4bae-cd52-464f-bdee-927150e3d7fa | improving-formality-style-transfer-with | 2106.00210 | null | https://arxiv.org/abs/2106.00210v1 | https://arxiv.org/pdf/2106.00210v1.pdf | Improving Formality Style Transfer with Context-Aware Rule Injection | Models pre-trained on large-scale regular text corpora often do not work well for user-generated data where the language styles differ significantly from the mainstream text. Here we present Context-Aware Rule Injection (CARI), an innovative method for formality style transfer (FST). CARI injects multiple rules into an... | ['Hong Yu', 'Zonghai Yao'] | 2021-06-01 | null | https://aclanthology.org/2021.acl-long.124 | https://aclanthology.org/2021.acl-long.124.pdf | acl-2021-5 | ['formality-style-transfer'] | ['natural-language-processing'] | [ 4.20230299e-01 2.97409594e-01 -4.35239375e-01 -6.35591745e-01
-7.68696666e-01 -6.24369025e-01 7.99948096e-01 -1.66171402e-01
-5.74355185e-01 8.53370011e-01 3.35282177e-01 -5.59023499e-01
4.43464696e-01 -5.88469267e-01 -8.99786294e-01 1.54776469e-01
1.55942202e-01 5.26521385e-01 2.61101902e-01 -5.73531866... | [11.370218276977539, 9.354607582092285] |
96db94f0-df8a-496e-a98e-149b249ae18b | melanogans-high-resolution-skin-lesion | 1804.04338 | null | http://arxiv.org/abs/1804.04338v1 | http://arxiv.org/pdf/1804.04338v1.pdf | MelanoGANs: High Resolution Skin Lesion Synthesis with GANs | Generative Adversarial Networks (GANs) have been successfully used to
synthesize realistically looking images of faces, scenery and even medical
images. Unfortunately, they usually require large training datasets, which are
often scarce in the medical field, and to the best of our knowledge GANs have
been only applied ... | ['Shadi Albarqouni', 'Nassir Navab', 'Christoph Baur'] | 2018-04-12 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 6.22446179e-01 4.30529267e-01 2.33372554e-01 -2.85094995e-02
-9.20638859e-01 -2.64573634e-01 7.16325760e-01 -3.92189652e-01
-2.11764991e-01 1.05723572e+00 8.91806930e-03 -3.27390470e-02
3.48737277e-03 -1.02139521e+00 -6.06995046e-01 -8.89395297e-01
2.63958156e-01 4.86240387e-01 6.47305474e-02 -3.54495764... | [14.102913856506348, -1.8880970478057861] |
16f163cd-5ac0-435e-82d8-bc845a3074cb | bias-in-pruned-vision-models-in-depth | 2304.12622 | null | https://arxiv.org/abs/2304.12622v1 | https://arxiv.org/pdf/2304.12622v1.pdf | Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures | Pruning - that is, setting a significant subset of the parameters of a neural network to zero - is one of the most popular methods of model compression. Yet, several recent works have raised the issue that pruning may induce or exacerbate bias in the output of the compressed model. Despite existing evidence for this ph... | ['Dan Alistarh', 'Alexandra Peste', 'Eugenia Iofinova'] | 2023-04-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Iofinova_Bias_in_Pruned_Vision_Models_In-Depth_Analysis_and_Countermeasures_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Iofinova_Bias_in_Pruned_Vision_Models_In-Depth_Analysis_and_Countermeasures_CVPR_2023_paper.pdf | cvpr-2023-1 | ['model-compression'] | ['methodology'] | [ 6.77335560e-01 3.92215967e-01 -3.22710425e-01 -4.73704457e-01
-3.03615838e-01 -2.14676559e-01 3.81665200e-01 2.78698713e-01
-6.20047748e-01 7.48810887e-01 1.10901810e-01 -1.83271155e-01
-1.98048949e-01 -8.24120760e-01 -1.08063877e+00 -7.70802081e-01
5.07177375e-02 3.56360704e-01 1.91056952e-01 5.93998022... | [8.55506706237793, 3.390861988067627] |
88129cdc-b1ea-4dad-b507-0ea87d5e2c58 | on-the-role-of-parallel-data-in-cross-lingual | 2212.10173 | null | https://arxiv.org/abs/2212.10173v1 | https://arxiv.org/pdf/2212.10173v1.pdf | On the Role of Parallel Data in Cross-lingual Transfer Learning | While prior work has established that the use of parallel data is conducive for cross-lingual learning, it is unclear if the improvements come from the data itself, or if it is the modeling of parallel interactions that matters. Exploring this, we examine the usage of unsupervised machine translation to generate synthe... | ['Mikel Artetxe', 'Machel Reid'] | 2022-12-20 | null | null | null | null | ['unsupervised-machine-translation', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.87594742e-01 7.06841098e-03 -4.06881809e-01 -3.71196687e-01
-1.16276717e+00 -9.44742143e-01 9.72150505e-01 5.44172861e-02
-6.05199933e-01 8.98929596e-01 6.17432833e-01 -1.00708663e+00
2.31600970e-01 -4.38965321e-01 -9.48700666e-01 -3.23569328e-01
3.13986808e-01 9.42653179e-01 -1.60678819e-01 -6.85971618... | [11.326520919799805, 10.20345687866211] |
7b2c78ba-8590-442a-80c2-10ad631345af | locality-preserving-sentence-encoding | null | null | https://aclanthology.org/2021.findings-emnlp.262 | https://aclanthology.org/2021.findings-emnlp.262.pdf | Locality Preserving Sentence Encoding | Although researches on word embeddings have made great progress in recent years, many tasks in natural language processing are on the sentence level. Thus, it is essential to learn sentence embeddings. Recently, Sentence BERT (SBERT) is proposed to learn embeddings on the sentence level, and it uses the inner product (... | ['Hongfei Lin', 'Bo Xu', 'Liang Yang', 'Yonghe Chu', 'Changrong Min'] | null | null | null | null | findings-emnlp-2021-11 | ['sentence-classification'] | ['natural-language-processing'] | [-2.21668798e-02 -2.11090237e-01 1.15875922e-01 -5.48170984e-01
-2.99697489e-01 -2.98459768e-01 5.74008346e-01 6.81852937e-01
-4.09434259e-01 2.44471934e-02 8.21080089e-01 -3.78316231e-02
-2.85946310e-01 -8.70301843e-01 -1.76865667e-01 -6.36992157e-01
2.81708956e-01 3.91599759e-02 2.07926422e-01 -4.05786693... | [10.682762145996094, 8.698083877563477] |
0b3d0f4f-b85f-4ddd-a311-9babe58bb55c | polar-ducks-and-where-to-find-them-enhancing | 2305.12027 | null | https://arxiv.org/abs/2305.12027v1 | https://arxiv.org/pdf/2305.12027v1.pdf | Polar Ducks and Where to Find Them: Enhancing Entity Linking with Duck Typing and Polar Box Embeddings | Entity linking methods based on dense retrieval are an efficient and widely used solution in large-scale applications, but they fall short of the performance of generative models, as they are sensitive to the structure of the embedding space. In order to address this issue, this paper introduces DUCK, an approach to in... | ['Nicola Cancedda', 'Louis Martin', 'Simone Merello', 'Nora Kassner', 'Frédéric A. Dreyer', 'Mikhail Plekhanov', 'Mattia Atzeni'] | 2023-05-19 | null | null | null | null | ['entity-linking', 'entity-disambiguation'] | ['natural-language-processing', 'natural-language-processing'] | [-3.74948382e-01 2.86391139e-01 -3.14812928e-01 -3.01070988e-01
-2.04625890e-01 -9.11285937e-01 7.42384017e-01 5.89565158e-01
-5.05331457e-01 5.42107284e-01 3.09633493e-01 -3.46125029e-02
-4.50128764e-01 -1.40640700e+00 -8.26624393e-01 -5.41312397e-01
-1.89646676e-01 1.17589748e+00 3.90490144e-01 -4.47073549... | [9.160633087158203, 8.163503646850586] |
ae6a2521-706b-4cbc-a4f9-b491758503c4 | a-joint-3d-unet-graph-neural-network-based | 1908.08588 | null | https://arxiv.org/abs/1908.08588v1 | https://arxiv.org/pdf/1908.08588v1.pdf | A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs | We present an end-to-end deep learning segmentation method by combining a 3D UNet architecture with a graph neural network (GNN) model. In this approach, the convolutional layers at the deepest level of the UNet are replaced by a GNN-based module with a series of graph convolutions. The dense feature maps at this level... | ['Marleen de Bruijne', 'Antonio Garcia-Uceda Juarez', 'Zaigham Saghir', 'Raghavendra Selvan'] | 2019-08-22 | null | null | null | null | ['3d-medical-imaging-segmentation'] | ['medical'] | [ 3.58109206e-01 7.67589927e-01 6.72381744e-02 -4.34577733e-01
-1.22034624e-01 -4.21898663e-01 2.31192186e-01 5.15327275e-01
-5.98567486e-01 2.09193960e-01 9.34729687e-05 -7.43517458e-01
-1.81179449e-01 -1.38070297e+00 -7.54003346e-01 -5.53969324e-01
-2.63780266e-01 7.68976986e-01 7.53722727e-01 -2.46062085... | [7.070760250091553, 5.998892784118652] |
ab5067f9-e408-4646-bb11-7dbe3b211f58 | lensless-hyperspectral-imaging-by-fourier | 2002.10886 | null | http://arxiv.org/abs/2002.10886v2 | http://arxiv.org/pdf/2002.10886v2.pdf | Lensless hyperspectral imaging by Fourier transform spectroscopy for broadband visible light: phase retrieval technique | A novel phase retrieval algorithm for broadband hyperspectral phase imaging
from noisy intensity observations is proposed. It utilizes advantages of the
Fourier Transform spectroscopy in the self-referencing optical setup and
provides, additionally beyond spectral intensity distribution, reconstruction
of the investiga... | [] | 2020-03-16 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 8.33750129e-01 -2.11665481e-01 4.57343280e-01 -3.43137234e-01
-4.20770347e-01 -2.25703061e-01 1.20108336e-01 -4.37265366e-01
-6.17667854e-01 8.75848472e-01 -3.35451961e-01 1.42181635e-01
-9.61354792e-01 -6.62068784e-01 -7.34328628e-02 -1.33879101e+00
8.98364708e-02 5.01551628e-01 -1.37902960e-01 -1.31951738... | [10.957226753234863, -2.5223119258880615] |
065b07d5-21dd-4eea-9383-348a8788ac77 | sparsely-annotated-object-detection-a-region | 2201.04620 | null | https://arxiv.org/abs/2201.04620v1 | https://arxiv.org/pdf/2201.04620v1.pdf | Sparsely Annotated Object Detection: A Region-based Semi-supervised Approach | Research shows a noticeable drop in performance of object detectors when the training data has missing annotations, i.e. sparsely annotated data. Contemporary methods focus on proxies for missing ground-truth annotations either in the form of pseudo-labels or by re-weighing gradients for unlabeled boxes during training... | ['Abhinav Shrivastava', 'Rama Chellappa', 'Saksham Suri', 'Sai Saketh Rambhatla'] | 2022-01-12 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 3.76340091e-01 2.34795153e-01 -3.14327866e-01 -5.11825085e-01
-9.27752197e-01 -6.74220622e-01 5.78473687e-01 3.82867716e-02
-3.61082971e-01 6.54586613e-01 7.83223659e-02 6.71670958e-02
4.00576353e-01 -2.38545418e-01 -8.15008938e-01 -6.76815093e-01
6.92160577e-02 5.75506687e-01 8.84760737e-01 2.63272315... | [9.280949592590332, 1.0392982959747314] |
2eeda650-5814-4783-aef9-7d5c7a5a673b | weakly-supervised-monocular-3d-object | 2303.08686 | null | https://arxiv.org/abs/2303.08686v1 | https://arxiv.org/pdf/2303.08686v1.pdf | Weakly Supervised Monocular 3D Object Detection using Multi-View Projection and Direction Consistency | Monocular 3D object detection has become a mainstream approach in automatic driving for its easy application. A prominent advantage is that it does not need LiDAR point clouds during the inference. However, most current methods still rely on 3D point cloud data for labeling the ground truths used in the training phase.... | ['Jianbing Shen', 'Cheng-Zhong Xu', 'Zhongying Qiu', 'Wencheng Han', 'Runzhou Tao'] | 2023-03-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tao_Weakly_Supervised_Monocular_3D_Object_Detection_Using_Multi-View_Projection_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tao_Weakly_Supervised_Monocular_3D_Object_Detection_Using_Multi-View_Projection_and_CVPR_2023_paper.pdf | cvpr-2023-1 | ['monocular-3d-object-detection'] | ['computer-vision'] | [-3.51918727e-01 -1.58346623e-01 -7.02573180e-01 -6.31078541e-01
-5.72672844e-01 -6.38415873e-01 5.82021594e-01 -3.12780440e-01
-2.15767324e-01 3.57920229e-01 -8.41692239e-02 -6.35759115e-01
3.64499986e-01 -6.29376411e-01 -9.60244775e-01 -4.93980557e-01
7.49681294e-01 5.52616656e-01 6.25114858e-01 -1.15300409... | [7.938394069671631, -2.670121669769287] |
e93972f1-ecb7-4947-b843-926795d8cbb8 | optimal-vaccination-policy-to-prevent | 2306.13633 | null | https://arxiv.org/abs/2306.13633v1 | https://arxiv.org/pdf/2306.13633v1.pdf | Optimal Vaccination Policy to Prevent Endemicity: A Stochastic Model | We examine here the effects of recurrent vaccination and waning immunity on the establishment of an endemic equilibrium in a population. An individual-based model that incorporates memory effects for transmission rate during infection and subsequent immunity is introduced, considering stochasticity at the individual le... | ['Hélène Guérin', 'Arthur Charpentier', 'Félix Foutel-Rodier'] | 2023-06-23 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [ 1.20595127e-01 2.17136764e-03 -3.69401485e-01 2.05062166e-01
2.02004790e-01 -4.62120473e-01 3.96605313e-01 7.51797080e-01
-6.28679633e-01 6.93024158e-01 1.90879181e-01 -6.26501143e-01
-5.73056757e-01 -1.04819250e+00 -7.41019905e-01 -9.52735126e-01
-5.19819498e-01 5.20111799e-01 2.68425465e-01 -5.76255858... | [5.928583145141602, 4.383437156677246] |
64da1ad2-3850-4543-9a7b-44f794fa2a63 | physically-explainable-cnn-for-sar-image | 2110.14144 | null | https://arxiv.org/abs/2110.14144v2 | https://arxiv.org/pdf/2110.14144v2.pdf | Physically Explainable CNN for SAR Image Classification | Integrating the special electromagnetic characteristics of Synthetic Aperture Radar (SAR) in deep neural networks is essential in order to enhance the explainability and physics awareness of deep learning. In this paper, we first propose a novel physically explainable convolutional neural network for SAR image classifi... | ['Corneliu Octavian Dumitru', 'Ying Liu', 'Junwei Han', 'Mihai Datcu', 'Xiwen Yao', 'Zhongling Huang'] | 2021-10-27 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 2.44614705e-01 5.13015032e-01 8.54648724e-02 -9.01628435e-01
-4.76236731e-01 -2.38505125e-01 8.57056856e-01 1.11429393e-01
2.92210400e-01 8.97259712e-01 2.83216745e-01 -6.15179598e-01
-7.43635535e-01 -9.82299387e-01 -1.05066931e+00 -6.85444355e-01
-2.85500348e-01 5.32888114e-01 -5.39030768e-02 -2.93332189... | [9.682846069335938, -1.391238808631897] |
7c6ad6d1-0be5-4f53-8d7a-6864cc4ef0e1 | rolling-unrolling-lstms-for-action | 2005.02190 | null | https://arxiv.org/abs/2005.02190v2 | https://arxiv.org/pdf/2005.02190v2.pdf | Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video | In this paper, we tackle the problem of egocentric action anticipation, i.e., predicting what actions the camera wearer will perform in the near future and which objects they will interact with. Specifically, we contribute Rolling-Unrolling LSTM, a learning architecture to anticipate actions from egocentric videos. The... | ['Giovanni Maria Farinella', 'Antonino Furnari'] | 2020-05-04 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 3.64385188e-01 1.53126568e-01 -1.53064281e-01 -3.82334113e-01
-2.78416127e-01 5.74954860e-02 7.02600181e-01 -5.47330439e-01
-3.53355706e-01 5.19417346e-01 7.29175687e-01 4.89110313e-02
7.34314546e-02 -2.21741036e-01 -1.01228642e+00 -7.01763988e-01
-2.32960522e-01 1.74238801e-01 3.21976058e-02 1.06096052... | [8.24116039276123, 0.4687145948410034] |
8470e817-0764-4b8c-ae3a-de5f4b523287 | from-competition-to-collaboration-making-toy | 2211.06212 | null | https://arxiv.org/abs/2211.06212v1 | https://arxiv.org/pdf/2211.06212v1.pdf | From Competition to Collaboration: Making Toy Datasets on Kaggle Clinically Useful for Chest X-Ray Diagnosis Using Federated Learning | Chest X-ray (CXR) datasets hosted on Kaggle, though useful from a data science competition standpoint, have limited utility in clinical use because of their narrow focus on diagnosing one specific disease. In real-world clinical use, multiple diseases need to be considered since they can co-exist in the same patient. I... | ['Vishwa S. Parekh', 'Paul H. Yi', 'Adway Kanhere', 'Pranav Kulkarni'] | 2022-11-11 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [-5.75329969e-03 5.44739738e-02 -7.71058574e-02 -3.29687059e-01
-1.02897716e+00 -5.08787513e-01 2.25455180e-01 5.48579693e-01
-4.25062478e-01 7.27484822e-01 1.34935334e-01 -7.65650749e-01
-2.74319202e-01 -7.56929934e-01 -5.11425555e-01 -7.34851003e-01
-1.87540680e-01 6.87542796e-01 7.42259771e-02 2.90517271... | [15.235568046569824, -1.9749969244003296] |
78ea0c60-de56-4f8d-92fc-0221a633f62e | state-of-the-art-of-user-simulation | 2201.03435 | null | https://arxiv.org/abs/2201.03435v1 | https://arxiv.org/pdf/2201.03435v1.pdf | State of the Art of User Simulation approaches for conversational information retrieval | Conversational Information Retrieval (CIR) is an emerging field of Information Retrieval (IR) at the intersection of interactive IR and dialogue systems for open domain information needs. In order to optimize these interactions and enhance the user experience, it is necessary to improve IR models by taking into account... | ['Ludovic Denoyer', 'Laure Soulier', 'Pierre Erbacher'] | 2022-01-10 | null | null | null | null | ['user-simulation'] | ['natural-language-processing'] | [ 8.16427171e-02 4.82004315e-01 -1.91920117e-01 -1.91813424e-01
-8.89149964e-01 -7.27636397e-01 6.62917256e-01 2.68866211e-01
-7.01269031e-01 5.24838448e-01 4.27933514e-01 -6.61125958e-01
-4.12612140e-01 -2.74930477e-01 -7.43987458e-03 -1.00785650e-01
1.02167893e-02 8.94880235e-01 -5.31752296e-02 -8.92512798... | [12.321198463439941, 7.656312465667725] |
36c55e2d-98ba-4663-b085-cb9a0e4f138c | camouflaged-object-detection-with-feature-1 | 2307.03943 | null | https://arxiv.org/abs/2307.03943v1 | https://arxiv.org/pdf/2307.03943v1.pdf | Camouflaged Object Detection with Feature Grafting and Distractor Aware | The task of Camouflaged Object Detection (COD) aims to accurately segment camouflaged objects that integrated into the environment, which is more challenging than ordinary detection as the texture between the target and background is visually indistinguishable. In this paper, we proposed a novel Feature Grafting and Di... | ['Lin Qi', 'Xinyue Li', 'Yuxuan Song'] | 2023-07-08 | null | null | null | null | ['object-detection'] | ['computer-vision'] | [ 7.96175972e-02 -3.11716676e-01 7.11865872e-02 2.84276102e-02
-4.89221364e-01 -5.49357235e-01 5.15172541e-01 -4.61136401e-01
-1.77461296e-01 5.26832581e-01 -4.10449877e-03 -1.64083481e-01
3.68644476e-01 -5.39575934e-01 -8.70127678e-01 -7.25304425e-01
2.58447498e-01 -7.13972077e-02 7.34633863e-01 -3.91301811... | [9.713326454162598, -0.15557964146137238] |
cfac9c9e-9202-4de2-b22e-21453216c570 | a-sequence-to-set-network-for-nested-named | 2105.08901 | null | https://arxiv.org/abs/2105.08901v2 | https://arxiv.org/pdf/2105.08901v2.pdf | A Sequence-to-Set Network for Nested Named Entity Recognition | Named entity recognition (NER) is a widely studied task in natural language processing. Recently, a growing number of studies have focused on the nested NER. The span-based methods, considering the entity recognition as a span classification task, can deal with nested entities naturally. But they suffer from the huge s... | ['Yueting Zhuang', 'Weiming Lu', 'Shuai Zhang', 'Yongliang Shen', 'Zeqi Tan'] | 2021-05-19 | null | null | null | null | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-6.68154210e-02 -1.31317645e-01 -4.55519138e-03 -4.74609256e-01
-8.48328412e-01 -7.04899371e-01 2.11005628e-01 3.77463102e-01
-8.34536612e-01 7.83526421e-01 2.96974152e-01 -2.15287268e-01
-7.47501478e-02 -8.68826807e-01 -8.41667235e-01 -4.28228766e-01
-4.36020829e-02 5.16263485e-01 1.43749118e-01 -8.10313970... | [9.559680938720703, 9.40312385559082] |
302932ad-18d1-497b-8f1c-7fac3e88d95e | double-retrieval-and-ranking-for-accurate | 2201.05981 | null | https://arxiv.org/abs/2201.05981v1 | https://arxiv.org/pdf/2201.05981v1.pdf | Double Retrieval and Ranking for Accurate Question Answering | Recent work has shown that an answer verification step introduced in Transformer-based answer selection models can significantly improve the state of the art in Question Answering. This step is performed by aggregating the embeddings of top $k$ answer candidates to support the verification of a target answer. Although ... | ['Alessandro Moschitti', 'Thuy Vu', 'Zeyu Zhang'] | 2022-01-16 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [ 1.79152563e-01 2.22747609e-01 -2.49138884e-02 -2.25863248e-01
-1.20611954e+00 -7.02981532e-01 7.09828138e-01 7.33821690e-01
-6.32329166e-01 5.91375113e-01 4.92188960e-01 -3.06211978e-01
-5.83421052e-01 -8.30377579e-01 -4.46685940e-01 -2.49768376e-01
4.12761569e-01 9.53428805e-01 8.58323812e-01 -6.16249561... | [11.309521675109863, 8.019085884094238] |
a9b87f4a-11f3-4a23-ae16-14dc662abc1d | eiffel-tower-a-deep-sea-underwater-dataset | 2305.05301 | null | https://arxiv.org/abs/2305.05301v1 | https://arxiv.org/pdf/2305.05301v1.pdf | Eiffel Tower: A Deep-Sea Underwater Dataset for Long-Term Visual Localization | Visual localization plays an important role in the positioning and navigation of robotics systems within previously visited environments. When visits occur over long periods of time, changes in the environment related to seasons or day-night cycles present a major challenge. Under water, the sources of variability are ... | ['Vincent Hugel', 'Loïc Van Audenhaege', 'Marjolaine Matabos', 'Ricard Marxer', 'Aurélien Arnaubec', 'Maxime Ferrera', 'Claire Dune', 'Clémentin Boittiaux'] | 2023-05-09 | null | null | null | null | ['visual-localization'] | ['computer-vision'] | [-3.81268144e-01 -3.48390162e-01 4.83954012e-01 -3.86201382e-01
-5.06748497e-01 -1.10723341e+00 5.90280950e-01 3.47708017e-01
-9.69376445e-01 7.19679832e-01 2.86542714e-01 2.23826356e-02
-1.62627593e-01 -5.79823792e-01 -8.66615355e-01 -7.74374485e-01
-3.64046365e-01 3.15887719e-01 5.29568017e-01 -5.89149535... | [7.641105651855469, -1.7525416612625122] |
dfe06575-018b-4b90-9ef1-11bc495a4f71 | vector-space-and-matrix-methods-in-signal-and | 1909.05128 | null | http://arxiv.org/abs/1909.05128v1 | http://arxiv.org/pdf/1909.05128v1.pdf | Vector Space and Matrix Methods in Signal and System Theory | The tools, ideas, and insights from linear algebra, abstract algebra, and
functional analysis can be extremely useful to signal processing and system
theory in various areas of engineering, science, and social science including
approximation, optimization, parameter identification, big data, etc. Indeed,
many important... | [] | 2019-09-11 | null | null | null | null | ['abstract-algebra'] | ['reasoning'] | [-6.34012744e-02 -3.63780826e-01 -6.43018633e-02 -1.42548224e-02
2.43799552e-01 -3.67932528e-01 1.22813433e-01 2.03031570e-01
-8.89821872e-02 5.10878325e-01 1.74415812e-01 -3.94895554e-01
-5.16734302e-01 -5.53231597e-01 -3.11113685e-01 -8.13515782e-01
-4.07949448e-01 -1.88603371e-01 -2.02753991e-01 -7.39181459... | [7.26041841506958, 4.269649982452393] |
a8a32c59-0c94-48a9-b245-e3eb98dbb408 | enhancing-document-level-relation-extraction | 2207.11433 | null | https://arxiv.org/abs/2207.11433v1 | https://arxiv.org/pdf/2207.11433v1.pdf | Enhancing Document-level Relation Extraction by Entity Knowledge Injection | Document-level relation extraction (RE) aims to identify the relations between entities throughout an entire document. It needs complex reasoning skills to synthesize various knowledge such as coreferences and commonsense. Large-scale knowledge graphs (KGs) contain a wealth of real-world facts, and can provide valuable... | ['Wei Hu', 'Weijian Sun', 'Zitao Wang', 'Xinyi Wang'] | 2022-07-23 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 1.15595227e-02 8.79520476e-01 -9.42295969e-01 -9.48147327e-02
-4.14058000e-01 -7.78506517e-01 1.03060889e+00 5.13570547e-01
1.09369457e-01 1.15646863e+00 6.96440816e-01 -3.39708090e-01
-6.35393023e-01 -1.31553769e+00 -5.67672431e-01 -8.63998681e-02
-2.89056040e-02 6.81289554e-01 4.09135550e-01 -6.10916793... | [9.193384170532227, 8.273058891296387] |
703bd989-bcd5-4bea-8ffe-9ab094bdc300 | ast-audio-spectrogram-transformer | 2104.01778 | null | https://arxiv.org/abs/2104.01778v3 | https://arxiv.org/pdf/2104.01778v3.pdf | AST: Audio Spectrogram Transformer | In the past decade, convolutional neural networks (CNNs) have been widely adopted as the main building block for end-to-end audio classification models, which aim to learn a direct mapping from audio spectrograms to corresponding labels. To better capture long-range global context, a recent trend is to add a self-atten... | ['James Glass', 'Yu-An Chung', 'Yuan Gong'] | 2021-04-05 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 1.11060448e-01 -2.56446242e-01 5.96553646e-02 -4.37396228e-01
-8.59274209e-01 -4.84490931e-01 3.46247584e-01 2.54727364e-01
-3.47495079e-01 3.41371685e-01 4.17361498e-01 -1.73970908e-01
1.56266943e-01 -5.77812672e-01 -6.81370437e-01 -3.02910775e-01
-6.88788593e-02 4.72938307e-02 2.30269909e-01 -1.90117300... | [15.215167045593262, 5.230347633361816] |
09bd61ae-56e7-4be3-9c5d-e10c56009731 | graph-generation-with-destination-driven | 2302.03596 | null | https://arxiv.org/abs/2302.03596v2 | https://arxiv.org/pdf/2302.03596v2.pdf | Graph Generation with Destination-Predicting Diffusion Mixture | Generation of graphs is a major challenge for real-world tasks that require understanding the complex nature of their non-Euclidean structures. Although diffusion models have achieved notable success in graph generation recently, they are ill-suited for modeling the structural information of graphs since learning to de... | ['Sung Ju Hwang', 'DongKi Kim', 'Jaehyeong Jo'] | 2023-02-07 | null | null | null | null | ['3d-molecule-generation'] | ['medical'] | [ 3.37152570e-01 4.60686594e-01 -5.85097373e-02 -2.93727797e-02
-3.86668324e-01 -7.36662805e-01 1.02189934e+00 3.59638304e-01
7.97920488e-03 8.09233367e-01 2.44396061e-01 -3.86280745e-01
-2.30405435e-01 -1.18453038e+00 -8.90431106e-01 -1.00391722e+00
-1.72779575e-01 1.06079257e+00 -1.39575154e-01 -2.47518182... | [5.257402420043945, 5.686735153198242] |
746a2e56-397f-4713-b722-acfd3ef109fd | cmd-self-supervised-3d-action-representation | 2208.12448 | null | https://arxiv.org/abs/2208.12448v3 | https://arxiv.org/pdf/2208.12448v3.pdf | CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual Distillation | In 3D action recognition, there exists rich complementary information between skeleton modalities. Nevertheless, how to model and utilize this information remains a challenging problem for self-supervised 3D action representation learning. In this work, we formulate the cross-modal interaction as a bidirectional knowle... | ['Houqiang Li', 'Jiajun Deng', 'Zhenbo Lu', 'Wengang Zhou', 'Yunyao Mao'] | 2022-08-26 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 1.69003904e-01 2.76314318e-01 -6.33273304e-01 -2.82982767e-01
-4.75507259e-01 -2.93304592e-01 7.55660355e-01 -1.34435534e-01
-3.33728969e-01 8.06577206e-01 3.55982244e-01 -1.62291244e-01
-2.44949773e-01 -7.24899709e-01 -6.72422945e-01 -9.68734860e-01
2.17414588e-01 4.86917913e-01 3.36787909e-01 -1.92679137... | [8.528983116149902, 0.7721011638641357] |
2014ac80-4c89-4c6a-91b0-31b7b6b8e061 | spatiotemporal-enhanced-network-for-click | 2209.09427 | null | https://arxiv.org/abs/2209.09427v1 | https://arxiv.org/pdf/2209.09427v1.pdf | Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location-based Services | In Location-Based Services(LBS), user behavior naturally has a strong dependence on the spatiotemporal information, i.e., in different geographical locations and at different times, user click behavior will change significantly. Appropriate spatiotemporal enhancement modeling of user click behavior and large-scale spar... | ['Ning Hu', 'Guodong Cao', 'Jia Jia', 'Zisen Sang', 'Hengxu He', 'Taotao Zhou', 'Xiyu Ji', 'Yicong Yu', 'Shaochuan Lin'] | 2022-09-20 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-7.97829553e-02 -7.81920075e-01 -5.80232024e-01 -5.83086371e-01
-4.24366564e-01 -3.15946281e-01 3.01491469e-01 9.05915257e-03
-4.53794338e-02 4.70641762e-01 3.78143191e-01 -4.07251656e-01
-6.28556073e-01 -7.09160924e-01 -5.72044611e-01 -4.33608592e-01
-1.50059938e-01 6.67929463e-03 5.27763009e-01 -3.16628963... | [10.128922462463379, 5.543710708618164] |
60479713-1ddf-47a5-9c9d-89de87b8465b | parallel-reasoning-network-for-human-object | 2301.03510 | null | https://arxiv.org/abs/2301.03510v1 | https://arxiv.org/pdf/2301.03510v1.pdf | Parallel Reasoning Network for Human-Object Interaction Detection | Human-Object Interaction (HOI) detection aims to learn how human interacts with surrounding objects. Previous HOI detection frameworks simultaneously detect human, objects and their corresponding interactions by using a predictor. Using only one shared predictor cannot differentiate the attentive field of instance-leve... | ['Changxin Gao', 'Nong Sang', 'Jing Shao', 'Bin Huang', 'Yangguang Li', 'Fenggang Liu', 'Huan Peng'] | 2023-01-09 | null | null | null | null | ['human-object-interaction-detection'] | ['computer-vision'] | [-1.68942120e-02 3.43828231e-01 -2.58553118e-01 -1.33160979e-01
-6.16717935e-02 3.47964764e-02 7.07092941e-01 -6.07523555e-03
-5.78727946e-02 3.47347826e-01 2.18569860e-01 5.82346506e-02
-2.72386491e-01 -9.80446577e-01 -6.49437606e-01 -3.81365120e-01
-2.39690050e-01 7.33162165e-01 8.17589283e-01 -6.67277053... | [9.591537475585938, 1.3660993576049805] |
ed2698f9-a194-4819-b5a4-4b35b7fae021 | discovering-causal-relations-and-equations | 2305.13341 | null | https://arxiv.org/abs/2305.13341v1 | https://arxiv.org/pdf/2305.13341v1.pdf | Discovering Causal Relations and Equations from Data | Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the phenomena. Discovering equations, laws and principles that are invariant, robust and causal explanations of the world has been fundamental in... | ['Jakob Runge', 'Laure Zanna', 'Emiliano Diaz', 'Ricardo Vinuesa', 'Emili Balaguer-Ballester', 'Georg Martius', 'Gherardo Varando', 'Urmi Ninad', 'Andreas Gerhardus', 'Gustau Camps-Valls'] | 2023-05-21 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [ 9.29092690e-02 -1.90173060e-01 -4.50134248e-01 -1.25508755e-01
8.50278556e-01 -5.22525549e-01 9.99718249e-01 2.23473817e-01
2.08623022e-01 1.00063503e+00 2.69885957e-01 -5.38630486e-01
-7.70564198e-01 -8.60552490e-01 -4.92717624e-01 -9.00245667e-01
-5.93594968e-01 2.81685770e-01 6.60387501e-02 -4.20742661... | [7.6531853675842285, 5.185644626617432] |
634ec481-c82e-40c5-aa13-892828acd086 | sparse-subspace-clustering-algorithm-theory | 1203.1005 | null | http://arxiv.org/abs/1203.1005v3 | http://arxiv.org/pdf/1203.1005v3.pdf | Sparse Subspace Clustering: Algorithm, Theory, and Applications | In many real-world problems, we are dealing with collections of
high-dimensional data, such as images, videos, text and web documents, DNA
microarray data, and more. Often, high-dimensional data lie close to
low-dimensional structures corresponding to several classes or categories the
data belongs to. In this paper, we... | ['Rene Vidal', 'Ehsan Elhamifar'] | 2012-03-05 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [ 2.54651159e-01 -2.07835272e-01 -3.00459653e-01 -2.16545478e-01
-6.18629932e-01 -6.32058918e-01 2.64763720e-02 8.28841999e-02
-1.26392320e-01 5.47445416e-01 1.51254758e-01 6.26814812e-02
-4.59104657e-01 -5.16808450e-01 -7.47924924e-01 -1.08789790e+00
5.94287775e-02 7.10342944e-01 -1.35750517e-01 2.32932374... | [7.756388187408447, 4.373954772949219] |
289d6984-1363-4f2f-9469-3c0e1c77a7db | cnn-model-tuning-for-global-road-damage | 2103.09512 | null | https://arxiv.org/abs/2103.09512v1 | https://arxiv.org/pdf/2103.09512v1.pdf | CNN Model & Tuning for Global Road Damage Detection | This paper provides a report on our solution including model selection, tuning strategy and results obtained for Global Road Damage Detection Challenge. This Big Data Cup Challenge was held as a part of IEEE International Conference on Big Data 2020. We assess single and multi-stage network architectures for object det... | ['Ravigopal Vennelakanti', 'Rahul Vishwakarma'] | 2021-03-17 | null | null | null | null | ['road-damage-detection'] | ['computer-vision'] | [-2.66475618e-01 -8.94651040e-02 -3.72801013e-02 5.61823137e-02
-8.03711414e-01 -2.88611263e-01 3.30962628e-01 -3.92027110e-01
-5.86511075e-01 3.74889016e-01 2.12831870e-01 -3.94579589e-01
6.72336817e-02 -1.19980454e+00 -7.67196774e-01 -4.89338815e-01
-2.20215861e-02 2.42411509e-01 6.89118087e-01 -2.09682122... | [7.6171722412109375, 1.0047687292099] |
b0db18ad-ed41-4504-b4bb-078d1e69bba0 | controllable-image-captioning | 2204.13324 | null | https://arxiv.org/abs/2204.13324v4 | https://arxiv.org/pdf/2204.13324v4.pdf | Controllable Image Captioning | State-of-the-art image captioners can generate accurate sentences to describe images in a sequence to sequence manner without considering the controllability and interpretability. This, however, is far from making image captioning widely used as an image can be interpreted in infinite ways depending on the target and t... | ['Luka Maxwell'] | 2022-04-28 | null | null | null | null | ['controllable-image-captioning'] | ['computer-vision'] | [ 7.04205573e-01 3.67189735e-01 -1.50693238e-01 -5.70214689e-01
-8.10055912e-01 -8.39558661e-01 8.49525571e-01 -4.12800759e-01
-1.47344798e-01 7.32114255e-01 4.16591883e-01 -2.97489583e-01
5.97382605e-01 -7.29041040e-01 -1.19468760e+00 -5.35658777e-01
4.97023612e-01 7.36691535e-01 -1.97481066e-02 -2.96814471... | [10.997586250305176, 0.9581395983695984] |
f851ba29-ee2c-4109-9a50-10bfb0d5ae63 | x-avatar-expressive-human-avatars | 2303.04805 | null | https://arxiv.org/abs/2303.04805v2 | https://arxiv.org/pdf/2303.04805v2.pdf | X-Avatar: Expressive Human Avatars | We present X-Avatar, a novel avatar model that captures the full expressiveness of digital humans to bring about life-like experiences in telepresence, AR/VR and beyond. Our method models bodies, hands, facial expressions and appearance in a holistic fashion and can be learned from either full 3D scans or RGB-D data. T... | ['Otmar Hilliges', 'Jie Song', 'Julien Valentin', 'Juan Jose Zarate', 'Manuel Kaufmann', 'Chen Guo', 'Kaiyue Shen'] | 2023-03-08 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Shen_X-Avatar_Expressive_Human_Avatars_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shen_X-Avatar_Expressive_Human_Avatars_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-human-reconstruction'] | ['computer-vision'] | [ 6.03631996e-02 4.15292799e-01 2.40714774e-01 -5.02324641e-01
-4.88981545e-01 -3.72098118e-01 6.26842439e-01 -6.84651136e-01
-7.30339512e-02 3.58933240e-01 3.99763644e-01 5.90619087e-01
3.35155040e-01 -6.54961467e-01 -7.48484254e-01 -4.79661644e-01
-2.45334953e-01 7.65074074e-01 6.40390441e-02 -5.93605042... | [7.216975688934326, -1.2516353130340576] |
9ba7ed44-b110-4f42-8c03-e6816d4e5a38 | inductive-relation-prediction-by-bert | 2103.07102 | null | https://arxiv.org/abs/2103.07102v1 | https://arxiv.org/pdf/2103.07102v1.pdf | Inductive Relation Prediction by BERT | Relation prediction in knowledge graphs is dominated by embedding based methods which mainly focus on the transductive setting. Unfortunately, they are not able to handle inductive learning where unseen entities and relations are present and cannot take advantage of prior knowledge. Furthermore, their inference process... | ['Xifeng Yan', 'Zhiyu Chen', 'Hanwen Zha'] | 2021-03-12 | null | null | null | null | ['inductive-relation-prediction'] | ['graphs'] | [-1.60228699e-01 7.94240355e-01 -7.86929011e-01 -2.29398087e-01
-2.83542365e-01 -5.38849354e-01 7.64307082e-01 4.02494252e-01
1.57287642e-02 9.24630642e-01 2.81887323e-01 -6.12609684e-01
-6.60703778e-01 -1.55448842e+00 -6.29928946e-01 -2.98422962e-01
-2.40950584e-01 9.53807116e-01 2.16791049e-01 -5.17870128... | [8.902005195617676, 7.902868747711182] |
f29050df-f5e8-4f8c-886b-ae92b39f7854 | deep-continuous-conditional-random-fields | 1806.01183 | null | http://arxiv.org/abs/1806.01183v1 | http://arxiv.org/pdf/1806.01183v1.pdf | Deep Continuous Conditional Random Fields with Asymmetric Inter-object Constraints for Online Multi-object Tracking | Online Multi-Object Tracking (MOT) is a challenging problem and has many
important applications including intelligence surveillance, robot navigation
and autonomous driving. In existing MOT methods, individual object's movements
and inter-object relations are mostly modeled separately and relations between
them are sti... | ['Jian Cheng', 'Hui Zhou', 'Hongsheng Li', 'Wanli Ouyang', 'Xiaogang Wang'] | 2018-06-04 | null | null | null | null | ['online-multi-object-tracking'] | ['computer-vision'] | [-2.24428892e-01 -2.81723142e-01 -1.99199885e-01 -3.78798753e-01
-4.75750834e-01 -4.57910001e-01 5.64734519e-01 8.83351173e-03
-4.66019213e-01 5.16324461e-01 -1.35483295e-01 7.98151568e-02
-3.09049010e-01 -6.40475929e-01 -1.23377824e+00 -8.69630337e-01
-7.81767219e-02 8.06260884e-01 9.62489069e-01 -1.80194169... | [6.356719017028809, -2.118842840194702] |
180e1e98-7c37-4a85-917f-8476fa3f1982 | identifying-clickbait-a-multi-strategy | 1710.01507 | null | http://arxiv.org/abs/1710.01507v4 | http://arxiv.org/pdf/1710.01507v4.pdf | Identifying Clickbait: A Multi-Strategy Approach Using Neural Networks | Online media outlets, in a bid to expand their reach and subsequently
increase revenue through ad monetisation, have begun adopting clickbait
techniques to lure readers to click on articles. The article fails to fulfill
the promise made by the headline. Traditional methods for clickbait detection
have relied heavily on... | ['Vasudeva Varma', 'Dhruv Khattar', 'Yash Kumar Lal', 'Siddhartha Gairola', 'Vaibhav Kumar'] | 2017-10-04 | null | null | null | null | ['clickbait-detection'] | ['natural-language-processing'] | [ 1.07639886e-01 -1.52946889e-01 -2.66012222e-01 -3.94066542e-01
-8.70449543e-01 -5.39662957e-01 8.95506680e-01 4.37836260e-01
-7.36734033e-01 4.81503874e-01 1.62696496e-01 -3.65554929e-01
9.37460270e-03 -7.42538929e-01 -9.83136475e-01 -3.58240396e-01
9.78907570e-02 2.65908748e-01 2.90453464e-01 -1.24734096... | [7.758794784545898, 9.77188491821289] |
d1bf2be8-feea-46b6-85cc-e5cadb401f81 | camelparser-a-system-for-arabic-syntactic | null | null | https://aclanthology.org/C16-2048 | https://aclanthology.org/C16-2048.pdf | CamelParser: A system for Arabic Syntactic Analysis and Morphological Disambiguation | In this paper, we present CamelParser, a state-of-the-art system for Arabic syntactic dependency analysis aligned with contextually disambiguated morphological features. CamelParser uses a state-of-the-art morphological disambiguator and improves its results using syntactically driven features. The system offers a numb... | ['Dima Taji', 'Salam Khalifa', 'Anas Shahrour', 'Nizar Habash'] | 2016-12-01 | camelparser-a-system-for-arabic-syntactic-1 | https://aclanthology.org/C16-2048 | https://aclanthology.org/C16-2048.pdf | coling-2016-12 | ['morphological-disambiguation'] | ['natural-language-processing'] | [-5.55157840e-01 -7.67123103e-02 4.37229797e-02 -6.74616694e-01
-7.22530127e-01 -1.01721084e+00 2.46902764e-01 6.27005458e-01
-2.08753198e-01 6.22748971e-01 3.54451478e-01 -7.46111870e-01
-2.87728570e-02 -6.50844932e-01 5.99071234e-02 -4.18026984e-01
-4.75429863e-01 6.41786218e-01 2.70218849e-01 -1.30133259... | [10.314044952392578, 10.383138656616211] |
ea5c9ac0-113c-4830-8610-043023cfe842 | adding-knowledge-to-unsupervised-algorithms | 2011.06219 | null | https://arxiv.org/abs/2011.06219v1 | https://arxiv.org/pdf/2011.06219v1.pdf | Adding Knowledge to Unsupervised Algorithms for the Recognition of Intent | Computer vision algorithms performance are near or superior to humans in the visual problems including object recognition (especially those of fine-grained categories), segmentation, and 3D object reconstruction from 2D views. Humans are, however, capable of higher-level image analyses. A clear example, involving theor... | ['Aleix Martinez', 'Qianli Feng', 'Stuart Synakowski'] | 2020-11-12 | null | null | null | null | ['3d-object-reconstruction'] | ['computer-vision'] | [ 4.13759410e-01 2.62785703e-01 -1.36671692e-01 -2.98306733e-01
8.78816396e-02 -6.50633097e-01 1.26376295e+00 -6.01758137e-02
-4.09726411e-01 4.12500650e-01 1.24306932e-01 -2.58306205e-01
1.20298967e-01 -5.59518337e-01 -8.12702954e-01 -8.69750679e-01
-1.54521361e-01 6.62641823e-01 3.52609307e-01 3.54560576... | [8.913902282714844, -0.2537354826927185] |
ceb7609b-ed76-4fd7-9c2f-64ba6e92ba0a | simcsum-joint-learning-of-simplification-and | 2304.01621 | null | https://arxiv.org/abs/2304.01621v1 | https://arxiv.org/pdf/2304.01621v1.pdf | SimCSum: Joint Learning of Simplification and Cross-lingual Summarization for Cross-lingual Science Journalism | Cross-lingual science journalism generates popular science stories of scientific articles different from the source language for a non-expert audience. Hence, a cross-lingual popular summary must contain the salient content of the input document, and the content should be coherent, comprehensible, and in a local langua... | ['Michael Strube', 'Katja Markert', 'Tim Kolber', 'Mehwish Fatima'] | 2023-04-04 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 2.89976168e-02 3.32581162e-01 -3.05741161e-01 -2.50218511e-01
-1.91912258e+00 -6.84547484e-01 7.58273602e-01 5.21060824e-01
-3.97692472e-01 1.09047544e+00 9.74143028e-01 -1.99221373e-01
3.23719978e-01 -3.94607812e-01 -1.23230803e+00 -3.77091646e-01
3.61468792e-01 6.37481570e-01 -2.06826553e-01 2.30607148... | [12.45589542388916, 9.59455680847168] |
065d6b75-ced1-4949-b5e0-2f3a8cb2fbd3 | encoder-decoder-multimodal-speaker-change | 2306.00680 | null | https://arxiv.org/abs/2306.00680v1 | https://arxiv.org/pdf/2306.00680v1.pdf | Encoder-decoder multimodal speaker change detection | The task of speaker change detection (SCD), which detects points where speakers change in an input, is essential for several applications. Several studies solved the SCD task using audio inputs only and have shown limited performance. Recently, multimodal SCD (MMSCD) models, which utilise text modality in addition to a... | ['Bong-Jin Lee', 'Minjae Lee', 'Young-ki Kwon', 'You Jin Kim', 'Geonmin Kim', 'Hee-Soo Heo', 'Soonshin Seo', 'Jee-weon Jung'] | 2023-06-01 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 4.40033346e-01 3.24041881e-02 1.09592073e-01 -4.30313259e-01
-1.16744864e+00 -3.67555887e-01 1.00838017e+00 4.12850201e-01
-6.18526042e-01 3.37508231e-01 5.96507490e-01 -1.39798641e-01
3.28023404e-01 -2.97120661e-01 -3.92112762e-01 -5.35612881e-01
2.16654763e-01 1.30511358e-01 4.21530664e-01 -2.70129085... | [14.391064643859863, 5.936723709106445] |
365abaaf-03ae-44fd-9190-93efb6e57e71 | completed-local-derivative-pattern-for | 1812.04183 | null | http://arxiv.org/abs/1812.04183v1 | http://arxiv.org/pdf/1812.04183v1.pdf | Completed Local Derivative Pattern for Rotation Invariant Texture Classification | In this paper, we propose a new texture descriptor, completed local
derivative pattern (CLDP). In contrast to completed local binary pattern
(CLBP), which involves only local differences at each scale, CLDP encodes the
directional variation of the local differences of two scales as a complementary
component to local pa... | [] | 2018-12-11 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 9.48691368e-02 -7.03496337e-01 -2.28636473e-01 -3.83228660e-01
-6.00760281e-01 -2.36624330e-01 6.64275527e-01 3.11865926e-01
-1.45119637e-01 4.83349502e-01 6.01392351e-02 1.60317287e-01
-3.70057940e-01 -1.07396483e+00 -2.12533891e-01 -1.01210868e+00
-8.34190771e-02 -5.40124141e-02 9.23646986e-01 -3.18872303... | [10.458242416381836, -0.36429280042648315] |
7145562b-73a9-4b2a-bc2d-d5af855eeddb | causal-augmentation-for-causal-sentence-1 | null | null | https://openreview.net/forum?id=q985hMM6EvA | https://openreview.net/pdf?id=q985hMM6EvA | Causal Augmentation for Causal Sentence Classification | Scarcity of corpora with annotated causal texts can lead to poor robustness when training state-of-the-art language models for causal sentence classification. In particular, we find that these models misclassify on augmented sentences that have been negated or strengthened in terms of their causal meaning. This is worr... | ['Anonymous'] | 2021-05-16 | null | null | null | acl-arr-may-2021-5 | ['sentence-classification'] | ['natural-language-processing'] | [ 5.44670641e-01 5.29680729e-01 -3.86394322e-01 -6.93873882e-01
-8.89119148e-01 -6.38775408e-01 1.17970753e+00 5.23891389e-01
-3.01231265e-01 1.34333086e+00 9.82178628e-01 -5.10637641e-01
-1.00901775e-01 -6.18779421e-01 -8.94396305e-01 -2.63604254e-01
-2.25636870e-01 1.97190285e-01 6.11362159e-02 -3.25328082... | [9.915254592895508, 8.100992202758789] |
73405f01-c73c-44db-b44a-45c126490d95 | multimodal-open-vocabulary-video | 2207.07646 | null | https://arxiv.org/abs/2207.07646v1 | https://arxiv.org/pdf/2207.07646v1.pdf | Multimodal Open-Vocabulary Video Classification via Pre-Trained Vision and Language Models | Utilizing vision and language models (VLMs) pre-trained on large-scale image-text pairs is becoming a promising paradigm for open-vocabulary visual recognition. In this work, we extend this paradigm by leveraging motion and audio that naturally exist in video. We present \textbf{MOV}, a simple yet effective method for ... | ['Yin Cui', 'Serge Belongie', 'Ming-Hsuan Yang', 'Zheng Xu', 'Yeqing Li', 'Rui Qian'] | 2022-07-15 | null | null | null | null | ['zero-shot-action-recognition', 'video-classification'] | ['computer-vision', 'computer-vision'] | [ 6.23807684e-02 -6.41786456e-01 -5.26116014e-01 -1.81449473e-01
-9.87309933e-01 -5.44649661e-01 7.58002520e-01 -1.12302460e-01
-6.13529742e-01 4.73521739e-01 4.14708763e-01 -1.29938781e-01
2.90120423e-01 -2.57056952e-01 -9.27132368e-01 -5.39796948e-01
-1.32511910e-02 -1.71341542e-02 3.12539548e-01 -9.59778950... | [10.213282585144043, 0.9673560857772827] |
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