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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e6c2122d-4aad-486e-97ed-5c88553661b2 | 3d-human-pose-estimation-using-convolutional | 1608.03075 | null | http://arxiv.org/abs/1608.03075v2 | http://arxiv.org/pdf/1608.03075v2.pdf | 3D Human Pose Estimation Using Convolutional Neural Networks with 2D Pose Information | While there has been a success in 2D human pose estimation with convolutional
neural networks (CNNs), 3D human pose estimation has not been thoroughly
studied. In this paper, we tackle the 3D human pose estimation task with
end-to-end learning using CNNs. Relative 3D positions between one joint and the
other joints are... | ['Sungheon Park', 'Jihye Hwang', 'Nojun Kwak'] | 2016-08-10 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-3.47203463e-01 1.83646798e-01 2.61323247e-02 -3.74214262e-01
-3.43907237e-01 -7.22541139e-02 3.56840253e-01 -1.61469266e-01
-9.59913015e-01 5.06062269e-01 3.82728577e-01 5.15494287e-01
2.80435383e-01 -3.60644966e-01 -8.07033777e-01 -2.07712725e-01
-1.87683463e-01 8.31013739e-01 2.95772016e-01 -3.81934583... | [6.962892055511475, -0.840390682220459] |
0ee6144d-1376-4b26-b79b-aea92ee504a7 | domain-independent-svm-for-transfer-learning | 1903.11020 | null | http://arxiv.org/abs/1903.11020v1 | http://arxiv.org/pdf/1903.11020v1.pdf | Domain Independent SVM for Transfer Learning in Brain Decoding | Brain imaging data are important in brain sciences yet expensive to obtain,
with big volume (i.e., large p) but small sample size (i.e., small n). To
tackle this problem, transfer learning is a promising direction that leverages
source data to improve performance on related, target data. Most transfer
learning methods ... | ['Christopher R. Cox', 'Wenwen Li', 'Shuo Zhou', 'Haiping Lu'] | 2019-03-26 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 1.57630205e-01 -2.65299201e-01 -1.54091418e-01 -6.75713718e-01
-9.82543945e-01 -4.24300194e-01 4.17369574e-01 -3.01888227e-01
-5.67281306e-01 1.09355962e+00 1.70934811e-01 -7.74724931e-02
-1.97985709e-01 -2.33119577e-01 -7.88969994e-01 -6.15321517e-01
4.47516255e-02 4.81182545e-01 2.13069871e-01 -9.94444191... | [12.626970291137695, 3.3440327644348145] |
e18237a4-c0e6-4c7e-b31a-c75cc4a657e4 | bayesian-bilinear-neural-network-for | 2203.03613 | null | https://arxiv.org/abs/2203.03613v2 | https://arxiv.org/pdf/2203.03613v2.pdf | Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets | The prediction of financial markets is a challenging yet important task. In modern electronically-driven markets, traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. While recent research has established the effe... | ['Alexandros Iosifidis', 'Mostafa Shabani', 'Martin Magris'] | 2022-03-07 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-5.00262678e-01 -3.08599651e-01 -1.14146687e-01 -4.18428123e-01
-8.40305030e-01 -4.42868143e-01 9.94430304e-01 -1.46305770e-01
-2.85807759e-01 6.86093569e-01 8.80517811e-02 -7.14597583e-01
-7.41222680e-01 -6.88887894e-01 -6.55476928e-01 -6.87717915e-01
-3.66259009e-01 9.37064648e-01 -2.18857870e-01 -1.70672536... | [4.811517715454102, 4.05569314956665] |
a6bf7e0b-c1c1-4c5e-8794-62c813565fd6 | less-is-more-micro-expression-recognition | 1606.01721 | null | http://arxiv.org/abs/1606.01721v3 | http://arxiv.org/pdf/1606.01721v3.pdf | Less is More: Micro-expression Recognition from Video using Apex Frame | Despite recent interest and advances in facial micro-expression research,
there is still plenty room for improvement in terms of micro-expression
recognition. Conventional feature extraction approaches for micro-expression
video consider either the whole video sequence or a part of it, for
representation. However, with... | ['Raphael C. -W. Phan', 'Sze-Teng Liong', 'John See', 'KokSheik Wong'] | 2016-06-06 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 3.64538312e-01 -2.61323571e-01 -2.27996796e-01 -4.64041084e-01
-3.67931873e-01 -6.15492538e-02 4.18727398e-01 -2.76964605e-01
-5.14225185e-01 7.21173584e-01 2.23657023e-03 3.55247498e-01
2.53004599e-02 -3.43911111e-01 -2.24911898e-01 -9.92410958e-01
-2.06684560e-01 -4.17798489e-01 -1.06540270e-01 -5.50248563... | [13.653576850891113, 1.8200126886367798] |
425fa612-2b2b-46f4-9f01-d8d06f5ffd3b | deep-learning-with-convolutional-neural | 1703.05051 | null | http://arxiv.org/abs/1703.05051v5 | http://arxiv.org/pdf/1703.05051v5.pdf | Deep learning with convolutional neural networks for EEG decoding and visualization | PLEASE READ AND CITE THE REVISED VERSION at Human Brain Mapping:
http://onlinelibrary.wiley.com/doi/10.1002/hbm.23730/full
Code available here: https://github.com/robintibor/braindecode | ['Jost Tobias Springenberg', 'Michael Tangermann', 'Robin Tibor Schirrmeister', 'Lukas Dominique Josef Fiederer', 'Frank Hutter', 'Wolfram Burgard', 'Tonio Ball', 'Martin Glasstetter', 'Katharina Eggensperger'] | 2017-03-15 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [-7.51829803e-01 1.99708879e-01 -4.72662300e-01 -2.20674157e-01
-8.00899863e-01 -2.94661760e-01 3.96612525e-01 4.92117912e-01
-4.56484944e-01 1.02347851e+00 5.13282776e-01 -2.36375257e-01
2.54575282e-01 -5.28913379e-01 -6.63588881e-01 -4.78447497e-01
-5.30666746e-02 6.02029562e-01 2.14636430e-01 1.17580965... | [14.180715560913086, -2.182055711746216] |
5909854d-d8e9-4c76-9801-e2ddaed44f90 | parameter-efficient-transfer-learning-of-pre | 2210.16032 | null | https://arxiv.org/abs/2210.16032v1 | https://arxiv.org/pdf/2210.16032v1.pdf | Parameter-efficient transfer learning of pre-trained Transformer models for speaker verification using adapters | Recently, the pre-trained Transformer models have received a rising interest in the field of speech processing thanks to their great success in various downstream tasks. However, most fine-tuning approaches update all the parameters of the pre-trained model, which becomes prohibitive as the model size grows and sometim... | ['Jan Černocký', 'Lukáš Burget', 'Ladislav Mošner', 'Oldřich Plchot', 'Rongzhi Gu', 'Themos Stafylakis', 'Junyi Peng'] | 2022-10-28 | null | null | null | null | ['speaker-verification'] | ['speech'] | [ 1.36633426e-01 1.54685929e-01 -4.14274558e-02 -5.38570344e-01
-1.15159833e+00 -7.67376840e-01 3.57342780e-01 -1.34156853e-01
-5.25896609e-01 6.31707788e-01 8.83677378e-02 -5.76500893e-01
1.33013561e-01 -4.18617398e-01 -7.65941978e-01 -5.53083956e-01
3.01698774e-01 4.87383991e-01 2.74232239e-01 -1.22486748... | [14.131278991699219, 6.620329856872559] |
5412226f-ed55-4552-a7c7-e302fc3c2177 | real-time-aerial-detection-and-reasoning-on | 2305.12414 | null | https://arxiv.org/abs/2305.12414v1 | https://arxiv.org/pdf/2305.12414v1.pdf | Real-time Aerial Detection and Reasoning on Embedded-UAVs | We present a unified pipeline architecture for a real-time detection system on an embedded system for UAVs. Neural architectures have been the industry standard for computer vision. However, most existing works focus solely on concatenating deeper layers to achieve higher accuracy with run-time performance as the trade... | ['Tin Lai'] | 2023-05-21 | null | null | null | null | ['pedestrian-detection'] | ['computer-vision'] | [ 4.52202186e-02 -5.11016250e-01 -7.58173689e-02 -3.69092703e-01
-2.77397811e-01 -7.49439061e-01 2.39974365e-01 -1.39783874e-01
-4.72877532e-01 2.33874246e-01 -2.88922846e-01 -3.11532050e-01
1.93038985e-01 -7.58229971e-01 -6.13241553e-01 -4.95328724e-01
-6.04766488e-01 -2.45680854e-01 8.55844378e-01 3.67805362... | [6.6969757080078125, -1.9073266983032227] |
42df154a-63da-4468-b5df-94887436ac65 | ego-body-pose-estimation-via-ego-head-pose | 2212.04636 | null | https://arxiv.org/abs/2212.04636v2 | https://arxiv.org/pdf/2212.04636v2.pdf | Ego-Body Pose Estimation via Ego-Head Pose Estimation | Estimating 3D human motion from an egocentric video sequence plays a critical role in human behavior understanding and has various applications in VR/AR. However, naively learning a mapping between egocentric videos and human motions is challenging, because the user's body is often unobserved by the front-facing camera... | ['Jiajun Wu', 'C. Karen Liu', 'Jiaman Li'] | 2022-12-09 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Ego-Body_Pose_Estimation_via_Ego-Head_Pose_Estimation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Ego-Body_Pose_Estimation_via_Ego-Head_Pose_Estimation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['head-pose-estimation'] | ['computer-vision'] | [-4.48454887e-01 -1.21350512e-01 -1.60784706e-01 -2.38181353e-01
-5.86952209e-01 -4.03129488e-01 3.77318293e-01 -7.79032052e-01
-2.21805602e-01 4.86716300e-01 7.73737848e-01 3.10055375e-01
2.90836602e-01 -2.96221763e-01 -7.78642356e-01 -5.29332221e-01
-1.91159435e-02 4.20241505e-01 1.82130158e-01 -1.08647458... | [7.055445671081543, -0.820791482925415] |
f7fd1b9a-690a-48f0-b911-660fda1d4e53 | two-way-fixed-effects-and-differences-in | 2112.04565 | null | https://arxiv.org/abs/2112.04565v6 | https://arxiv.org/pdf/2112.04565v6.pdf | Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey | Linear regressions with period and group fixed effects are widely used to estimate policies' effects: 26 of the 100 most cited papers published by the American Economic Review from 2015 to 2019 estimate such regressions. It has recently been shown that those regressions may produce misleading estimates, if the policy's... | ["Xavier D'Haultfœuille", 'Clément de Chaisemartin'] | 2021-12-08 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-3.72124404e-01 -3.70229296e-02 -1.54396737e+00 -9.16159078e-02
-6.12289965e-01 -6.48490131e-01 8.86924565e-01 2.97620475e-01
-8.06343973e-01 1.05293179e+00 7.63284862e-01 -1.11882615e+00
-4.02006626e-01 -4.21794176e-01 -5.64119399e-01 -3.62058073e-01
1.99728622e-03 -3.68655436e-02 -5.10515571e-02 2.48280883... | [7.955697059631348, 5.194067478179932] |
fbc61c07-c19b-4d4d-b379-34f262c27cb3 | a-distance-geometric-method-for-recovering | 2301.02051 | null | https://arxiv.org/abs/2301.02051v2 | https://arxiv.org/pdf/2301.02051v2.pdf | A Distance-Geometric Method for Recovering Robot Joint Angles From an RGB Image | Autonomous manipulation systems operating in domains where human intervention is difficult or impossible (e.g., underwater, extraterrestrial or hazardous environments) require a high degree of robustness to sensing and communication failures. Crucially, motion planning and control algorithms require a stream of accurat... | ['Ivan Petrović', 'Ivan Marković', 'Filip Marić', 'Ivan Bilić'] | 2023-01-05 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 4.12827849e-01 1.13874517e-01 2.23550275e-01 7.97688738e-02
-4.46634650e-01 -6.68349802e-01 3.23228270e-01 1.79083839e-01
-7.07763791e-01 6.02106333e-01 -4.97775882e-01 -2.48623028e-01
-6.21254623e-01 -5.90172350e-01 -1.02012765e+00 -7.67435014e-01
-4.06825632e-01 6.17957532e-01 2.16090679e-01 -5.57001948... | [6.996329307556152, -1.8138326406478882] |
1a7238f8-25aa-4f25-9975-30c41939ab37 | identification-and-classification-of-1 | 2003.08209 | null | https://arxiv.org/abs/2003.08209v1 | https://arxiv.org/pdf/2003.08209v1.pdf | Identification and Classification of Phenomena in Multispectral Satellite Imagery Using a New Image Smoother Method and its Applications in Environmental Remote Sensing | In this paper a new method of image smoothing for satellite imagery and its applications in environmental remote sensing are presented. This method is based on the global gradient minimization over the whole image. With respect to the image discrete identity, the continuous minimization problem is discretized. Using th... | ['M. Kiani'] | 2020-03-17 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [ 6.16917193e-01 -3.14007849e-01 5.77077866e-01 -3.66408616e-01
-6.59246981e-01 -3.23497772e-01 4.31459188e-01 -3.23178172e-01
-7.15585709e-01 8.41808796e-01 -2.06143349e-01 -2.70733118e-01
-3.09248865e-01 -9.29081976e-01 -5.15287369e-02 -1.28815329e+00
-1.44342244e-01 -1.45850897e-01 1.20817460e-02 -2.65744209... | [10.126562118530273, -2.042008876800537] |
efd9500c-0a62-4700-976d-6d594cc9bdfb | nlnde-at-cantemist-neural-sequence-labeling | 2010.12322 | null | https://arxiv.org/abs/2010.12322v1 | https://arxiv.org/pdf/2010.12322v1.pdf | NLNDE at CANTEMIST: Neural Sequence Labeling and Parsing Approaches for Clinical Concept Extraction | The recognition and normalization of clinical information, such as tumor morphology mentions, is an important, but complex process consisting of multiple subtasks. In this paper, we describe our system for the CANTEMIST shared task, which is able to extract, normalize and rank ICD codes from Spanish electronic health r... | ['Jannik Strötgen', 'Heike Adel', 'Xiang Dai', 'Lukas Lange'] | 2020-10-23 | null | null | null | null | ['clinical-concept-extraction'] | ['medical'] | [ 3.17384988e-01 1.01820409e-01 -4.58253145e-01 -6.99412942e-01
-1.30556071e+00 -5.61634600e-01 3.18874955e-01 9.20404613e-01
-8.73294473e-01 8.63198817e-01 4.68142390e-01 -4.91579235e-01
-1.51186615e-01 -4.74307895e-01 -5.89227557e-01 -6.32560968e-01
7.22957999e-02 8.42987359e-01 -1.16667353e-01 7.57605061... | [8.512078285217285, 8.726417541503906] |
2229a0e1-d5bb-4aca-aff0-c1adceeaa51e | detecting-human-object-interaction-via | 2103.08214 | null | https://arxiv.org/abs/2103.08214v2 | https://arxiv.org/pdf/2103.08214v2.pdf | Detecting Human-Object Interaction via Fabricated Compositional Learning | Human-Object Interaction (HOI) detection, inferring the relationships between human and objects from images/videos, is a fundamental task for high-level scene understanding. However, HOI detection usually suffers from the open long-tailed nature of interactions with objects, while human has extremely powerful compositi... | ['DaCheng Tao', 'Xiaojiang Peng', 'Yu Qiao', 'Baosheng Yu', 'Zhi Hou'] | 2021-03-15 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Hou_Detecting_Human-Object_Interaction_via_Fabricated_Compositional_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Hou_Detecting_Human-Object_Interaction_via_Fabricated_Compositional_Learning_CVPR_2021_paper.pdf | cvpr-2021-1 | ['affordance-recognition'] | ['computer-vision'] | [ 2.44271576e-01 -1.95998296e-01 -4.84981090e-02 -2.21301258e-01
-5.18142402e-01 -2.51394928e-01 4.12727535e-01 -9.38400999e-02
1.42494217e-01 4.30727988e-01 4.14855421e-01 2.96554267e-01
-1.15728475e-01 -6.77304089e-01 -8.58775675e-01 -6.90148771e-01
1.83578417e-01 4.82293636e-01 2.27655888e-01 1.24181136... | [9.527586936950684, 1.4299191236495972] |
8b7a17bf-0957-47d9-8237-746facca3e7f | sumsum-fns-2020-shared-task | null | null | https://aclanthology.org/2020.fnp-1.25 | https://aclanthology.org/2020.fnp-1.25.pdf | SUMSUM@FNS-2020 Shared Task | This paper describes the SUMSUM systems submitted to the Financial Narrative Summarization Shared Task (FNS-2020). We explore a section-based extractive summarization method tailored to the structure of financial reports: our best system parses the report Table of Contents (ToC), splits the report into narrative sectio... | ['Claire Cardie', 'Anneliese Lu', 'Siyan Zheng'] | null | null | null | null | fnp-coling-2020-12 | ['extractive-summarization'] | ['natural-language-processing'] | [-1.45736799e-01 5.68689525e-01 -5.90007961e-01 -5.56505211e-02
-1.43340147e+00 -9.03407037e-01 9.28650677e-01 9.21408117e-01
-2.77951926e-01 1.12863958e+00 1.11142051e+00 -4.06054407e-01
-4.90281910e-01 -6.39906108e-01 -3.26067716e-01 -6.13421015e-02
-1.03711203e-01 4.66300666e-01 3.49842072e-01 -1.72251716... | [12.43258285522461, 9.521369934082031] |
a7b5fd4d-c0d8-4a8b-a3ba-aea02586cb0f | standardized-cyclegan-training-for | 2301.13128 | null | https://arxiv.org/abs/2301.13128v1 | https://arxiv.org/pdf/2301.13128v1.pdf | Standardized CycleGAN training for unsupervised stain adaptation in invasive carcinoma classification for breast histopathology | Generalization is one of the main challenges of computational pathology. Slide preparation heterogeneity and the diversity of scanners lead to poor model performance when used on data from medical centers not seen during training. In order to achieve stain invariance in breast invasive carcinoma patch classification, w... | ['Stéphane Sockeel', 'Marie Sockeel', 'Rémy Peyret', 'Nicolas Nerrienet'] | 2023-01-30 | null | null | null | null | ['unsupervised-image-to-image-translation'] | ['computer-vision'] | [ 7.48346150e-01 3.35664541e-01 -1.28690854e-01 -2.48559490e-01
-8.03691447e-01 -6.93406045e-01 4.41358089e-01 5.78010269e-02
-7.44051516e-01 7.11528003e-01 -4.58073795e-01 -6.15902781e-01
1.33059710e-01 -6.49639189e-01 -5.38899899e-01 -1.19846487e+00
1.85839981e-01 5.33692896e-01 2.00026125e-01 -5.15421759... | [15.032538414001465, -3.01418137550354] |
2c5dbde1-d665-4809-af30-42650dc31a9b | drug-synergistic-combinations-predictions-via | 2301.05931 | null | https://arxiv.org/abs/2301.05931v1 | https://arxiv.org/pdf/2301.05931v1.pdf | Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning | Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. However, identifying novel drug combinations through wet-lab experiments is resource intensive due to the vast combinatorial search space. Recently, computational approaches, specifically... | ['Yu Li', 'Le Song', 'Xin Gao', 'Irwin King', 'Taifeng Wang', 'Yucheng Guo', 'Qinze Yu', 'Zhihang Hu'] | 2023-01-14 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 3.50655466e-01 -1.73459694e-01 -5.83593249e-01 5.53641878e-02
-6.99570954e-01 -4.93139118e-01 4.92248744e-01 6.91741407e-01
1.27615303e-01 1.32271874e+00 3.59818600e-02 -6.07458055e-01
-5.63372135e-01 -9.71300602e-01 -8.14607680e-01 -8.69622171e-01
-2.53142893e-01 8.16693902e-01 -5.62228151e-02 -3.80068243... | [5.334403991699219, 5.772904396057129] |
316ff7df-ffe3-462d-933e-f87997d6a6a1 | using-a-waffle-iron-for-automotive-point | 2301.10100 | null | https://arxiv.org/abs/2301.10100v1 | https://arxiv.org/pdf/2301.10100v1.pdf | Using a Waffle Iron for Automotive Point Cloud Semantic Segmentation | Semantic segmentation of point clouds in autonomous driving datasets requires techniques that can process large numbers of points over large field of views. Today, most deep networks designed for this task exploit 3D sparse convolutions to reduce memory and computational loads. The best methods then further exploit spe... | ['Renaud Marlet', 'Alexandre Boulch', 'Gilles Puy'] | 2023-01-24 | null | null | null | null | ['robust-3d-semantic-segmentation'] | ['computer-vision'] | [-9.48955268e-02 -5.87263033e-02 -6.35818206e-03 -5.97950697e-01
-4.58066761e-01 -6.58330798e-01 6.73383296e-01 -7.02971965e-02
-6.08204722e-01 3.26049447e-01 -5.56070626e-01 -3.62117320e-01
-3.57710458e-02 -1.18892574e+00 -1.24081218e+00 -4.09504086e-01
-7.67630860e-02 1.00062680e+00 7.16834903e-01 -2.99035668... | [8.070289611816406, -3.0376627445220947] |
46e68fb8-afa7-4d6b-8091-3f484bfb9871 | computer-aided-diagnosis-of-lung-carcinoma | 1803.05471 | null | http://arxiv.org/abs/1803.05471v1 | http://arxiv.org/pdf/1803.05471v1.pdf | Computer-aided diagnosis of lung carcinoma using deep learning - a pilot study | Aim: Early detection and correct diagnosis of lung cancer are the most
important steps in improving patient outcome. This study aims to assess which
deep learning models perform best in lung cancer diagnosis. Methods: Non-small
cell lung carcinoma and small cell lung carcinoma biopsy specimens were
consecutively obtain... | ['Qiang Li', 'Tao Tan', 'Guoping Cai', 'Geert Litjens', 'Yuling Tang', 'Quchang Ouyang', 'Jun Tang', 'Jiaolong Xu', 'Zhi Duan', 'Ping Liu', 'Hui Chen', 'Zheyu Hu', 'Zhang Li'] | 2018-03-14 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [-1.96325183e-01 -1.33808509e-01 -6.45904422e-01 2.13088155e-01
-9.68719363e-01 -4.53333467e-01 1.56298965e-01 2.55885780e-01
-4.89221126e-01 8.58154058e-01 -1.68590039e-01 -7.58945942e-01
2.41296012e-02 -8.59725475e-01 6.47465363e-02 -1.11760342e+00
9.84228402e-02 1.00179374e+00 4.26866204e-01 4.13834363... | [15.337623596191406, -2.5019073486328125] |
2bb8dd60-57c2-457c-bb46-20a1f4a4f04b | visual-commonsense-aware-representation | 2211.09469 | null | https://arxiv.org/abs/2211.09469v1 | https://arxiv.org/pdf/2211.09469v1.pdf | Visual Commonsense-aware Representation Network for Video Captioning | Generating consecutive descriptions for videos, i.e., Video Captioning, requires taking full advantage of visual representation along with the generation process. Existing video captioning methods focus on making an exploration of spatial-temporal representations and their relationships to produce inferences. However, ... | ['Heng Tao Shen', 'Jin Qian', 'Xiangpeng Li', 'Lianli Gao', 'Haonan Zhang', 'Pengpeng Zeng'] | 2022-11-17 | null | null | null | null | ['video-question-answering'] | ['computer-vision'] | [ 2.80860513e-01 -1.06063783e-01 -3.03148746e-01 -2.54307419e-01
-6.49140179e-01 -6.38304532e-01 6.53093636e-01 4.33141887e-02
-2.77973595e-03 6.41263962e-01 6.20042443e-01 -1.92484275e-01
-8.12599994e-03 -6.50957823e-01 -9.18866754e-01 -4.13555443e-01
2.89972693e-01 1.04589999e-01 4.67118397e-02 -1.64468601... | [10.523992538452148, 0.9040728211402893] |
7161098c-98ac-4415-a522-1bbec4714843 | analogical-inference-enhanced-knowledge-graph | 2301.00982 | null | https://arxiv.org/abs/2301.00982v2 | https://arxiv.org/pdf/2301.00982v2.pdf | Analogical Inference Enhanced Knowledge Graph Embedding | Knowledge graph embedding (KGE), which maps entities and relations in a knowledge graph into continuous vector spaces, has achieved great success in predicting missing links in knowledge graphs. However, knowledge graphs often contain incomplete triples that are difficult to inductively infer by KGEs. To address this c... | ['Huajun Chen', 'Yi Yang', 'Yufeng Huang', 'Mingyang Chen', 'Wen Zhang', 'Zhen Yao'] | 2023-01-03 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-1.53769672e-01 5.98119378e-01 -6.30066693e-01 -1.89158216e-01
-2.41393875e-02 -4.05298620e-01 4.83484983e-01 4.54886883e-01
-1.62317678e-01 9.20675159e-01 1.37745544e-01 -4.29441631e-01
-6.71687841e-01 -1.51976800e+00 -1.19678009e+00 -5.03246449e-02
-5.54190576e-02 8.34862888e-01 2.38630280e-01 -4.74057049... | [8.798065185546875, 7.869781970977783] |
c96f82c7-7dff-4ae5-a3ab-0cddd938af54 | bootstrapping-a-user-centered-task-oriented | 2207.05223 | null | https://arxiv.org/abs/2207.05223v2 | https://arxiv.org/pdf/2207.05223v2.pdf | Bootstrapping a User-Centered Task-Oriented Dialogue System | We present TacoBot, a task-oriented dialogue system built for the inaugural Alexa Prize TaskBot Challenge, which assists users in completing multi-step cooking and home improvement tasks. TacoBot is designed with a user-centered principle and aspires to deliver a collaborative and accessible dialogue experience. Toward... | ['Huan Sun', 'Yu Su', 'Tianshu Zhang', 'Xiang Yue', 'Zhen Wang', 'Samuel Stevens', 'Lingbo Mo', 'Ashley Lewis', 'Xiang Deng', 'Ziru Chen', 'Shijie Chen'] | 2022-07-11 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [-3.37030947e-01 4.41020459e-01 2.38471419e-01 -5.08253098e-01
-7.34980702e-01 -7.43470609e-01 7.72864997e-01 3.98657061e-02
-4.55545008e-01 9.53439534e-01 5.52370071e-01 -4.08103853e-01
-1.16355876e-02 -4.51087087e-01 1.08154334e-01 6.69185519e-02
8.91732201e-02 9.45422888e-01 -2.19511464e-01 -1.09814465... | [12.780008316040039, 8.018976211547852] |
07d5084d-72de-42ac-9741-b5a74ea5b687 | boosting-cross-task-transferability-of | 2304.05402 | null | https://arxiv.org/abs/2304.05402v1 | https://arxiv.org/pdf/2304.05402v1.pdf | Boosting Cross-task Transferability of Adversarial Patches with Visual Relations | The transferability of adversarial examples is a crucial aspect of evaluating the robustness of deep learning systems, particularly in black-box scenarios. Although several methods have been proposed to enhance cross-model transferability, little attention has been paid to the transferability of adversarial examples ac... | ['Shunchang Liu', 'Yisong Xiao', 'Songze Li', 'Tony Ma'] | 2023-04-11 | null | null | null | null | ['object-recognition', 'visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'computer-vision', 'reasoning'] | [ 4.09344435e-01 2.50052005e-01 4.10235077e-01 -1.17893621e-01
-8.29594553e-01 -9.65060234e-01 9.19602215e-01 -1.11860305e-01
-3.53245795e-01 6.42217934e-01 -1.58058435e-01 -5.16077936e-01
4.41572629e-02 -7.08551645e-01 -1.11073387e+00 -4.60878372e-01
3.16680193e-01 1.61750734e-01 1.33697942e-01 -3.67541790... | [10.867973327636719, 1.8247935771942139] |
38f54ac0-d588-4fa6-b762-8ee1b768ce2c | cab-comprehensive-attention-benchmarking-on | 2210.07661 | null | https://arxiv.org/abs/2210.07661v3 | https://arxiv.org/pdf/2210.07661v3.pdf | CAB: Comprehensive Attention Benchmarking on Long Sequence Modeling | Transformer has achieved remarkable success in language, image, and speech processing. Recently, various efficient attention architectures have been proposed to improve transformer's efficiency while largely preserving its efficacy, especially in modeling long sequences. A widely-used benchmark to test these efficient ... | ['Lingpeng Kong', 'Lin Zheng', 'Jiangtao Feng', 'Shuyang Jiang', 'Jun Zhang'] | 2022-10-14 | null | null | null | null | ['long-range-modeling'] | ['natural-language-processing'] | [-9.55649912e-02 -5.03140092e-01 -3.62510473e-01 -1.81109041e-01
-4.93403345e-01 -1.60245553e-01 6.35571241e-01 -2.66482770e-01
-4.59025264e-01 6.84260249e-01 5.47587156e-01 -5.19697070e-01
-2.99801379e-01 -6.38184428e-01 -8.31889689e-01 -6.47878170e-01
-1.78841308e-01 3.66317689e-01 2.20849231e-01 -4.80902791... | [10.907670021057129, 6.636829853057861] |
dfd5bbb1-1753-4791-b36e-bafc909ff8bc | fuzzy-controller-of-reward-of-reinforcement | 1812.07028 | null | http://arxiv.org/abs/1812.07028v1 | http://arxiv.org/pdf/1812.07028v1.pdf | Fuzzy Controller of Reward of Reinforcement Learning For Handwritten Digit Recognition | Recognition of human environment with computer systems always was a big deal
in artificial intelligence. In this area handwriting recognition and
conceptualization of it to computer is an important area in it. In the past
years with growth of machine learning in artificial intelligence, efforts to
using this technique ... | ['Saber Malekzadeh'] | 2018-12-17 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 3.84629220e-01 1.37160450e-01 5.02111092e-02 -4.79644388e-01
5.49881101e-01 -7.84300268e-01 5.70481479e-01 -4.18589078e-02
-5.38606226e-01 8.86970460e-01 -8.07282999e-02 -3.53361368e-01
-2.59492368e-01 -8.28109980e-01 -3.29404712e-01 -3.46376121e-01
2.95600265e-01 7.22997844e-01 2.65265405e-01 -2.20080063... | [11.834882736206055, 2.7021188735961914] |
58dcbb3f-5b0e-4f54-a24a-3f4f9135df34 | centroid-distance-keypoint-detector-for | 2210.01298 | null | https://arxiv.org/abs/2210.01298v2 | https://arxiv.org/pdf/2210.01298v2.pdf | Centroid Distance Keypoint Detector for Colored Point Clouds | Keypoint detection serves as the basis for many computer vision and robotics applications. Despite the fact that colored point clouds can be readily obtained, most existing keypoint detectors extract only geometry-salient keypoints, which can impede the overall performance of systems that intend to (or have the potenti... | ['Konstantinos Karydis', 'Amit K. Roy-Chowdhury', 'Xinyue Kan', 'Dimitrios Chatziparaschis', 'Hanzhe Teng'] | 2022-10-04 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-9.12467837e-02 -3.36471051e-01 -9.82323885e-02 1.76463172e-01
-8.29005957e-01 -7.05040514e-01 8.03814232e-01 4.10036772e-01
-5.00860810e-01 1.49391919e-01 -2.07200870e-01 -6.58296198e-02
-1.63542241e-01 -5.03077328e-01 -6.81595504e-01 -5.97149253e-01
-1.35965601e-01 1.18116990e-01 6.48875117e-01 -2.12367341... | [7.8264031410217285, -2.1289138793945312] |
225b9973-1b80-4e45-a98b-894db95ec709 | balanced-mse-for-imbalanced-visual-regression | 2203.16427 | null | https://arxiv.org/abs/2203.16427v1 | https://arxiv.org/pdf/2203.16427v1.pdf | Balanced MSE for Imbalanced Visual Regression | Data imbalance exists ubiquitously in real-world visual regressions, e.g., age estimation and pose estimation, hurting the model's generalizability and fairness. Thus, imbalanced regression gains increasing research attention recently. Compared to imbalanced classification, imbalanced regression focuses on continuous l... | ['Ziwei Liu', 'Cunjun Yu', 'Mingyuan Zhang', 'Jiawei Ren'] | 2022-03-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ren_Balanced_MSE_for_Imbalanced_Visual_Regression_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ren_Balanced_MSE_for_Imbalanced_Visual_Regression_CVPR_2022_paper.pdf | cvpr-2022-1 | ['age-estimation', 'imbalanced-classification', 'age-estimation'] | ['computer-vision', 'miscellaneous', 'miscellaneous'] | [ 1.52396530e-01 -1.20372050e-01 -5.86261213e-01 -5.29568672e-01
-6.04761362e-01 -1.67330459e-01 5.88526092e-02 2.99440861e-01
-3.05987269e-01 1.02099741e+00 -2.53395379e-01 -3.05460483e-01
-6.12828229e-03 -5.07811010e-01 -5.65332651e-01 -6.74265981e-01
2.20403969e-01 3.43224287e-01 -3.94562602e-01 -9.87787545... | [9.052103996276855, 3.9695677757263184] |
9a45f69c-0bd8-4ef4-9f23-1e5c244fa501 | correlation-clustering-algorithm-for-dynamic | 2301.00384 | null | https://arxiv.org/abs/2301.00384v1 | https://arxiv.org/pdf/2301.00384v1.pdf | Correlation Clustering Algorithm for Dynamic Complete Signed Graphs: An Index-based Approach | In this paper, we reduce the complexity of approximating the correlation clustering problem from $O(m\times\left( 2+ \alpha (G) \right)+n)$ to $O(m+n)$ for any given value of $\varepsilon$ for a complete signed graph with $n$ vertices and $m$ positive edges where $\alpha(G)$ is the arboricity of the graph. Our approach... | ['Ali Shakiba'] | 2023-01-01 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 2.37501442e-01 3.10895920e-01 -3.52000110e-02 -1.18774809e-01
-6.17951572e-01 -8.05659175e-01 2.78151780e-02 4.81733501e-01
-7.22909808e-01 6.72571301e-01 -8.08393717e-01 -5.27591109e-01
-4.24590915e-01 -1.25309169e+00 -6.68787837e-01 -6.84321582e-01
-1.03686285e+00 9.49775457e-01 5.33192277e-01 -1.51424855... | [6.786737442016602, 5.0807271003723145] |
1a10a86b-613c-4dce-a274-93f7e3216078 | training-a-deep-q-learning-agent-inside-a | 2301.01913 | null | https://arxiv.org/abs/2301.01913v1 | https://arxiv.org/pdf/2301.01913v1.pdf | Training a Deep Q-Learning Agent Inside a Generic Constraint Programming Solver | Constraint programming is known for being an efficient approach for solving combinatorial problems. Important design choices in a solver are the branching heuristics, which are designed to lead the search to the best solutions in a minimum amount of time. However, developing these heuristics is a time-consuming process... | ['Louis-Martin Rousseau', 'Quentin Cappart', 'Louis Gauthier', 'Pierre Tessier', 'Tristan François', 'Tom Marty'] | 2023-01-05 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 0.19256894 0.25010923 -0.38713413 -0.13636525 -0.6208722 -0.64627755
0.11122739 0.45482364 -0.18934305 0.87879485 -0.4961141 -0.38190413
-0.45669284 -0.9181313 -0.62264544 -0.59778607 -0.1708463 0.9305864
0.22654107 -0.1953625 0.49476883 0.6688637 -1.4370329 0.01184747
1.091217 1.0171622 0.4... | [5.277121543884277, 3.0971405506134033] |
71368df0-3b95-40cf-a5e3-4a09d21fdbe9 | a-novel-multi-stage-approach-for-hierarchical | null | null | https://ieeexplore.ieee.org/document/10077796 | https://ieeexplore.ieee.org/document/10077796 | A Novel Multi-Stage Approach for Hierarchical Intrusion Detection | An intrusion detection system (IDS), traditionally an example of an effective security monitoring system, is facing significant challenges due to the ongoing digitization of our modern society. The growing number and variety of connected devices are not only causing a continuous emergence of new threats that are not re... | ['Filip De Turck', 'Bruno Volckaert', 'Tim Wauters', 'Ying-Dar Lin', 'Didik Sudyana', 'Laurens D’hooge', 'Miel Verkerken'] | 2023-03-21 | null | null | null | ieee-transactions-on-network-and-service-1 | ['network-intrusion-detection'] | ['miscellaneous'] | [ 7.05393329e-02 -4.65690792e-01 -9.61681604e-02 -1.86656371e-01
-4.15926635e-01 -7.68610477e-01 6.82644725e-01 6.62687659e-01
-7.12718964e-01 6.20948672e-01 -5.25590181e-01 -5.86454153e-01
-3.19346935e-01 -8.13187420e-01 -6.01668209e-02 -4.41311449e-01
-2.11934194e-01 4.74296808e-01 6.34019315e-01 -5.28036654... | [5.270878791809082, 7.198757648468018] |
bc7cd91c-1537-456c-82e0-d325ed68df36 | progressive-unsupervised-person-re | 1910.11560 | null | https://arxiv.org/abs/1910.11560v1 | https://arxiv.org/pdf/1910.11560v1.pdf | Progressive Unsupervised Person Re-identification by Tracklet Association with Spatio-Temporal Regularization | Existing methods for person re-identification (Re-ID) are mostly based on supervised learning which requires numerous manually labeled samples across all camera views for training. Such a paradigm suffers the scalability issue since in real-world Re-ID application, it is difficult to exhaustively label abundant identit... | ['Guo-Jun Qi', 'Qiaokang Xie', 'Wengang Zhou', 'Qi Tian', 'Houqiang Li'] | 2019-10-25 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-4.49089184e-02 -5.56527972e-01 4.42529377e-03 -3.49199086e-01
-5.87625682e-01 -7.19173551e-01 5.82454503e-01 2.78987288e-01
-5.31981051e-01 6.11174047e-01 2.86576867e-01 4.30511028e-01
-1.13164149e-01 -4.94897872e-01 -5.42923212e-01 -5.89836061e-01
2.20203519e-01 5.23622572e-01 2.44639833e-02 2.44904369... | [14.770283699035645, 1.0224322080612183] |
7f3fd312-3269-4e00-b429-c189779fbcd9 | iaunet-global-context-aware-feature-learning | 2009.01035 | null | https://arxiv.org/abs/2009.01035v1 | https://arxiv.org/pdf/2009.01035v1.pdf | IAUnet: Global Context-Aware Feature Learning for Person Re-Identification | Person re-identification (reID) by CNNs based networks has achieved favorable performance in recent years. However, most of existing CNNs based methods do not take full advantage of spatial-temporal context modeling. In fact, the global spatial-temporal context can greatly clarify local distractions to enhance the targ... | ['Xilin Chen', 'Ruibing Hou', 'Xinqian Gu', 'Bingpeng Ma', 'Shiguang Shan', 'Hong Chang'] | 2020-09-02 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [-3.78392965e-01 -6.13535702e-01 -1.63785443e-01 -6.66615725e-01
-3.69698524e-01 -1.83238909e-01 6.29750431e-01 -1.92668848e-02
-6.44139469e-01 4.90915805e-01 6.42451882e-01 1.09829336e-01
6.82070339e-03 -5.83735943e-01 -7.05482185e-01 -5.70306063e-01
-3.76141258e-02 -4.31650691e-02 -6.90617934e-02 -1.63138598... | [14.734582901000977, 0.939357578754425] |
2952de44-e827-464d-93d2-23f0daf14f6d | towards-human-cognition-level-based | 2211.00103 | null | https://arxiv.org/abs/2211.00103v1 | https://arxiv.org/pdf/2211.00103v1.pdf | Towards Human Cognition Level-based Experiment Design for Counterfactual Explanations (XAI) | Explainable Artificial Intelligence (XAI) has recently gained a swell of interest, as many Artificial Intelligence (AI) practitioners and developers are compelled to rationalize how such AI-based systems work. Decades back, most XAI systems were developed as knowledge-based or expert systems. These systems assumed reas... | ['Alessandro Bogliolo', 'Muhammad Yaseen Khan', 'Muhammad Suffian'] | 2022-10-31 | null | null | null | null | ['explanation-generation'] | ['natural-language-processing'] | [ 1.72183946e-01 8.38863254e-01 -2.25957766e-01 -5.24964750e-01
1.47157028e-01 -6.25861287e-01 8.24088693e-01 2.75441766e-01
9.60276946e-02 5.67738116e-01 5.02379358e-01 -1.08799064e+00
-7.25602210e-01 -6.45778716e-01 -3.71573687e-01 4.25656401e-02
3.37541431e-01 4.47962403e-01 -2.57922322e-01 -3.22454453... | [8.9550142288208, 6.122028350830078] |
bfe2e560-1c36-4022-b1c0-bfc2845360fc | online-action-detection-in-streaming-videos | 2010.03016 | null | https://arxiv.org/abs/2010.03016v1 | https://arxiv.org/pdf/2010.03016v1.pdf | Online Action Detection in Streaming Videos with Time Buffers | We formulate the problem of online temporal action detection in live streaming videos, acknowledging one important property of live streaming videos that there is normally a broadcast delay between the latest captured frame and the actual frame viewed by the audience. The standard setting of the online action detection... | ['Yuanjun Xiong', 'Meng Wang', 'Hao Chen', 'BoWen Zhang'] | 2020-10-06 | null | null | null | null | ['online-action-detection'] | ['computer-vision'] | [ 7.50979841e-01 6.70704991e-02 -4.24079567e-01 -6.97904602e-02
-6.31448567e-01 -4.36296165e-01 3.83858651e-01 3.95507887e-02
-5.64776301e-01 1.63953424e-01 2.91622430e-01 -1.80788100e-01
1.50603084e-02 -3.27183872e-01 -8.51213574e-01 -6.57314837e-01
-5.89168251e-01 -9.98615697e-02 1.02751613e+00 5.05111702... | [8.33199405670166, 0.4387865662574768] |
e1003fad-7ded-4482-ba1d-7bdef78ff3cf | iitk-detox-at-semeval-2021-task-5-semi | 2104.01566 | null | https://arxiv.org/abs/2104.01566v1 | https://arxiv.org/pdf/2104.01566v1.pdf | IITK@Detox at SemEval-2021 Task 5: Semi-Supervised Learning and Dice Loss for Toxic Spans Detection | In this work, we present our approach and findings for SemEval-2021 Task 5 - Toxic Spans Detection. The task's main aim was to identify spans to which a given text's toxicity could be attributed. The task is challenging mainly due to two constraints: the small training dataset and imbalanced class distribution. Our pap... | ['Ashutosh Modi', 'Abhay Kaushik', 'Archit Bansal'] | 2021-04-04 | null | https://aclanthology.org/2021.semeval-1.24 | https://aclanthology.org/2021.semeval-1.24.pdf | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [ 2.40998656e-01 -1.58610255e-01 -2.30885088e-01 -8.06929469e-02
-1.44629312e+00 -6.91278398e-01 6.31921053e-01 7.65833735e-01
-5.09518147e-01 1.43119979e+00 3.51005256e-01 -3.54508907e-01
-1.42875880e-01 -3.62669945e-01 -7.06503570e-01 -5.00384450e-01
-1.67112663e-01 4.76549804e-01 3.26094151e-01 6.98737502... | [8.959778785705566, 10.62716007232666] |
ba47adfc-d709-4a8e-b3d1-5f3923615826 | egocentric-image-captioning-for-privacy | 2107.00372 | null | https://arxiv.org/abs/2107.00372v2 | https://arxiv.org/pdf/2107.00372v2.pdf | Egocentric Image Captioning for Privacy-Preserved Passive Dietary Intake Monitoring | Camera-based passive dietary intake monitoring is able to continuously capture the eating episodes of a subject, recording rich visual information, such as the type and volume of food being consumed, as well as the eating behaviours of the subject. However, there currently is no method that is able to incorporate these... | ['Benny Lo', 'Gary Frost', 'Mingui Sun', 'Edward Sazonov', 'Megan A McCrory', 'Alex K. Anderson', 'Matilda Steiner-Asiedu', 'Tom Baranowski', 'Wenyan Jia', 'Modou L. Jobarteh', 'Xiao Gu', 'Frank P. -W. Lo', 'Jianing Qiu'] | 2021-07-01 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [ 3.45082581e-01 -5.58480583e-02 -3.75353247e-01 -7.32964277e-01
-3.69533300e-01 -7.41572380e-01 -1.47225946e-01 5.84751904e-01
-7.78950974e-02 9.02832225e-02 7.61675298e-01 2.35069185e-01
2.41809472e-01 -7.98427880e-01 -9.46620882e-01 -6.26864314e-01
1.68016609e-02 -1.12940729e-01 -5.87725282e-01 2.76777178... | [11.566555976867676, 4.403940200805664] |
2e52054e-c3c4-4aa3-b298-7ab88b24646a | knn-classification-with-one-step-computation | 2012.06047 | null | https://arxiv.org/abs/2012.06047v2 | https://arxiv.org/pdf/2012.06047v2.pdf | KNN Classification with One-step Computation | KNN classification is an improvisational learning mode, in which they are carried out only when a test data is predicted that set a suitable K value and search the K nearest neighbors from the whole training sample space, referred them to the lazy part of KNN classification. This lazy part has been the bottleneck probl... | ['Jiaye Li', 'Shichao Zhang'] | 2020-12-09 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 2.95988500e-01 -2.23655269e-01 -4.66049433e-01 -4.17767763e-01
-7.06658542e-01 -2.12617651e-01 2.40796641e-01 2.80880611e-02
-3.70956421e-01 6.36567175e-01 -1.43262848e-01 -1.32636353e-01
-6.48490071e-01 -7.95647681e-01 -5.00391126e-01 -9.51711297e-01
3.00500870e-01 4.73234683e-01 9.56970174e-03 2.33512625... | [8.878568649291992, 3.7284891605377197] |
5de5b855-5d66-4193-8f71-774c8cbbb53f | sentiment-analysis-of-twitter-data-for | 1610.09225 | null | http://arxiv.org/abs/1610.09225v1 | http://arxiv.org/pdf/1610.09225v1.pdf | Sentiment Analysis of Twitter Data for Predicting Stock Market Movements | Predicting stock market movements is a well-known problem of interest.
Now-a-days social media is perfectly representing the public sentiment and
opinion about current events. Especially, twitter has attracted a lot of
attention from researchers for studying the public sentiments. Stock market
prediction on the basis o... | ['Kamal Nayan Reddy Challa', 'Babita Majhi', 'Ganapati Panda', 'Venkata Sasank Pagolu'] | 2016-10-28 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-5.87009907e-01 -3.37210238e-01 -4.90469873e-01 -2.35862359e-01
4.61693220e-02 -6.01938605e-01 8.15326989e-01 6.21864557e-01
-3.69059443e-01 6.40487909e-01 6.29202604e-01 -2.70469040e-01
3.02771717e-01 -1.25802696e+00 -1.55138552e-01 -5.24848700e-01
2.15927199e-01 -5.54757416e-02 -1.78898182e-02 -9.71443534... | [4.52412223815918, 4.401846408843994] |
6b42a885-5ea4-4a54-81e9-677dd95e75fc | occupancy-networks-learning-3d-reconstruction | 1812.03828 | null | http://arxiv.org/abs/1812.03828v2 | http://arxiv.org/pdf/1812.03828v2.pdf | Occupancy Networks: Learning 3D Reconstruction in Function Space | With the advent of deep neural networks, learning-based approaches for 3D
reconstruction have gained popularity. However, unlike for images, in 3D there
is no canonical representation which is both computationally and memory
efficient yet allows for representing high-resolution geometry of arbitrary
topology. Many of t... | ['Sebastian Nowozin', 'Michael Niemeyer', 'Lars Mescheder', 'Michael Oechsle', 'Andreas Geiger'] | 2018-12-10 | occupancy-networks-learning-3d-reconstruction-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Mescheder_Occupancy_Networks_Learning_3D_Reconstruction_in_Function_Space_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Mescheder_Occupancy_Networks_Learning_3D_Reconstruction_in_Function_Space_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-shape-representation'] | ['computer-vision'] | [-3.52107920e-02 9.98338908e-02 -2.13734619e-02 -1.94531620e-01
-9.10866737e-01 -3.05608094e-01 7.28127182e-01 2.48576865e-01
-1.87988982e-01 5.00687480e-01 4.93807159e-02 -4.33982581e-01
-5.88852987e-02 -1.10597813e+00 -1.14877570e+00 -4.02876645e-01
-1.93364501e-01 1.16936994e+00 2.49939859e-01 1.77371785... | [8.419380187988281, -3.617124557495117] |
22fe418d-b6e2-4ad1-9638-2c0b29d1c8c6 | conditional-local-filters-with-explainers-for | 2101.01000 | null | https://arxiv.org/abs/2101.01000v3 | https://arxiv.org/pdf/2101.01000v3.pdf | Conditional Local Convolution for Spatio-temporal Meteorological Forecasting | Spatio-temporal forecasting is challenging attributing to the high nonlinearity in temporal dynamics as well as complex location-characterized patterns in spatial domains, especially in fields like weather forecasting. Graph convolutions are usually used for modeling the spatial dependency in meteorology to handle the ... | ['Ling Li', 'Yongjie Xu', 'Stan. Z. Li', 'Lirong Wu', 'Zhangyang Gao', 'Haitao Lin'] | 2021-01-04 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [-4.59215850e-01 -5.62853277e-01 3.08308184e-01 -4.44179475e-01
5.51308930e-01 -4.84240234e-01 7.24265695e-01 -3.72156613e-02
-1.48337319e-01 2.20987469e-01 5.05738080e-01 -7.76231945e-01
-4.33582425e-01 -1.08943570e+00 -4.89006817e-01 -6.63373470e-01
-7.70363927e-01 -3.65311354e-01 1.56971291e-01 -4.48185265... | [6.651744842529297, 2.76419997215271] |
5c7b38ef-4594-4d59-9728-f81fe1e27aa0 | elsed-enhanced-line-segment-drawing | 2108.03144 | null | https://arxiv.org/abs/2108.03144v1 | https://arxiv.org/pdf/2108.03144v1.pdf | ELSED: Enhanced Line SEgment Drawing | Detecting local features, such as corners, segments or blobs, is the first step in the pipeline of many Computer Vision applications. Its speed is crucial for real time applications. In this paper we present ELSED, the fastest line segment detector in the literature. The key for its efficiency is a local segment growin... | ['Luis Baumela', 'José M. Buenaposada', 'Iago Suárez'] | 2021-08-06 | null | null | null | null | ['line-segment-detection', 'line-detection'] | ['computer-vision', 'computer-vision'] | [ 3.20666373e-01 -2.69288510e-01 -3.64495307e-01 -1.52251154e-01
-4.02212143e-01 -7.34950483e-01 5.67988515e-01 6.89792216e-01
-4.42515284e-01 1.76807836e-01 -4.26962107e-01 -4.45762396e-01
2.76724756e-01 -6.60492361e-01 -6.50324643e-01 -4.16199714e-01
-1.21461980e-01 1.33906171e-01 1.22242820e+00 -9.55238193... | [8.287923812866211, -1.5581036806106567] |
a90d94d3-80ee-4a6c-9367-23364ecde054 | argument-pair-extraction-with-mutual-guidance | null | null | https://aclanthology.org/2021.emnlp-main.319 | https://aclanthology.org/2021.emnlp-main.319.pdf | Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph | Argument pair extraction (APE) aims to extract interactive argument pairs from two passages of a discussion. Previous work studied this task in the context of peer review and rebuttal, and decomposed it into a sequence labeling task and a sentence relation classification task. However, despite the promising performance... | ['Ruifeng Xu', 'Min Yang', 'Yice Zhang', 'Jingyi Sun', 'Bin Liang', 'Jianzhu Bao'] | null | null | null | null | emnlp-2021-11 | ['argument-pair-extraction-ape', 'relation-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.76968920e-01 5.31325102e-01 -3.05168301e-01 -3.53635579e-01
-6.35215282e-01 -5.77560425e-01 1.00259233e+00 9.14413154e-01
-2.93178290e-01 7.02553451e-01 4.96534109e-01 -6.59392953e-01
-3.39335322e-01 -8.67540359e-01 -4.79387224e-01 -2.96055913e-01
2.80734777e-01 3.43566090e-01 3.42607677e-01 -5.72207868... | [9.757883071899414, 9.205171585083008] |
71c45b6c-e393-4bf8-8930-f2dd3145574e | attention-enhanced-cross-modal-localization | 2212.02757 | null | https://arxiv.org/abs/2212.02757v1 | https://arxiv.org/pdf/2212.02757v1.pdf | Attention-Enhanced Cross-modal Localization Between 360 Images and Point Clouds | Visual localization plays an important role for intelligent robots and autonomous driving, especially when the accuracy of GNSS is unreliable. Recently, camera localization in LiDAR maps has attracted more and more attention for its low cost and potential robustness to illumination and weather changes. However, the com... | ['Sebastian Scherer', 'Wen Yang', 'Chenwei Lyv', 'Huai Yu', 'Zhipeng Zhao'] | 2022-12-06 | null | null | null | null | ['camera-localization', 'visual-localization'] | ['computer-vision', 'computer-vision'] | [-4.31053370e-01 -6.33511603e-01 -3.38815272e-01 -6.45702302e-01
-3.58893543e-01 -4.77438509e-01 4.01286274e-01 -1.35079324e-01
-7.53327549e-01 5.42485714e-01 -2.48142079e-01 -2.21474499e-01
-3.53189170e-01 -7.43225873e-01 -8.15236330e-01 -5.19892335e-01
7.91281834e-02 3.27692002e-01 1.04487561e-01 -1.17244750... | [7.6315155029296875, -2.075411319732666] |
9a02317b-c59c-46d6-954a-39783976076e | understanding-convolution-for-semantic | 1702.08502 | null | http://arxiv.org/abs/1702.08502v3 | http://arxiv.org/pdf/1702.08502v3.pdf | Understanding Convolution for Semantic Segmentation | Recent advances in deep learning, especially deep convolutional neural
networks (CNNs), have led to significant improvement over previous semantic
segmentation systems. Here we show how to improve pixel-wise semantic
segmentation by manipulating convolution-related operations that are of both
theoretical and practical ... | ['Garrison Cottrell', 'Ye Yuan', 'Xiaodi Hou', 'Pengfei Chen', 'Zehua Huang', 'Panqu Wang', 'Ding Liu'] | 2017-02-27 | null | null | null | null | ['thermal-image-segmentation'] | ['computer-vision'] | [ 3.80771250e-01 1.82933241e-01 1.31722074e-02 -5.27576387e-01
-6.89106286e-01 -3.54763538e-01 4.53637362e-01 -2.59012640e-01
-6.36886179e-01 6.15840435e-01 4.86335903e-03 -4.73552793e-01
3.97555947e-01 -9.99867857e-01 -9.21349704e-01 -6.66099548e-01
3.65783051e-02 8.94586146e-02 5.74021518e-01 -1.63661584... | [9.489218711853027, 0.010646681301295757] |
2e56439e-3810-4bc0-b020-0052367e1441 | efficient-domain-generalization-via-common | 2003.12815 | null | https://arxiv.org/abs/2003.12815v2 | https://arxiv.org/pdf/2003.12815v2.pdf | Efficient Domain Generalization via Common-Specific Low-Rank Decomposition | Domain generalization refers to the task of training a model which generalizes to new domains that are not seen during training. We present CSD (Common Specific Decomposition), for this setting,which jointly learns a common component (which generalizes to new domains) and a domain specific component (which overfits on ... | ['Sunita Sarawagi', 'Praneeth Netrapalli', 'Vihari Piratla'] | 2020-03-28 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/4649-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/4649-Paper.pdf | icml-2020-1 | ['rotated-mnist'] | ['computer-vision'] | [ 7.01192677e-01 2.34920606e-01 -4.07535434e-01 -4.22766060e-01
-4.03282911e-01 -1.00322473e+00 8.09556007e-01 1.97357405e-02
-3.59784245e-01 1.03716028e+00 3.20837379e-01 -4.69880790e-01
-5.48386097e-01 -5.46349227e-01 -8.63923132e-01 -6.17016852e-01
-3.09729874e-01 7.01933205e-01 -1.06032610e-01 -3.44367266... | [10.283004760742188, 3.016594886779785] |
b767a98c-bb52-45b8-9352-084d67e31eb5 | selfd-self-learning-large-scale-driving | 2204.10320 | null | https://arxiv.org/abs/2204.10320v1 | https://arxiv.org/pdf/2204.10320v1.pdf | SelfD: Self-Learning Large-Scale Driving Policies From the Web | Effectively utilizing the vast amounts of ego-centric navigation data that is freely available on the internet can advance generalized intelligent systems, i.e., to robustly scale across perspectives, platforms, environmental conditions, scenarios, and geographical locations. However, it is difficult to directly levera... | ['Eshed Ohn-Bar', 'Ruizhao Zhu', 'Jimuyang Zhang'] | 2022-04-21 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhang_SelfD_Self-Learning_Large-Scale_Driving_Policies_From_the_Web_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhang_SelfD_Self-Learning_Large-Scale_Driving_Policies_From_the_Web_CVPR_2022_paper.pdf | cvpr-2022-1 | ['self-learning'] | ['natural-language-processing'] | [-1.07769646e-01 -2.68424465e-03 -2.20820993e-01 -5.04845202e-01
-6.02213144e-01 -8.56564045e-01 5.78077018e-01 -3.92672420e-01
-6.03587151e-01 6.62648916e-01 1.31603286e-01 -5.56394994e-01
2.81196386e-01 -6.34070098e-01 -9.68993187e-01 -3.23866695e-01
1.72456399e-01 2.76580989e-01 3.08966756e-01 -4.10312951... | [4.504973888397217, 0.6570364832878113] |
11084c83-19fc-4220-8029-2c9a9d84eed3 | long-term-spatio-temporal-forecasting-via | 2204.11008 | null | https://arxiv.org/abs/2204.11008v4 | https://arxiv.org/pdf/2204.11008v4.pdf | Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention | Many real-world ubiquitous applications, such as parking recommendations and air pollution monitoring, benefit significantly from accurate long-term spatio-temporal forecasting (LSTF). LSTF makes use of long-term dependency between spatial and temporal domains, contextual information, and inherent pattern in the data. ... | ['Flora Salim', 'Junshan Zhang', 'Zhaofeng Zhang', 'Hamid Menouar', 'Xiao Xiao', 'Yufan Kang', 'Shuo Wang', 'Zhiling Jin', 'Wei Shao'] | 2022-04-23 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [-1.63199618e-01 -3.07588518e-01 -3.21155608e-01 -3.36360544e-01
-1.61100760e-01 -9.78109613e-02 3.25700015e-01 4.13762182e-01
1.12419620e-01 5.34409106e-01 3.93351883e-01 -3.73643935e-01
-5.74842036e-01 -1.04596353e+00 -5.98470628e-01 -6.62452459e-01
-3.13029528e-01 1.73797905e-01 3.33496243e-01 -2.83347964... | [6.680901050567627, 2.5162508487701416] |
3f5d9c57-497c-45bb-b838-130c56ee124b | bayesian-pseudo-labels-expectation | 2208.04435 | null | https://arxiv.org/abs/2208.04435v3 | https://arxiv.org/pdf/2208.04435v3.pdf | Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi-Supervised Segmentation | This paper concerns pseudo labelling in segmentation. Our contribution is fourfold. Firstly, we present a new formulation of pseudo-labelling as an Expectation-Maximization (EM) algorithm for clear statistical interpretation. Secondly, we propose a semi-supervised medical image segmentation method purely based on the o... | ['Joseph Jacob', 'Yipeng Hu', 'Neil P. Oxtoby', 'Daniel C. Alexander', 'Marius de Groot', 'Chen Jin', 'Yukun Zhou', 'Mou-Cheng Xu'] | 2022-08-08 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 4.84173983e-01 8.40558290e-01 2.22817019e-01 -5.09783149e-01
-1.41201377e+00 -4.37224984e-01 6.48756742e-01 6.32417202e-02
-6.56651735e-01 6.61379576e-01 -4.26632762e-02 -3.33801389e-01
-1.81126580e-01 -4.85263944e-01 -8.71733367e-01 -1.12116361e+00
2.16573969e-01 8.80789518e-01 4.44456488e-01 3.99505913... | [14.468591690063477, -2.095590829849243] |
e8995d9e-d7d5-467b-ae3c-ef42eb65e7ae | a-pretraining-numerical-reasoning-model-for | null | null | https://aclanthology.org/2021.findings-emnlp.159 | https://aclanthology.org/2021.findings-emnlp.159.pdf | A Pretraining Numerical Reasoning Model for Ordinal Constrained Question Answering on Knowledge Base | Knowledge Base Question Answering (KBQA) is to answer natural language questions posed over knowledge bases (KBs). This paper targets at empowering the IR-based KBQA models with the ability of numerical reasoning for answering ordinal constrained questions. A major challenge is the lack of explicit annotations about nu... | ['Hong Chen', 'Cuiping Li', 'Quan Liu', 'Lemao Liu', 'Wayne Xin Zhao', 'Gaole He', 'Jing Zhang', 'Yu Feng'] | null | null | null | null | findings-emnlp-2021-11 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-3.59326690e-01 6.16020739e-01 -3.38373452e-01 -6.18511915e-01
-1.03396285e+00 -6.80953801e-01 2.23712713e-01 9.96975601e-02
-3.15665960e-01 8.83301973e-01 2.47044295e-01 -6.31073773e-01
-1.91017315e-01 -1.38343489e+00 -9.93353248e-01 7.57441223e-02
1.24601431e-01 7.97855198e-01 1.40912786e-01 -9.39621806... | [10.413310050964355, 7.90489387512207] |
663a01bb-f12a-49e2-a30f-fa9fa5a9dee6 | cleaning-noisy-and-heterogeneous-metadata-for | 1906.08470 | null | https://arxiv.org/abs/1906.08470v1 | https://arxiv.org/pdf/1906.08470v1.pdf | Cleaning Noisy and Heterogeneous Metadata for Record Linking Across Scholarly Big Datasets | Automatically extracted metadata from scholarly documents in PDF formats is usually noisy and heterogeneous, often containing incomplete fields and erroneous values. One common way of cleaning metadata is to use a bibliographic reference dataset. The challenge is to match records between corpora with high precision. Th... | ['Jian Wu', 'Cornelia Caragea', 'Athar Sefid', 'Prasenjit Mitra', 'Lu Liu', 'Jing Zhao', 'C. Lee Giles', 'Allen C. Ge'] | 2019-06-20 | null | null | null | null | ['record-linking'] | ['natural-language-processing'] | [-4.23436791e-01 -3.91234048e-02 -5.45891285e-01 -4.43221107e-02
-1.82661486e+00 -8.73035669e-01 7.09043443e-01 8.21398377e-01
-6.13549471e-01 1.12037969e+00 5.96156001e-01 4.39746454e-02
-6.09196603e-01 -8.09251428e-01 -9.02251422e-01 -2.80431449e-01
4.13712770e-01 6.40868306e-01 2.30797917e-01 2.20513776... | [9.452275276184082, 8.38356876373291] |
d54cc66b-58a8-46de-9bb7-aafaf16ac898 | motion-capture-from-pan-tilt-cameras-with | 1908.11676 | null | https://arxiv.org/abs/1908.11676v1 | https://arxiv.org/pdf/1908.11676v1.pdf | Motion Capture from Pan-Tilt Cameras with Unknown Orientation | In sports, such as alpine skiing, coaches would like to know the speed and various biomechanical variables of their athletes and competitors. Existing methods use either body-worn sensors, which are cumbersome to setup, or manual image annotation, which is time consuming. We propose a method for estimating an athlete's... | ['Jörg Spörri', 'Pascal Fua', 'Roman Bachmann', 'Helge Rhodin'] | 2019-08-30 | null | null | null | null | ['markerless-motion-capture'] | ['computer-vision'] | [-1.08709857e-01 -9.41333398e-02 -2.59788960e-01 -5.08740768e-02
-6.39627695e-01 -7.58533716e-01 3.47222351e-02 -6.00520521e-02
-7.84424245e-01 3.75832230e-01 1.40156748e-03 4.07467902e-01
7.51483664e-02 -4.37431753e-01 -7.65378475e-01 -4.40264046e-01
6.99934289e-02 8.69117439e-01 5.82782567e-01 -3.09621811... | [7.207302093505859, -0.8509618639945984] |
f0fc307e-1ed5-441c-b04d-ff6dd9d5f4d4 | scan-a-spatial-context-attentive-network-for | 2102.00109 | null | https://arxiv.org/abs/2102.00109v2 | https://arxiv.org/pdf/2102.00109v2.pdf | SCAN: A Spatial Context Attentive Network for Joint Multi-Agent Intent Prediction | Safe navigation of autonomous agents in human centric environments requires the ability to understand and predict motion of neighboring pedestrians. However, predicting pedestrian intent is a complex problem. Pedestrian motion is governed by complex social navigation norms, is dependent on neighbors' trajectories, and ... | ['Cody Fleming', 'Jasmine Sekhon'] | 2021-01-29 | null | null | null | null | ['social-navigation'] | ['robots'] | [-3.63514632e-01 -4.72788885e-02 -3.26986849e-01 -4.35606688e-01
-1.34767100e-01 -4.52389330e-01 7.97566473e-01 1.05567843e-01
-7.37641335e-01 8.49255800e-01 7.77404606e-01 -5.96440494e-01
-8.95338282e-02 -9.66826916e-01 -7.45544612e-01 -3.69261146e-01
-3.73269767e-01 3.65925461e-01 5.84461689e-01 -4.76044953... | [6.018428802490234, 0.7580181360244751] |
635d1130-b1e4-43ea-8a3f-dda90cf8784b | drum-end-to-end-differentiable-rule-mining-on | 1911.00055 | null | https://arxiv.org/abs/1911.00055v1 | https://arxiv.org/pdf/1911.00055v1.pdf | DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs | In this paper, we study the problem of learning probabilistic logical rules for inductive and interpretable link prediction. Despite the importance of inductive link prediction, most previous works focused on transductive link prediction and cannot manage previously unseen entities. Moreover, they are black-box models ... | ['Patrick Ding', 'Ali Sadeghian', 'Mohammadreza Armandpour', 'Daisy Zhe Wang'] | 2019-10-31 | drum-end-to-end-differentiable-rule-mining-on-1 | http://papers.nips.cc/paper/9669-drum-end-to-end-differentiable-rule-mining-on-knowledge-graphs | http://papers.nips.cc/paper/9669-drum-end-to-end-differentiable-rule-mining-on-knowledge-graphs.pdf | neurips-2019-12 | ['inductive-link-prediction', 'inductive-knowledge-graph-completion'] | ['graphs', 'knowledge-base'] | [-6.09908924e-02 7.99934745e-01 -9.01262760e-01 -5.73846221e-01
-1.54630765e-01 -2.44173065e-01 2.92637080e-01 3.87071759e-01
2.98146486e-01 1.07120109e+00 4.70521688e-01 -9.82036293e-01
-9.93707955e-01 -1.22578967e+00 -1.03869498e+00 1.02684058e-01
-9.38401759e-01 1.05986333e+00 2.70085186e-01 -4.17848736... | [8.881119728088379, 7.766942977905273] |
7df7abcc-f2c5-40db-9449-3bed1894ddb0 | comstreamclust-a-communicative-text | 2010.05349 | null | https://arxiv.org/abs/2010.05349v2 | https://arxiv.org/pdf/2010.05349v2.pdf | ComStreamClust: a communicative multi-agent approach to text clustering in streaming data | Topic detection is the task of determining and tracking hot topics in social media. Twitter is arguably the most popular platform for people to share their ideas with others about different issues. One such prevalent issue is the COVID-19 pandemic. Detecting and tracking topics on these kinds of issues would help gover... | ['Meysam Asgari-Chenaghlu', 'Ali Mohammadpur-Fard', 'Rahim Dehkharghani', 'Araz Gholipour-Shilabin', 'Ali Najafi'] | 2020-10-11 | null | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [-3.26221168e-01 -2.54750121e-02 -1.37569591e-01 -8.83481652e-02
-5.24355292e-01 -3.68058056e-01 9.50931966e-01 1.06790841e+00
-4.96759832e-01 6.33993566e-01 5.40234327e-01 2.53521591e-01
-1.74599618e-01 -9.41558242e-01 6.57715797e-02 -7.34695852e-01
-2.27047116e-01 8.26845527e-01 3.03064734e-01 -1.95386261... | [10.335811614990234, 7.315977573394775] |
1dd50640-b359-4e8e-920a-3b75bd03b4f7 | videonavqa-bridging-the-gap-between-visual | 1908.04950 | null | https://arxiv.org/abs/1908.04950v1 | https://arxiv.org/pdf/1908.04950v1.pdf | VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering | Embodied Question Answering (EQA) is a recently proposed task, where an agent is placed in a rich 3D environment and must act based solely on its egocentric input to answer a given question. The desired outcome is that the agent learns to combine capabilities such as scene understanding, navigation and language underst... | ['Pietro Liò', 'Cătălina Cangea', 'Eugene Belilovsky', 'Aaron Courville'] | 2019-08-14 | null | null | null | null | ['embodied-question-answering'] | ['computer-vision'] | [-7.16202557e-02 1.86125696e-01 5.48892856e-01 -3.05613875e-01
-6.84470713e-01 -8.86790693e-01 1.00207233e+00 -1.78057671e-01
-5.38571775e-01 5.15705943e-01 2.28993624e-01 -5.98865032e-01
-2.21889228e-01 -7.26240396e-01 -7.42702365e-01 -2.70502895e-01
-3.87072526e-02 7.67980456e-01 2.62470454e-01 -7.65719354... | [4.390598773956299, 0.566823422908783] |
93ccd812-807d-44c2-8e0d-a0428ed80c51 | controlling-epidemic-spread-using | 2202.08296 | null | https://arxiv.org/abs/2202.08296v1 | https://arxiv.org/pdf/2202.08296v1.pdf | Controlling Epidemic Spread using Probabilistic Diffusion Models on Networks | The spread of an epidemic is often modeled by an SIR random process on a social network graph. The MinINF problem for optimal social distancing involves minimizing the expected number of infections, when we are allowed to break at most $B$ edges; similarly the MinINFNode problem involves removing at most $B$ vertices. ... | ['Anil Vullikanti', 'Leonidas Tsepenekas', 'Aravind Srinivasan', 'Michael Dinitz', 'Amy Babay'] | 2022-02-16 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 1.70436069e-01 6.84225261e-01 -1.59169883e-01 2.87201196e-01
8.09126720e-03 -5.05746901e-01 1.55731484e-01 3.42183322e-01
-4.59301680e-01 9.50722992e-01 -5.19216001e-01 -5.18800080e-01
-9.01540220e-01 -1.24281907e+00 -6.78467155e-01 -7.42641568e-01
-8.59795511e-01 1.13956904e+00 2.84098893e-01 -4.47575897... | [6.709329605102539, 5.157647132873535] |
0301a3c7-41a1-45d3-b39f-41069347986d | task-specific-alignment-and-multiple-level | 2307.01985 | null | https://arxiv.org/abs/2307.01985v1 | https://arxiv.org/pdf/2307.01985v1.pdf | Task-Specific Alignment and Multiple Level Transformer for Few-Shot Action Recognition | In the research field of few-shot learning, the main difference between image-based and video-based is the additional temporal dimension for videos. In recent years, many approaches for few-shot action recognition have followed the metric-based methods, especially, since some works use the Transformer to get the cross-... | ['YiWang Wang', 'Li Zhu', 'Fei Guo'] | 2023-07-05 | null | null | null | null | ['few-shot-action-recognition', 'action-recognition-in-videos', 'few-shot-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [-4.61013056e-02 -6.06072545e-01 -2.00124428e-01 -3.87405336e-01
-6.85456276e-01 1.96597576e-02 3.30541402e-01 -1.38403729e-01
-7.22914994e-01 3.45090598e-01 3.67319316e-01 3.34895551e-01
-1.14853464e-01 -5.17568350e-01 -6.50486350e-01 -7.89948940e-01
1.16407447e-01 -5.51714674e-02 8.07594895e-01 -2.36654505... | [8.585698127746582, 0.7590094208717346] |
730ae128-a07d-4e03-8b61-fedb329f4f08 | fast-and-large-scale-unsupervised-relation | null | null | https://aclanthology.org/Y15-1012 | https://aclanthology.org/Y15-1012.pdf | Fast and Large-scale Unsupervised Relation Extraction | null | ['Kentaro Inui', 'Sho Takase', 'Naoaki Okazaki'] | 2015-10-01 | fast-and-large-scale-unsupervised-relation-1 | https://aclanthology.org/Y15-1012 | https://aclanthology.org/Y15-1012.pdf | paclic-2015-10 | ['open-information-extraction'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.3260817527771, 3.858095407485962] |
270ab9b1-6ed5-4a5d-8619-6d04a481e469 | impara-impact-based-metric-for-gec-using | null | null | https://aclanthology.org/2022.coling-1.316 | https://aclanthology.org/2022.coling-1.316.pdf | IMPARA: Impact-Based Metric for GEC Using Parallel Data | Automatic evaluation of grammatical error correction (GEC) is essential in developing useful GEC systems. Existing methods for automatic evaluation require multiple reference sentences or manual scores. However, such resources are expensive, thereby hindering automatic evaluation for various domains and correction styl... | ['Naoaki Okazaki', 'Masahiro Kaneko', 'Koki Maeda'] | null | null | null | null | coling-2022-10 | ['grammatical-error-correction'] | ['natural-language-processing'] | [-1.45016998e-01 -8.35871026e-02 4.21961963e-01 -7.16518760e-01
-1.07954311e+00 -7.38080442e-01 1.78345799e-01 8.81907165e-01
-7.81060576e-01 7.56904721e-01 2.95957088e-01 -2.46749640e-01
1.62639152e-02 -7.57595062e-01 -3.73663813e-01 5.48118092e-02
4.71228331e-01 5.36669970e-01 3.25914919e-02 -6.57922804... | [11.07904052734375, 10.680402755737305] |
a043866f-f34d-4f63-8433-a251fe257fab | exploiting-pseudo-future-contexts-for-emotion | 2306.15376 | null | https://arxiv.org/abs/2306.15376v1 | https://arxiv.org/pdf/2306.15376v1.pdf | Exploiting Pseudo Future Contexts for Emotion Recognition in Conversations | With the extensive accumulation of conversational data on the Internet, emotion recognition in conversations (ERC) has received increasing attention. Previous efforts of this task mainly focus on leveraging contextual and speaker-specific features, or integrating heterogeneous external commonsense knowledge. Among them... | ['Guanglu Wan', 'Tong Mo', 'Wei Ye', 'Hailei Yan', 'Shuaipeng Liu', 'Yinyi Wei'] | 2023-06-27 | null | null | null | null | ['emotion-recognition'] | ['computer-vision'] | [ 2.57098436e-01 -1.05171613e-01 -1.02217562e-01 -6.29530907e-01
-5.80410957e-01 -5.20850718e-01 8.72847736e-01 7.42293596e-02
-2.30404615e-01 8.64721179e-01 8.40950012e-01 -1.26083910e-01
1.95333421e-01 -5.68672061e-01 -1.74265712e-01 -5.39920926e-01
1.27349928e-01 -1.51301801e-01 -2.45232120e-01 -7.24152684... | [13.028871536254883, 6.092994689941406] |
bd843c68-eb47-4b24-a4f7-9ead75bac21d | benchmarking-joint-face-spoofing-and-forgery | 2208.05401 | null | https://arxiv.org/abs/2208.05401v1 | https://arxiv.org/pdf/2208.05401v1.pdf | Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues | Face anti-spoofing (FAS) and face forgery detection play vital roles in securing face biometric systems from presentation attacks (PAs) and vicious digital manipulation (e.g., deepfakes). Despite promising performance upon large-scale data and powerful deep models, the generalization problem of existing approaches is s... | ['Alex C. Kot', 'Jingang Shi', 'Wenhan Yang', 'Zhi Li', 'Rizhao Cai', 'Zitong Yu'] | 2022-08-10 | null | null | null | null | ['face-anti-spoofing'] | ['computer-vision'] | [ 1.58967614e-01 -3.87912333e-01 5.80675602e-02 -2.57928163e-01
-5.76579452e-01 -5.50667882e-01 4.54367578e-01 -6.73666894e-01
8.95655379e-02 4.61318880e-01 7.67611191e-02 -4.61115967e-03
8.61042961e-02 -5.58471680e-01 -5.85227907e-01 -1.32628882e+00
-6.46984801e-02 -5.30085266e-01 -2.01703146e-01 -2.66928166... | [13.0045804977417, 1.1496691703796387] |
56b284ca-4438-44d1-8431-f76a7bfe8e82 | sequential-edge-detection-using-joint | 2302.14247 | null | https://arxiv.org/abs/2302.14247v1 | https://arxiv.org/pdf/2302.14247v1.pdf | Sequential edge detection using joint hierarchical Bayesian learning | This paper introduces a new sparse Bayesian learning (SBL) algorithm that jointly recovers a temporal sequence of edge maps from noisy and under-sampled Fourier data. The new method is cast in a Bayesian framework and uses a prior that simultaneously incorporates intra-image information to promote sparsity in each indi... | ['Guohui Song', 'Anne Gelb', 'Yao Xiao'] | 2023-02-28 | null | null | null | null | ['edge-detection'] | ['computer-vision'] | [ 3.98407727e-01 8.58263019e-03 4.47222143e-02 -3.52061808e-01
-9.05588627e-01 -2.53976583e-01 6.50739551e-01 -1.33321825e-02
-4.18463349e-01 7.25424349e-01 1.84180737e-01 3.52605432e-01
-3.94127369e-01 -5.73326766e-01 -6.30729258e-01 -9.23066258e-01
-3.43835384e-01 9.44812819e-02 5.65255165e-01 2.04513714... | [11.511202812194824, -2.377856492996216] |
08f6cb65-7bb1-4229-a53f-2fc0de4947ef | picture-word-interference-in-language | 2303.09201 | null | https://arxiv.org/abs/2303.09201v1 | https://arxiv.org/pdf/2303.09201v1.pdf | Picture-word interference in language production studies: Exploring the roles of attention and processing times | The picture-word interference paradigm (participants name target pictures while ignoring distractor words) is often used to model the planning processes involved in word production. The participants' naming times are delayed in the presence of a distractor (general interference). The size of this effect depends on the ... | ['Sylvain Madec', 'Audrey Bürki'] | 2023-03-16 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [ 1.75444201e-01 -5.49713314e-01 2.56124824e-01 5.80976077e-04
2.67979354e-01 -5.63324869e-01 8.24932218e-01 4.23859864e-01
-9.06567693e-01 9.27141979e-02 4.95826155e-01 -1.49183795e-01
-8.06780607e-02 -6.04810953e-01 -2.75869697e-01 -7.01397419e-01
-1.61165781e-02 1.33589908e-01 1.98524907e-01 1.15493797... | [10.290511131286621, 2.286548376083374] |
e46ee0f2-65aa-4bee-a05b-d95bbcb43938 | 3d-cartoon-face-generation-with-controllable | 2207.14425 | null | https://arxiv.org/abs/2207.14425v1 | https://arxiv.org/pdf/2207.14425v1.pdf | 3D Cartoon Face Generation with Controllable Expressions from a Single GAN Image | In this paper, we investigate an open research task of generating 3D cartoon face shapes from single 2D GAN generated human faces and without 3D supervision, where we can also manipulate the facial expressions of the 3D shapes. To this end, we discover the semantic meanings of StyleGAN latent space, such that we are ab... | ['Chunyan Miao', 'Steven C. H. Hoi', 'Guosheng Lin', 'Hao Wang'] | 2022-07-29 | null | null | null | null | ['face-model'] | ['computer-vision'] | [ 2.18107626e-01 4.80863124e-01 2.63682783e-01 -3.69584531e-01
-9.70856845e-02 -1.04528451e+00 6.70813203e-01 -1.07635057e+00
2.44771898e-01 4.93805081e-01 2.70428240e-01 7.32524320e-02
5.43174446e-01 -8.42996478e-01 -8.01968694e-01 -6.71600878e-01
4.82703239e-01 4.96687412e-01 -5.37460864e-01 -1.70682460... | [12.568704605102539, -0.2928411066532135] |
01524ae4-7456-4065-b20a-f3df08591c0a | computational-tradeoff-in-minimum-obstacle | 2302.07114 | null | https://arxiv.org/abs/2302.07114v1 | https://arxiv.org/pdf/2302.07114v1.pdf | Computational Tradeoff in Minimum Obstacle Displacement Planning for Robot Navigation | In this paper, we look into the minimum obstacle displacement (MOD) planning problem from a mobile robot motion planning perspective. This problem finds an optimal path to goal by displacing movable obstacles when no path exists due to collision with obstacles. However this problem is computationally expensive and grow... | ['Michela Robba', 'Fulvio Mastrogiovanni', 'Giulio Ferro', 'Antony Thomas'] | 2023-02-14 | null | null | null | null | ['robot-navigation', 'motion-planning'] | ['robots', 'robots'] | [ 2.51867384e-01 4.86672699e-01 -4.39849764e-01 1.73642144e-01
-4.87319291e-01 -7.84537435e-01 2.87566453e-01 2.15983853e-01
-9.17687535e-01 1.03587198e+00 -3.38088810e-01 -8.42291832e-01
-3.18800807e-01 -1.15537727e+00 -4.37048972e-01 -4.33970094e-01
-6.83042824e-01 8.49543393e-01 9.55098152e-01 -8.71499419... | [5.020934581756592, 1.637241244316101] |
811a0604-fdf8-4b16-b57b-5596a38b50a8 | pseco-pseudo-labeling-and-consistency | 2203.16317 | null | https://arxiv.org/abs/2203.16317v2 | https://arxiv.org/pdf/2203.16317v2.pdf | PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised Object Detection | In this paper, we delve into two key techniques in Semi-Supervised Object Detection (SSOD), namely pseudo labeling and consistency training. We observe that these two techniques currently neglect some important properties of object detection, hindering efficient learning on unlabeled data. Specifically, for pseudo labe... | ['Shanshan Zhang', 'Ding Liang', 'Yichao Wu', 'Yujie Wang', 'Xiang Li', 'Gang Li'] | 2022-03-30 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [-1.56191215e-02 4.66665486e-03 -4.27934378e-01 -4.17753279e-01
-1.09965491e+00 -7.07249820e-01 5.88373065e-01 1.12505727e-01
-2.98387200e-01 4.57673609e-01 -2.25813851e-01 -3.11401375e-02
5.28065823e-02 -4.05615240e-01 -7.25015104e-01 -8.65258634e-01
2.25113556e-01 5.20770967e-01 7.32651830e-01 1.89590991... | [9.203326225280762, 1.2724902629852295] |
b1e03163-6a80-4c82-a6cd-c835b8cdf860 | deep-smart-contract-intent-detection | 2211.10724 | null | https://arxiv.org/abs/2211.10724v1 | https://arxiv.org/pdf/2211.10724v1.pdf | Deep Smart Contract Intent Detection | Nowadays, security activities in smart contracts concentrate on vulnerability detection. Despite early success, we find that developers' intent to write smart contracts is a more noteworthy security concern because smart contracts with malicious intent have caused significant users' financial loss. Unfortunately, curre... | ['Youshuai Tan', 'Sen Fang', 'Tao Zhang', 'Youwei Huang'] | 2022-11-19 | null | null | null | null | ['vulnerability-detection', 'intent-detection'] | ['miscellaneous', 'natural-language-processing'] | [ 1.57174021e-01 1.26879215e-01 -2.29798734e-01 -4.82312977e-01
-1.06567073e+00 -8.21023285e-01 5.29073179e-01 -4.33428675e-01
1.19756185e-01 1.78521663e-01 5.82454801e-01 -8.28356802e-01
2.20063999e-01 -6.41030133e-01 -3.35256666e-01 -6.39161348e-01
1.79901332e-01 4.00312282e-02 -1.69610139e-02 -7.87008703... | [6.83836030960083, 7.345325946807861] |
588f1a80-5f74-41e7-be54-9e749e4ff8a9 | confidence-estimation-for-knowledge-base | null | null | https://aclanthology.org/R13-1051 | https://aclanthology.org/R13-1051.pdf | Confidence Estimation for Knowledge Base Population | null | ['Xiang Li', 'Ralph Grishman'] | 2013-09-01 | confidence-estimation-for-knowledge-base-1 | https://aclanthology.org/R13-1051 | https://aclanthology.org/R13-1051.pdf | ranlp-2013-9 | ['knowledge-base-population'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.507970333099365, 3.5807580947875977] |
8e85a784-fd63-4712-8f57-cba955109769 | deep-learning-framework-for-real-time-fetal | 2205.01675 | null | https://arxiv.org/abs/2205.01675v1 | https://arxiv.org/pdf/2205.01675v1.pdf | Deep Learning Framework for Real-time Fetal Brain Segmentation in MRI | Fetal brain segmentation is an important first step for slice-level motion correction and slice-to-volume reconstruction in fetal MRI. Fast and accurate segmentation of the fetal brain on fetal MRI is required to achieve real-time fetal head pose estimation and motion tracking for slice re-acquisition and steering. To ... | ['Ali Gholipour', 'Deniz Erdogmus', 'Davood Karimi', 'Razieh Faghihpirayesh'] | 2022-05-02 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [ 7.90898949e-02 1.72539920e-01 2.39790052e-01 -8.59382629e-01
-6.83073938e-01 -4.50513870e-01 4.60289791e-02 -1.35233253e-01
-5.48822284e-01 4.90153104e-01 -2.27402281e-02 -4.11688685e-01
-1.94729269e-01 -4.98146325e-01 -6.55970991e-01 -6.59167588e-01
-6.06442511e-01 5.64649343e-01 3.12226593e-01 4.36501980... | [14.019425392150879, -2.402336359024048] |
d7d5452c-cb3e-4ff7-a7cc-73d9d0c0f3d6 | pointit-a-fast-tracking-framework-based-on-3d | 1902.06379 | null | http://arxiv.org/abs/1902.06379v1 | http://arxiv.org/pdf/1902.06379v1.pdf | PointIT: A Fast Tracking Framework Based on 3D Instance Segmentation | Recently most popular tracking frameworks focus on 2D image sequences. They
seldom track the 3D object in point clouds. In this paper, we propose PointIT,
a fast, simple tracking method based on 3D on-road instance segmentation.
Firstly, we transform 3D LiDAR data into the spherical image with the size of
64 x 512 x 4 ... | ['Yu-An Wang', 'Yang Yu', 'Ming Liu'] | 2019-02-18 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [-1.89425558e-01 -3.58056575e-01 -3.93184721e-01 -2.67527908e-01
-5.36538363e-01 -4.67031300e-01 3.71442378e-01 -5.50572515e-01
-4.99109656e-01 4.01964337e-01 -4.03388232e-01 -3.52627307e-01
9.58524495e-02 -8.40713263e-01 -1.04850340e+00 -3.34604979e-01
2.94710308e-01 8.59563410e-01 9.07720625e-01 3.00945491... | [6.643890857696533, -2.3702685832977295] |
7f1c506f-fe82-4eec-99c1-58ae65e565c9 | bbs-net-rgb-d-salient-object-detection-with-a | 2007.02713 | null | https://arxiv.org/abs/2007.02713v3 | https://arxiv.org/pdf/2007.02713v3.pdf | Bifurcated backbone strategy for RGB-D salient object detection | Multi-level feature fusion is a fundamental topic in computer vision. It has been exploited to detect, segment and classify objects at various scales. When multi-level features meet multi-modal cues, the optimal feature aggregation and multi-modal learning strategy become a hot potato. In this paper, we leverage the in... | ['Ling Shao', 'Deng-Ping Fan', 'Liang Wang', 'Junwei Han', 'Ali Borji', 'Jufeng Yang', 'Yingjie Zhai'] | 2020-07-06 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 7.21291676e-02 -3.07859719e-01 1.24837393e-02 -4.26604062e-01
-9.67988372e-01 -2.49038920e-01 3.85618001e-01 -1.93208363e-02
-3.35770667e-01 3.09017032e-01 3.87165025e-02 1.56993028e-02
-2.53171504e-01 -7.91107595e-01 -4.77711350e-01 -8.84056151e-01
8.56262520e-02 -2.03234926e-01 7.80181944e-01 -2.03410760... | [9.653342247009277, -0.8716728091239929] |
9434b4dd-f47e-4871-9520-5aa9edf9d809 | unsupervised-machine-translation-using | 1711.00043 | null | http://arxiv.org/abs/1711.00043v2 | http://arxiv.org/pdf/1711.00043v2.pdf | Unsupervised Machine Translation Using Monolingual Corpora Only | Machine translation has recently achieved impressive performance thanks to
recent advances in deep learning and the availability of large-scale parallel
corpora. There have been numerous attempts to extend these successes to
low-resource language pairs, yet requiring tens of thousands of parallel
sentences. In this wor... | ["Marc'Aurelio Ranzato", 'Ludovic Denoyer', 'Guillaume Lample', 'Alexis Conneau'] | 2017-10-31 | unsupervised-machine-translation-using-1 | https://openreview.net/forum?id=rkYTTf-AZ | https://openreview.net/pdf?id=rkYTTf-AZ | iclr-2018-1 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [-4.27492447e-02 -3.21608931e-01 -2.99138814e-01 -4.28526253e-01
-1.46244109e+00 -8.73060107e-01 9.56133544e-01 -1.41275272e-01
-5.46635747e-01 1.14360964e+00 2.38805115e-01 -5.75823843e-01
3.73307556e-01 -5.20261467e-01 -9.73862112e-01 -4.02416885e-01
1.83596030e-01 9.27873850e-01 -2.35040531e-01 -2.89711207... | [11.597250938415527, 10.271446228027344] |
5f184a27-26b2-4d98-8f2d-7ae84d5fc4a0 | tsanet-temporal-and-scale-alignment-for | 2303.04376 | null | https://arxiv.org/abs/2303.04376v1 | https://arxiv.org/pdf/2303.04376v1.pdf | TSANET: Temporal and Scale Alignment for Unsupervised Video Object Segmentation | Unsupervised Video Object Segmentation (UVOS) refers to the challenging task of segmenting the prominent object in videos without manual guidance. In other words, the network detects the accurate region of the target object in a sequence of RGB frames without prior knowledge. In recent works, two approaches for UVOS ha... | ['Sangyoun Lee', 'Minhyeok Lee', 'Dogyoon Lee', 'Suhwan Cho', 'Seunghoon Lee'] | 2023-03-08 | null | null | null | null | ['video-object-segmentation', 'video-semantic-segmentation', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.44170094e-01 -3.36661965e-01 -4.07615840e-01 -1.64502397e-01
-3.61981869e-01 -4.63758349e-01 2.31099427e-01 -2.61399359e-01
-4.56377566e-01 4.93270665e-01 9.78143588e-02 3.68264675e-01
1.34359136e-01 -4.11446512e-01 -7.37972260e-01 -9.01528656e-01
2.73427039e-01 -3.58417273e-01 8.31434429e-01 -9.71215963... | [9.282495498657227, -0.2823661267757416] |
de9108b7-7521-4266-ad1b-317aa4161265 | dr-vic-decomposition-and-reasoning-for-video | 2203.12335 | null | https://arxiv.org/abs/2203.12335v2 | https://arxiv.org/pdf/2203.12335v2.pdf | DR.VIC: Decomposition and Reasoning for Video Individual Counting | Pedestrian counting is a fundamental tool for understanding pedestrian patterns and crowd flow analysis. Existing works (e.g., image-level pedestrian counting, crossline crowd counting et al.) either only focus on the image-level counting or are constrained to the manual annotation of lines. In this work, we propose to... | ['Wanli Ouyang', 'Qi Wang', 'Junyu Gao', 'Lei Bai', 'Tao Han'] | 2022-03-23 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Han_DR.VIC_Decomposition_and_Reasoning_for_Video_Individual_Counting_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Han_DR.VIC_Decomposition_and_Reasoning_for_Video_Individual_Counting_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-individual-counting'] | ['computer-vision'] | [-3.39859843e-01 -5.96963644e-01 1.64921302e-02 -1.75026566e-01
-7.37846419e-02 -2.21353248e-01 4.70575333e-01 3.49959657e-02
-8.43101203e-01 9.24693584e-01 1.15484498e-01 -2.56686479e-01
6.95055962e-01 -1.02492821e+00 -5.39830923e-01 -6.66150630e-01
3.57181090e-03 3.59712899e-01 6.50363028e-01 1.93543971... | [8.282828330993652, -0.42254510521888733] |
7220972b-2e5d-4563-a51b-b9b3cc9278be | covidcare-transferring-knowledge-from | 2007.08848 | null | https://arxiv.org/abs/2007.08848v1 | https://arxiv.org/pdf/2007.08848v1.pdf | CovidCare: Transferring Knowledge from Existing EMR to Emerging Epidemic for Interpretable Prognosis | Due to the characteristics of COVID-19, the epidemic develops rapidly and overwhelms health service systems worldwide. Many patients suffer from systemic life-threatening problems and need to be carefully monitored in ICUs. Thus the intelligent prognosis is in an urgent need to assist physicians to take an early interv... | ['Yasha Wang', 'Xinyu Ma', 'Wenjie Ruan', 'Jiangtao Wang', 'Liantao Ma', 'Chaohe Zhang', 'Xianfeng Jiao', 'Wen Tang', 'Junyi Gao', 'Zhihao Yu'] | 2020-07-17 | null | null | null | null | ['length-of-stay-prediction'] | ['medical'] | [-2.17565939e-01 -3.00772697e-01 -1.49214402e-01 -2.31714606e-01
-3.58435750e-01 -1.39826730e-01 -2.70028800e-01 4.94438916e-01
-5.44677734e-01 1.00890827e+00 2.68911391e-01 -4.74919289e-01
-5.64290822e-01 -7.67080843e-01 -2.83308923e-01 -9.37519908e-01
-3.78805518e-01 8.68830979e-01 -4.95076627e-01 9.04868320... | [7.924874305725098, 6.213657855987549] |
0abdd4cd-814b-431f-9b25-eebde80ac787 | a-cnn-bilstm-model-with-attention-mechanism | 2112.13444 | null | https://arxiv.org/abs/2112.13444v1 | https://arxiv.org/pdf/2112.13444v1.pdf | A CNN-BiLSTM Model with Attention Mechanism for Earthquake Prediction | Earthquakes, as natural phenomena, have continuously caused damage and loss of human life historically. Earthquake prediction is an essential aspect of any society's plans and can increase public preparedness and reduce damage to a great extent. Nevertheless, due to the stochastic character of earthquakes and the chall... | ['Amin Ramezani', 'Ehsan Jahani', 'Mohammadreza Kavianpour', 'Parisa Kavianpour'] | 2021-12-26 | null | null | null | null | ['earthquake-prediction'] | ['computer-vision'] | [-2.25244656e-01 -4.93861258e-01 2.33379647e-01 -1.93847105e-01
-2.80222327e-01 3.63799155e-01 1.51789472e-01 8.15165490e-02
-5.78145802e-01 6.59664571e-01 5.32029688e-01 -2.99345911e-01
-1.70525700e-01 -1.27364063e+00 -3.59640628e-01 -8.31872523e-01
-3.11123461e-01 -1.30264282e-01 3.49671185e-01 -4.35024649... | [6.843992710113525, 2.69747257232666] |
9c5fc76f-d85c-40c2-9b5e-7587b5bbabe4 | gesturediffuclip-gesture-diffusion-model-with | 2303.14613 | null | https://arxiv.org/abs/2303.14613v3 | https://arxiv.org/pdf/2303.14613v3.pdf | GestureDiffuCLIP: Gesture Diffusion Model with CLIP Latents | The automatic generation of stylized co-speech gestures has recently received increasing attention. Previous systems typically allow style control via predefined text labels or example motion clips, which are often not flexible enough to convey user intent accurately. In this work, we present GestureDiffuCLIP, a neural... | ['Libin Liu', 'Zeyi Zhang', 'Tenglong Ao'] | 2023-03-26 | null | null | null | null | ['gesture-generation'] | ['robots'] | [ 5.59004188e-01 -6.31191209e-02 -1.83613777e-01 -6.89341128e-01
-8.11091423e-01 -8.98109555e-01 9.34578061e-01 -6.28806949e-01
-2.80628979e-01 2.28009343e-01 6.65987194e-01 3.55718732e-02
2.83341974e-01 -3.80598634e-01 -5.48736274e-01 -4.64555055e-01
2.77599633e-01 4.99294907e-01 6.42266646e-02 -2.89572895... | [5.667873859405518, -0.13692258298397064] |
66221ca0-3020-43a8-ab4a-7af2c136176e | achieving-diversity-in-objective-space-for | 2306.13780 | null | https://arxiv.org/abs/2306.13780v1 | https://arxiv.org/pdf/2306.13780v1.pdf | Achieving Diversity in Objective Space for Sample-efficient Search of Multiobjective Optimization Problems | Efficiently solving multi-objective optimization problems for simulation optimization of important scientific and engineering applications such as materials design is becoming an increasingly important research topic. This is due largely to the expensive costs associated with said applications, and the resulting need f... | ['Michael McCourt', 'Bolong Cheng', 'Eric Hans Lee'] | 2023-06-23 | null | null | null | null | ['multiobjective-optimization'] | ['methodology'] | [ 1.30527526e-01 -5.71342647e-01 -2.56641805e-01 -2.09958062e-01
-9.96624529e-01 -5.50728798e-01 -4.53977957e-02 3.35429877e-01
-3.10845971e-01 9.70395148e-01 -2.41813455e-02 -4.45206165e-01
-9.21149075e-01 -6.22017086e-01 -3.86704415e-01 -7.26625741e-01
-5.04574813e-02 7.52346754e-01 -1.85300216e-01 -8.20828453... | [5.876138687133789, 3.575511932373047] |
91805cec-e934-40f4-ab42-0f266e5e88cb | a-tutorial-on-vaes-from-bayes-rule-to | 2006.10273 | null | https://arxiv.org/abs/2006.10273v2 | https://arxiv.org/pdf/2006.10273v2.pdf | A Tutorial on VAEs: From Bayes' Rule to Lossless Compression | The Variational Auto-Encoder (VAE) is a simple, efficient, and popular deep maximum likelihood model. Though usage of VAEs is widespread, the derivation of the VAE is not as widely understood. In this tutorial, we will provide an overview of the VAE and a tour through various derivations and interpretations of the VAE ... | ['Ronald Yu'] | 2020-06-18 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 2.33878851e-01 1.21127404e-01 -3.08171269e-02 -2.88221836e-01
-8.71172428e-01 -1.63777113e-01 4.32007909e-01 -2.41310418e-01
-2.29641676e-01 9.27341104e-01 2.54253149e-01 -6.02174520e-01
-5.50594151e-01 -5.46457052e-01 -7.26939738e-01 -7.17930496e-01
-4.56184387e-01 5.33199161e-02 -2.38190562e-01 2.01908499... | [7.211519241333008, 3.9171230792999268] |
6c3afd81-54d6-4d2b-94ee-4ffbf23d9aa0 | roto-translation-equivariant-convolutional | 2002.08725 | null | https://arxiv.org/abs/2002.08725v1 | https://arxiv.org/pdf/2002.08725v1.pdf | Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis | Rotation-invariance is a desired property of machine-learning models for medical image analysis and in particular for computational pathology applications. We propose a framework to encode the geometric structure of the special Euclidean motion group SE(2) in convolutional networks to yield translation and rotation equ... | ['Erik J. Bekkers', 'Maxime W. Lafarge', 'Mitko Veta', 'Josien P. W. Pluim', 'Remco Duits'] | 2020-02-20 | null | null | null | null | ['colorectal-gland-segmentation', 'breast-tumour-classification', 'multi-tissue-nucleus-segmentation', 'mitosis-detection'] | ['medical', 'medical', 'medical', 'medical'] | [ 5.49839795e-01 2.92515427e-01 -2.76434928e-01 -2.97438949e-01
-2.54828066e-01 -4.63384449e-01 6.70080602e-01 1.09161168e-01
-6.87099695e-01 5.51763296e-01 -1.18167199e-01 -4.29039508e-01
-1.54766276e-01 -5.64651549e-01 -6.21888041e-01 -1.15145445e+00
-7.43352771e-02 1.64967120e-01 -2.94724270e-03 -2.81922907... | [15.073692321777344, -2.874262809753418] |
f80c7ef9-0930-4a4d-9bc0-4617be34850e | object-level-depth-reconstruction-for | 2204.01586 | null | https://arxiv.org/abs/2204.01586v2 | https://arxiv.org/pdf/2204.01586v2.pdf | Object Level Depth Reconstruction for Category Level 6D Object Pose Estimation From Monocular RGB Image | Recently, RGBD-based category-level 6D object pose estimation has achieved promising improvement in performance, however, the requirement of depth information prohibits broader applications. In order to relieve this problem, this paper proposes a novel approach named Object Level Depth reconstruction Network (OLD-Net) ... | ['Jun He', 'Hongyan Liu', 'Kejian Wu', 'Zhicheng Wang', 'Jian Xu', 'Zhenbo Song', 'Zhaoxin Fan'] | 2022-04-04 | null | null | null | null | ['6d-pose-estimation'] | ['computer-vision'] | [ 1.63395405e-01 2.96357483e-01 -2.71958500e-01 -6.70931876e-01
-9.21600342e-01 -3.22896570e-01 4.15882677e-01 -3.56799364e-01
-3.22026938e-01 2.59052962e-01 1.01233259e-01 1.44115835e-01
6.53026924e-02 -7.53808916e-01 -9.19777811e-01 -6.32214844e-01
4.64132071e-01 6.98105276e-01 4.60187763e-01 3.94597590... | [7.613234519958496, -2.665158271789551] |
36577112-5b5a-40fe-bdfb-0e3cc4b43722 | on-class-imbalance-and-background-filtering | 1903.08456 | null | http://arxiv.org/abs/1903.08456v2 | http://arxiv.org/pdf/1903.08456v2.pdf | On Class Imbalance and Background Filtering in Visual Relationship Detection | In this paper we investigate the problems of class imbalance and irrelevant
relationships in Visual Relationship Detection (VRD). State-of-the-art deep VRD
models still struggle to predict uncommon classes, limiting their
applicability. Moreover, many methods are incapable of properly filtering out
background relations... | ['Tingting Mu', 'Alessio Sarullo'] | 2019-03-20 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 1.58670112e-01 1.63789257e-01 -2.83662111e-01 -3.72193724e-01
-5.91660403e-02 -4.16899413e-01 7.33377755e-01 6.53368294e-01
-2.93876737e-01 9.30043697e-01 1.21417986e-02 -4.44345832e-01
-5.81236959e-01 -7.55053341e-01 -3.79861534e-01 -5.43936789e-01
-1.56888649e-01 5.97997010e-01 5.00939965e-01 -3.86367947... | [10.258487701416016, 1.7238986492156982] |
7eb3b3b1-705d-4745-845b-ab2d3bd526aa | understanding-label-bias-in-single-positive | 2305.15584 | null | https://arxiv.org/abs/2305.15584v1 | https://arxiv.org/pdf/2305.15584v1.pdf | Understanding Label Bias in Single Positive Multi-Label Learning | Annotating data for multi-label classification is prohibitively expensive because every category of interest must be confirmed to be present or absent. Recent work on single positive multi-label (SPML) learning shows that it is possible to train effective multi-label classifiers using only one positive label per image.... | ['Elijah Cole', 'Pietro Perona', 'Julio Arroyo'] | 2023-05-24 | null | null | null | null | ['multi-label-learning'] | ['methodology'] | [ 7.58361220e-01 2.76632365e-02 -7.39589155e-01 -8.36472809e-01
-1.17304647e+00 -9.14467156e-01 4.74787354e-01 7.28341639e-01
-8.00830364e-01 1.02634966e+00 -4.01179284e-01 -2.29486689e-01
7.42169991e-02 -5.42466760e-01 -5.75662017e-01 -9.76866066e-01
3.34964618e-02 8.63390028e-01 1.85514659e-01 5.39759696... | [9.52323055267334, 4.256229400634766] |
1b712f84-0eb4-4963-9fc0-ce24d46c3c21 | reconfigurable-intelligent-surface-assisted-24 | 2307.04438 | null | https://arxiv.org/abs/2307.04438v1 | https://arxiv.org/pdf/2307.04438v1.pdf | Reconfigurable Intelligent Surface Assisted Railway Communications: A survey | The number of train passengers and the demand for high data rates to handle new technologies such as video streaming and IoT technologies are continuously increasing. Therefore the exploration of millimeter waves (mmWave) band is a key technology to meet this demand. However, the high penetration loss makes mmWave very... | ['Marion Berbineau', 'Charlotte Langlais', 'Ammar El Falou', 'Aline Habib'] | 2023-07-10 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [ 9.56258923e-02 1.08314551e-01 -1.27276167e-01 -1.56005710e-01
-3.02252293e-01 -3.14969182e-01 -1.27908155e-01 -3.53081018e-01
-1.96521297e-01 9.09161866e-01 -5.91490418e-03 -3.74472439e-01
-5.84855080e-01 -1.39528382e+00 -2.27725387e-01 -1.12241435e+00
-7.67409950e-02 2.09530115e-01 1.46418065e-01 -5.90529978... | [6.257157325744629, 1.2192310094833374] |
c62afdee-2fd1-4cac-9df7-72a58e21fe13 | refteacher-a-strong-baseline-for-semi | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sun_RefTeacher_A_Strong_Baseline_for_Semi-Supervised_Referring_Expression_Comprehension_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sun_RefTeacher_A_Strong_Baseline_for_Semi-Supervised_Referring_Expression_Comprehension_CVPR_2023_paper.pdf | RefTeacher: A Strong Baseline for Semi-Supervised Referring Expression Comprehension | Referring expression comprehension (REC) often requires a large number of instance-level annotations for fully supervised learning, which are laborious and expensive. In this paper, we present the first attempt of semi-supervised learning for REC and propose a strong baseline method called RefTeacher. Inspired by t... | ['Rongrong Ji', 'Zhiyu Wang', 'Guannan Jiang', 'Xiaoshuai Sun', 'Yiyi Zhou', 'Gen Luo', 'Jiamu Sun'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['referring-expression', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [ 2.40825608e-01 4.21503812e-01 -5.08956552e-01 -6.24591470e-01
-9.59487677e-01 -4.23995852e-01 3.60367775e-01 -1.91735268e-01
-4.80233431e-01 6.67740583e-01 -1.19217369e-03 -1.35803863e-01
1.91600934e-01 -4.30782050e-01 -8.48258018e-01 -6.74140394e-01
5.85629702e-01 4.33235735e-01 1.39027357e-01 -8.96780491... | [9.563163757324219, 3.3704612255096436] |
9cdc4038-f846-41ac-8d9a-74fa7caaccaa | technical-report-temporal-aggregate | 2106.03152 | null | https://arxiv.org/abs/2106.03152v2 | https://arxiv.org/pdf/2106.03152v2.pdf | Technical Report: Temporal Aggregate Representations | This technical report extends our work presented in [9] with more experiments. In [9], we tackle long-term video understanding, which requires reasoning from current and past or future observations and raises several fundamental questions. How should temporal or sequential relationships be modelled? What temporal exten... | ['Angela Yao', 'Dibyadip Chatterjee', 'Fadime Sener'] | 2021-06-06 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 1.31402820e-01 -7.22194016e-02 -4.37391520e-01 -4.81902063e-01
-2.08054036e-01 -5.67000508e-01 6.49233043e-01 1.98914960e-01
-4.87571329e-01 7.75866568e-01 4.21878695e-01 -2.30538800e-01
-5.87246954e-01 -5.37815213e-01 -4.18721706e-01 -2.50343263e-01
-7.50202537e-01 8.88586044e-04 7.74966955e-01 -2.11588979... | [8.408903121948242, 0.5090969204902649] |
8015cfdc-1926-4cb3-a9b2-0964eb55ccd5 | doublemix-simple-interpolation-based-data | 2209.05297 | null | https://arxiv.org/abs/2209.05297v1 | https://arxiv.org/pdf/2209.05297v1.pdf | DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification | This paper proposes a simple yet effective interpolation-based data augmentation approach termed DoubleMix, to improve the robustness of models in text classification. DoubleMix first leverages a couple of simple augmentation operations to generate several perturbed samples for each training data, and then uses the per... | ['Soujanya Poria', 'Diyi Yang', 'Wei Han', 'Hui Chen'] | 2022-09-12 | null | https://aclanthology.org/2022.coling-1.409 | https://aclanthology.org/2022.coling-1.409.pdf | coling-2022-10 | ['text-augmentation'] | ['natural-language-processing'] | [ 2.60092437e-01 5.89272380e-02 -3.09881121e-01 -3.54935855e-01
-1.03572738e+00 -3.07275534e-01 7.82672405e-01 3.68651420e-01
-4.59862500e-01 6.57978833e-01 3.89270514e-01 -4.59060848e-01
5.42659998e-01 -5.18522263e-01 -6.61062479e-01 -5.29253900e-01
2.32116878e-01 2.68115461e-01 -2.49876425e-01 -2.40238935... | [10.749519348144531, 8.146260261535645] |
ec1dd753-fd80-4732-b757-f46fd192bcd1 | masonnlp-at-semeval-2023-task-8-extracting | 2304.13875 | null | https://arxiv.org/abs/2304.13875v1 | https://arxiv.org/pdf/2304.13875v1.pdf | MasonNLP+ at SemEval-2023 Task 8: Extracting Medical Questions, Experiences and Claims from Social Media using Knowledge-Augmented Pre-trained Language Models | In online forums like Reddit, users share their experiences with medical conditions and treatments, including making claims, asking questions, and discussing the effects of treatments on their health. Building systems to understand this information can effectively monitor the spread of misinformation and verify user cl... | ['Ozlem Uzuner', 'Kevin Lybarger', 'Haritha Gangavarapu', 'Giridhar Kaushik Ramachandran'] | 2023-04-26 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [ 1.11290209e-01 8.19570065e-01 -4.64434296e-01 -4.13935632e-01
-1.22930551e+00 -5.73201656e-01 3.41983199e-01 1.30749297e+00
-4.27802086e-01 8.58989477e-01 1.02250540e+00 -1.79630473e-01
-4.22718078e-01 -4.75157797e-01 -2.44533911e-01 -6.87575191e-02
-3.58371772e-02 7.16976225e-01 -8.38036835e-02 -2.43202031... | [8.644426345825195, 8.87440013885498] |
972ea3d8-83f6-40a5-b6bf-be861edb9ee7 | co-optimal-transport | 2002.03731 | null | https://arxiv.org/abs/2002.03731v3 | https://arxiv.org/pdf/2002.03731v3.pdf | CO-Optimal Transport | Optimal transport (OT) is a powerful geometric and probabilistic tool for finding correspondences and measuring similarity between two distributions. Yet, its original formulation relies on the existence of a cost function between the samples of the two distributions, which makes it impractical when they are supported ... | ['Rémi Flamary', 'Titouan Vayer', 'Ievgen Redko', 'Nicolas Courty'] | 2020-02-10 | null | http://proceedings.neurips.cc/paper/2020/hash/cc384c68ad503482fb24e6d1e3b512ae-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/cc384c68ad503482fb24e6d1e3b512ae-Paper.pdf | neurips-2020-12 | ['data-summarization'] | ['miscellaneous'] | [ 1.29509479e-01 -3.20294350e-01 -2.27824777e-01 -2.85381675e-01
-1.07393932e+00 -6.47394180e-01 6.13841891e-01 6.22026145e-01
-2.58781374e-01 5.78718543e-01 1.38587564e-01 -5.17655574e-02
-7.63339520e-01 -6.55330062e-01 -5.39059937e-01 -9.29071069e-01
-1.86751246e-01 8.84922147e-01 2.74833113e-01 -8.35109726... | [7.779326438903809, 4.109753608703613] |
623db609-5e1a-40b2-9e0b-0c3ae3b7dbef | parameterized-pseudo-differential-operators | null | null | https://openreview.net/forum?id=Y45i-hDynr | https://openreview.net/pdf?id=Y45i-hDynr | Parameterized Pseudo-Differential Operators for Graph Convolutional Neural Networks | We present a novel graph convolutional layer that is fast, conceptually simple, and provides high accuracy with reduced overfitting. Based on pseudo-differential operators, our layer operates on graphs with relative position information available for each pair of connected nodes. The new layer outperforms multiple rece... | ['John Tencer', 'Matthew David Smith', 'Steven Richard Sleder', 'Kevin M. Potter'] | 2021-01-01 | null | null | null | null | ['superpixel-image-classification'] | ['computer-vision'] | [ 8.44125077e-02 4.95876342e-01 -3.27721417e-01 -3.54458869e-01
-7.76832640e-01 -6.19572639e-01 5.04643321e-01 1.88837320e-01
-6.94209158e-01 7.87824988e-01 -2.95787454e-01 -2.41544187e-01
3.87267247e-02 -8.87513578e-01 -9.35900867e-01 -7.24959791e-01
-2.55332291e-01 5.29056966e-01 4.14087474e-01 3.07575196... | [9.553826332092285, 1.1225996017456055] |
50c32550-27d1-482d-aea3-577030681431 | a-simple-attempt-for-3d-occupancy-estimation | 2303.10076 | null | https://arxiv.org/abs/2303.10076v2 | https://arxiv.org/pdf/2303.10076v2.pdf | A Simple Attempt for 3D Occupancy Estimation in Autonomous Driving | The task of estimating 3D occupancy from surrounding view images is an exciting development in the field of autonomous driving, following the success of Birds Eye View (BEV) perception.This task provides crucial 3D attributes of the driving environment, enhancing the overall understanding and perception of the surround... | ['Naoto Yokoya', 'Hongbin Xu', 'Ningkai Mo', 'Wanshui Gan'] | 2023-03-17 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [-1.24029882e-01 -2.78645843e-01 -1.31766647e-01 -5.15280068e-01
-1.56399041e-01 -4.76586878e-01 5.98393261e-01 -1.60145342e-01
-6.18319988e-01 4.88891363e-01 2.06977576e-01 -2.13748515e-01
1.33688614e-01 -8.68218958e-01 -7.66684473e-01 -7.44163692e-01
1.52042732e-01 1.44785181e-01 3.20493042e-01 -3.05387348... | [8.161608695983887, -2.4065141677856445] |
ec22ec02-5314-4bf6-8575-1a29972e3787 | automated-detection-of-oral-pre-cancerous | 1909.08987 | null | https://arxiv.org/abs/1909.08987v1 | https://arxiv.org/pdf/1909.08987v1.pdf | Automated detection of oral pre-cancerous tongue lesions using deep learning for early diagnosis of oral cavity cancer | Discovering oral cavity cancer (OCC) at an early stage is an effective way to increase patient survival rate. However, current initial screening process is done manually and is expensive for the average individual, especially in developing countries worldwide. This problem is further compounded due to the lack of speci... | ['Mohammed Usman', 'Syed Ali', 'Mohammad Shiblee', 'Mohammed Zubair M. Shamim', 'Sadatullah Syed'] | 2019-09-18 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 1.65045068e-01 2.85419524e-01 -4.33772683e-01 3.52566987e-02
-1.02321386e+00 -5.00350356e-01 1.14050023e-01 2.89580673e-01
-3.74709755e-01 6.09646678e-01 1.57160774e-01 -8.10178220e-01
-1.28505439e-01 -7.22553849e-01 -9.64220706e-03 -9.60797727e-01
1.84419289e-01 8.60925317e-01 9.45150945e-03 -1.70904607... | [15.403705596923828, -3.0137808322906494] |
cf6fdbc3-4d3d-4b11-a1cc-a74503d74ef2 | the-devil-is-in-the-middle-exploiting-mid | 1711.08106 | null | http://arxiv.org/abs/1711.08106v2 | http://arxiv.org/pdf/1711.08106v2.pdf | The Devil is in the Middle: Exploiting Mid-level Representations for Cross-Domain Instance Matching | Many vision problems require matching images of object instances across
different domains. These include fine-grained sketch-based image retrieval
(FG-SBIR) and Person Re-identification (person ReID). Existing approaches
attempt to learn a joint embedding space where images from different domains
can be directly compar... | ['Yi-Zhe Song', 'Qian Yu', 'Timothy M. Hospedales', 'Tao Xiang', 'Xiaobin Chang'] | 2017-11-22 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [-7.79135004e-02 -2.69832641e-01 -1.03255831e-01 -5.46844184e-01
-5.74044943e-01 -6.01228893e-01 1.01787722e+00 8.95542577e-02
-6.71723723e-01 4.71972108e-01 1.18196115e-01 3.40085238e-01
-2.64390826e-01 -9.18855071e-01 -6.78119540e-01 -3.45715404e-01
1.48829445e-01 5.05215704e-01 2.85962135e-01 -1.92108050... | [11.689029693603516, 0.6059616208076477] |
b73b0c82-258d-455d-9fb8-54a9e04540dd | unsupervised-summarization-for-chat-logs-with | 2012.07300 | null | https://arxiv.org/abs/2012.07300v2 | https://arxiv.org/pdf/2012.07300v2.pdf | Unsupervised Summarization for Chat Logs with Topic-Oriented Ranking and Context-Aware Auto-Encoders | Automatic chat summarization can help people quickly grasp important information from numerous chat messages. Unlike conventional documents, chat logs usually have fragmented and evolving topics. In addition, these logs contain a quantity of elliptical and interrogative sentences, which make the chat summarization high... | ['Xiaozhong Liu', 'Xuanjing Huang', 'Qi Zhang', 'Changlong Sun', 'Zhuoren Jiang', 'Yangyang Kang', 'Lujun Zhao', 'Jun Lin', 'Yicheng Zou'] | 2020-12-14 | null | null | null | null | ['topic-coverage'] | ['natural-language-processing'] | [ 2.36874506e-01 2.11055696e-01 3.25174600e-01 -5.33973992e-01
-1.32291365e+00 -3.29253972e-01 7.74633408e-01 6.37668312e-01
-2.17648655e-01 7.45177746e-01 9.70914602e-01 4.41790164e-01
2.44974103e-02 -3.97819728e-01 -1.27446935e-01 -5.28593242e-01
2.15523466e-01 9.14270401e-01 3.08569938e-01 -2.11917654... | [12.629449844360352, 9.178641319274902] |
f7b08058-a775-4d14-bc94-b8ccf210e06d | learning-a-natural-language-interface-with | 1611.08945 | null | http://arxiv.org/abs/1611.08945v4 | http://arxiv.org/pdf/1611.08945v4.pdf | Learning a Natural Language Interface with Neural Programmer | Learning a natural language interface for database tables is a challenging
task that involves deep language understanding and multi-step reasoning. The
task is often approached by mapping natural language queries to logical forms
or programs that provide the desired response when executed on the database. To
our knowle... | ['Martin Abadi', 'Andrew McCallum', 'Quoc V. Le', 'Dario Amodei', 'Arvind Neelakantan'] | 2016-11-28 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 2.09756494e-01 6.73001885e-01 -4.99511153e-01 -8.14111233e-01
-1.04630971e+00 -5.70536375e-01 3.59108627e-01 4.96807665e-01
-5.07686853e-01 4.16895241e-01 -8.97368267e-02 -8.63985240e-01
1.73997924e-01 -1.09175718e+00 -1.45813298e+00 2.58003920e-01
-4.45899367e-02 1.01708508e+00 4.61525768e-01 -4.60127354... | [9.653413772583008, 7.628544807434082] |
fda1c7bd-d331-49b8-bcf1-20b635caf6cc | vision-deduction-and-alignment-an-empirical | 2302.08774 | null | https://arxiv.org/abs/2302.08774v2 | https://arxiv.org/pdf/2302.08774v2.pdf | Vision, Deduction and Alignment: An Empirical Study on Multi-modal Knowledge Graph Alignment | Entity alignment (EA) for knowledge graphs (KGs) plays a critical role in knowledge engineering. Existing EA methods mostly focus on utilizing the graph structures and entity attributes (including literals), but ignore images that are common in modern multi-modal KGs. In this study we first constructed Multi-OpenEA -- ... | ['Hai-Tao Zheng', 'Xi Chen', 'Yuejia Xiang', 'Yinghui Li', 'Jiaoyan Chen', 'Yangning Li'] | 2023-02-17 | null | null | null | null | ['entity-alignment', 'multi-modal-knowledge-graph', 'entity-alignment'] | ['knowledge-base', 'knowledge-base', 'natural-language-processing'] | [-3.80634815e-01 7.39949718e-02 -3.47380012e-01 -5.99446567e-03
-4.27901089e-01 -5.73894262e-01 3.24368924e-01 3.64549845e-01
-1.12937979e-01 5.45444906e-01 3.30390394e-01 -1.51081353e-01
-3.92013550e-01 -1.39693582e+00 -7.63841391e-01 -3.18904519e-01
-2.83812344e-01 5.14723718e-01 2.93890357e-01 -3.81077409... | [8.692697525024414, 7.732250213623047] |
be6a0186-a83a-47d0-9aa9-e75277bebd46 | learning-invariant-visual-representations-for | 2206.00415 | null | https://arxiv.org/abs/2206.00415v3 | https://arxiv.org/pdf/2206.00415v3.pdf | Learning Invariant Visual Representations for Compositional Zero-Shot Learning | Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions using knowledge learned from seen attribute-object compositions in the training set. Previous works mainly project an image and a composition into a common embedding space to measure their compatibility score. However, both attributes and obje... | ['Jun Guo', 'Zhanyu Ma', 'Xian Sun', 'Ruoyi Du', 'Kongming Liang', 'Tian Zhang'] | 2022-06-01 | null | null | null | null | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 2.87868530e-01 -1.95484683e-01 -3.85776520e-01 -5.89948535e-01
-6.15404725e-01 -5.56495726e-01 8.24802935e-01 6.17807396e-02
-1.54966488e-01 3.11579198e-01 2.76037782e-01 5.37144959e-01
4.46294853e-03 -7.12104559e-01 -7.53172755e-01 -1.02908266e+00
1.30109787e-01 4.87164021e-01 1.78642452e-01 9.70852375... | [10.1381196975708, 2.286712408065796] |
497d12a9-8b8e-4c7a-a865-5ba68537a63a | reliable-and-interpretable-drift-detection-in | 2305.17750 | null | https://arxiv.org/abs/2305.17750v1 | https://arxiv.org/pdf/2305.17750v1.pdf | Reliable and Interpretable Drift Detection in Streams of Short Texts | Data drift is the change in model input data that is one of the key factors leading to machine learning models performance degradation over time. Monitoring drift helps detecting these issues and preventing their harmful consequences. Meaningful drift interpretation is a fundamental step towards effective re-training o... | ['Ateret Anaby-Tavor', 'Samuel Ackerman', 'Matan Vetzler', 'Ella Rabinovich'] | 2023-05-28 | null | null | null | null | ['intent-classification', 'change-point-detection'] | ['natural-language-processing', 'time-series'] | [ 3.69379856e-02 -2.08425045e-01 -3.46118212e-01 -8.93837869e-01
-6.17763221e-01 -8.64779770e-01 6.36542261e-01 3.89074445e-01
-2.58124471e-01 6.75469160e-01 1.39253423e-01 -4.86619115e-01
-5.19170798e-02 4.42182831e-02 -3.31514567e-01 -2.43235663e-01
-1.34470224e-01 1.09460425e+00 4.23161060e-01 -4.03771818... | [12.58251667022705, 7.667848110198975] |
cc5f0d5c-efc9-4c32-b091-c8af153e5a67 | deep-ehr-spotlight-a-framework-and-mechanism | 2103.14161 | null | https://arxiv.org/abs/2103.14161v1 | https://arxiv.org/pdf/2103.14161v1.pdf | Deep EHR Spotlight: a Framework and Mechanism to Highlight Events in Electronic Health Records for Explainable Predictions | The wide adoption of Electronic Health Records (EHR) has resulted in large amounts of clinical data becoming available, which promises to support service delivery and advance clinical and informatics research. Deep learning techniques have demonstrated performance in predictive analytic tasks using EHRs yet they typica... | ['Joao H. Bettencourt-Silva', 'Gurdeep S. Mannu', 'Natasha Mulligan', 'Thanh Nguyen-Duc'] | 2021-03-25 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 2.13408470e-01 2.60899395e-01 -9.89768282e-03 -4.95784342e-01
-4.47805315e-01 -3.60962331e-01 5.66791654e-01 9.75557685e-01
-5.91092110e-02 6.55410171e-01 6.13706231e-01 -6.68741882e-01
-3.00176471e-01 -7.69942880e-01 -4.28891182e-01 -4.68311489e-01
-6.33015871e-01 3.64953339e-01 -5.24569988e-01 3.36758643... | [7.899590969085693, 6.374433994293213] |
c49523bb-7983-4a06-bf6d-08ca4830b753 | about-the-cost-of-global-privacy-in-density | 2306.14535 | null | https://arxiv.org/abs/2306.14535v1 | https://arxiv.org/pdf/2306.14535v1.pdf | About the Cost of Global Privacy in Density Estimation | We study non-parametric density estimation for densities in Lipschitz and Sobolev spaces, and under global privacy. In particular, we investigate regimes where the privacy budget is not supposed to be constant. We consider the classical definition of global differential privacy, but also the more recent notion of globa... | ['Rémi Gribonval', 'Aurélien Garivier', 'Clément Lalanne'] | 2023-06-26 | null | null | null | null | ['density-estimation'] | ['methodology'] | [ 1.08003048e-02 5.11984885e-01 -2.87407245e-02 -1.43878907e-01
-8.44522893e-01 -6.91318154e-01 2.01025113e-01 2.20144346e-01
-5.40964723e-01 1.00658834e+00 3.25118154e-01 -1.50048777e-01
-3.16232920e-01 -9.17140603e-01 -8.80264759e-01 -1.10628235e+00
-1.96312353e-01 1.06338702e-01 -6.56673778e-03 9.39953327... | [7.094205379486084, 4.288792610168457] |
6d96f3c0-1d97-483d-9604-f97213c44762 | hybrid-window-attention-based-transformer | 2209.07704 | null | https://arxiv.org/abs/2209.07704v1 | https://arxiv.org/pdf/2209.07704v1.pdf | Hybrid Window Attention Based Transformer Architecture for Brain Tumor Segmentation | As intensities of MRI volumes are inconsistent across institutes, it is essential to extract universal features of multi-modal MRIs to precisely segment brain tumors. In this concept, we propose a volumetric vision transformer that follows two windowing strategies in attention for extracting fine features and local dis... | ['Mehrtash Harandi', 'Gary Egan', 'Zhaolin Chen', 'Munawar Hayat', 'Himashi Peiris'] | 2022-09-16 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [-1.94270629e-02 2.56224066e-01 -1.57263860e-01 -4.80189621e-01
-1.08270919e+00 -4.13714796e-01 4.27025378e-01 -7.56686479e-02
-6.26335621e-01 8.53371620e-01 1.29117459e-01 -3.28120470e-01
-5.82406558e-02 -4.53728557e-01 -5.98588765e-01 -8.11266124e-01
-2.04793841e-01 2.84818769e-01 3.46444786e-01 5.16650546... | [14.389050483703613, -2.342435836791992] |
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