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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
348d5a26-213b-4d11-9d77-0942fc9320b8 | recursive-euclidean-distance-based-robust | 2303.11337 | null | https://arxiv.org/abs/2303.11337v1 | https://arxiv.org/pdf/2303.11337v1.pdf | Recursive Euclidean Distance Based Robust Aggregation Technique For Federated Learning | Federated learning has gained popularity as a solution to data availability and privacy challenges in machine learning. However, the aggregation process of local model updates to obtain a global model in federated learning is susceptible to malicious attacks, such as backdoor poisoning, label-flipping, and membership i... | ['Xiaolan Liu', 'Yogachandran Rahulamathavan', 'Charuka Herath'] | 2023-03-20 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [-1.61802247e-01 -1.33663481e-02 -2.35492066e-01 -1.82710111e-01
-9.20341611e-01 -8.51619780e-01 4.44534898e-01 5.49993873e-01
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-1.44962758e-01 -9.65260863e-01 -6.87460184e-01 -8.99484098e-01
-9.95321423e-02 3.33657086e-01 5.05427942e-02 1.09028876... | [5.800374507904053, 6.7127366065979] |
c526904a-bb0f-4dec-a743-22490650a03c | multi-view-audio-and-music-classification | 2103.02420 | null | https://arxiv.org/abs/2103.02420v1 | https://arxiv.org/pdf/2103.02420v1.pdf | Multi-view Audio and Music Classification | We propose in this work a multi-view learning approach for audio and music classification. Considering four typical low-level representations (i.e. different views) commonly used for audio and music recognition tasks, the proposed multi-view network consists of four subnetworks, each handling one input types. The learn... | ['Alfred Mertins', 'Ian McLoughlin', 'Philipp Koch', 'Lam Pham', 'Oliver Y. Chén', 'Huy Le Nguyen', 'Huy Phan'] | 2021-03-03 | null | null | null | null | ['multi-view-learning', 'music-classification'] | ['computer-vision', 'music'] | [ 4.11248095e-02 -1.18414283e-01 -2.09734946e-01 -2.69228876e-01
-7.07995892e-01 -6.10730052e-01 3.85246724e-01 7.77706727e-02
-1.16452098e-01 2.67950267e-01 4.00002480e-01 2.73832530e-01
-1.86997905e-01 -6.88255012e-01 -4.38267291e-01 -9.81266141e-01
-3.45445052e-02 4.10760194e-01 3.57654512e-01 -1.52165085... | [15.599387168884277, 5.236428737640381] |
a355f26b-ddcd-487f-8dc5-66a2738be732 | tracking-by-natural-language-specification | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Li_Tracking_by_Natural_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Tracking_by_Natural_CVPR_2017_paper.pdf | Tracking by Natural Language Specification | This paper strives to track a target object in a video. Rather than specifying the target in the first frame of a video by a bounding box, we propose to track the object based on a natural language specification of the target, which provides a more natural human-machine interaction as well as a means to improve trackin... | ['Arnold W. M. Smeulders', 'Efstratios Gavves', 'Zhenyang Li', 'Ran Tao', 'Cees G. M. Snoek'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['referring-expression-segmentation'] | ['computer-vision'] | [ 9.41877365e-02 7.49777481e-02 -4.88668770e-01 -2.73317307e-01
-5.02199948e-01 -1.07084811e+00 9.63057280e-01 -3.08624148e-01
-4.04675901e-01 4.32149768e-01 2.30709538e-01 -4.12896395e-01
1.09848395e-01 -4.61584955e-01 -8.36648762e-01 -4.36028361e-01
-1.99969113e-01 3.45721245e-01 7.29233801e-01 1.19707994... | [6.32230806350708, -2.019019365310669] |
3b88ec31-39f3-4328-a058-671bd90c019e | is-a-video-worth-n-times-n-images-a-highly | 2305.09107 | null | https://arxiv.org/abs/2305.09107v1 | https://arxiv.org/pdf/2305.09107v1.pdf | Is a Video worth $n\times n$ Images? A Highly Efficient Approach to Transformer-based Video Question Answering | Conventional Transformer-based Video Question Answering (VideoQA) approaches generally encode frames independently through one or more image encoders followed by interaction between frames and question. However, such schema would incur significant memory use and inevitably slow down the training and inference speed. In... | ['Jennifer Foster', 'Yvette Graham', 'Tianbo Ji', 'Chenyang Lyu'] | 2023-05-16 | null | null | null | null | ['video-question-answering'] | ['computer-vision'] | [ 1.59395993e-01 -1.64692372e-01 7.47932419e-02 -5.53016365e-01
-1.09170759e+00 -6.44043028e-01 1.77898437e-01 -1.28375337e-01
-6.36739790e-01 5.91214359e-01 -1.34789944e-01 -7.49366581e-01
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1.75733984e-01 1.88784853e-01 5.54089963e-01 -9.67142805... | [10.010981559753418, 0.7779089212417603] |
f5a473a3-5e2c-439f-8b7b-bdfb718c441a | reconsider-re-ranking-using-span-focused | 2010.10757 | null | https://arxiv.org/abs/2010.10757v1 | https://arxiv.org/pdf/2010.10757v1.pdf | RECONSIDER: Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering | State-of-the-art Machine Reading Comprehension (MRC) models for Open-domain Question Answering (QA) are typically trained for span selection using distantly supervised positive examples and heuristically retrieved negative examples. This training scheme possibly explains empirical observations that these models achieve... | ['Wen-tau Yih', 'Yashar Mehdad', 'Sewon Min', 'Srinivasan Iyer'] | 2020-10-21 | null | null | null | null | ['triviaqa'] | ['miscellaneous'] | [ 3.72304946e-01 5.66551805e-01 6.98076710e-02 -3.97291481e-01
-2.01929522e+00 -8.03774059e-01 2.37444356e-01 6.27169371e-01
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-2.18544304e-01 -7.38925993e-01 -7.48139799e-01 1.21217072e-01
1.11574881e-01 1.00039446e+00 8.31317723e-01 -7.69170403... | [11.325313568115234, 8.036806106567383] |
9b7c3b4c-6637-4f2d-9af8-c84cebdb2859 | does-interpretability-of-neural-networks | 1912.03430 | null | https://arxiv.org/abs/1912.03430v6 | https://arxiv.org/pdf/1912.03430v6.pdf | An Empirical Study on the Relation between Network Interpretability and Adversarial Robustness | Deep neural networks (DNNs) have had many successes, but they suffer from two major issues: (1) a vulnerability to adversarial examples and (2) a tendency to elude human interpretation. Interestingly, recent empirical and theoretical evidence suggests these two seemingly disparate issues are actually connected. In part... | ['Adam Noack', 'Isaac Ahern', 'Dejing Dou', 'Boyang Li'] | 2019-12-07 | null | null | null | null | ['network-interpretation'] | ['computer-vision'] | [ 2.92613178e-01 3.22248489e-01 -1.01094969e-01 -5.28012395e-01
-4.98722196e-01 -6.39931977e-01 4.94832039e-01 -2.06589878e-01
-4.00179416e-01 6.82499170e-01 2.92746633e-01 -5.19944072e-01
-1.76515535e-01 -4.29878503e-01 -8.75968993e-01 -6.33885324e-01
-1.02757573e-01 6.01710938e-02 -2.21080072e-02 -4.81143117... | [5.7304606437683105, 7.8366899490356445] |
de56a4a5-8302-465b-a8a4-6cc7c9b3f3e3 | decentralized-data-governance-as-part-of-a | 2307.02357 | null | https://arxiv.org/abs/2307.02357v1 | https://arxiv.org/pdf/2307.02357v1.pdf | Decentralized Data Governance as Part of a Data Mesh Platform: Concepts and Approaches | Data mesh is a socio-technical approach to decentralized analytics data management. To manage this decentralization efficiently, data mesh relies on automation provided by a self-service data infrastructure platform. A key aspect of this platform is to enable decentralized data governance. Because data mesh is a young ... | ['Atif Akhtar', 'Sumedha Verma', 'Arif Wider'] | 2023-07-05 | null | null | null | null | ['management'] | ['miscellaneous'] | [-1.01390827e+00 5.18398225e-01 -5.14411509e-01 -2.51417220e-01
-1.97332442e-01 -7.47669876e-01 9.35272634e-01 7.84329653e-01
3.69993001e-02 1.54806957e-01 8.93648684e-01 -4.62601304e-01
-4.82098579e-01 -9.52334523e-01 -1.62306845e-01 -2.55257726e-01
2.05410391e-01 4.90806311e-01 -5.52914590e-02 -6.01866305... | [8.99071979522705, 7.404989242553711] |
d0cd290d-94fd-48ad-9f71-cdd2e981abe3 | foreground-segmentation-based-on-multi | 1402.2013 | null | http://arxiv.org/abs/1402.2013v1 | http://arxiv.org/pdf/1402.2013v1.pdf | Foreground segmentation based on multi-resolution and matting | We propose a foreground segmentation algorithm that does foreground
extraction under different scales and refines the result by matting. First, the
input image is filtered and resampled to 5 different resolutions. Then each of
them is segmented by adaptive figure-ground classification and the best
segmentation is autom... | ['Xiaohan Liu', 'Xintong Yu', 'Yisong Chen'] | 2014-02-10 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 7.01720059e-01 1.11886345e-01 -1.32278740e-01 -3.09656739e-01
-7.63716578e-01 -5.50663829e-01 6.82235733e-02 -4.58799936e-02
-2.81108230e-01 6.40724480e-01 -3.50492060e-01 -1.75415412e-01
2.93145567e-01 -9.56372857e-01 -6.11866593e-01 -8.21543574e-01
5.25678955e-02 7.36258447e-01 1.05473661e+00 3.34500939... | [9.1506929397583, -0.36330392956733704] |
f93e558b-28c9-4768-8392-8bb487c3a41f | learning-to-shoot-in-first-person-shooter | 1806.05117 | null | http://arxiv.org/abs/1806.05117v1 | http://arxiv.org/pdf/1806.05117v1.pdf | Learning to Shoot in First Person Shooter Games by Stabilizing Actions and Clustering Rewards for Reinforcement Learning | While reinforcement learning (RL) has been applied to turn-based board games
for many years, more complex games involving decision-making in real-time are
beginning to receive more attention. A challenge in such environments is that
the time that elapses between deciding to take an action and receiving a reward
based o... | ['Frank G. Glavin', 'Michael G. Madden'] | 2018-06-13 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 3.57287347e-01 -4.01268080e-02 1.84347793e-01 5.65508306e-02
-3.83908540e-01 -5.58783114e-01 2.81666458e-01 4.67712402e-01
-9.88269508e-01 9.45712030e-01 -1.76045552e-01 -9.11151767e-02
-3.37874770e-01 -9.87181365e-01 -2.66855985e-01 -5.50346136e-01
-4.11519885e-01 6.15154028e-01 7.14561105e-01 -6.40502036... | [3.5988399982452393, 1.5587623119354248] |
d699e548-ed80-44f9-a0f4-aa52f0797eeb | survey-of-face-detection-on-low-quality | 1804.07362 | null | http://arxiv.org/abs/1804.07362v1 | http://arxiv.org/pdf/1804.07362v1.pdf | Survey of Face Detection on Low-quality Images | Face detection is a well-explored problem. Many challenges on face detectors
like extreme pose, illumination, low resolution and small scales are studied in
the previous work. However, previous proposed models are mostly trained and
tested on good-quality images which are not always the case for practical
applications ... | ['Yuqian Zhou', 'Thomas Huang', 'Ding Liu'] | 2018-04-19 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [-9.55385789e-02 -5.86145401e-01 1.33025259e-01 -3.83192807e-01
-1.60653442e-01 -5.21970093e-01 3.74788672e-01 -6.59503818e-01
-4.09935087e-01 4.35524434e-01 -2.85874844e-01 -8.68379045e-03
2.47491255e-01 -6.30838156e-01 -4.85153764e-01 -7.28605330e-01
-1.78738028e-01 8.28141645e-02 4.27440703e-01 -1.52123600... | [13.319787979125977, 0.7565113306045532] |
e262ed2a-ac0b-4487-bdf8-67a7fa73f630 | apicontext2com-code-comment-generation-by | 2303.01645 | null | https://arxiv.org/abs/2303.01645v1 | https://arxiv.org/pdf/2303.01645v1.pdf | APIContext2Com: Code Comment Generation by Incorporating Pre-Defined API Documentation | Code comments are significantly helpful in comprehending software programs and also aid developers to save a great deal of time in software maintenance. Code comment generation aims to automatically predict comments in natural language given a code snippet. Several works investigate the effect of integrating external k... | ['Fatemeh Fard', 'Ramin Shahbazi'] | 2023-03-03 | null | null | null | null | ['code-comment-generation', 'comment-generation'] | ['computer-code', 'natural-language-processing'] | [ 2.25120991e-01 2.39996746e-01 -1.60300732e-01 -4.27349716e-01
-7.68830180e-01 -7.29639411e-01 4.36413705e-01 2.92381912e-01
-7.35263452e-02 5.40123343e-01 4.91536438e-01 -2.88589925e-01
2.44511798e-01 -6.67245030e-01 -7.32065380e-01 -1.58158675e-01
1.36809140e-01 -1.30281389e-01 1.96325406e-01 -1.75609514... | [7.669431686401367, 7.907370567321777] |
f1ecb4a4-4176-4e6d-b23e-b035552cbf1f | improving-nonparametric-classification-via | 2112.13951 | null | https://arxiv.org/abs/2112.13951v2 | https://arxiv.org/pdf/2112.13951v2.pdf | Improving Nonparametric Classification via Local Radial Regression with an Application to Stock Prediction | For supervised classification problems, this paper considers estimating the query's label probability through local regression using observed covariates. Well-known nonparametric kernel smoother and $k$-nearest neighbor ($k$-NN) estimator, which take label average over a ball around the query, are consistent but asympt... | ['Hidetoshi Shimodaira', 'Kei Nakagawa', 'Akifumi Okuno', 'Ruixing Cao'] | 2021-12-28 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-3.36529613e-01 -2.43968833e-02 -5.59434474e-01 -6.49120152e-01
-1.16941690e+00 -3.41484159e-01 9.30631757e-02 1.54158501e-02
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-5.94766021e-01 -9.02508199e-01 -8.28577518e-01 -9.24686253e-01
-5.07622719e-01 2.78614610e-01 7.73403645e-02 9.70613360... | [7.735414505004883, 4.217560768127441] |
113ff1cc-2475-4f99-be34-b0e54b027bd6 | improving-visual-image-reconstruction-from | 2306.11536 | null | https://arxiv.org/abs/2306.11536v1 | https://arxiv.org/pdf/2306.11536v1.pdf | Improving visual image reconstruction from human brain activity using latent diffusion models via multiple decoded inputs | The integration of deep learning and neuroscience has been advancing rapidly, which has led to improvements in the analysis of brain activity and the understanding of deep learning models from a neuroscientific perspective. The reconstruction of visual experience from human brain activity is an area that has particular... | ['Shinji Nishimoto', 'Yu Takagi'] | 2023-06-20 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [-3.53290200e-01 -2.35472545e-01 1.85383469e-01 -2.62756288e-01
-5.68230569e-01 -3.30730915e-01 8.01522315e-01 -2.87243277e-01
-6.05847418e-01 7.51187325e-01 5.76217532e-01 -8.21564421e-02
1.48393691e-01 -5.17684937e-01 -7.08783388e-01 -8.12355399e-01
-2.64904723e-02 1.67683855e-01 7.52898529e-02 2.79977292... | [10.756355285644531, 2.503872871398926] |
02ec9f7c-f7b7-4967-9963-ed166f656ff5 | cross-domain-few-shot-meta-learning-using | 2205.05831 | null | https://arxiv.org/abs/2205.05831v2 | https://arxiv.org/pdf/2205.05831v2.pdf | Feature Extractor Stacking for Cross-domain Few-shot Meta-learning | Cross-domain few-shot meta-learning (CDFSML) addresses learning problems where knowledge needs to be transferred from several source domains into an instance-scarce target domain with an explicitly different distribution. Recently published CDFSML methods generally construct a "universal model" that combines knowledge ... | ['Geoffrey Holmes', 'Michael Mayo', 'Bernhard Pfahringer', 'Eibe Frank', 'Hongyu Wang'] | 2022-05-12 | null | null | null | null | ['cross-domain-few-shot'] | ['computer-vision'] | [ 4.80673224e-01 -2.60564476e-01 -4.69045013e-01 -4.30409700e-01
-1.20109594e+00 -5.27941167e-01 7.45819926e-01 1.63205966e-01
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-3.28648299e-01 -9.29475129e-01 -1.07201910e+00 -7.58976698e-01
1.40277222e-01 8.27471018e-01 6.09391809e-01 -3.90690446... | [9.977662086486816, 3.0858027935028076] |
69e6f9a6-22b0-47fd-8f8a-c25ee2786272 | iplan-intent-aware-planning-in-heterogeneous | 2306.06236 | null | https://arxiv.org/abs/2306.06236v1 | https://arxiv.org/pdf/2306.06236v1.pdf | iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning | Navigating safely and efficiently in dense and heterogeneous traffic scenarios is challenging for autonomous vehicles (AVs) due to their inability to infer the behaviors or intentions of nearby drivers. In this work, we propose a distributed multi-agent reinforcement learning (MARL) algorithm with trajectory and intent... | ['Dinesh Manocha', 'Amrit Singh Bedi', 'Tianrui Guan', 'Rohan Chandra', 'Xiyang Wu'] | 2023-06-09 | null | null | null | null | ['autonomous-vehicles', 'multi-agent-reinforcement-learning'] | ['computer-vision', 'methodology'] | [-5.51787436e-01 5.23203790e-01 -3.89662027e-01 -3.52428705e-01
-8.35747361e-01 -4.08971459e-01 6.34402394e-01 2.44137552e-02
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-2.26586670e-01 -9.65544939e-01 -6.71797693e-01 -5.00853479e-01
-5.23117185e-01 1.10719526e+00 5.47386587e-01 -6.18062794... | [5.253256320953369, 1.37967050075531] |
9a4d15cb-6293-423f-aef5-faad64214d93 | 2305-14952 | 2305.14952 | null | https://arxiv.org/abs/2305.14952v1 | https://arxiv.org/pdf/2305.14952v1.pdf | Focus Your Attention (with Adaptive IIR Filters) | We present a new layer in which dynamic (i.e.,input-dependent) Infinite Impulse Response (IIR) filters of order two are used to process the input sequence prior to applying conventional attention. The input is split into chunks, and the coefficients of these filters are determined based on previous chunks to maintain c... | ['Lior Wolf', 'Itamar Zimerman', 'Shahar Lutati'] | 2023-05-24 | null | null | null | null | ['long-range-modeling'] | ['natural-language-processing'] | [ 5.12723505e-01 1.90665230e-01 -1.34329855e-01 -5.47032282e-02
-2.78042674e-01 -5.85715771e-01 6.76879764e-01 2.23054796e-01
-7.88708687e-01 6.36430740e-01 4.97170717e-01 -3.43581140e-01
-9.26840901e-02 -6.37932718e-01 -8.58884335e-01 -5.68284631e-01
-2.70484686e-01 2.49835595e-01 7.20895290e-01 -3.45885187... | [10.798678398132324, 6.784905910491943] |
b88d89f6-93b7-42fc-b5e7-ccfd80169b78 | vietnamese-open-domain-complaint-detection-in | 2104.11969 | null | https://arxiv.org/abs/2104.11969v3 | https://arxiv.org/pdf/2104.11969v3.pdf | Vietnamese Complaint Detection on E-Commerce Websites | Customer product reviews play a role in improving the quality of products and services for business organizations or their brands. Complaining is an attitude that expresses dissatisfaction with an event or a product not meeting customer expectations. In this paper, we build a Open-domain Complaint Detection dataset (UI... | ['Phuong Phan-Dieu Ha', 'Ngan Luu-Thuy Nguyen', 'Kiet Van Nguyen', 'Luan Thanh Nguyen', 'Nhung Thi-Hong Nguyen'] | 2021-04-24 | null | null | null | null | ['toxic-comment-classification', 'vietnamese-datasets', 'complaint-comment-classification'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-4.28641707e-01 2.44537771e-01 -2.86637723e-01 -5.46159506e-01
-8.39311361e-01 -5.73807657e-01 1.73882633e-01 4.56997186e-01
-3.14147204e-01 4.89530116e-01 2.90085465e-01 -1.47645166e-02
3.29685152e-01 -5.48236251e-01 -1.37836665e-01 -2.34115288e-01
3.94881576e-01 4.67157722e-01 5.56632392e-02 -3.31946552... | [11.26709270477295, 6.749553203582764] |
389de4a4-c4dd-4040-accb-67304931edb6 | how-to-fool-radiologists-with-generative | 1710.09762 | null | http://arxiv.org/abs/1710.09762v2 | http://arxiv.org/pdf/1710.09762v2.pdf | How to Fool Radiologists with Generative Adversarial Networks? A Visual Turing Test for Lung Cancer Diagnosis | Discriminating lung nodules as malignant or benign is still an underlying
challenge. To address this challenge, radiologists need computer aided
diagnosis (CAD) systems which can assist in learning discriminative imaging
features corresponding to malignant and benign nodules. However, learning
highly discriminative ima... | ['Maria J. M. Chuquicusma', 'Sarfaraz Hussein', 'Jeremy Burt', 'Ulas Bagci'] | 2017-10-26 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [ 3.67970377e-01 6.84591174e-01 1.45080671e-01 -1.86764508e-01
-8.33321095e-01 -5.52802503e-01 4.98405576e-01 -4.36466187e-01
-5.83769870e-04 6.09938145e-01 -1.77617744e-02 -4.94475514e-01
1.47987213e-02 -7.99608052e-01 -6.71259642e-01 -8.41630399e-01
-5.44773489e-02 9.21223640e-01 2.08868101e-01 2.59591490... | [15.102221488952637, -2.0883188247680664] |
f235f1a2-ce0e-4aab-9c8a-2842e43ccd03 | spectral-unmixing-of-raman-microscopic-images | 2110.13189 | null | https://arxiv.org/abs/2110.13189v1 | https://arxiv.org/pdf/2110.13189v1.pdf | Spectral unmixing of Raman microscopic images of single human cells using Independent Component Analysis | Application of independent component analysis (ICA) as an unmixing and image clustering technique for high spatial resolution Raman maps is reported. A hyperspectral map of a fixed human cell was collected by a Raman micro spectrometer in a raster pattern on a 0.5um grid. Unlike previously used unsupervised machine lea... | ['Li-Lin Tay', 'M. Hamed Mozaffari'] | 2021-10-25 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 8.29802632e-01 -4.02359128e-01 5.48092246e-01 1.06710836e-01
-2.56384671e-01 -6.70810461e-01 4.30059731e-01 -3.27938795e-01
-4.69250888e-01 7.40554810e-01 -1.11323975e-01 -2.07358330e-01
-2.72191972e-01 -6.86765552e-01 -2.43026182e-01 -1.56507242e+00
2.06498653e-02 8.96848679e-01 -4.27520424e-01 1.83011174... | [10.003641128540039, -1.9984899759292603] |
5e781d96-5adf-49a3-9eab-a5fe1c87a07e | cluster-head-detection-for-hierarchical-uav | 2203.04311 | null | https://arxiv.org/abs/2203.04311v1 | https://arxiv.org/pdf/2203.04311v1.pdf | Cluster Head Detection for Hierarchical UAV Swarm With Graph Self-supervised Learning | In this paper, we study the cluster head detection problem of a two-level unmanned aerial vehicle (UAV) swarm network (USNET) with multiple UAV clusters, where the inherent follow strategy (IFS) of low-level follower UAVs (FUAVs) with respect to high-level cluster head UAVs (HUAVs) is unknown. We first propose a graph ... | ['Qihui Wu', 'Feifei Gao', 'Xiang Yun', 'Jun Liu', 'Zhiyu Mou'] | 2022-03-08 | null | null | null | null | ['head-detection'] | ['computer-vision'] | [-4.03769672e-01 6.61812769e-03 -6.67102411e-02 4.34796065e-01
1.76391378e-02 -7.28690624e-01 8.94320011e-02 2.65988052e-01
-9.76575315e-02 4.75848973e-01 -7.42642283e-01 -2.34544486e-01
-4.46674705e-01 -8.06248963e-01 -4.98593181e-01 -1.11435723e+00
-8.56189668e-01 3.60707849e-01 6.53673410e-01 -2.00583175... | [5.960819721221924, 1.7112061977386475] |
0331815d-d4a3-4e93-b41c-1293160cafd9 | point2vec-for-self-supervised-representation | 2303.16570 | null | https://arxiv.org/abs/2303.16570v1 | https://arxiv.org/pdf/2303.16570v1.pdf | Point2Vec for Self-Supervised Representation Learning on Point Clouds | Recently, the self-supervised learning framework data2vec has shown inspiring performance for various modalities using a masked student-teacher approach. However, it remains open whether such a framework generalizes to the unique challenges of 3D point clouds. To answer this question, we extend data2vec to the point cl... | ['Bastian Leibe', 'Alexander Hermans', 'Jonas Schult', 'Karim Abou Zeid'] | 2023-03-29 | null | null | null | null | ['3d-point-cloud-classification', '3d-part-segmentation', 'few-shot-3d-point-cloud-classification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-7.10942000e-02 3.66825283e-01 -1.56206116e-01 -5.31335831e-01
-8.30713212e-01 -7.04461396e-01 7.91320801e-01 2.48813063e-01
7.95017462e-03 -4.53447439e-02 1.66641816e-01 -2.14323595e-01
1.17567249e-01 -1.02510333e+00 -9.83189583e-01 -6.46465123e-01
5.63786589e-02 7.20128834e-01 3.52362573e-01 -3.03850830... | [8.110060691833496, -3.350904703140259] |
82d59c78-a3d8-4564-b0f0-6bd0d35dc9cc | achieving-domain-generalization-in-underwater | 2104.02230 | null | https://arxiv.org/abs/2104.02230v6 | https://arxiv.org/pdf/2104.02230v6.pdf | Achieving Domain Generalization in Underwater Object Detection by Domain Mixup and Contrastive Learning | The performance of existing underwater object detection methods degrades seriously when facing domain shift caused by complicated underwater environments. Due to the limitation of the number of domains in the dataset, deep detectors easily memorize a few seen domains, which leads to low generalization ability. There ar... | ['Hong Liu', 'Pinhao Song', 'Shengquan Li', 'Runwei Ding', 'Xiaochuan Zhang', 'Linhui Dai', 'Yang Chen'] | 2021-04-06 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 2.10585594e-01 -2.72183806e-01 3.71688664e-01 -4.12951320e-01
-2.68605024e-01 -6.63479924e-01 4.42182690e-01 -2.77571350e-01
-5.61356723e-01 6.08776927e-01 2.20255628e-02 2.36614466e-01
-3.28867100e-02 -1.01430488e+00 -7.11190939e-01 -9.81822550e-01
-9.51836780e-02 1.22797377e-01 6.25339031e-01 -4.34886277... | [10.310648918151855, 2.7740635871887207] |
30b69c69-17d8-4fc1-ae59-2b4751afe9f8 | a-convolutional-decoder-for-point-clouds | 1906.11478 | null | https://arxiv.org/abs/1906.11478v1 | https://arxiv.org/pdf/1906.11478v1.pdf | A Convolutional Decoder for Point Clouds using Adaptive Instance Normalization | Automatic synthesis of high quality 3D shapes is an ongoing and challenging area of research. While several data-driven methods have been proposed that make use of neural networks to generate 3D shapes, none of them reach the level of quality that deep learning synthesis approaches for images provide. In this work we p... | ['Moritz Ibing', 'Leif Kobbelt', 'Isaak Lim'] | 2019-06-27 | null | null | null | null | ['point-cloud-generation'] | ['computer-vision'] | [ 3.48210812e-01 1.73311338e-01 3.42659682e-01 -3.97968709e-01
-6.33454263e-01 -5.17980516e-01 9.46328342e-01 7.20397756e-02
-6.33078292e-02 3.78674775e-01 1.35185793e-01 -1.33636519e-01
1.29759638e-02 -1.10569072e+00 -1.10247493e+00 -3.72861415e-01
1.23132885e-01 7.90092766e-01 1.39291301e-01 -5.36795795... | [8.69271183013916, -3.463822364807129] |
b00423f8-5938-40fd-bc6c-51fb543fe1bc | tribert-full-body-human-centric-audio-visual | 2110.13412 | null | https://arxiv.org/abs/2110.13412v1 | https://arxiv.org/pdf/2110.13412v1.pdf | TriBERT: Full-body Human-centric Audio-visual Representation Learning for Visual Sound Separation | The recent success of transformer models in language, such as BERT, has motivated the use of such architectures for multi-modal feature learning and tasks. However, most multi-modal variants (e.g., ViLBERT) have limited themselves to visual-linguistic data. Relatively few have explored its use in audio-visual modalitie... | ['Leonid Sigal', 'Mengyu Yang', 'Tanzila Rahman'] | 2021-10-26 | null | null | null | null | ['pose-retrieval'] | ['computer-vision'] | [ 3.51070464e-02 -3.90082598e-01 1.48831487e-01 -1.48186460e-01
-1.51320434e+00 -9.05327559e-01 7.66869485e-01 6.88760579e-02
-3.61821890e-01 1.38124436e-01 5.32459438e-01 1.67329069e-02
-2.58039594e-01 -2.82195151e-01 -8.68830979e-01 -5.46292782e-01
-1.73386917e-01 3.13673615e-01 8.05724710e-02 -1.56703189... | [14.762632369995117, 4.942720890045166] |
08e041a7-1f84-49d8-a605-83fa381d3dc0 | discriminative-functional-connectivity | 1402.5684 | null | http://arxiv.org/abs/1402.5684v2 | http://arxiv.org/pdf/1402.5684v2.pdf | Discriminative Functional Connectivity Measures for Brain Decoding | We propose a statistical learning model for classifying cognitive processes
based on distributed patterns of neural activation in the brain, acquired via
functional magnetic resonance imaging (fMRI). In the proposed learning method,
local meshes are formed around each voxel. The distance between voxels in the
mesh is d... | ['Orhan Firat', 'Mete Ozay', 'Fatos T. Yarman Vural', 'Ilke Oztekin'] | 2014-02-23 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 2.66949505e-01 -9.81413573e-02 -2.24229530e-01 -4.62546021e-01
-2.55658239e-01 -2.48283967e-01 6.07915163e-01 6.34823859e-01
-6.22864366e-01 6.56005323e-01 2.74726957e-01 2.55181909e-01
-8.43886733e-01 -1.06338406e+00 -4.16319102e-01 -7.65468359e-01
-3.87623370e-01 4.96287137e-01 2.57805526e-01 1.29284695... | [12.575154304504395, 3.380384683609009] |
156a1fe2-0648-4a06-9a3f-a0bc302db011 | dosa-a-system-to-accelerate-annotations-on | 2211.04934 | null | https://arxiv.org/abs/2211.04934v1 | https://arxiv.org/pdf/2211.04934v1.pdf | DoSA : A System to Accelerate Annotations on Business Documents with Human-in-the-Loop | Business documents come in a variety of structures, formats and information needs which makes information extraction a challenging task. Due to these variations, having a document generic model which can work well across all types of documents and for all the use cases seems far-fetched. For document-specific models, w... | ['Amit Vaid', 'Raghu Katikeri', 'Msp Raja', 'Neelesh K Shukla'] | 2022-11-09 | null | null | null | null | ['document-ai', 'key-information-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 8.68421644e-02 4.76951182e-01 -3.18595976e-01 -6.32564425e-01
-7.59962022e-01 -9.46986914e-01 8.68001282e-01 4.36137170e-01
5.28454408e-02 6.89309180e-01 1.36138439e-01 -4.45098400e-01
-1.69795737e-01 -5.69810808e-01 -5.06127000e-01 -2.38669798e-01
3.00611526e-01 9.22231019e-01 5.02042890e-01 -1.02181196... | [9.292482376098633, 8.2388916015625] |
fcaadeac-dc5b-46e2-b36d-404c7178f234 | lungbrn-a-smart-digital-stethoscope-for | null | null | https://ieeexplore.ieee.org/document/8919021 | https://yongfu-li.github.io/papers/LungBRN_A_Smart_Digital_Stethoscope_for_Detecting_Respiratory_Disease_Using_bi-ResNet_Deep_Learning_Algorithm.pdf | LungBRN: A Smart Digital Stethoscope for Detecting Respiratory Disease Using bi-ResNet Deep Learning Algorithm | Improving access to health care services for the medically under-served population is vital to ensure that critical illness can be addressed immediately. In the scenarios where there is a severely lacking of skilled medical staff, a basic lung sound classification through a digital stethoscope can be used to provide an... | ['Jian Zhao and Guoxing Wang', 'Yongfu Li', 'Yuhang Zhang', 'Qing Yu', 'Xinzi Xu', 'Yi Ma'] | 2019-12-05 | null | null | null | ieee-biomedical-circuits-and-systems-biocas | ['sound-classification'] | ['audio'] | [-2.09739301e-02 2.15919688e-02 3.49086598e-02 1.97573006e-01
-7.11465776e-01 -8.65631178e-02 9.33694169e-02 1.86731011e-01
-6.72303796e-01 6.92860663e-01 2.16119632e-01 -6.17643833e-01
-2.80249804e-01 -7.27626860e-01 -3.80404711e-01 -6.05907679e-01
1.70662384e-02 6.43660963e-01 1.91212177e-01 -1.92903560... | [14.536773681640625, 3.8200149536132812] |
d84ad23f-9e2b-422f-9e40-4718c8f91d9c | self-motivated-multi-agent-exploration | 2301.02083 | null | https://arxiv.org/abs/2301.02083v1 | https://arxiv.org/pdf/2301.02083v1.pdf | Self-Motivated Multi-Agent Exploration | In cooperative multi-agent reinforcement learning (CMARL), it is critical for agents to achieve a balance between self-exploration and team collaboration. However, agents can hardly accomplish the team task without coordination and they would be trapped in a local optimum where easy cooperation is accessed without enou... | ['De-Chuan Zhan', 'Yang Yu', 'Lei Yuan', 'Jiahan Cao', 'Shaowei Zhang'] | 2023-01-05 | null | null | null | null | ['starcraft-ii', 'smac-1', 'starcraft', 'smac'] | ['playing-games', 'playing-games', 'playing-games', 'playing-games'] | [-6.13157570e-01 1.29746497e-01 -6.30822182e-02 1.97397858e-01
-4.87528622e-01 -3.04784983e-01 4.86616492e-01 1.93774626e-01
-6.21343315e-01 1.11437488e+00 -1.65043578e-01 -5.93096018e-02
-3.22336197e-01 -6.16711259e-01 -3.33017558e-01 -1.20728385e+00
-5.73664784e-01 7.18883514e-01 2.44740516e-01 -7.45450616... | [3.7766709327697754, 2.0727603435516357] |
51390ecb-f035-4412-abcc-f136e2e519e5 | tap-dlnd-10-a-corpus-for-document-level | 1802.06950 | null | http://arxiv.org/abs/1802.06950v1 | http://arxiv.org/pdf/1802.06950v1.pdf | TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection | Detecting novelty of an entire document is an Artificial Intelligence (AI)
frontier problem that has widespread NLP applications, such as extractive
document summarization, tracking development of news events, predicting impact
of scholarly articles, etc. Important though the problem is, we are unaware of
any benchmark... | ['Amitra Salam', 'Swati Tiwari', 'Asif Ekbal', 'Tirthankar Ghosal', 'Pushpak Bhattacharyya'] | 2018-02-20 | tap-dlnd-10-a-corpus-for-document-level-2 | https://aclanthology.org/L18-1559 | https://aclanthology.org/L18-1559.pdf | lrec-2018-5 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 1.65114999e-01 1.94040313e-01 -3.25915337e-01 4.59896326e-02
-1.06220710e+00 -8.75042379e-01 1.22507858e+00 1.02302754e+00
-3.60342622e-01 1.02388120e+00 7.17852235e-01 -6.34352714e-02
-4.84341741e-01 -5.37354231e-01 -5.12329459e-01 -4.46832031e-01
-1.54980332e-01 5.00889599e-01 3.48883331e-01 -7.43204281... | [12.383914947509766, 9.380431175231934] |
5ecdcb37-84a7-42d0-a245-902da67498ff | anchor-based-adversarially-robust-zero-shot | 2301.13096 | null | https://arxiv.org/abs/2301.13096v2 | https://arxiv.org/pdf/2301.13096v2.pdf | Language-Driven Anchors for Zero-Shot Adversarial Robustness | Deep neural networks are known to be susceptible to adversarial attacks. In this work, we focus on improving adversarial robustness in the challenging zero-shot image classification setting. To address this issue, we propose LAAT, a novel Language-driven, Anchor-based Adversarial Training strategy. LAAT utilizes a text... | ['Xiaolin Hu', 'Bo Zhang', 'Zhanhao Hu', 'Yining Liu', 'Wei zhang', 'Xiao Li'] | 2023-01-30 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 2.74206847e-01 1.31133720e-01 4.96050790e-02 -2.56174505e-01
-9.60849881e-01 -8.39225233e-01 7.83561349e-01 -1.84722930e-01
-4.66385543e-01 4.52966213e-01 2.40711719e-01 -2.71262914e-01
2.19542608e-01 -7.58807540e-01 -1.09559917e+00 -5.45099556e-01
8.94197226e-02 8.11584946e-03 4.42832261e-01 -4.17404354... | [5.6343536376953125, 7.9309210777282715] |
d649496a-c945-4294-b2f2-2d0ccee236c6 | towards-efficient-and-exact-map-inference-for | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Kappes_Towards_Efficient_and_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Kappes_Towards_Efficient_and_2013_CVPR_paper.pdf | Towards Efficient and Exact MAP-Inference for Large Scale Discrete Computer Vision Problems via Combinatorial Optimization | Discrete graphical models (also known as discrete Markov random fields) are a major conceptual tool to model the structure of optimization problems in computer vision. While in the last decade research has focused on fast approximative methods, algorithms that provide globally optimal solutions have come more into the ... | ['Christoph Schnorr', 'Gerhard Reinelt', 'Markus Speth', 'Jorg Hendrik Kappes'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['2048'] | ['playing-games'] | [ 2.30143756e-01 2.28617758e-01 -4.20023613e-02 -2.52205402e-01
-7.16464996e-01 -5.44966996e-01 6.06793284e-01 5.36441207e-01
-6.06113732e-01 8.45084608e-01 -3.02598774e-01 -3.67777526e-01
-3.66591781e-01 -8.42622876e-01 -6.40904307e-01 -8.11350346e-01
-1.18308030e-01 8.93835485e-01 4.38602239e-01 9.77943167... | [9.27899169921875, 0.0793975442647934] |
35484adf-3ca1-4944-b95e-ffce70da98b6 | on-a-relation-between-the-rate-distortion | 2307.00246 | null | https://arxiv.org/abs/2307.00246v1 | https://arxiv.org/pdf/2307.00246v1.pdf | On a Relation Between the Rate-Distortion Function and Optimal Transport | We discuss a relationship between rate-distortion and optimal transport (OT) theory, even though they seem to be unrelated at first glance. In particular, we show that a function defined via an extremal entropic OT distance is equivalent to the rate-distortion function. We numerically verify this result as well as prev... | ['Shirin Saeedi Bidokhti', 'Hamed Hassani', 'Eric Lei'] | 2023-07-01 | null | null | null | null | ['quantization'] | ['methodology'] | [-1.09411187e-01 2.56780475e-01 -1.86734617e-01 -1.42033890e-01
-5.77744186e-01 -8.55580151e-01 4.66167510e-01 1.79585651e-01
-5.99313915e-01 1.11299348e+00 2.39990026e-01 -4.06027734e-01
-5.56790352e-01 -3.92292351e-01 -5.88342071e-01 -9.13902700e-01
-2.17051163e-01 1.41960666e-01 -1.47436112e-01 -4.44339097... | [7.17299747467041, 3.966876268386841] |
21fc1d7d-2056-4bb9-9cbf-8a637787e9f0 | neural-body-fitting-unifying-deep-learning | 1808.05942 | null | http://arxiv.org/abs/1808.05942v1 | http://arxiv.org/pdf/1808.05942v1.pdf | Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation | Direct prediction of 3D body pose and shape remains a challenge even for
highly parameterized deep learning models. Mapping from the 2D image space to
the prediction space is difficult: perspective ambiguities make the loss
function noisy and training data is scarce. In this paper, we propose a novel
approach (Neural B... | ['Gerard Pons-Moll', 'Christoph Lassner', 'Peter V. Gehler', 'Mohamed Omran', 'Bernt Schiele'] | 2018-08-17 | null | null | null | null | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-3.04684192e-02 4.23892766e-01 -2.99048066e-01 -4.85264271e-01
-8.27089608e-01 -6.08511329e-01 1.44813895e-01 -1.72281340e-01
-3.05805534e-01 4.34596509e-01 2.35354900e-01 1.28727630e-01
1.91034079e-01 -3.80381227e-01 -1.12036860e+00 -2.53302217e-01
-3.32490206e-02 9.01265264e-01 2.02268854e-01 -1.56721652... | [7.032674312591553, -1.0687885284423828] |
fc602566-534f-4401-86dd-71f82e962306 | automatic-covid-19-disease-diagnosis-using-1d | 2112.07285 | null | https://arxiv.org/abs/2112.07285v1 | https://arxiv.org/pdf/2112.07285v1.pdf | Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice | The issue in respiratory sound classification has attained good attention from the clinical scientists and medical researcher's group in the last year to diagnosing COVID-19 disease. To date, various models of Artificial Intelligence (AI) entered into the real-world to detect the COVID-19 disease from human-generated s... | ['Alphonse Pja', 'Kranthi Kumar Lella'] | 2021-12-14 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 7.21014813e-02 -1.21486656e-01 2.22082034e-01 4.64682244e-02
-1.21680349e-01 -2.28671521e-01 1.84953868e-01 -3.31326388e-02
-4.05011624e-01 3.77703279e-01 2.65537620e-01 -4.95741278e-01
-5.79800494e-02 -7.99073517e-01 -1.23696618e-01 -5.59344471e-01
1.79251637e-02 1.37258187e-01 -2.85062809e-02 -1.02529936... | [14.525776863098145, 3.883249282836914] |
1b804c48-1245-4476-b64c-1cb63b5e2781 | implementation-of-an-automatic-sign-language | 1403.6392 | null | http://arxiv.org/abs/1403.6392v2 | http://arxiv.org/pdf/1403.6392v2.pdf | Implementation of an Automatic Sign Language Lexical Annotation Framework based on Propositional Dynamic Logic | In this paper, we present the implementation of an automatic Sign Language
(SL) sign annotation framework based on a formal logic, the Propositional
Dynamic Logic (PDL). Our system relies heavily on the use of a specific variant
of PDL, the Propositional Dynamic Logic for Sign Language (PDLSL), which lets
us describe S... | ['Arturo Curiel', 'Christophe Collet'] | 2014-03-25 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 2.09682196e-01 5.27013302e-01 -7.59525076e-02 -2.31163770e-01
-1.78552210e-01 -7.82839060e-01 9.44641888e-01 -2.97049880e-01
-2.28861779e-01 4.62982684e-01 1.88226521e-01 -3.62593830e-01
-4.57975596e-01 -5.49662650e-01 -3.13645065e-01 -1.77172646e-01
-1.88486412e-01 5.39260864e-01 8.71739626e-01 -3.37336123... | [9.134469032287598, -6.406210899353027] |
9116608a-194f-4ec5-bfe6-c9a4030b6bbe | pushing-the-limits-of-self-supervised-resnets | 2201.05119 | null | https://arxiv.org/abs/2201.05119v2 | https://arxiv.org/pdf/2201.05119v2.pdf | Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet? | Despite recent progress made by self-supervised methods in representation learning with residual networks, they still underperform supervised learning on the ImageNet classification benchmark, limiting their applicability in performance-critical settings. Building on prior theoretical insights from ReLIC [Mitrovic et a... | ['Jovana Mitrovic', 'Charles Blundell', 'Razvan Pascanu', 'Lars Buesing', 'Brian McWilliams', 'Ioana Bica', 'Nenad Tomasev'] | 2022-01-13 | null | null | null | null | ['self-supervised-image-classification', 'semi-supervised-image-classification'] | ['computer-vision', 'computer-vision'] | [ 4.20556188e-01 3.46074730e-01 -4.54954475e-01 -6.09178424e-01
-9.85138834e-01 -4.50919122e-01 6.11829400e-01 -1.25535488e-01
-6.33593738e-01 7.86883652e-01 2.21503794e-01 -5.39120957e-02
-1.36071384e-01 -8.02644491e-01 -9.38724101e-01 -4.40926433e-01
-7.00128004e-02 5.54025471e-01 1.56722903e-01 -2.18240440... | [9.476653099060059, 2.542053461074829] |
e518baa6-735c-4330-a24a-c1ee735addd2 | dptnet-a-dual-path-transformer-architecture | 2208.09878 | null | https://arxiv.org/abs/2208.09878v1 | https://arxiv.org/pdf/2208.09878v1.pdf | DPTNet: A Dual-Path Transformer Architecture for Scene Text Detection | The prosperity of deep learning contributes to the rapid progress in scene text detection. Among all the methods with convolutional networks, segmentation-based ones have drawn extensive attention due to their superiority in detecting text instances of arbitrary shapes and extreme aspect ratios. However, the bottom-up ... | ['Hanzi Wang', 'Wei Liu', 'Hongfa Wang', 'Chunchao Guo', 'Yan Yan', 'Jie Jiang', 'Jingyu Lin'] | 2022-08-21 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 8.94252909e-04 -4.67577815e-01 9.59574655e-02 -2.01107845e-01
-4.77826834e-01 -3.37848693e-01 7.18671024e-01 5.34415580e-02
-2.69385815e-01 -1.28614539e-02 1.78211972e-01 -3.48852724e-01
3.19539100e-01 -8.89378250e-01 -4.07941073e-01 -6.77187741e-01
4.59835768e-01 3.63931537e-01 9.30841804e-01 -3.76705587... | [12.07999324798584, 2.282796621322632] |
5962c919-7648-44cc-8ec3-be0f93a47443 | dddm-vc-decoupled-denoising-diffusion-models | 2305.15816 | null | https://arxiv.org/abs/2305.15816v1 | https://arxiv.org/pdf/2305.15816v1.pdf | DDDM-VC: Decoupled Denoising Diffusion Models with Disentangled Representation and Prior Mixup for Verified Robust Voice Conversion | Diffusion-based generative models have exhibited powerful generative performance in recent years. However, as many attributes exist in the data distribution and owing to several limitations of sharing the model parameters across all levels of the generation process, it remains challenging to control specific styles for... | ['Seong-Whan Lee', 'Sang-Hoon Lee', 'Ha-Yeong Choi'] | 2023-05-25 | null | null | null | null | ['voice-conversion', 'style-transfer', 'voice-conversion'] | ['audio', 'computer-vision', 'speech'] | [-7.35099539e-02 -1.21055685e-01 2.62832157e-02 -1.87576607e-01
-8.60216796e-01 -8.19642723e-01 7.35685229e-01 -6.40189886e-01
1.44642398e-01 6.14739120e-01 8.17139447e-01 1.02198444e-01
-4.30880897e-02 -8.86865318e-01 -3.34334403e-01 -1.06959295e+00
6.13266110e-01 3.89813989e-01 -5.43067455e-01 -3.07077408... | [14.97694206237793, 6.492598056793213] |
3b1ebd24-82eb-404a-a155-81d1f25f7887 | room-geometry-estimation-from-room-impulse | 1904.00869 | null | http://arxiv.org/abs/1904.00869v4 | http://arxiv.org/pdf/1904.00869v4.pdf | Room Geometry Estimation from Room Impulse Responses using Convolutional Neural Networks | We describe a new method to estimate the geometry of a room given room
impulse responses. The method utilises convolutional neural networks to
estimate the room geometry and uses the mean square error as the loss function.
In contrast to existing methods, we do not require the position or distance of
sources or receive... | [] | 2019-05-15 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [ 1.13451093e-01 1.71126246e-01 9.77030396e-01 -5.36184072e-01
-1.17159665e+00 -5.96875250e-01 3.26601446e-01 3.41015905e-01
-4.14954782e-01 4.31380928e-01 7.00409189e-02 -5.30175447e-01
-9.70035642e-02 -1.03838277e+00 -7.86705732e-01 -5.54989755e-01
-3.29389393e-01 1.24430388e-01 -1.19369172e-01 -1.53501689... | [15.172327041625977, 5.665417671203613] |
28164876-de67-46d4-8d32-893510ed5efd | hand-pose-estimation-via-multiview | 2302.00988 | null | https://arxiv.org/abs/2302.00988v1 | https://arxiv.org/pdf/2302.00988v1.pdf | Hand Pose Estimation via Multiview Collaborative Self-Supervised Learning | 3D hand pose estimation has made significant progress in recent years. However, the improvement is highly dependent on the emergence of large-scale annotated datasets. To alleviate the label-hungry limitation, we propose a multi-view collaborative self-supervised learning framework, HaMuCo, that estimates hand pose onl... | ['Jingyu Wang', 'Zhou Xue', 'Chao Wen', 'Xiaozheng Zheng'] | 2023-02-02 | null | null | null | null | ['3d-hand-pose-estimation', '3d-hand-pose-estimation'] | ['computer-vision', 'graphs'] | [-2.13315710e-01 -3.31150919e-01 -4.03655946e-01 -3.30700874e-01
-1.02604103e+00 -7.18346477e-01 1.70975685e-01 -5.14569640e-01
-2.51160920e-01 4.85621125e-01 4.85587209e-01 4.28540856e-01
-8.11321139e-02 -3.03516328e-01 -4.97510910e-01 -7.70257711e-01
2.38421917e-01 5.93730152e-01 3.66592348e-01 1.31859884... | [6.82996940612793, -0.8220939636230469] |
f7ba0939-cc37-4a86-9bc8-fe888203b753 | blobgan-3d-a-spatially-disentangled-3d-aware | 2303.14706 | null | https://arxiv.org/abs/2303.14706v1 | https://arxiv.org/pdf/2303.14706v1.pdf | BlobGAN-3D: A Spatially-Disentangled 3D-Aware Generative Model for Indoor Scenes | 3D-aware image synthesis has attracted increasing interest as it models the 3D nature of our real world. However, performing realistic object-level editing of the generated images in the multi-object scenario still remains a challenge. Recently, a 2D GAN termed BlobGAN has demonstrated great multi-object editing capabi... | ['Peter Wonka', 'Michael Birsak', 'Yiqun Wang', 'Qian Wang'] | 2023-03-26 | null | null | null | null | ['3d-aware-image-synthesis'] | ['computer-vision'] | [ 2.68420458e-01 1.34577081e-01 3.45262915e-01 -9.60973576e-02
-6.36108220e-01 -7.02888966e-01 5.63495338e-01 -3.99397850e-01
-1.46795763e-02 5.59447825e-01 1.60174206e-01 3.92089598e-02
6.26596436e-02 -6.05198741e-01 -1.08351827e+00 -5.95146537e-01
3.42413485e-01 6.26646340e-01 5.07693768e-01 -1.57778442... | [9.245267868041992, -3.092047929763794] |
9a28977d-6024-491d-8fe6-c5294761751e | weakly-supervised-deep-learning-for-thoracic | 1807.06067 | null | http://arxiv.org/abs/1807.06067v1 | http://arxiv.org/pdf/1807.06067v1.pdf | Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays | Chest X-rays is one of the most commonly available and affordable
radiological examinations in clinical practice. While detecting thoracic
diseases on chest X-rays is still a challenging task for machine intelligence,
due to 1) the highly varied appearance of lesion areas on X-rays from patients
of different thoracic d... | ['Junzhou Huang', 'Ruoyu Li', 'Jiawen Yao', 'Chaochao Yan', 'Zheng Xu'] | 2018-07-16 | null | null | null | null | ['thoracic-disease-classification'] | ['computer-vision'] | [ 4.10425693e-01 1.41540587e-01 -3.06415081e-01 -3.92628402e-01
-1.23906446e+00 -2.72947520e-01 2.71277726e-01 1.29149362e-01
-3.78526777e-01 5.67853987e-01 1.96273793e-02 -6.46669507e-01
-2.09296077e-01 -4.60252553e-01 -5.09172797e-01 -8.50715458e-01
1.13953814e-01 4.43004370e-01 6.53334439e-01 3.15765172... | [15.234587669372559, -2.1561169624328613] |
882833c6-d1a0-407c-9cd1-3068d711ac1e | a-practical-stereo-depth-system-for-smart | 2211.10551 | null | https://arxiv.org/abs/2211.10551v2 | https://arxiv.org/pdf/2211.10551v2.pdf | A Practical Stereo Depth System for Smart Glasses | We present the design of a productionized end-to-end stereo depth sensing system that does pre-processing, online stereo rectification, and stereo depth estimation with a fallback to monocular depth estimation when rectification is unreliable. The output of our depth sensing system is then used in a novel view generati... | ['Matt Uyttendaele', 'Michael F. Cohen', 'Peter Vajda', 'Zijian He', 'Jan-Michael Frahm', 'Sam Tsai', 'Yanghan Wang', 'Suhib Alsisan', 'Jonathan Lehman', 'Matthew Yu', 'Kevin Blackburn-Matzen', 'Akash Bapat', 'Daniel Scharstein', 'Jialiang Wang'] | 2022-11-19 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_A_Practical_Stereo_Depth_System_for_Smart_Glasses_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_A_Practical_Stereo_Depth_System_for_Smart_Glasses_CVPR_2023_paper.pdf | cvpr-2023-1 | ['stereo-depth-estimation'] | ['computer-vision'] | [ 4.27792460e-01 6.29012287e-02 2.70399064e-01 -4.05592918e-01
-8.32316637e-01 -5.63888311e-01 1.52023807e-01 -3.53680849e-01
-3.10476601e-01 4.27945673e-01 1.50307566e-01 -6.09385848e-01
4.83315766e-01 -5.74426770e-01 -6.96392179e-01 -3.66608351e-01
3.39476883e-01 4.78509277e-01 5.55255353e-01 -9.49918926... | [9.012533187866211, -2.534329652786255] |
abcf2e7f-a9b1-4fe6-a052-fd7da43e2304 | learning-from-sibling-mentions-with-scalable | null | null | https://aclanthology.org/2022.acl-long.147 | https://aclanthology.org/2022.acl-long.147.pdf | Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing | In this paper, we firstly empirically find that existing models struggle to handle hard mentions due to their insufficient contexts, which consequently limits their overall typing performance. To this end, we propose to exploit sibling mentions for enhancing the mention representations.Specifically, we present two diff... | ['Ruifeng Xu', 'Shuming Shi', 'Haisong Zhang', 'Lemao Liu', 'Haiyun Jiang', 'Jiayang Cheng', 'Yi Chen'] | null | null | null | null | acl-2022-5 | ['entity-typing'] | ['natural-language-processing'] | [ 9.98621434e-02 5.19556582e-01 -4.16724235e-01 -4.44310278e-01
-5.77043355e-01 -6.13618851e-01 5.08005679e-01 4.69906002e-01
-5.47772944e-01 9.24196482e-01 2.27520362e-01 -6.63415611e-01
-6.32387325e-02 -7.92575300e-01 -5.07383406e-01 -2.33695552e-01
-1.66655630e-01 2.69434392e-01 5.13981581e-01 -2.20422581... | [9.337523460388184, 8.66987133026123] |
635533c8-592d-4a00-b877-d2c6e76d0ade | cross-corpus-training-with-treelstm-for-the | null | null | https://openreview.net/forum?id=S1LXVnxRb | https://openreview.net/pdf?id=S1LXVnxRb | Cross-Corpus Training with TreeLSTM for the Extraction of Biomedical Relationships from Text | A bottleneck problem in machine learning-based relationship extraction (RE) algorithms, and particularly of deep learning-based ones, is the availability of training data in the form of annotated corpora. For specific domains, such as biomedicine, the long time and high expertise required for the development of manuall... | ['Chedy Raïssi', 'Yannick Toussaint', 'Legrand Joël', 'Adrien Coulet'] | 2018-01-01 | null | null | null | iclr-2018-1 | ['cross-corpus', 'relationship-extraction-distant-supervised'] | ['computer-vision', 'natural-language-processing'] | [ 2.67222226e-01 6.40523076e-01 -2.66260475e-01 -2.52453208e-01
-6.22113645e-01 -1.69053108e-01 5.43505549e-01 9.45439816e-01
-9.06869054e-01 1.44196153e+00 7.65935406e-02 -3.95241439e-01
-2.92675018e-01 -9.12658572e-01 -7.17194617e-01 -5.56951702e-01
-1.98018458e-02 7.88034022e-01 2.08381400e-01 -4.53412384... | [8.773669242858887, 8.78752613067627] |
fd5c49c0-a765-432b-9f70-da9b448a84c2 | vgf-net-visual-geometric-fusion-learning-for | 2104.03109 | null | https://arxiv.org/abs/2104.03109v1 | https://arxiv.org/pdf/2104.03109v1.pdf | VGF-Net: Visual-Geometric Fusion Learning for Simultaneous Drone Navigation and Height Mapping | The drone navigation requires the comprehensive understanding of both visual and geometric information in the 3D world. In this paper, we present a Visual-Geometric Fusion Network(VGF-Net), a deep network for the fusion analysis of visual/geometric data and the construction of 2.5D height maps for simultaneous drone na... | ['Hui Huang', 'Ke Xie', 'Yilin Liu'] | 2021-04-07 | null | null | null | null | ['drone-navigation'] | ['computer-vision'] | [-1.55440927e-01 -2.97971874e-01 2.85727590e-01 -6.51407242e-01
-3.64053071e-01 -4.70166087e-01 2.84887612e-01 1.05851687e-01
-3.72655004e-01 4.28525090e-01 2.24755853e-01 7.45349750e-02
-4.59475338e-01 -1.12992835e+00 -5.92824519e-01 -5.30904233e-01
-2.39182189e-02 3.37751895e-01 4.00808632e-01 -8.48223507... | [7.71895170211792, -1.95010507106781] |
93130024-2de1-4105-b877-3f6f6dd84774 | cgans-with-auxiliary-discriminative | 2107.10060 | null | https://arxiv.org/abs/2107.10060v5 | https://arxiv.org/pdf/2107.10060v5.pdf | Conditional GANs with Auxiliary Discriminative Classifier | Conditional generative models aim to learn the underlying joint distribution of data and labels to achieve conditional data generation. Among them, the auxiliary classifier generative adversarial network (AC-GAN) has been widely used, but suffers from the problem of low intra-class diversity of the generated samples. T... | ['Xueqi Cheng', 'Xiaoshuang Li', 'Siyuan Pan', 'HuaWei Shen', 'Qi Cao', 'Liang Hou'] | 2021-07-21 | conditional-gans-with-auxiliary | https://openreview.net/forum?id=Yn4CPz_LRKO | https://openreview.net/pdf?id=Yn4CPz_LRKO | null | ['conditional-image-generation'] | ['computer-vision'] | [ 4.07524943e-01 8.35682303e-02 -1.65351957e-01 -1.45653889e-01
-9.19862747e-01 -5.21527410e-01 5.87543726e-01 -4.35972393e-01
8.53971988e-02 1.00168276e+00 -3.44377779e-03 4.54346314e-02
1.63421422e-01 -1.07736409e+00 -4.71667171e-01 -1.51209748e+00
5.07273018e-01 5.25601208e-01 -2.10778669e-01 1.29347950... | [11.646604537963867, -0.2559076249599457] |
c64cafb6-c8ce-4f01-a9b2-0f5e57a5d788 | training-robots-without-robots-deep-imitation | 2202.09574 | null | https://arxiv.org/abs/2202.09574v1 | https://arxiv.org/pdf/2202.09574v1.pdf | Training Robots without Robots: Deep Imitation Learning for Master-to-Robot Policy Transfer | Deep imitation learning is a promising method for dexterous robot manipulation because it only requires demonstration samples for learning manipulation skills. In this paper, deep imitation learning is applied to tasks that require force feedback, such as bottle opening. However, simple visual feedback systems, such as... | ['Yasuo Kuniyoshi', 'Akihiko Nagakubo', 'Yoshiyuki Ohmura', 'Heecheol Kim'] | 2022-02-19 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [-3.15551102e-01 4.42473263e-01 -5.18019274e-02 -1.32759944e-01
8.25924203e-02 -4.65587020e-01 1.59002662e-01 -6.21442258e-01
-4.60985482e-01 6.87214613e-01 -7.99602449e-01 -2.63242871e-01
-1.56479422e-02 -2.64911294e-01 -1.04269922e+00 -4.80257571e-01
2.25369573e-01 3.38161260e-01 3.42667341e-01 -6.45466030... | [4.6889824867248535, 0.6868337392807007] |
c73a0e1c-7a0f-4fb6-b2c2-8414f1a27230 | latent-transformations-for-discrete-data | 2006.06346 | null | https://arxiv.org/abs/2006.06346v1 | https://arxiv.org/pdf/2006.06346v1.pdf | Latent Transformations for Discrete-Data Normalising Flows | Normalising flows (NFs) for discrete data are challenging because parameterising bijective transformations of discrete variables requires predicting discrete/integer parameters. Having a neural network architecture predict discrete parameters takes a non-differentiable activation function (eg, the step function) which ... | ['Wilker Aziz', 'Rob Hesselink'] | 2020-06-11 | null | null | null | null | ['normalising-flows'] | ['methodology'] | [ 4.43486810e-01 2.68200010e-01 -2.28761032e-01 -6.66956365e-01
-9.45396960e-01 -8.08248699e-01 8.96071255e-01 -3.15832049e-01
-5.02471387e-01 1.08282030e+00 8.77930894e-02 -6.10409975e-01
-2.84016043e-01 -7.40532815e-01 -8.17977607e-01 -7.22439289e-01
-1.97180703e-01 6.54392123e-01 8.54476988e-02 6.82114363... | [7.2146735191345215, 3.839672565460205] |
6d696364-ae25-4780-95b1-5ec38907c372 | on-the-generalizability-of-ecg-based-stress | 2210.06225 | null | https://arxiv.org/abs/2210.06225v1 | https://arxiv.org/pdf/2210.06225v1.pdf | On the Generalizability of ECG-based Stress Detection Models | Stress is prevalent in many aspects of everyday life including work, healthcare, and social interactions. Many works have studied handcrafted features from various bio-signals that are indicators of stress. Recently, deep learning models have also been proposed to detect stress. Typically, stress models are trained and... | ['Elisabeth André', 'Pooja Prajod'] | 2022-10-12 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [-6.90328330e-02 -3.37498486e-01 2.79942714e-02 -6.39476120e-01
-3.89079526e-02 -2.80476570e-01 -2.25048438e-02 6.05712414e-01
-4.86769825e-01 8.04794073e-01 -9.39298570e-02 -8.74107033e-02
-2.69413620e-01 -7.84165204e-01 -2.75387347e-01 -5.48492432e-01
-2.86960274e-01 -1.09998807e-02 -2.91367918e-01 -3.79224926... | [13.833788871765137, 3.1314306259155273] |
2416754b-1115-46f2-9540-18df29b65b24 | deep-speech-denoising-with-vector-space | 1804.10669 | null | http://arxiv.org/abs/1804.10669v1 | http://arxiv.org/pdf/1804.10669v1.pdf | Deep Speech Denoising with Vector Space Projections | We propose an algorithm to denoise speakers from a single microphone in the
presence of non-stationary and dynamic noise. Our approach is inspired by the
recent success of neural network models separating speakers from other speakers
and singers from instrumental accompaniment. Unlike prior art, we leverage
embedding s... | ['Karl Ni', 'Paul Gamble', 'Maria Barrios', 'Jeff Hetherly', 'Cory Stephenson'] | 2018-04-27 | null | null | null | null | ['speech-denoising'] | ['speech'] | [ 4.00307417e-01 1.16840079e-01 3.10801771e-02 -2.21044794e-01
-1.10696089e+00 -7.89349735e-01 5.78790367e-01 -1.92021132e-01
-4.88817424e-01 4.97448295e-01 7.16598988e-01 -8.71769339e-02
-2.07871333e-01 -4.85274374e-01 -3.73374969e-01 -8.59985769e-01
4.88816835e-02 9.70264301e-02 -2.62483716e-01 -2.36184910... | [15.244105339050293, 5.721889495849609] |
718c5de8-83ec-4188-a22e-a6740b841b1e | voice-cloning-a-multi-speaker-text-to-speech | 2102.05630 | null | https://arxiv.org/abs/2102.05630v1 | https://arxiv.org/pdf/2102.05630v1.pdf | Voice Cloning: a Multi-Speaker Text-to-Speech Synthesis Approach based on Transfer Learning | Deep learning models are becoming predominant in many fields of machine learning. Text-to-Speech (TTS), the process of synthesizing artificial speech from text, is no exception. To this end, a deep neural network is usually trained using a corpus of several hours of recorded speech from a single speaker. Trying to prod... | ['Vincent Pollet', 'Luigi di Caro', 'Enrico Zovato', 'Giuseppe Ruggiero'] | 2021-02-10 | null | null | null | null | ['voice-cloning'] | ['speech'] | [ 1.96395606e-01 2.71944940e-01 4.72145587e-01 -3.91311467e-01
-5.93949854e-01 -4.07880992e-01 7.12795138e-01 1.61773980e-01
-2.92360812e-01 6.11851931e-01 -3.09416149e-02 -2.89465576e-01
2.46201813e-01 -4.85031486e-01 -7.99394786e-01 -7.94354677e-01
2.63079077e-01 8.56783748e-01 9.35021415e-02 -6.56758398... | [14.799856185913086, 6.526928901672363] |
e61e562c-f3d4-4788-9c73-4343e5eb6319 | blockchain-based-federated-learning-for-2 | 2306.17186 | null | https://arxiv.org/abs/2306.17186v1 | https://arxiv.org/pdf/2306.17186v1.pdf | Blockchain-based Federated Learning for Decentralized Energy Management Systems | The Internet of Energy (IoE) is a distributed paradigm that leverages smart networks and distributed system technologies to enable decentralized energy systems. In contrast to the traditional centralized energy systems, distributed Energy Internet systems comprise multiple components and communication requirements that... | ['Öznur Özkasap', 'Abdulrezzak Zekiye'] | 2023-06-23 | null | null | null | null | ['management', 'energy-management'] | ['miscellaneous', 'time-series'] | [-8.95073056e-01 -5.28457128e-02 -8.02151740e-01 -1.70203656e-01
-2.96325713e-01 -1.29095411e+00 1.09029019e+00 -1.38879880e-01
3.16846400e-01 1.01481783e+00 3.54056716e-01 -6.64346755e-01
-1.88586838e-03 -9.71835256e-01 -5.24970233e-01 -1.10017383e+00
-9.50290710e-02 3.78409624e-01 -1.01152249e-01 -2.11021543... | [5.876958847045898, 6.422941207885742] |
a6b3e783-031c-4ed1-b951-7c551dcd441d | lt-net-label-transfer-by-learning-reversible | 2003.07072 | null | https://arxiv.org/abs/2003.07072v3 | https://arxiv.org/pdf/2003.07072v3.pdf | LT-Net: Label Transfer by Learning Reversible Voxel-wise Correspondence for One-shot Medical Image Segmentation | We introduce a one-shot segmentation method to alleviate the burden of manual annotation for medical images. The main idea is to treat one-shot segmentation as a classical atlas-based segmentation problem, where voxel-wise correspondence from the atlas to the unlabelled data is learned. Subsequently, segmentation label... | ['Renzhen Wang', 'Yefeng Zheng', 'Dong Wei', 'Shuxin Wang', 'Shilei Cao', 'Liansheng Wang', 'Kai Ma', 'Deyu Meng'] | 2020-03-16 | lt-net-label-transfer-by-learning-reversible-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Wang_LT-Net_Label_Transfer_by_Learning_Reversible_Voxel-Wise_Correspondence_for_One-Shot_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_LT-Net_Label_Transfer_by_Learning_Reversible_Voxel-Wise_Correspondence_for_One-Shot_CVPR_2020_paper.pdf | cvpr-2020-6 | ['one-shot-segmentation'] | ['computer-vision'] | [ 1.96742192e-01 3.79792511e-01 -2.52774030e-01 -6.38165772e-01
-1.17072809e+00 -3.67636591e-01 3.05425555e-01 -3.79057191e-02
-3.66188258e-01 5.24638295e-01 -7.67388269e-02 5.19606769e-02
-1.06692594e-02 -6.47914231e-01 -5.96675754e-01 -9.59334373e-01
2.83462793e-01 7.45837450e-01 6.86412752e-01 -5.09739555... | [14.547277450561523, -2.082487106323242] |
391055b1-14e1-410b-9a7b-c954d6f66446 | reconciling-a-centroid-hypothesis-conflict-in | 2212.03795 | null | https://arxiv.org/abs/2212.03795v1 | https://arxiv.org/pdf/2212.03795v1.pdf | Reconciling a Centroid-Hypothesis Conflict in Source-Free Domain Adaptation | Source-free domain adaptation (SFDA) aims to transfer knowledge learned from a source domain to an unlabeled target domain, where the source data is unavailable during adaptation. Existing approaches for SFDA focus on self-training usually including well-established entropy minimization techniques. One of the main chal... | ['Arnon Netzer', 'Hai Victor Habi', 'Oranit Dror', 'Roy H. Jennings', 'Idit Diamant'] | 2022-12-07 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 3.56716365e-01 2.88548261e-01 -1.18224978e-01 -6.88687801e-01
-9.42647815e-01 -5.51867127e-01 6.94800794e-01 1.24575227e-01
-4.83767062e-01 1.08893180e+00 4.42554653e-02 1.85224816e-01
-1.27784848e-01 -4.85070109e-01 -7.18523204e-01 -7.55825162e-01
1.31822243e-01 8.27430606e-01 1.10777304e-01 -3.56441624... | [10.277645111083984, 3.1751914024353027] |
a442695f-0408-48d3-989d-6e6bd67941bb | fast-and-incremental-loop-closure-detection | 1911.10752 | null | https://arxiv.org/abs/1911.10752v1 | https://arxiv.org/pdf/1911.10752v1.pdf | Fast and Incremental Loop Closure Detection Using Proximity Graphs | Visual loop closure detection, which can be considered as an image retrieval task, is an important problem in SLAM (Simultaneous Localization and Mapping) systems. The frequently used bag-of-words (BoW) models can achieve high precision and moderate recall. However, the requirement for lower time costs and fewer memory... | ['Xianglong Liu', 'Guangfu Che', 'Yu Chen', 'Shan An', 'Fangru Zhou', 'Xin Ma'] | 2019-11-25 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [-1.32106140e-01 -2.85518497e-01 -3.02619845e-01 -1.53861642e-01
-6.91129625e-01 -2.45683253e-01 5.94677329e-01 6.38446987e-01
-7.20324457e-01 3.93489867e-01 -2.55875647e-01 -4.32642579e-01
-6.87866211e-02 -1.07273650e+00 -7.86750436e-01 -4.59172755e-01
-2.26190597e-01 4.17241752e-01 6.77152097e-01 -3.71621728... | [7.4652628898620605, -2.0874693393707275] |
a5ea7ba9-90ac-4251-824f-f893aa965215 | tcn-aa-a-wi-fi-based-temporal-convolution | 2305.18211 | null | https://arxiv.org/abs/2305.18211v1 | https://arxiv.org/pdf/2305.18211v1.pdf | TCN AA: A Wi Fi based Temporal Convolution Network for Human to Human Interaction Recognition with Augmentation and Attention | The utilization of Wi-Fi-based human activity recognition (HAR) has gained considerable interest in recent times, primarily owing to its applications in various domains such as healthcare for monitoring breath and heart rate, security, elderly care, and others. These Wi-Fi-based methods exhibit several advantages over ... | ['Timothy K. Shih', 'Chih-Yang Lin', 'Yu-Tso Liu', 'Chia-Yu Lin'] | 2023-05-21 | null | null | null | null | ['human-interaction-recognition', 'human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'computer-vision', 'time-series'] | [ 5.14231265e-01 -3.17844659e-01 -2.20692948e-01 -2.01688975e-01
-6.37834311e-01 -1.65863603e-01 2.64504373e-01 -1.65263101e-01
-4.92058009e-01 8.73752475e-01 3.15520614e-01 -1.38085827e-01
1.83258597e-02 -6.87143624e-01 -4.90962207e-01 -4.85082150e-01
-1.26063138e-01 -2.53922820e-01 3.35998207e-01 -5.62571920... | [7.452597141265869, 0.7845702767372131] |
8909705d-2a4a-4674-abd9-f0e5af7d42d3 | topology-preserving-segmentation-network-a | 2202.13331 | null | https://arxiv.org/abs/2202.13331v1 | https://arxiv.org/pdf/2202.13331v1.pdf | Topology-Preserving Segmentation Network: A Deep Learning Segmentation Framework for Connected Component | Medical image segmentation, which aims to automatically extract anatomical or pathological structures, plays a key role in computer-aided diagnosis and disease analysis. Despite the problem has been widely studied, existing methods are prone to topological errors. In medical imaging, the topology of the structure, such... | ['Lok Ming Lui', 'Han Zhang'] | 2022-02-27 | null | null | null | null | ['unet-segmentation'] | ['computer-vision'] | [ 2.63381988e-01 2.35481501e-01 1.76401976e-02 -3.40633780e-01
-3.77729803e-01 -4.46268290e-01 2.32314765e-01 1.57252029e-02
-3.00958842e-01 5.59343576e-01 -1.90363973e-01 -5.41785024e-02
-2.61802852e-01 -9.35380697e-01 -5.41939735e-01 -8.94233882e-01
3.30238119e-02 5.76807499e-01 4.65087980e-01 1.87681019... | [14.233458518981934, -2.6007449626922607] |
e840f4ec-ee6c-4621-adb6-bce21261b263 | bongard-hoi-benchmarking-few-shot-visual | 2205.13803 | null | https://arxiv.org/abs/2205.13803v2 | https://arxiv.org/pdf/2205.13803v2.pdf | Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions | A significant gap remains between today's visual pattern recognition models and human-level visual cognition especially when it comes to few-shot learning and compositional reasoning of novel concepts. We introduce Bongard-HOI, a new visual reasoning benchmark that focuses on compositional learning of human-object inte... | ['Song-Chun Zhu', 'Anima Anandkumar', 'Yuke Zhu', 'Zhiding Yu', 'Weili Nie', 'Xiaojian Ma', 'Huaizu Jiang'] | 2022-05-27 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Jiang_Bongard-HOI_Benchmarking_Few-Shot_Visual_Reasoning_for_Human-Object_Interactions_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Jiang_Bongard-HOI_Benchmarking_Few-Shot_Visual_Reasoning_for_Human-Object_Interactions_CVPR_2022_paper.pdf | cvpr-2022-1 | ['few-shot-image-classification', 'novel-concepts'] | ['computer-vision', 'reasoning'] | [ 3.36352319e-01 1.83451682e-01 -1.41860709e-01 -3.13555032e-01
-3.91341716e-01 -3.28708023e-01 7.91980684e-01 4.76854183e-02
-9.33184996e-02 4.04256374e-01 1.25444695e-01 -4.17214334e-01
-3.32101405e-01 -5.81921756e-01 -7.03548133e-01 -3.15732002e-01
6.94052279e-02 5.56219280e-01 6.51213229e-01 -3.69580239... | [10.237709045410156, 2.292825937271118] |
c5396760-3563-4378-8869-ef1f1b3b8441 | metaassist-robust-dialogue-state-tracking | 2210.12397 | null | https://arxiv.org/abs/2210.12397v1 | https://arxiv.org/pdf/2210.12397v1.pdf | MetaASSIST: Robust Dialogue State Tracking with Meta Learning | Existing dialogue datasets contain lots of noise in their state annotations. Such noise can hurt model training and ultimately lead to poor generalization performance. A general framework named ASSIST has recently been proposed to train robust dialogue state tracking (DST) models. It introduces an auxiliary model to ge... | ['Emine Yilmaz', 'Samuel Stern', 'Shenghui Li', 'Jie Huang', 'Xi Wang', 'Fanghua Ye'] | 2022-10-22 | null | null | null | null | ['dialogue-state-tracking'] | ['natural-language-processing'] | [ 1.78995475e-01 4.95582938e-01 -5.33819497e-01 -6.35570049e-01
-1.13521159e+00 -6.28511012e-01 8.75366986e-01 2.27941647e-02
-4.68709022e-01 9.61596131e-01 2.99069166e-01 -2.29724109e-01
2.11639658e-01 -3.97863984e-01 -3.11402231e-01 -5.22592545e-01
2.12860629e-01 6.13993764e-01 4.21447903e-01 -7.19763517... | [12.730851173400879, 7.819100856781006] |
3bc80990-d067-404e-89c7-ea3b50d11d39 | cosea-convolutional-code-search-with-layer | 2010.09520 | null | https://arxiv.org/abs/2010.09520v1 | https://arxiv.org/pdf/2010.09520v1.pdf | COSEA: Convolutional Code Search with Layer-wise Attention | Semantic code search, which aims to retrieve code snippets relevant to a given natural language query, has attracted many research efforts with the purpose of accelerating software development. The huge amount of online publicly available code repositories has prompted the employment of deep learning techniques to buil... | ['Tie-Yan Liu', 'Chao Zhang', 'Jiang Bian', 'Yingce Xia', 'Jia Zhang', 'Hao Wang'] | 2020-10-19 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-2.97154963e-01 -4.62554127e-01 -5.21493495e-01 -3.75141382e-01
-7.02933371e-01 -5.17839551e-01 2.93044180e-01 3.47857356e-01
-8.96096528e-02 -1.22600757e-01 1.71035856e-01 -4.67752188e-01
-9.22468826e-02 -7.72748113e-01 -6.79987788e-01 -1.11898705e-01
1.71558291e-01 9.81558338e-02 3.04634124e-01 -2.56830335... | [7.498051166534424, 8.086078643798828] |
69848df8-d548-493c-8b79-90cae961d7ba | blind-image-deblurring-a-review | 2201.10522 | null | https://arxiv.org/abs/2201.10522v1 | https://arxiv.org/pdf/2201.10522v1.pdf | Blind Image Deblurring: a Review | This is a review on blind image deblurring. First, we formulate the blind image deblurring problem and explain why it is challenging. Next, we bring some psychological and cognitive studies on the way our human vision system deblurs. Then, relying on several previous reviews, we discuss the topic of metrics and dataset... | ['Zhengrong Xue'] | 2022-01-22 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 1.77882373e-01 -4.04538959e-01 -3.20750296e-01 9.03774947e-02
-3.93377036e-01 -4.88026828e-01 4.38377529e-01 -5.58220804e-01
-5.50227225e-01 6.59796596e-01 8.82114172e-01 -3.44705701e-01
-6.83110207e-03 1.98561341e-01 -3.08377028e-01 -6.88661695e-01
1.61198214e-01 -3.65647376e-01 -3.48630399e-01 1.57782927... | [11.654312133789062, -2.781005382537842] |
16455db5-1ac4-4441-b7fc-7fc02aac4d0b | time-contrastive-learning-based-dnn | 1704.02373 | null | https://arxiv.org/abs/1704.02373v3 | https://arxiv.org/pdf/1704.02373v3.pdf | Time-Contrastive Learning Based DNN Bottleneck Features for Text-Dependent Speaker Verification | In this paper, we present a time-contrastive learning (TCL) based bottleneck (BN)feature extraction method for speech signals with an application to text-dependent (TD) speaker verification (SV). It is well-known that speech signals exhibit quasi-stationary behavior in and only in a short interval, and the TCL method a... | ['Zheng-Hua Tan', 'Achintya Kr. Sarkar'] | 2017-04-06 | null | null | null | null | ['text-dependent-speaker-verification'] | ['speech'] | [ 1.15139969e-01 -4.85034198e-01 -3.40365797e-01 -6.50564730e-01
-9.80368376e-01 -5.85089087e-01 6.10423505e-01 1.50558382e-01
-6.91767097e-01 5.40717125e-01 3.23393673e-01 -7.54546225e-01
-3.74291353e-02 -1.74645334e-01 -1.74022660e-01 -1.05561185e+00
-3.81033570e-01 1.47402152e-01 -6.34759367e-02 -2.01648429... | [14.406624794006348, 6.110065937042236] |
29e2059a-ea14-4fb3-9e40-e6a4995f41e6 | improving-the-generalizability-and-robustness | 2306.01925 | null | https://arxiv.org/abs/2306.01925v2 | https://arxiv.org/pdf/2306.01925v2.pdf | Improving the generalizability and robustness of large-scale traffic signal control | A number of deep reinforcement-learning (RL) approaches propose to control traffic signals. In this work, we study the robustness of such methods along two axes. First, sensor failures and GPS occlusions create missing-data challenges and we show that recent methods remain brittle in the face of these missing data. Sec... | ['Laurent Charlin', 'Denis Larocque', 'Francois-Xavier Devailly', 'Tianyu Shi'] | 2023-06-02 | null | null | null | null | ['distributional-reinforcement-learning', 'multi-agent-reinforcement-learning'] | ['methodology', 'methodology'] | [-1.19626805e-01 8.86580572e-02 -4.20370042e-01 -8.34798589e-02
-7.57956862e-01 -3.82194132e-01 6.07495308e-01 -1.49235100e-01
-2.49269038e-01 1.25355113e+00 6.71148673e-02 -6.98768675e-01
-6.74512625e-01 -1.30932367e+00 -9.97719467e-01 -5.90619922e-01
-4.33326930e-01 6.88348889e-01 5.27154922e-01 -8.34093511... | [5.298422336578369, 1.4953187704086304] |
2c4542f9-737d-40eb-823f-bc46914c32aa | hltsuda-at-semeval-2019-task-1-ucca-graph | 1903.04153 | null | http://arxiv.org/abs/1903.04153v2 | http://arxiv.org/pdf/1903.04153v2.pdf | HLT@SUDA at SemEval 2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing | This paper describes a simple UCCA semantic graph parsing approach. The key
idea is to convert a UCCA semantic graph into a constituent tree, in which
extra labels are deliberately designed to mark remote edges and discontinuous
nodes for future recovery. In this way, we can make use of existing syntactic
parsing techn... | ['Wei Jiang', 'Zhenghua Li', 'Min Zhang', 'Yu Zhang'] | 2019-03-11 | null | null | null | null | ['ucca-parsing'] | ['natural-language-processing'] | [-7.09655136e-02 6.06640577e-01 -4.56022322e-01 -4.18398112e-01
-1.20067620e+00 -8.41700554e-01 4.99731958e-01 2.02905223e-01
-5.38647652e-01 4.63597417e-01 2.94753551e-01 -4.93465871e-01
1.44522220e-01 -8.97919595e-01 -7.49107897e-01 -5.50414741e-01
1.10742196e-01 5.81562221e-01 2.79619455e-01 -2.19849944... | [10.555159568786621, 9.654207229614258] |
737f529c-9265-4178-b1ed-f7de84433e93 | neural-sign-reenactor-deep-photorealistic | 2209.01470 | null | https://arxiv.org/abs/2209.01470v2 | https://arxiv.org/pdf/2209.01470v2.pdf | Neural Sign Reenactor: Deep Photorealistic Sign Language Retargeting | In this paper, we introduce a neural rendering pipeline for transferring the facial expressions, head pose, and body movements of one person in a source video to another in a target video. We apply our method to the challenging case of Sign Language videos: given a source video of a sign language user, we can faithfull... | ['Petros Maragos', 'Anastasios Roussos', 'Athanasia-Lida Dimou', 'Panagiotis P. Filntisis', 'Christina O. Tze'] | 2022-09-03 | null | null | null | null | ['sign-language-production'] | ['natural-language-processing'] | [ 3.09312463e-01 -3.31782456e-03 1.97873726e-01 -5.62036037e-01
-3.77483815e-01 -8.49784791e-01 6.97041631e-01 -8.79674196e-01
-2.88395852e-01 5.36208928e-01 5.38733423e-01 1.88754544e-01
3.05948645e-01 -3.54932956e-02 -6.27625287e-01 -5.99741399e-01
8.73151273e-02 2.81746499e-02 6.91663614e-03 -8.94414112... | [13.06446647644043, -0.45164573192596436] |
15e415a0-6fe1-4278-8f07-081fa9b90403 | a-greedy-graph-search-algorithm-based-on | 2102.03538 | null | https://arxiv.org/abs/2102.03538v1 | https://arxiv.org/pdf/2102.03538v1.pdf | A Greedy Graph Search Algorithm Based on Changepoint Analysis for Automatic QRS Complex Detection | The electrocardiogram (ECG) signal is the most widely used non-invasive tool for the investigation of cardiovascular diseases. Automatic delineation of ECG fiducial points, in particular the R-peak, serves as the basis for ECG processing and analysis. This study proposes a new method of ECG signal analysis by introduci... | ['Fatemeh Afghah', 'Toby Hocking', 'Atiyeh Fotoohinasab'] | 2021-02-06 | null | null | null | null | ['qrs-complex-detection'] | ['medical'] | [ 2.79494166e-01 4.69705835e-02 -2.02912822e-01 -8.39842558e-02
-5.10695040e-01 -7.04826593e-01 -1.23853631e-01 5.40115595e-01
-2.12599173e-01 6.00455999e-01 -4.61705387e-01 -4.31878328e-01
-3.05439383e-01 -4.76913124e-01 -2.23910257e-01 -7.36036062e-01
-5.07049143e-01 2.54078329e-01 1.41259730e-01 3.40234905... | [14.232513427734375, 3.225764274597168] |
0f7ab870-70fb-4549-a9ec-f34f97496b31 | unified-language-model-pre-training-for | 1905.03197 | null | https://arxiv.org/abs/1905.03197v3 | https://arxiv.org/pdf/1905.03197v3.pdf | Unified Language Model Pre-training for Natural Language Understanding and Generation | This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling tasks: unidirectional, bidirectional, and sequence-to-sequence prediction. The unified modeling is ach... | ['Hsiao-Wuen Hon', 'Nan Yang', 'Furu Wei', 'Yu Wang', 'Xiaodong Liu', 'Ming Zhou', 'Jianfeng Gao', 'Wenhui Wang', 'Li Dong'] | 2019-05-08 | unified-language-model-pre-training-for-1 | http://papers.nips.cc/paper/9464-unified-language-model-pre-training-for-natural-language-understanding-and-generation | http://papers.nips.cc/paper/9464-unified-language-model-pre-training-for-natural-language-understanding-and-generation.pdf | neurips-2019-12 | ['generative-question-answering'] | ['natural-language-processing'] | [ 1.38031691e-01 3.27187628e-01 7.41914138e-02 -3.45732629e-01
-1.58711660e+00 -8.44906449e-01 1.03072846e+00 -3.06799877e-02
-5.01877964e-01 1.09725571e+00 7.13643014e-01 -5.37459970e-01
3.62859815e-01 -7.73369849e-01 -5.70353448e-01 -4.12110150e-01
4.72916037e-01 9.58103776e-01 8.92073754e-03 -7.34454453... | [11.913802146911621, 8.937972068786621] |
45eb7dd5-a5b3-4599-9e56-f904d397bf45 | contrastive-audio-visual-masked-autoencoder | 2210.07839 | null | https://arxiv.org/abs/2210.07839v4 | https://arxiv.org/pdf/2210.07839v4.pdf | Contrastive Audio-Visual Masked Autoencoder | In this paper, we first extend the recent Masked Auto-Encoder (MAE) model from a single modality to audio-visual multi-modalities. Subsequently, we propose the Contrastive Audio-Visual Masked Auto-Encoder (CAV-MAE) by combining contrastive learning and masked data modeling, two major self-supervised learning frameworks... | ['James Glass', 'Hilde Kuehne', 'Leonid Karlinsky', 'David Harwath', 'Alexander H. Liu', 'Andrew Rouditchenko', 'Yuan Gong'] | 2022-10-02 | null | null | null | null | ['audio-tagging', 'multi-modal-classification'] | ['audio', 'miscellaneous'] | [ 1.11062244e-01 -2.02082232e-01 -2.23907053e-01 -3.82041752e-01
-1.54947162e+00 -2.17784211e-01 5.83835602e-01 -3.66196670e-02
-1.55294627e-01 4.21323270e-01 3.79579425e-01 3.04895520e-01
2.62631565e-01 -3.99933040e-01 -9.52742696e-01 -4.83780146e-01
-1.49112076e-01 1.91962183e-01 -7.08853304e-02 1.98827267... | [14.532896041870117, 4.977655410766602] |
81e65d71-23f7-4dc6-aec9-9a66be98dc21 | omnilayout-room-layout-reconstruction-from | 2104.09403 | null | https://arxiv.org/abs/2104.09403v1 | https://arxiv.org/pdf/2104.09403v1.pdf | OmniLayout: Room Layout Reconstruction from Indoor Spherical Panoramas | Given a single RGB panorama, the goal of 3D layout reconstruction is to estimate the room layout by predicting the corners, floor boundary, and ceiling boundary. A common approach has been to use standard convolutional networks to predict the corners and boundaries, followed by post-processing to generate the 3D layout... | ['Ankur Mali', 'Lee Giles', 'Daniel Kifer', 'Vikas Kumar', 'Shivansh Rao'] | 2021-04-19 | null | null | null | null | ['3d-room-layouts-from-a-single-rgb-panorama'] | ['computer-vision'] | [ 2.08479896e-01 1.22177251e-01 5.87678730e-01 -3.95793289e-01
-2.73928910e-01 -6.03588045e-01 7.32000053e-01 -1.87506855e-01
-2.68317580e-01 4.02277589e-01 3.57567251e-01 -3.35988522e-01
-1.48931220e-01 -9.71388578e-01 -1.12360787e+00 -5.60540497e-01
3.69416438e-02 1.85882255e-01 -1.78177282e-01 -1.76919624... | [8.685505867004395, -2.8139398097991943] |
44efb708-5a05-4fda-9e23-973b4950f5aa | open-vocabulary-affordance-detection-in-3d | 2303.02401 | null | https://arxiv.org/abs/2303.02401v2 | https://arxiv.org/pdf/2303.02401v2.pdf | Open-Vocabulary Affordance Detection in 3D Point Clouds | Affordance detection is a challenging problem with a wide variety of robotic applications. Traditional affordance detection methods are limited to a predefined set of affordance labels, hence potentially restricting the adaptability of intelligent robots in complex and dynamic environments. In this paper, we present th... | ['Toan Nguyen', 'Anh Nguyen', 'Ngan Le', 'Thieu Vo', 'Dzung Nguyen', 'An Vuong', 'Minh Nhat Vu'] | 2023-03-04 | null | null | null | null | ['affordance-detection'] | ['computer-vision'] | [-1.16319515e-01 -1.57121301e-01 -2.12008640e-01 -6.86273873e-02
-3.70983928e-01 -7.47780442e-01 5.54483712e-01 5.16974293e-02
-4.16758597e-01 2.35241383e-01 -2.98357010e-02 -1.68032214e-01
-1.35022506e-01 -3.94834250e-01 -6.22433364e-01 -3.95883501e-01
-3.37417096e-01 3.79267365e-01 7.31740713e-01 -2.75156170... | [5.166907787322998, -0.12807539105415344] |
1ff331c0-fa24-4c24-a5b8-66b7bfe5b854 | data-augmentation-for-sign-language-gloss | 2105.07476 | null | https://arxiv.org/abs/2105.07476v1 | https://arxiv.org/pdf/2105.07476v1.pdf | Data Augmentation for Sign Language Gloss Translation | Sign language translation (SLT) is often decomposed into video-to-gloss recognition and gloss-to-text translation, where a gloss is a sequence of transcribed spoken-language words in the order in which they are signed. We focus here on gloss-to-text translation, which we treat as a low-resource neural machine translati... | ['Yoav Goldberg', 'Graham Neubig', 'Kayo Yin', 'Amit Moryossef'] | 2021-05-16 | null | https://aclanthology.org/2021.mtsummit-at4ssl.1 | https://aclanthology.org/2021.mtsummit-at4ssl.1.pdf | mtsummit-2021-8 | ['sign-language-translation', 'low-resource-neural-machine-translation'] | ['computer-vision', 'natural-language-processing'] | [ 7.42178440e-01 6.86528906e-02 -1.34638622e-01 -7.66070068e-01
-1.36688221e+00 -7.26134300e-01 7.54268289e-01 -5.18411517e-01
-5.98073244e-01 1.03206158e+00 5.66737473e-01 -3.00495207e-01
3.48700613e-01 -4.27196473e-01 -8.89705420e-01 -5.57901740e-01
3.86447698e-01 9.81265426e-01 -1.55151725e-01 -1.66243955... | [9.205947875976562, -6.5340471267700195] |
20951556-b454-48d2-b1e9-47d66d4dcaf0 | detecting-offensive-language-in-tweets-using | 1801.04433 | null | http://arxiv.org/abs/1801.04433v1 | http://arxiv.org/pdf/1801.04433v1.pdf | Detecting Offensive Language in Tweets Using Deep Learning | This paper addresses the important problem of discerning hateful content in
social media. We propose a detection scheme that is an ensemble of Recurrent
Neural Network (RNN) classifiers, and it incorporates various features
associated with user-related information, such as the users' tendency towards
racism or sexism. ... | ['Helge Langseth', 'Georgios K. Pitsilis', 'Heri Ramampiaro'] | 2018-01-13 | null | null | null | null | ['abuse-detection'] | ['natural-language-processing'] | [ 1.79630890e-01 -2.06065401e-01 -5.60091019e-01 -2.37259522e-01
-2.97012419e-01 -2.17549235e-01 9.04909849e-01 3.84146214e-01
-5.90927303e-01 4.70396727e-01 6.63937509e-01 -3.84835452e-01
1.74910963e-01 -6.98622227e-01 9.09235477e-02 -6.04421318e-01
-9.99588296e-02 -4.34250720e-02 -3.38931590e-01 -7.05931127... | [8.77604866027832, 10.55078125] |
7a2a6e81-9122-43d5-9520-d9535fc02364 | orthographic-features-for-bilingual-lexicon | null | null | https://aclanthology.org/P18-2062 | https://aclanthology.org/P18-2062.pdf | Orthographic Features for Bilingual Lexicon Induction | Recent embedding-based methods in bilingual lexicon induction show good results, but do not take advantage of orthographic features, such as edit distance, which can be helpful for pairs of related languages. This work extends embedding-based methods to incorporate these features, resulting in significant accuracy gain... | ['Parker Riley', 'Daniel Gildea'] | 2018-07-01 | null | null | null | acl-2018-7 | ['multilingual-word-embeddings', 'unsupervised-machine-translation'] | ['methodology', 'natural-language-processing'] | [-6.39975369e-01 -3.34944814e-01 -6.22566938e-01 -3.43771070e-01
-5.05878687e-01 -7.32296705e-01 5.47715664e-01 5.90992868e-01
-8.35678220e-01 6.87175393e-01 5.39865077e-01 -4.35841233e-01
1.40876949e-01 -9.23299909e-01 -2.49971107e-01 -2.85654932e-01
-3.27446252e-01 5.54932654e-01 1.07996970e-01 -7.12677419... | [11.15463924407959, 10.157903671264648] |
73426e07-36f9-4cd8-ae39-3f08094b5f42 | 190501964 | 1905.01964 | null | http://arxiv.org/abs/1905.01964v1 | http://arxiv.org/pdf/1905.01964v1.pdf | Neural Chinese Named Entity Recognition via CNN-LSTM-CRF and Joint Training with Word Segmentation | Chinese named entity recognition (CNER) is an important task in Chinese
natural language processing field. However, CNER is very challenging since
Chinese entity names are highly context-dependent. In addition, Chinese texts
lack delimiters to separate words, making it difficult to identify the boundary
of entities. Be... | ['Xing Xie', 'Yongfeng Huang', 'Junxin Liu', 'Fangzhao Wu', 'Chuhan Wu'] | 2019-04-26 | null | null | null | null | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-1.06489815e-01 -2.91618913e-01 -1.49718627e-01 -4.90109682e-01
-6.35029137e-01 -5.58847904e-01 8.96898881e-02 -4.21753479e-03
-9.59864914e-01 8.55665743e-01 2.43664339e-01 -3.38058889e-01
5.62661171e-01 -9.19541121e-01 -4.17412996e-01 -4.16686505e-01
4.63734478e-01 3.18543732e-01 2.72638112e-01 1.14545174... | [9.816316604614258, 9.853153228759766] |
77c44fe8-2e6e-4598-b171-9856f25c3101 | extending-multi-object-tracking-systems-to | 1912.11651 | null | https://arxiv.org/abs/1912.11651v1 | https://arxiv.org/pdf/1912.11651v1.pdf | Extending Multi-Object Tracking systems to better exploit appearance and 3D information | Tracking multiple objects in real time is essential for a variety of real-world applications, with self-driving industry being at the foremost. This work involves exploiting temporally varying appearance and motion information for tracking. Siamese networks have recently become highly successful at appearance based sin... | ['Sahan Liyanaarachchi', 'Mayuka Jayawardhana', 'Kanchana Ranasinghe', 'Harsha Ranasinghe'] | 2019-12-25 | null | null | null | null | ['real-time-multi-object-tracking'] | ['computer-vision'] | [-2.73882389e-01 -7.31085360e-01 -3.59860510e-01 2.87570879e-02
-2.57920653e-01 -6.50846064e-01 4.26057577e-01 -4.51586276e-01
-5.43785691e-01 4.55514193e-01 -1.43906608e-01 -7.73186143e-03
-2.40989700e-01 -1.52096376e-01 -3.52188051e-01 -5.48754156e-01
-1.30066901e-01 5.80001712e-01 6.58656955e-01 -7.32637644... | [6.299977779388428, -2.0391201972961426] |
9ecaa0c9-bc13-4e8b-9af9-15d44b497aec | zero-shot-visual-reasoning-through | 2209.15087 | null | https://arxiv.org/abs/2209.15087v1 | https://arxiv.org/pdf/2209.15087v1.pdf | Zero-shot visual reasoning through probabilistic analogical mapping | Human reasoning is grounded in an ability to identify highly abstract commonalities governing superficially dissimilar visual inputs. Recent efforts to develop algorithms with this capacity have largely focused on approaches that require extensive direct training on visual reasoning tasks, and yield limited generalizat... | ['Hongjing Lu', 'Keith J. Holyoak', 'Trevor Bihl', 'Shuhao Fu', 'Taylor W. Webb'] | 2022-09-29 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 1.57051787e-01 1.10513397e-01 1.34344071e-01 -4.11995113e-01
-1.40793800e-01 -7.31842458e-01 1.05135739e+00 5.19939601e-01
-4.20260727e-01 2.30175838e-01 2.29026407e-01 -7.02061713e-01
-4.20397997e-01 -7.36967146e-01 -6.98378980e-01 -1.09980494e-01
2.45615810e-01 8.61118972e-01 2.60935456e-01 -3.58141631... | [10.580645561218262, 2.2955355644226074] |
b83ecfe7-940f-4fcb-982f-915346ad36d2 | learning-from-synthetic-human-group | 2306.16772 | null | https://arxiv.org/abs/2306.16772v1 | https://arxiv.org/pdf/2306.16772v1.pdf | Learning from Synthetic Human Group Activities | The understanding of complex human interactions and group activities has garnered attention in human-centric computer vision. However, the advancement of the related tasks is hindered due to the difficulty of obtaining large-scale labeled real-world datasets. To mitigate the issue, we propose M3Act, a multi-view multi-... | ['Mubbasir Kapadia', 'Vladimir Pavlovic', 'Sejong Yoon', 'Samuel S. Sohn', 'Seonghyeon Moon', 'Aditya Bhat', 'Parth Goel', 'Honglu Zhou', 'Che-Jui Chang'] | 2023-06-29 | null | null | null | null | ['pose-tracking', 'activity-recognition', 'person-recognition', 'unity', 'group-activity-recognition', 'instance-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.00848207e-01 -1.60929263e-02 2.29759485e-01 -3.16730738e-01
-8.28648031e-01 -5.50625443e-01 7.27707505e-01 -5.28047979e-01
-3.43287140e-01 5.15588403e-01 3.31973135e-01 3.43155265e-01
2.42768899e-01 -4.98310447e-01 -7.06661940e-01 -5.39188445e-01
-8.27402622e-03 9.94802296e-01 2.84457147e-01 -6.69272915... | [7.091365337371826, -0.8283817172050476] |
a2c2c0d8-8341-44a0-813f-7047890cca8d | rethinking-vision-transformer-and-masked | 2302.05744 | null | https://arxiv.org/abs/2302.05744v1 | https://arxiv.org/pdf/2302.05744v1.pdf | Rethinking Vision Transformer and Masked Autoencoder in Multimodal Face Anti-Spoofing | Recently, vision transformer (ViT) based multimodal learning methods have been proposed to improve the robustness of face anti-spoofing (FAS) systems. However, there are still no works to explore the fundamental natures (\textit{e.g.}, modality-aware inputs, suitable multimodal pre-training, and efficient finetuning) i... | ['Alex Kot', 'Yongjian Hu', 'Xin Liu', 'Yawen Cui', 'Rizhao Cai', 'Zitong Yu'] | 2023-02-11 | null | null | null | null | ['face-anti-spoofing'] | ['computer-vision'] | [ 4.16408837e-01 -1.20301038e-01 -2.27612123e-01 -2.61531413e-01
-6.44085526e-01 -7.39260614e-01 6.66597962e-01 -3.63719642e-01
-2.05287904e-01 5.89999974e-01 1.26714513e-01 -4.73130554e-01
-3.08852941e-01 -7.53479719e-01 -7.43540108e-01 -1.19646788e+00
2.74976701e-01 -4.62672040e-02 -5.59844226e-02 -4.69496220... | [13.107885360717773, 1.196211338043213] |
8d347808-15a9-4cbd-b220-50617b2d9e42 | interactive-query-clarification-and | 2205.15918 | null | https://arxiv.org/abs/2205.15918v1 | https://arxiv.org/pdf/2205.15918v1.pdf | Interactive Query Clarification and Refinement via User Simulation | When users initiate search sessions, their queries are often unclear or might lack of context; this resulting in inefficient document ranking. Multiple approaches have been proposed by the Information Retrieval community to add context and retrieve documents aligned with users' intents. While some work focus on query d... | ['Laure Soulier', 'Ludovic Denoyer', 'Pierre Erbacher'] | 2022-05-31 | null | null | null | null | ['user-simulation', 'document-ranking'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.59776121e-01 1.50129393e-01 -3.82581919e-01 -4.00025785e-01
-6.97274148e-01 -9.77338433e-01 9.07128930e-01 5.27725577e-01
-6.62309408e-01 7.07089603e-01 3.69399011e-01 -4.40803319e-01
-7.35555470e-01 -4.26795483e-01 2.54417568e-01 -7.09846094e-02
3.03493619e-01 9.33992863e-01 5.53957582e-01 -6.23064339... | [12.107382774353027, 7.713661193847656] |
cd8cf347-136d-4a06-8ddb-8df2c9362d20 | federated-learning-based-energy-demand | 2210.15850 | null | https://arxiv.org/abs/2210.15850v1 | https://arxiv.org/pdf/2210.15850v1.pdf | Federated Learning based Energy Demand Prediction with Clustered Aggregation | To reduce negative environmental impacts, power stations and energy grids need to optimize the resources required for power production. Thus, predicting the energy consumption of clients is becoming an important part of every energy management system. Energy usage information collected by the clients' smart homes can b... | ['Choong Seon Hong', 'Chu Myaet Thwal', 'Kyi Thar', 'Ye Lin Tun'] | 2022-10-28 | null | null | null | null | ['energy-management'] | ['time-series'] | [-5.97027063e-01 -2.79712230e-01 -2.48774767e-01 -6.72064602e-01
-4.03132677e-01 -2.34797210e-01 -6.64784759e-03 1.15530849e-01
-5.19212596e-02 6.17346823e-01 7.03212842e-02 9.14308950e-02
-1.00149423e-01 -1.31433105e+00 -3.84866953e-01 -1.07073593e+00
2.41121203e-01 7.67830551e-01 -7.47841522e-02 2.95019835... | [5.911602973937988, 2.7267258167266846] |
9e44e32f-b733-4ff5-9d08-425c3cadb4c5 | happydb-a-corpus-of-100000-crowdsourced-happy | 1801.07746 | null | http://arxiv.org/abs/1801.07746v2 | http://arxiv.org/pdf/1801.07746v2.pdf | HappyDB: A Corpus of 100,000 Crowdsourced Happy Moments | The science of happiness is an area of positive psychology concerned with
understanding what behaviors make people happy in a sustainable fashion.
Recently, there has been interest in developing technologies that help
incorporate the findings of the science of happiness into users' daily lives by
steering them towards ... | ['Wang-Chiew Tan', 'Yinzhan Xu', 'Vivian Li', 'Sara Evensen', 'Daniela Stepanov', 'Alon Halevy', 'Yoshihiko Suhara', 'Behzad Golshan', 'Andrei Lopatenko', 'Akari Asai'] | 2018-01-23 | happydb-a-corpus-of-100000-crowdsourced-happy-1 | https://aclanthology.org/L18-1103 | https://aclanthology.org/L18-1103.pdf | lrec-2018-5 | ['art-analysis'] | ['computer-vision'] | [-4.08067346e-01 3.50209713e-01 -7.96598613e-01 -9.49855983e-01
-4.88348961e-01 -2.26874560e-01 4.96078938e-01 4.73761559e-01
-3.33795816e-01 7.09595025e-01 1.01064754e+00 1.92767277e-01
3.05075794e-01 -8.02145720e-01 -1.54157802e-01 -2.00076386e-01
2.22366646e-01 9.52863991e-02 -6.80864215e-01 -7.76190519... | [12.620023727416992, 6.549898624420166] |
649554dc-d811-4cfa-b5df-022aa6200798 | lvit-language-meets-vision-transformer-in | 2206.14718 | null | https://arxiv.org/abs/2206.14718v4 | https://arxiv.org/pdf/2206.14718v4.pdf | LViT: Language meets Vision Transformer in Medical Image Segmentation | Deep learning has been widely used in medical image segmentation and other aspects. However, the performance of existing medical image segmentation models has been limited by the challenge of obtaining sufficient high-quality labeled data due to the prohibitive data annotation cost. To alleviate this limitation, we pro... | ['Dazhou Guo', 'Puyang Wang', 'Qingqi Hong', 'Dakai Jin', 'Le Lu', 'You Zhang', 'Qingde Li', 'Yunxiang Li', 'Zihan Li'] | 2022-06-29 | null | null | null | null | ['text-annotation'] | ['natural-language-processing'] | [ 5.94706714e-01 4.25582260e-01 -4.80910242e-01 -7.40278244e-01
-1.20371616e+00 -3.19431573e-01 2.04090685e-01 8.77315253e-02
-6.77002132e-01 6.05686307e-01 7.02843741e-02 -2.72289932e-01
2.46911496e-01 -4.70114648e-01 -7.26534486e-01 -7.41024137e-01
6.52517200e-01 6.14347637e-01 1.63863465e-01 2.42032036... | [14.673624038696289, -2.138914108276367] |
d89a50e5-971e-453e-ba81-447b9f12feaa | zero-shot-framework-for-satellite-image | 2306.02921 | null | https://arxiv.org/abs/2306.02921v1 | https://arxiv.org/pdf/2306.02921v1.pdf | Zero shot framework for satellite image restoration | Satellite images are typically subject to multiple distortions. Different factors affect the quality of satellite images, including changes in atmosphere, surface reflectance, sun illumination, viewing geometries etc., limiting its application to downstream tasks. In supervised networks, the availability of paired data... | ['A. N. Rajagopalan', 'Praveen Kandula'] | 2023-06-05 | null | null | null | null | ['image-restoration', 'disentanglement'] | ['computer-vision', 'methodology'] | [ 6.37714744e-01 -1.01904362e-01 1.86803922e-01 -4.13964301e-01
-6.55915022e-01 -8.50771308e-01 8.43061924e-01 -4.32875723e-01
-2.79142916e-01 8.84121716e-01 1.06468514e-01 -1.76155895e-01
-4.42640670e-02 -1.03183246e+00 -8.69712591e-01 -9.79290485e-01
2.54135996e-01 5.16832545e-02 -1.84337422e-01 -1.91863164... | [10.896642684936523, -3.0721733570098877] |
a118c60f-e5e4-4be6-88a4-752b1ac1ffa1 | capacity-bandwidth-and-compositionality-in | 1910.11424 | null | https://arxiv.org/abs/1910.11424v3 | https://arxiv.org/pdf/1910.11424v3.pdf | Capacity, Bandwidth, and Compositionality in Emergent Language Learning | Many recent works have discussed the propensity, or lack thereof, for emergent languages to exhibit properties of natural languages. A favorite in the literature is learning compositionality. We note that most of those works have focused on communicative bandwidth as being of primary importance. While important, it is ... | ['Cinjon Resnick', 'Kyunghyun Cho', 'Jakob Foerster', 'Abhinav Gupta', 'Andrew M. Dai'] | 2019-10-24 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 1.27623454e-01 2.99019337e-01 -2.93328285e-01 -6.28086105e-02
-1.69228628e-01 -6.12265885e-01 7.60929465e-01 1.89705223e-01
-3.87925655e-01 7.26236582e-01 4.40436512e-01 -7.13049293e-01
-3.33920181e-01 -6.40578449e-01 -9.66296732e-01 -7.56958783e-01
-1.87161312e-01 7.06627369e-02 1.69944137e-01 -3.43057245... | [8.080018997192383, 3.4648947715759277] |
449112bd-0beb-4575-9cbd-43ba55f14aea | findvehicle-and-vehiclefinder-a-ner-dataset | 2304.10893 | null | https://arxiv.org/abs/2304.10893v1 | https://arxiv.org/pdf/2304.10893v1.pdf | FindVehicle and VehicleFinder: A NER dataset for natural language-based vehicle retrieval and a keyword-based cross-modal vehicle retrieval system | Natural language (NL) based vehicle retrieval is a task aiming to retrieve a vehicle that is most consistent with a given NL query from among all candidate vehicles. Because NL query can be easily obtained, such a task has a promising prospect in building an interactive intelligent traffic system (ITS). Current solutio... | ['Yutao Yue', 'Eng Gee Lim', 'Jeremy Smith', 'Xiaohui Zhu', 'Rongsheng Hu', 'Shanliang Yao', 'Feifan Chen', 'Ka Lok Man', 'Runwei Guan'] | 2023-04-21 | null | null | null | null | ['named-entity-recognition-ner'] | ['natural-language-processing'] | [-2.99053282e-01 -4.91667360e-01 -5.22207141e-01 -4.96244758e-01
-1.07158947e+00 -5.83878636e-01 7.53755867e-01 -4.25074808e-03
-5.60252368e-01 5.08605421e-01 -1.85244754e-02 -5.08006394e-01
4.72646020e-02 -1.09858394e+00 -9.19070303e-01 -4.97356027e-01
1.95987776e-01 4.79970336e-01 4.06178534e-01 -8.14784542... | [8.09343147277832, -1.0507643222808838] |
dbdf73de-92a5-484a-9322-52a7b4af6c59 | training-dynamics-for-curriculum-learning-a-1 | 2210.12499 | null | https://arxiv.org/abs/2210.12499v2 | https://arxiv.org/pdf/2210.12499v2.pdf | Training Dynamics for Curriculum Learning: A Study on Monolingual and Cross-lingual NLU | Curriculum Learning (CL) is a technique of training models via ranking examples in a typically increasing difficulty trend with the aim of accelerating convergence and improving generalisability. Current approaches for Natural Language Understanding (NLU) tasks use CL to improve in-distribution data performance often v... | ['Ignacio Iacobacci', 'Gerasimos Lampouras', 'Fenia Christopoulou'] | 2022-10-22 | null | null | null | null | ['zero-shot-cross-lingual-transfer'] | ['natural-language-processing'] | [-1.08156249e-01 -5.42458110e-02 -3.19883078e-01 -3.36510032e-01
-1.09201646e+00 -7.99711168e-01 7.27993488e-01 3.66672516e-01
-8.32767069e-01 7.14892864e-01 1.15365833e-01 -5.60246050e-01
-3.76059383e-01 -3.77313912e-01 -7.71383166e-01 -4.30636287e-01
-1.15518920e-01 8.65080535e-01 2.76174396e-01 -3.40494156... | [10.782576560974121, 8.510193824768066] |
3bf3cb76-6735-44ff-9ba9-c10468e10443 | investigation-of-ensemble-methods-for-the | 2304.07395 | null | https://arxiv.org/abs/2304.07395v1 | https://arxiv.org/pdf/2304.07395v1.pdf | Investigation of ensemble methods for the detection of deepfake face manipulations | The recent wave of AI research has enabled a new brand of synthetic media, called deepfakes. Deepfakes have impressive photorealism, which has generated exciting new use cases but also raised serious threats to our increasingly digital world. To mitigate these threats, researchers have tried to come up with new methods... | ['Ioannis Kompatsiaris', 'Symeon Papadopoulos', 'Nikolaos Giatsoglou'] | 2023-04-14 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 2.45689601e-01 -2.59646207e-01 -1.18002348e-01 1.47286475e-01
-2.65376478e-01 -1.04353130e+00 9.45058405e-01 -5.30224182e-02
-3.52889419e-01 5.48599958e-01 1.79186374e-01 -3.09218854e-01
-2.11962387e-01 -7.27997422e-01 -5.24355352e-01 -4.16364253e-01
-9.64270905e-02 1.06424227e-01 7.55294785e-02 -2.75000960... | [12.566681861877441, 1.1231505870819092] |
20b7818a-04d0-4c50-aafb-d5cd2d8bd33a | cad-based-design-optimization-of-four-bar | 2201.01590 | null | https://arxiv.org/abs/2201.01590v1 | https://arxiv.org/pdf/2201.01590v1.pdf | CAD Based Design Optimization of Four-bar Mechanisms: a coronaventilator case study | Design optimization of mechanisms is a promising research area as it results in more energy-efficient machines without compromising performance. However, machine builders do not actually use the design methods described in the literature as these algorithms require too much theoretical analysis. Moreover, the design sy... | ['Stijn Derammelaere', 'Annie Cuyt', 'Bart Vanwalleghem', 'Jan Herregodts', 'Stijn Herregodts', 'Simon Houwen', 'Ferre Knaepkens', 'Nick Van Oosterwyck', 'Abdelmajid Ben Yahya'] | 2022-01-05 | null | null | null | null | ['design-synthesis'] | ['adversarial'] | [-2.39110053e-01 5.95651194e-02 -5.72640181e-01 1.51288137e-01
-1.67480946e-01 -4.40598905e-01 2.88298484e-02 8.57863352e-02
-9.78506505e-02 7.34786391e-01 -3.90716761e-01 -2.90963709e-01
-9.74935532e-01 -8.18163812e-01 -5.06682932e-01 -6.75829291e-01
3.52730118e-02 4.38381255e-01 -1.94028303e-01 -2.12009057... | [5.994898796081543, 3.210148334503174] |
a32f74c5-a344-43e4-af63-b69d7724cf2b | video-in-10-bits-few-bit-videoqa-for | 2210.08391 | null | https://arxiv.org/abs/2210.08391v2 | https://arxiv.org/pdf/2210.08391v2.pdf | Video in 10 Bits: Few-Bit VideoQA for Efficiency and Privacy | In Video Question Answering (VideoQA), answering general questions about a video requires its visual information. Yet, video often contains redundant information irrelevant to the VideoQA task. For example, if the task is only to answer questions similar to "Is someone laughing in the video?", then all other informatio... | ['Gunnar A. Sigurdsson', 'Shih-Fu Chang', 'Robinson Piramuthu', 'Shiyuan Huang'] | 2022-10-15 | null | null | null | null | ['feature-compression', 'video-question-answering'] | ['computer-vision', 'computer-vision'] | [ 3.73126298e-01 -4.80821840e-02 3.46598811e-02 -4.56852406e-01
-9.12917256e-01 -7.78146446e-01 3.68186422e-02 1.33912116e-01
-5.28314054e-01 5.65088689e-01 2.70340025e-01 -3.22495997e-01
5.31602278e-02 -6.51773155e-01 -1.12966549e+00 -6.53759778e-01
-1.77998632e-01 -2.57936209e-01 1.09340258e-01 -9.41631198... | [5.865006923675537, 6.700124740600586] |
2c551d92-6d47-466f-864e-3e413604cfba | the-value-equivalence-principle-for-model | 2011.03506 | null | https://arxiv.org/abs/2011.03506v1 | https://arxiv.org/pdf/2011.03506v1.pdf | The Value Equivalence Principle for Model-Based Reinforcement Learning | Learning models of the environment from data is often viewed as an essential component to building intelligent reinforcement learning (RL) agents. The common practice is to separate the learning of the model from its use, by constructing a model of the environment's dynamics that correctly predicts the observed state t... | ['David Silver', 'Satinder Singh', 'André Barreto', 'Christopher Grimm'] | 2020-11-06 | null | http://proceedings.neurips.cc/paper/2020/hash/3bb585ea00014b0e3ebe4c6dd165a358-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/3bb585ea00014b0e3ebe4c6dd165a358-Paper.pdf | neurips-2020-12 | ['value-prediction'] | ['computer-code'] | [ 1.05060957e-01 3.80081773e-01 -6.69126630e-01 -2.56218106e-01
-3.63366008e-01 -7.28648365e-01 7.33472288e-01 1.43968895e-01
-6.54683411e-01 1.11458635e+00 6.61193803e-02 -4.73924518e-01
-5.44035852e-01 -8.76635909e-01 -6.19211018e-01 -6.19218588e-01
-2.39508316e-01 7.20769882e-01 5.04246950e-02 -3.30512285... | [4.14655876159668, 1.7943673133850098] |
c60359c1-c553-4d70-8bf1-04bfdc514139 | fast-learning-of-dynamic-hand-gesture | 2212.08363 | null | https://arxiv.org/abs/2212.08363v1 | https://arxiv.org/pdf/2212.08363v1.pdf | Fast Learning of Dynamic Hand Gesture Recognition with Few-Shot Learning Models | We develop Few-Shot Learning models trained to recognize five or ten different dynamic hand gestures, respectively, which are arbitrarily interchangeable by providing the model with one, two, or five examples per hand gesture. All models were built in the Few-Shot Learning architecture of the Relation Network (RN), in ... | ['Michael Bücker', 'Niels Schlüsener'] | 2022-12-16 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.88064426e-01 -2.31217891e-02 -1.54873505e-01 -3.82289380e-01
-6.51324928e-01 -4.37269419e-01 6.51886702e-01 -6.06456995e-01
-6.43610895e-01 2.34270304e-01 2.95221299e-01 9.24355071e-03
-2.54771203e-01 -5.20045042e-01 -3.77243102e-01 -7.31673241e-01
-1.51826560e-01 4.42571312e-01 2.53985971e-01 1.52014166... | [6.673305034637451, -0.15557779371738434] |
25375f7e-6411-4d00-abdb-3c514c91dc3c | from-nerflix-to-nerflix-a-general-nerf | 2306.06388 | null | https://arxiv.org/abs/2306.06388v2 | https://arxiv.org/pdf/2306.06388v2.pdf | From NeRFLiX to NeRFLiX++: A General NeRF-Agnostic Restorer Paradigm | Neural radiance fields (NeRF) have shown great success in novel view synthesis. However, recovering high-quality details from real-world scenes is still challenging for the existing NeRF-based approaches, due to the potential imperfect calibration information and scene representation inaccuracy. Even with high-quality ... | ['Jiangbo Lu', 'Xiaoguang Han', 'Nianjuan Jiang', 'Wenbo Li', 'Kun Zhou'] | 2023-06-10 | null | null | null | null | ['novel-view-synthesis'] | ['computer-vision'] | [ 3.29810739e-01 -3.11649919e-01 3.53335202e-01 -2.43672401e-01
-8.85365188e-01 -3.92828226e-01 4.64488119e-01 -4.72880423e-01
3.11146319e-01 6.59124792e-01 6.44622386e-01 5.40547073e-02
-1.40396640e-01 -9.07911003e-01 -1.01462913e+00 -7.23592699e-01
4.25892979e-01 -1.31012321e-01 -1.58210322e-02 -5.38148105... | [10.051887512207031, -2.5255212783813477] |
a48fb4c1-acbd-47dc-9185-6013723230b9 | cct-code-cross-consistency-training-for | 2305.11626 | null | https://arxiv.org/abs/2305.11626v1 | https://arxiv.org/pdf/2305.11626v1.pdf | CCT-Code: Cross-Consistency Training for Multilingual Clone Detection and Code Search | We consider the clone detection and information retrieval problems for source code, well-known tasks important for any programming language. Although it is also an important and interesting problem to find code snippets that operate identically but are written in different programming languages, to the best of our know... | ['Valentin Malykh', 'Sergey Nikolenko', 'Dmitry Abulkhanov', 'Nikita Sorokin'] | 2023-05-19 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-1.79647833e-01 -5.17146230e-01 -5.52461326e-01 2.18446419e-01
-1.08822906e+00 -7.84933388e-01 4.41831589e-01 5.28951228e-01
-1.02037393e-01 2.66525447e-01 -2.47648954e-01 -6.72819972e-01
6.43932447e-02 -2.74440914e-01 -8.33992958e-01 -1.40606642e-01
-1.92984641e-01 2.51087308e-01 4.69003648e-01 6.03435338... | [7.600022792816162, 7.987106800079346] |
fa084714-3f81-4322-a54d-dd51dc27f935 | multi-cpr-a-multi-domain-chinese-dataset-for | 2203.03367 | null | https://arxiv.org/abs/2203.03367v2 | https://arxiv.org/pdf/2203.03367v2.pdf | Multi-CPR: A Multi Domain Chinese Dataset for Passage Retrieval | Passage retrieval is a fundamental task in information retrieval (IR) research, which has drawn much attention recently. In the English field, the availability of large-scale annotated dataset (e.g, MS MARCO) and the emergence of deep pre-trained language models (e.g, BERT) has resulted in a substantial improvement of ... | ['Ping Yang', 'Luxi Xing', 'Guanjun Jiang', 'Jian Xu', 'Ruijie Guo', 'Pengjun Xie', 'Guangwei Xu', 'Kuan Zou', 'Qiong Gao', 'Dingkun Long'] | 2022-03-07 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [-3.01593274e-01 -7.92688608e-01 -2.60614008e-01 -1.01127438e-01
-1.61018109e+00 -8.27351332e-01 6.11129105e-01 3.43094110e-01
-8.17342758e-01 8.52556884e-01 4.24800366e-01 -7.11605698e-02
-2.69439608e-01 -6.73283637e-01 -4.21321541e-01 -3.32493871e-01
1.46519557e-01 5.50381958e-01 5.03064454e-01 -8.10133100... | [11.50001049041748, 7.736717224121094] |
b3f498fe-c6fa-47e2-910e-5dc9e11d6b7d | infrared-and-visible-image-fusion-via | 2203.15337 | null | https://arxiv.org/abs/2203.15337v1 | https://arxiv.org/pdf/2203.15337v1.pdf | Infrared and Visible Image Fusion via Interactive Compensatory Attention Adversarial Learning | The existing generative adversarial fusion methods generally concatenate source images and extract local features through convolution operation, without considering their global characteristics, which tends to produce an unbalanced result and is biased towards the infrared image or visible image. Toward this end, we pr... | ['Xiaoqin Zhang', 'Jiawei Xu', 'Yanlin Chen', 'Wenyu Shao', 'Zhishe Wang'] | 2022-03-29 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 9.61396843e-02 -3.38794082e-01 8.49816874e-02 -2.94281393e-01
-7.33488202e-01 -4.17901307e-01 4.73386317e-01 -4.77360010e-01
-1.56717692e-02 5.25834560e-01 4.29774553e-01 -1.23939849e-01
1.57129839e-02 -1.07293701e+00 -5.19719005e-01 -1.01383114e+00
2.69782305e-01 -4.13938880e-01 -1.30567372e-01 -3.25726509... | [10.569764137268066, -1.824640154838562] |
bd78bb69-626f-4e82-b6cf-d126cfc92a84 | conceptual-design-generation-using-large | 2306.01779 | null | https://arxiv.org/abs/2306.01779v1 | https://arxiv.org/pdf/2306.01779v1.pdf | Conceptual Design Generation Using Large Language Models | Concept generation is a creative step in the conceptual design phase, where designers often turn to brainstorming, mindmapping, or crowdsourcing design ideas to complement their own knowledge of the domain. Recent advances in natural language processing (NLP) and machine learning (ML) have led to the rise of Large Lang... | ['Kosa Goucher-Lambert', 'Christopher McComb', 'Daniele Grandi', 'Kevin Ma'] | 2023-05-30 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [ 1.36401564e-01 2.89473802e-01 3.99556290e-03 -1.65365994e-01
-9.14648652e-01 -9.03307557e-01 7.04404533e-01 1.89698175e-01
5.01410775e-02 5.00296950e-01 7.64472902e-01 -2.78316081e-01
-1.75170079e-01 -8.39118242e-01 -3.52419764e-01 1.59812495e-01
5.54138541e-01 5.31876028e-01 -9.49224234e-02 -4.03726548... | [11.69510269165039, 8.6053466796875] |
3d62d309-84dc-438c-ab97-ac003c9a5b3f | the-asnr-miccai-brain-tumor-segmentation | 2305.07642 | null | https://arxiv.org/abs/2305.07642v1 | https://arxiv.org/pdf/2305.07642v1.pdf | The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma | Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring; yet... | ['Evan Calabrese', 'Benedikt Wiestler', 'Javier Villanueva-Meyer', 'Nourel Hoda Tahon', 'Jeff Rudie', 'Andreas M Rauschecker', 'Ayman Nada', 'Bjoern Menze', 'Marius George Linguraru', 'Goldey Khanna', 'Anastasia Janas', 'Adam Flanders', 'Spyridon Bakas', 'Udunna Anazodo', 'Jake Albrecht', 'Mariam Aboian', 'Walter Wiggi... | 2023-05-12 | null | null | null | null | ['tumor-segmentation', 'brain-image-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical', 'medical'] | [ 2.15401486e-01 3.39679360e-01 7.70889595e-02 -3.71657282e-01
-1.20792687e+00 -5.88374853e-01 6.98093176e-01 5.69522440e-01
-7.47820020e-01 5.34854949e-01 3.72238219e-01 -4.61564630e-01
-3.04033637e-01 -3.24688315e-01 9.60394666e-02 -6.69481397e-01
-5.22116184e-01 1.17880833e+00 5.13609290e-01 5.66800907... | [14.501385688781738, -2.5454719066619873] |
8dd014ca-7d09-42bd-879b-3e5881e2b9a9 | on-the-information-bottleneck-theory-of-deep | null | null | https://openreview.net/forum?id=ry_WPG-A- | https://openreview.net/pdf?id=ry_WPG-A- | On the Information Bottleneck Theory of Deep Learning | The practical successes of deep neural networks have not been matched by theoretical progress that satisfyingly explains their behavior. In this work, we study the information bottleneck (IB) theory of deep learning, which makes three specific claims: first, that deep networks undergo two distinct phases consisting of ... | ['Artemy Kolchinsky', 'Joel Dapello', 'David Daniel Cox', 'Brendan Daniel Tracey', 'Yamini Bansal', 'Madhu Advani', 'Andrew Michael Saxe'] | 2018-01-01 | null | null | null | iclr-2018-1 | ['information-plane'] | ['methodology'] | [ 3.07772249e-01 3.97812501e-02 -9.48947445e-02 -1.00210905e-01
-5.72504364e-02 -4.32904243e-01 7.15208888e-01 2.69480616e-01
-8.17399800e-01 6.75590634e-01 1.79005444e-01 -5.93485892e-01
-5.42775095e-01 -5.50449431e-01 -8.89314651e-01 -1.01513326e+00
-1.27062812e-01 2.99428552e-01 4.34811145e-01 -4.29335624... | [7.986981391906738, 3.506277322769165] |
46fd77d2-0c04-4c9b-853a-8c5678e6804c | smoothnet-a-plug-and-play-network-for | 2112.13715 | null | https://arxiv.org/abs/2112.13715v2 | https://arxiv.org/pdf/2112.13715v2.pdf | SmoothNet: A Plug-and-Play Network for Refining Human Poses in Videos | When analyzing human motion videos, the output jitters from existing pose estimators are highly-unbalanced with varied estimation errors across frames. Most frames in a video are relatively easy to estimate and only suffer from slight jitters. In contrast, for rarely seen or occluded actions, the estimated positions of... | ['Qiang Xu', 'Jianyi Wang', 'Jiefeng Li', 'Xuan Ju', 'Lei Yang', 'Ailing Zeng'] | 2021-12-27 | null | null | null | null | ['3d-pose-estimation', '3d-human-reconstruction', '2d-human-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.71134943e-01 3.99508812e-02 -4.36588645e-01 -2.69195080e-01
-4.85232800e-01 -2.60106236e-01 2.24364415e-01 -4.00334328e-01
-2.72666305e-01 6.78138375e-01 2.45135263e-01 5.90560377e-01
-2.26018488e-01 -3.18422079e-01 -9.00717854e-01 -6.20231628e-01
-2.79220670e-01 2.47233093e-01 5.70767343e-01 -1.65453777... | [7.179355144500732, -0.7009904384613037] |
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