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49c0077b-0098-4edb-82f3-7119a13ea579 | ji-yu-zhi-shi-jian-du-de-biao-qian-jiang-zao | null | null | https://aclanthology.org/2022.ccl-1.25 | https://aclanthology.org/2022.ccl-1.25.pdf | 基于知识监督的标签降噪实体对齐(Refined De-noising for Labeled Entity Alignment from Auxiliary Evidence Knowledge) | “大多数现有的实体对齐解决方案都依赖于干净的标记数据来训练模型,很少关注种子噪声。为了解决实体对齐中的噪声问题,本文提出了一个标签降噪框架,在实体对齐中注入辅助知识和附带监督,以纠正标记和引导过程中的种子错误。特别是,考虑到以前基于邻域嵌入方法的弱点,本文应用了一种新的对偶关系注意力匹配编码器来加速知识图谱的结构学习,同时使用辅助知识来弥补结构表征的不足。然后,通过对抗训练来执行弱监督标签降噪。对于误差累积的问题,本文进一步使用对齐精化模块来提高模型的性能。实验结果表明,所提的框架能够轻松应对含噪声环境下的实体对齐问题,在多个真实数据集上的对齐准确性和噪声辨别能力始终优于其他基线方法。” | ['Ning Jing', 'Fenglong Su'] | null | null | null | null | ccl-2022-10 | ['entity-alignment', 'entity-alignment'] | ['knowledge-base', 'natural-language-processing'] | [-8.16761732e-01 -9.26098645e-01 7.49491453e-01 4.34055597e-01
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b4f637b4-b1fd-4dea-a580-8770d64d8de5 | node-copying-a-random-graph-model-for | 2208.02435 | null | https://arxiv.org/abs/2208.02435v1 | https://arxiv.org/pdf/2208.02435v1.pdf | Node Copying: A Random Graph Model for Effective Graph Sampling | There has been an increased interest in applying machine learning techniques on relational structured-data based on an observed graph. Often, this graph is not fully representative of the true relationship amongst nodes. In these settings, building a generative model conditioned on the observed graph allows to take the... | ['Mark Coates', 'Yanhui Geng', 'Yingxue Zhang', 'Jianing Sun', 'Soumyasundar Pal', 'Florence Regol'] | 2022-08-04 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 2.31798500e-01 5.18402040e-01 -3.21625054e-01 -2.89456248e-01
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-3.17349374e-01 6.43330276e-01 4.73864436e-01 8.03129375... | [6.97433614730835, 5.543204307556152] |
0ab5c369-d664-4eda-985e-b42bd4bde5d6 | adaptive-graph-based-feature-normalization | 2207.11123 | null | https://arxiv.org/abs/2207.11123v1 | https://arxiv.org/pdf/2207.11123v1.pdf | Adaptive Graph-Based Feature Normalization for Facial Expression Recognition | Facial Expression Recognition (FER) suffers from data uncertainties caused by ambiguous facial images and annotators' subjectiveness, resulting in excursive semantic and feature covariate shifting problem. Existing works usually correct mislabeled data by estimating noise distribution, or guide network training with kn... | ['Yujie Xiong', 'Qingqing Wang', 'Yangtao Du'] | 2022-07-22 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 2.17708528e-01 2.10110545e-01 8.22720379e-02 -7.99778521e-01
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-2.38684174e-02 -7.73847103e-01 -6.40644193e-01 -7.03712583e-01
4.73200642e-02 3.45825404e-01 -8.26278329e-02 -2.41185635... | [13.599101066589355, 1.6315535306930542] |
feefe2dc-292b-4d1b-ba95-1c51d4781f8a | independent-sign-language-recognition-with-3d | 2012.05698 | null | https://arxiv.org/abs/2012.05698v1 | https://arxiv.org/pdf/2012.05698v1.pdf | Independent Sign Language Recognition with 3D Body, Hands, and Face Reconstruction | Independent Sign Language Recognition is a complex visual recognition problem that combines several challenging tasks of Computer Vision due to the necessity to exploit and fuse information from hand gestures, body features and facial expressions. While many state-of-the-art works have managed to deeply elaborate on th... | ['Petros Maragos', 'Georgios Pavlakos', 'Agelos Kratimenos'] | 2020-11-24 | null | null | null | null | ['3d-human-action-recognition', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 6.23266697e-02 -2.07238436e-01 -2.23348260e-01 -2.52048194e-01
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1.43870279e-01 5.10266721e-01 2.94875801e-01 -2.01420084... | [9.148006439208984, -6.456523418426514] |
80e39430-9a81-4bf0-bfb8-269c07d4d4d2 | semantic-embedded-unsupervised-spectral | 2108.06659 | null | https://arxiv.org/abs/2108.06659v2 | https://arxiv.org/pdf/2108.06659v2.pdf | Semantic-embedded Unsupervised Spectral Reconstruction from Single RGB Images in the Wild | This paper investigates the problem of reconstructing hyperspectral (HS) images from single RGB images captured by commercial cameras, \textbf{without} using paired HS and RGB images during training. To tackle this challenge, we propose a new lightweight and end-to-end learning-based framework. Specifically, on the bas... | ['Qingfu Zhang', 'Huanqiang Zeng', 'Junhui Hou', 'Hui Liu', 'Zhiyu Zhu'] | 2021-08-15 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhu_Semantic-Embedded_Unsupervised_Spectral_Reconstruction_From_Single_RGB_Images_in_the_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhu_Semantic-Embedded_Unsupervised_Spectral_Reconstruction_From_Single_RGB_Images_in_the_ICCV_2021_paper.pdf | iccv-2021-1 | ['spectral-reconstruction'] | ['computer-vision'] | [ 8.50814402e-01 -8.66369382e-02 4.71987613e-02 -2.89970458e-01
-1.03247344e+00 -6.95765078e-01 1.21713422e-01 -5.53140640e-01
-3.47550839e-01 7.90044904e-01 -1.95139959e-01 -1.42924264e-01
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5.26366830e-01 -1.29682377e-01 -6.16847686e-02 -4.09209207... | [10.299968719482422, -2.3907926082611084] |
a59535c1-e5bc-4d41-ac5f-fec0e47244af | convolutional-random-walk-networks-for | 1605.07681 | null | http://arxiv.org/abs/1605.07681v3 | http://arxiv.org/pdf/1605.07681v3.pdf | Convolutional Random Walk Networks for Semantic Image Segmentation | Most current semantic segmentation methods rely on fully convolutional
networks (FCNs). However, their use of large receptive fields and many pooling
layers cause low spatial resolution inside the deep layers. This leads to
predictions with poor localization around the boundaries. Prior work has
attempted to address th... | ['Jianbo Shi', 'Lorenzo Torresani', 'Gedas Bertasius', 'Stella X. Yu'] | 2016-05-24 | convolutional-random-walk-networks-for-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Bertasius_Convolutional_Random_Walk_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Bertasius_Convolutional_Random_Walk_CVPR_2017_paper.pdf | cvpr-2017-7 | ['scene-labeling'] | ['computer-vision'] | [ 2.62180328e-01 2.44906768e-01 -9.56180841e-02 -7.04056561e-01
-3.01656336e-01 -3.65472734e-01 4.90109265e-01 9.24025178e-02
-6.88315630e-01 6.11132979e-01 -1.56615332e-01 -1.62391722e-01
-6.35255035e-03 -1.01697123e+00 -8.57850611e-01 -4.29108411e-01
1.26092359e-01 3.80694270e-01 1.05344415e+00 1.02668345... | [9.534236907958984, 0.36487188935279846] |
fdb8170c-49c9-4915-a86b-10903f70ff1c | the-dreem-headband-as-an-alternative-to | null | null | https://doi.org/10.1101/662734 | https://www.biorxiv.org/content/biorxiv/early/2019/06/10/662734.full-text.pdf | The Dreem Headband as an Alternative to Polysomnography for EEG Signal Acquisition and Sleep Staging | Despite the central role of sleep in our lives and the high prevalence of sleep disorders, sleep is still poorly understood. The development of ambulatory technologies capable of monitoring brain activity during sleep longitudinally is critical to advancing sleep science and facilitating the diagnosis of sleep disorder... | ['Fabien Sauvet', 'Pascal Van Beers', 'Mathias Guillard', 'Pierrick J. Arnal', 'Albert Bou Hernandez', 'Mounir Chennaoui', 'Michael E. Ballard', 'Mason Harris', 'Hugo Jourde', 'Antoine Guillot', 'Valentin Thorey'] | 2019-06-10 | null | null | null | biorxiv-2019-6 | ['sleep-stage-detection', 'sleep-quality-prediction', 'sleep-staging'] | ['medical', 'medical', 'medical'] | [-1.51363596e-01 -2.83964664e-01 2.39245221e-01 -3.74318004e-01
-3.61796260e-01 -4.39056724e-01 -1.81486011e-01 2.05628306e-01
-6.29806161e-01 9.83644426e-01 2.37264801e-02 -2.27845103e-01
1.59881487e-01 -2.89990187e-01 -7.60809854e-02 -6.76539004e-01
-3.88589203e-01 1.31179184e-01 9.33641791e-02 1.33777857... | [13.521849632263184, 3.429280996322632] |
5ee8fb85-8700-4f98-a1a3-7ec6a61cef68 | defect-detection-on-semiconductor-wafers-by | 2111.03727 | null | https://arxiv.org/abs/2111.03727v1 | https://arxiv.org/pdf/2111.03727v1.pdf | Defect Detection on Semiconductor Wafers by Distribution Analysis | A method for object classification that is based on distribution analysis is proposed. In addition, a method for finding relevant features and the unification of this algorithm with another classification algorithm is proposed. The presented classification algorithm has been applied successfully to real-world measureme... | ['Thomas Olschewski'] | 2021-11-05 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 2.71459937e-01 -8.71672928e-02 -2.36163169e-01 -6.92029655e-01
-6.90078259e-01 -1.10867225e-01 1.82368517e-01 4.52750474e-01
-2.08071977e-01 8.32248926e-01 -5.74891210e-01 -2.58031845e-01
-1.02057207e+00 -1.03837955e+00 -1.28411084e-01 -8.41619849e-01
-8.42880756e-02 1.08907175e+00 3.71191442e-01 -1.92055851... | [8.240446090698242, 4.236546516418457] |
21fd3f9c-f26b-4c72-a880-9583cd4c4098 | neural-architecture-search-as-multiobjective | 2208.04321 | null | https://arxiv.org/abs/2208.04321v2 | https://arxiv.org/pdf/2208.04321v2.pdf | Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment | The ongoing advancements in network architecture design have led to remarkable achievements in deep learning across various challenging computer vision tasks. Meanwhile, the development of neural architecture search (NAS) has provided promising approaches to automating the design of network architectures for lower pred... | ['Kalyanmoy Deb', 'Kay Chen Tan', 'Yaochu Jin', 'Ran Cheng', 'Zhichao Lu'] | 2022-08-08 | null | null | null | null | ['multiobjective-optimization'] | ['methodology'] | [ 1.27306581e-01 -4.27804202e-01 2.09154829e-01 -3.15797418e-01
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-7.23391414e-01 -5.38503587e-01 -5.86831927e-01 -7.51125693e-01
2.52150986e-02 1.69742808e-01 -2.07841501e-01 -2.39325523... | [8.270698547363281, 3.2147719860076904] |
646f6bbf-ce99-441e-bb52-0436d69f83e8 | norm-of-word-embedding-encodes-information | 2212.09663 | null | https://arxiv.org/abs/2212.09663v2 | https://arxiv.org/pdf/2212.09663v2.pdf | Norm of Word Embedding Encodes Information Gain | Distributed representations of words encode lexical semantic information, but what type of information is encoded, and how? Focusing on the skip-gram with negative-sampling method, we found that the squared norm of static word embedding encodes the information gain conveyed by the word; the information gain is defined ... | ['Hidetoshi Shimodaira', 'Sho Yokoi', 'Momose Oyama'] | 2022-12-19 | null | null | null | null | ['keyword-extraction'] | ['natural-language-processing'] | [ 2.19863802e-01 3.26079458e-01 -4.55873996e-01 -4.41477507e-01
-7.35532820e-01 -6.97148204e-01 4.92524922e-01 6.54227257e-01
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-1.52364343e-01 -8.19572747e-01 -3.28790635e-01 -7.51196027e-01
-3.10420662e-01 2.77514964e-01 9.31656137e-02 -1.84701949... | [10.434910774230957, 8.782041549682617] |
801b43a3-1d8f-4f27-b1cb-81953d953a4c | realistic-data-enrichment-for-robust-image | 2304.09534 | null | https://arxiv.org/abs/2304.09534v1 | https://arxiv.org/pdf/2304.09534v1.pdf | Realistic Data Enrichment for Robust Image Segmentation in Histopathology | Poor performance of quantitative analysis in histopathological Whole Slide Images (WSI) has been a significant obstacle in clinical practice. Annotating large-scale WSIs manually is a demanding and time-consuming task, unlikely to yield the expected results when used for fully supervised learning systems. Rarely observ... | ['Bernhard Kainz', 'Candice Roufosse', 'Callum Arthurs', 'James Ball', 'Sarah Cechnicka'] | 2023-04-19 | null | null | null | null | ['whole-slide-images', 'image-augmentation'] | ['computer-vision', 'computer-vision'] | [ 6.64247990e-01 5.97859740e-01 -1.55453429e-01 -3.82683605e-01
-1.22878492e+00 -6.10855103e-01 4.67175901e-01 7.58980095e-01
-5.09693682e-01 6.79787576e-01 6.49567395e-02 -6.46701813e-01
-1.57535583e-01 -7.22841442e-01 -7.79084921e-01 -9.31985915e-01
1.25197947e-01 9.88192141e-01 2.04846069e-01 2.30534151... | [14.997896194458008, -2.799880027770996] |
55faf512-12c1-47f8-88bf-2c11a38b3982 | robust-federated-learning-with-noisy | 1911.00251 | null | https://arxiv.org/abs/1911.00251v1 | https://arxiv.org/pdf/1911.00251v1.pdf | Robust Federated Learning with Noisy Communication | Federated learning is a communication-efficient training process that alternates between local training at the edge devices and averaging the updated local model at the central server. Nevertheless, it is impractical to achieve a perfect acquisition of the local models in wireless communication due to noise, which also... | ['Yunfei Chen', 'Weidong Wang', 'Fan Ang', 'Li Chen', 'Nan Zhao', 'F. Richard Yu'] | 2019-11-01 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 7.32192583e-03 9.32375193e-02 -1.37245730e-01 -2.06606627e-01
-9.28042650e-01 -1.33409083e-01 -1.61015272e-01 -4.96582547e-03
-3.46189648e-01 9.09845352e-01 -1.02398917e-01 -3.93248677e-01
-5.50800383e-01 -7.68241405e-01 -8.15452635e-01 -1.09491467e+00
-7.55363563e-03 -2.00364426e-01 -4.10845339e-01 2.76579529... | [5.966122150421143, 5.673704147338867] |
8e8b6aca-d79c-404c-b456-8da05c14694b | deep-double-self-expressive-subspace | 2306.11592 | null | https://arxiv.org/abs/2306.11592v1 | https://arxiv.org/pdf/2306.11592v1.pdf | Deep Double Self-Expressive Subspace Clustering | Deep subspace clustering based on auto-encoder has received wide attention. However, most subspace clustering based on auto-encoder does not utilize the structural information in the self-expressive coefficient matrix, which limits the clustering performance. In this paper, we propose a double self-expressive subspace ... | ['Jun Zhou', 'Shanxiong Chen', 'Yunpeng Ma', 'Ling Zhao'] | 2023-06-20 | null | null | null | null | ['contrastive-learning', 'contrastive-learning', 'clustering'] | ['computer-vision', 'methodology', 'methodology'] | [-3.67706746e-01 -4.81113195e-01 -5.00949435e-02 -2.79139698e-01
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-2.35462174e-01 -6.43027306e-01 -4.69542325e-01 -1.09462237e+00
1.90464631e-01 7.86635578e-02 9.77649242e-02 6.71655163... | [8.659581184387207, 3.9919283390045166] |
86907ee1-8538-4dae-a021-b8e7cbd11583 | spatial-temporal-recurrent-graph-neural | 2210.15177 | null | https://arxiv.org/abs/2210.15177v1 | https://arxiv.org/pdf/2210.15177v1.pdf | Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems | Fault diagnostics are extremely important to decide proper actions toward fault isolation and system restoration. The growing integration of inverter-based distributed energy resources imposes strong influences on fault detection using traditional overcurrent relays. This paper utilizes emerging graph learning techniqu... | ['Rob Hovsapian', 'Mayank Panwar', 'Thai-Thanh Nguyen', 'Tuyen Vu', 'Bang Nguyen'] | 2022-10-27 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-3.43582898e-01 -3.23172957e-01 7.43489265e-02 1.63933873e-01
-9.31805596e-02 -4.98178154e-01 1.81539521e-01 3.45476687e-01
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-6.33201241e-01 -8.23470950e-01 -7.16641396e-02 -9.55994844e-01
-6.51326478e-01 2.30754554e-01 8.13412443e-02 -3.31746578... | [6.207886219024658, 2.491515636444092] |
446d08c5-1151-495f-9cd9-16bc4c0149af | an-experiment-in-integrating-sentiment | null | null | https://aclanthology.org/W12-5503 | https://aclanthology.org/W12-5503.pdf | An Experiment in Integrating Sentiment Features for Tech Stock Prediction in Twitter | null | ['Tien Thanh Vu', 'Nigel Collier', 'Shu Chang', 'Quang Thuy Ha'] | 2012-12-01 | an-experiment-in-integrating-sentiment-1 | https://aclanthology.org/W12-5503 | https://aclanthology.org/W12-5503.pdf | ws-2012-12 | ['twitter-sentiment-analysis', 'stock-market-prediction', 'stock-prediction'] | ['natural-language-processing', 'time-series', 'time-series'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.315954208374023, 3.6651344299316406] |
5451e98b-f360-4e83-beb2-476a46c5359a | towards-exploiting-sticker-for-multimodal | null | null | https://aclanthology.org/2022.coling-1.591 | https://aclanthology.org/2022.coling-1.591.pdf | Towards Exploiting Sticker for Multimodal Sentiment Analysis in Social Media: A New Dataset and Baseline | Sentiment analysis in social media is challenging since posts are short of context. As a popular way to express emotion on social media, stickers related to these posts can supplement missing sentiments and help identify sentiments precisely. However, research about stickers has not been investigated further. To this e... | ['Yi Cai', 'Haopeng Ren', 'Weizhao Li', 'Feng Ge'] | null | null | null | null | coling-2022-10 | ['multimodal-sentiment-analysis', 'multimodal-sentiment-analysis'] | ['computer-vision', 'natural-language-processing'] | [-5.08209355e-02 -4.19506490e-01 -1.06771417e-01 -6.31312847e-01
-6.46841884e-01 -5.27900100e-01 5.54603696e-01 3.21024477e-01
-2.25480929e-01 1.98554993e-01 6.02209032e-01 4.06270981e-01
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2.70489365e-01 -1.79577231e-01 -6.80639073e-02 -8.71872842... | [13.063501358032227, 5.246790885925293] |
e6d91af0-2758-4a75-9dac-37ef5a70372e | target-conditioned-sampling-optimizing-data | 1905.08212 | null | https://arxiv.org/abs/1905.08212v1 | https://arxiv.org/pdf/1905.08212v1.pdf | Target Conditioned Sampling: Optimizing Data Selection for Multilingual Neural Machine Translation | To improve low-resource Neural Machine Translation (NMT) with multilingual corpora, training on the most related high-resource language only is often more effective than using all data available (Neubig and Hu, 2018). However, it is possible that an intelligent data selection strategy can further improve low-resource N... | ['Xinyi Wang', 'Graham Neubig'] | 2019-05-20 | target-conditioned-sampling-optimizing-data-1 | https://aclanthology.org/P19-1583 | https://aclanthology.org/P19-1583.pdf | acl-2019-7 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 1.19914465e-01 -1.31187662e-01 -6.09809995e-01 -3.23676050e-01
-1.53187037e+00 -5.51123083e-01 5.74709237e-01 -1.61795706e-01
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4.22450751e-01 -5.19014001e-01 -8.82782340e-01 -4.84351367e-01
3.12799096e-01 8.00551534e-01 -4.22522843e-01 -8.97463560... | [11.637834548950195, 10.279297828674316] |
14cda3bf-61d4-49c2-a04c-a32bd0e9cf20 | an-overview-on-the-evaluated-video-retrieval | 2306.13118 | null | https://arxiv.org/abs/2306.13118v1 | https://arxiv.org/pdf/2306.13118v1.pdf | An overview on the evaluated video retrieval tasks at TRECVID 2022 | The TREC Video Retrieval Evaluation (TRECVID) is a TREC-style video analysis and retrieval evaluation with the goal of promoting progress in research and development of content-based exploitation and retrieval of information from digital video via open, tasks-based evaluation supported by metrology. Over the last twent... | ['Georges Quenot', 'Yvette Graham', 'Jeffrey Liu', 'Lukas Diduch', 'Eliot Godard', 'Andrew Delgado', 'Yooyoung Lee', 'Afzal Godil', 'Jonathan Fiscus', 'Asad Butt', 'Keith Curtis', 'George Awad'] | 2023-06-22 | null | null | null | null | ['video-retrieval', 'video-understanding', 'ad-hoc-video-search', 'retrieval'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology'] | [ 3.59854877e-01 -6.17948174e-01 -2.04009414e-02 -4.39307302e-01
-1.47969043e+00 -8.54912043e-01 8.62499177e-01 5.77403486e-01
-8.07411134e-01 4.24099475e-01 7.19062746e-01 3.13072592e-01
-5.62091172e-02 -1.49380490e-01 -2.97055215e-01 -3.73250932e-01
-2.88379669e-01 3.59600186e-01 3.74018699e-01 -7.16248378... | [10.463508605957031, 0.7297830581665039] |
27733d3e-7e30-449d-8563-3c2cc6203e59 | discriminative-representation-combinations | 1808.08802 | null | http://arxiv.org/abs/1808.08802v2 | http://arxiv.org/pdf/1808.08802v2.pdf | Discriminative Representation Combinations for Accurate Face Spoofing Detection | Three discriminative representations for face presentation attack detection
are introduced in this paper. Firstly we design a descriptor called spatial
pyramid coding micro-texture (SPMT) feature to characterize local appearance
information. Secondly we utilize the SSD, which is a deep learning framework
for detection,... | ['Tianwei Lin', 'Xu Zhao', 'Xiao Song', 'Liangji Fang'] | 2018-08-27 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 1.04253873e-01 -7.57527530e-01 3.91742811e-02 -2.42376864e-01
-8.29163432e-01 -4.13683534e-01 5.67815304e-01 -2.55097449e-01
2.06981927e-01 -8.78933445e-03 3.80585700e-01 -1.33358359e-01
8.03512782e-02 -6.95467591e-01 -3.95561874e-01 -8.29163790e-01
1.10292390e-01 -4.21737462e-01 2.72951216e-01 -1.66901469... | [13.023133277893066, 1.1014189720153809] |
5a9f235b-99da-4a01-82c2-16df69d54bab | rethinking-cross-entropy-loss-for-stereo | 2306.15612 | null | https://arxiv.org/abs/2306.15612v1 | https://arxiv.org/pdf/2306.15612v1.pdf | Rethinking Cross-Entropy Loss for Stereo Matching Networks | Despite the great success of deep learning in stereo matching, recovering accurate and clearly-contoured disparity map is still challenging. Currently, L1 loss and cross-entropy loss are the two most widely used loss functions for training the stereo matching networks. Comparing with the former, the latter can usually ... | ['Xijun Zhao', 'Jingyun Fu', 'Chenyu Qiao', 'Zhiyu Xiang', 'Peng Xu'] | 2023-06-27 | null | null | null | null | ['stereo-matching-1', 'domain-generalization'] | ['computer-vision', 'methodology'] | [ 9.98689383e-02 -1.87134504e-01 -4.26247418e-01 -5.08305430e-01
-9.56075191e-01 -1.30063266e-01 4.17975932e-01 -2.60188341e-01
-4.17846173e-01 9.17980909e-01 2.01911822e-01 -4.14292179e-02
8.64188373e-02 -9.52558756e-01 -8.93879712e-01 -8.09820712e-01
4.01775897e-01 2.76639014e-01 2.38555625e-01 -9.54610705... | [8.879772186279297, -2.2902231216430664] |
8205c95c-0339-4449-ab50-e3579afd58e0 | back-to-reality-leveraging-pattern-driven | 2110.08604 | null | https://arxiv.org/abs/2110.08604v3 | https://arxiv.org/pdf/2110.08604v3.pdf | Improving Implicit Sentiment Learning via Local Sentiment Aggregation | Aspect-based sentiment classification (ABSC) has revealed the potential dependency of sentiment polarities among different aspects. Our study further explores this phenomenon, positing that adjacent aspects often exhibit similar sentiments, a concept we term "aspect sentiment coherency." We argue that the current resea... | ['Ke Li', 'Heng Yang'] | 2021-10-16 | null | null | null | null | ['sentiment-dependency-learning'] | ['natural-language-processing'] | [-1.51161075e-01 -5.94823472e-02 -4.97588009e-01 -6.63262844e-01
-7.12627113e-01 -6.67105556e-01 8.45211327e-01 4.89930838e-01
-1.17148072e-01 3.30672085e-01 8.90338421e-01 -3.72267306e-01
8.44581202e-02 -8.40386331e-01 -2.35146105e-01 -5.18554211e-01
1.57260805e-01 -9.41932499e-02 -3.17062050e-01 -6.12077475... | [11.472517013549805, 6.725875377655029] |
8afeedb6-48ae-43d2-83ac-5c7bcafb3492 | ef-bv-a-unified-theory-of-error-feedback-and | 2205.04180 | null | https://arxiv.org/abs/2205.04180v4 | https://arxiv.org/pdf/2205.04180v4.pdf | EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization | In distributed or federated optimization and learning, communication between the different computing units is often the bottleneck and gradient compression is widely used to reduce the number of bits sent within each communication round of iterative methods. There are two classes of compression operators and separate a... | ['Peter Richtárik', 'Kai Yi', 'Laurent Condat'] | 2022-05-09 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [ 7.11763501e-02 3.12127713e-02 -2.96769500e-01 1.10682569e-01
-7.57625401e-01 -5.12932420e-01 4.99145627e-01 2.83590555e-01
-6.76832676e-01 9.60713923e-01 4.35374267e-02 -3.39399517e-01
-4.59881365e-01 -9.37147081e-01 -8.98630083e-01 -1.13569629e+00
-3.78538996e-01 6.43677831e-01 1.50857985e-01 -2.26297930... | [6.310446739196777, 4.84208345413208] |
6d9cee5e-66ce-464e-be2e-bfaa03372fa4 | non-rigid-medical-image-registration-using | 2302.10343 | null | https://arxiv.org/abs/2302.10343v1 | https://arxiv.org/pdf/2302.10343v1.pdf | Non-rigid Medical Image Registration using Physics-informed Neural Networks | Biomechanical modelling of soft tissue provides a non-data-driven method for constraining medical image registration, such that the estimated spatial transformation is considered biophysically plausible. This has not only been adopted in real-world clinical applications, such as the MR-to-ultrasound registration for pr... | ['Yipeng Hu', 'Zeike A. Taylor', 'Dean C. Barratt', 'Mark Emberton', 'Shaheer U. Saeed', 'Zachary M. C. Baum', 'Zhe Min'] | 2023-02-20 | null | null | null | null | ['medical-image-registration'] | ['medical'] | [ 5.57132304e-01 4.19316322e-01 -1.79214016e-01 -2.59046972e-01
-4.98275667e-01 -5.62604308e-01 8.19626451e-01 3.50316793e-01
-5.79750896e-01 4.44706023e-01 3.06476951e-01 -2.30906606e-01
-8.73821795e-01 -4.84996825e-01 -5.31362712e-01 -6.60534561e-01
-5.65437794e-01 8.27147067e-01 1.11877054e-01 -2.91998386... | [14.023699760437012, -2.6408796310424805] |
f80b3898-5fc6-4375-96c9-a2d1299fa591 | distilling-vision-language-pre-training-to | 2212.09335 | null | https://arxiv.org/abs/2212.09335v1 | https://arxiv.org/pdf/2212.09335v1.pdf | Distilling Vision-Language Pre-training to Collaborate with Weakly-Supervised Temporal Action Localization | Weakly-supervised temporal action localization (WTAL) learns to detect and classify action instances with only category labels. Most methods widely adopt the off-the-shelf Classification-Based Pre-training (CBP) to generate video features for action localization. However, the different optimization objectives between c... | ['Qi Tian', 'Yanfeng Wang', 'Jianlong Chang', 'Ya zhang', 'Peisen Zhao', 'Jinxiang Liu', 'Kunhao Zheng', 'Chen Ju'] | 2022-12-19 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ju_Distilling_Vision-Language_Pre-Training_To_Collaborate_With_Weakly-Supervised_Temporal_Action_Localization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ju_Distilling_Vision-Language_Pre-Training_To_Collaborate_With_Weakly-Supervised_Temporal_Action_Localization_CVPR_2023_paper.pdf | cvpr-2023-1 | ['weakly-supervised-temporal-action', 'action-localization'] | ['computer-vision', 'computer-vision'] | [ 3.06526959e-01 -1.21735111e-02 -6.67196274e-01 -1.49552315e-01
-8.54440272e-01 -4.70769048e-01 6.96306229e-01 -3.18835676e-01
-4.44344074e-01 7.86265075e-01 2.47135729e-01 1.74460579e-02
2.83138275e-01 -4.30651993e-01 -6.96241975e-01 -1.06736434e+00
8.20913613e-02 4.31906953e-02 7.89071620e-01 6.46759644... | [8.466673851013184, 0.633354663848877] |
97568580-b168-47ac-899a-b65fec7d8cc8 | dynamic-survival-prediction-in-intensive-care | 1909.07214 | null | https://arxiv.org/abs/1909.07214v2 | https://arxiv.org/pdf/1909.07214v2.pdf | Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or pre-processing | We present a machine learning pipeline and model that uses the entire uncurated EHR for prediction of in-hospital mortality at arbitrary time intervals, using all available chart, lab and output events, without the need for pre-processing or feature engineering. Data for more than 45,000 American ICU patients from the ... | ['Pietro Liò', 'Jacob Deasy', 'Ari Ercole'] | 2019-09-13 | null | null | null | null | ['icu-mortality'] | ['medical'] | [ 3.04278079e-02 -7.71968290e-02 2.82289907e-02 -3.10031086e-01
-8.21961045e-01 -5.17196715e-01 9.03877318e-02 1.30159712e+00
-6.56517446e-01 5.91206968e-01 6.15238488e-01 -9.82114673e-01
-5.55823207e-01 -6.42157555e-01 -6.06561825e-02 -3.88623983e-01
-5.30667067e-01 6.43128335e-01 -2.69024074e-01 3.32180679... | [8.014100074768066, 6.169409275054932] |
ef772e52-4b54-444c-ac11-05cf06a46002 | prores-exploring-degradation-aware-visual | 2306.13653 | null | https://arxiv.org/abs/2306.13653v1 | https://arxiv.org/pdf/2306.13653v1.pdf | ProRes: Exploring Degradation-aware Visual Prompt for Universal Image Restoration | Image restoration aims to reconstruct degraded images, e.g., denoising or deblurring. Existing works focus on designing task-specific methods and there are inadequate attempts at universal methods. However, simply unifying multiple tasks into one universal architecture suffers from uncontrollable and undesired predicti... | ['Lefei Zhang', 'Xinggang Wang', 'Qian Zhang', 'Guoli Wang', 'Tianheng Cheng', 'Jiaqi Ma'] | 2023-06-23 | null | null | null | null | ['deblurring', 'image-restoration'] | ['computer-vision', 'computer-vision'] | [ 2.38105908e-01 -3.48325133e-01 1.45708367e-01 -2.92991042e-01
-7.52300680e-01 -4.53764081e-01 5.34158647e-01 -6.92100942e-01
-5.28340079e-02 4.49857712e-01 4.89753097e-01 -4.00411963e-01
6.61954805e-02 -1.57587767e-01 -8.11595380e-01 -7.93731034e-01
3.12470287e-01 -3.86477053e-01 1.30788192e-01 -2.61593282... | [11.21603775024414, -2.267251491546631] |
fb1e42af-38fb-469b-aee6-bbaddc08abc8 | modeling-irregular-time-series-with | 2111.11344 | null | https://arxiv.org/abs/2111.11344v3 | https://arxiv.org/pdf/2111.11344v3.pdf | Modeling Irregular Time Series with Continuous Recurrent Units | Recurrent neural networks (RNNs) are a popular choice for modeling sequential data. Modern RNN architectures assume constant time-intervals between observations. However, in many datasets (e.g. medical records) observation times are irregular and can carry important information. To address this challenge, we propose co... | ['Maja Rudolph', 'Stefan Lessmann', 'Mazin Eltayeb', 'Mona Schirmer'] | 2021-11-22 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [ 1.25253901e-01 -1.53388269e-02 -2.94768397e-04 -3.83143872e-01
-6.18789017e-01 -1.22369312e-01 3.99198234e-01 -1.01870336e-01
-4.33173686e-01 6.13801658e-01 2.73196578e-01 -5.79935133e-01
8.93785283e-02 -5.47051072e-01 -7.28915155e-01 -6.85618401e-01
-2.42090523e-01 2.33843446e-01 -7.43333325e-02 -9.18387249... | [7.168252468109131, 3.352914571762085] |
08802fa6-ffe0-47b2-9702-467a7702ef4c | study-on-unsupervised-statistical-machine | null | null | https://aclanthology.org/R19-1068 | https://aclanthology.org/R19-1068.pdf | Study on Unsupervised Statistical Machine Translation for Backtranslation | Machine Translation systems have drastically improved over the years for several language pairs. Monolingual data is often used to generate synthetic sentences to augment the training data which has shown to improve the performance of machine translation models. In our paper, we make use of an Unsupervised Statistical ... | ['Mydhili K. Nair', 'Ch', 'Anush Kumar', 'Nihal V. Nayak', 'Aditya ra'] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 6.01551473e-01 2.95545101e-01 -2.86725789e-01 -4.43405420e-01
-1.38461804e+00 -5.18893719e-01 1.17292166e+00 -4.32895720e-01
-4.35454696e-01 1.69275248e+00 4.42391813e-01 -8.18511963e-01
8.29994261e-01 -5.56122243e-01 -9.07375395e-01 -2.13373721e-01
9.14881706e-01 1.10205019e+00 -3.57373595e-01 -6.62555873... | [11.599553108215332, 10.398356437683105] |
6b0c30a0-daf8-48c2-9cd9-6e3d89405010 | expanded-parts-model-for-human-attribute-and | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Sharma_Expanded_Parts_Model_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Sharma_Expanded_Parts_Model_2013_CVPR_paper.pdf | Expanded Parts Model for Human Attribute and Action Recognition in Still Images | We propose a new model for recognizing human attributes (e.g. wearing a suit, sitting, short hair) and actions (e.g. running, riding a horse) in still images. The proposed model relies on a collection of part templates which are learnt discriminatively to explain specific scale-space locations in the images (in human c... | ['Gaurav Sharma', 'Frederic Jurie', 'Cordelia Schmid'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['action-recognition-in-still-images'] | ['computer-vision'] | [ 3.44261557e-01 2.34875068e-01 -2.10322529e-01 -7.41514146e-01
-8.73582840e-01 -3.01939875e-01 7.24083126e-01 -2.87746161e-01
-2.72093773e-01 5.48808873e-01 3.74496460e-01 3.62630039e-01
-8.80158618e-02 -1.62187755e-01 -8.13354969e-01 -6.40197754e-01
-1.40873984e-01 8.64102483e-01 4.81180876e-01 -7.18474761... | [7.991342067718506, 0.2295798510313034] |
4a337c14-c595-4269-a157-4b7566f7fa5c | a-harmonic-mean-linear-discriminant-analysis | 1610.04631 | null | http://arxiv.org/abs/1610.04631v2 | http://arxiv.org/pdf/1610.04631v2.pdf | A Harmonic Mean Linear Discriminant Analysis for Robust Image Classification | Linear Discriminant Analysis (LDA) is a widely-used supervised dimensionality
reduction method in computer vision and pattern recognition. In null space
based LDA (NLDA), a well-known LDA extension, between-class distance is
maximized in the null space of the within-class scatter matrix. However, there
are some limitat... | ['Shuai Zheng', 'Heng Huang', 'Chris Ding', 'Feiping Nie'] | 2016-10-14 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [-2.24074826e-01 -5.00396132e-01 -3.77381235e-01 -4.52756017e-01
-6.79874420e-01 -5.53552926e-01 3.18655163e-01 1.84430435e-01
-1.45097882e-01 5.19220650e-01 8.90281722e-02 -1.12039275e-01
-5.23056388e-01 -6.67127609e-01 1.67872787e-01 -1.22866344e+00
-6.26220256e-02 4.04573590e-01 -4.45840582e-02 1.15984187... | [7.8944621086120605, 4.257002353668213] |
b8c5a30e-1a20-4e19-8610-b1d770412841 | dualhgnn-a-dual-hypergraph-neural-network-for | 2306.04214 | null | https://arxiv.org/abs/2306.04214v1 | https://arxiv.org/pdf/2306.04214v1.pdf | DualHGNN: A Dual Hypergraph Neural Network for Semi-Supervised Node Classification based on Multi-View Learning and Density Awareness | Graph-based semi-supervised node classification has been shown to become a state-of-the-art approach in many applications with high research value and significance. Most existing methods are only based on the original intrinsic or artificially established graph structure which may not accurately reflect the "true" corr... | ['Qian Tao', 'Jun Yan', 'Jianpeng Liao'] | 2023-06-07 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-2.24402696e-01 3.64219725e-01 -6.59532666e-01 -3.78932118e-01
-2.99427807e-01 -1.56302452e-01 2.83255070e-01 2.80947592e-02
1.08387634e-01 5.77252328e-01 1.20756581e-01 -1.33912086e-01
-4.30938482e-01 -1.06220126e+00 -6.04375660e-01 -8.64014030e-01
-8.78586918e-02 5.41613936e-01 2.28806853e-01 8.08525160... | [7.372117519378662, 6.193046569824219] |
d00b7803-33a6-4671-9f15-a3917459586c | understanding-hindsight-goal-relabeling | 2209.13046 | null | https://arxiv.org/abs/2209.13046v2 | https://arxiv.org/pdf/2209.13046v2.pdf | Understanding Hindsight Goal Relabeling from a Divergence Minimization Perspective | Hindsight goal relabeling has become a foundational technique in multi-goal reinforcement learning (RL). The essential idea is that any trajectory can be seen as a sub-optimal demonstration for reaching its final state. Intuitively, learning from those arbitrary demonstrations can be seen as a form of imitation learnin... | ['Bradly C. Stadie', 'Lunjun Zhang'] | 2022-09-26 | null | null | null | null | ['multi-goal-reinforcement-learning'] | ['methodology'] | [-1.17965210e-02 1.28512517e-01 -4.38304037e-01 -1.61254965e-02
-6.26973033e-01 -6.36981130e-01 4.81806666e-01 -9.72292125e-02
-6.18578851e-01 9.04340863e-01 2.28311330e-01 -3.37908447e-01
-4.24288541e-01 -5.04186332e-01 -7.44761944e-01 -9.80494082e-01
-2.16095060e-01 1.48118839e-01 -5.11936890e-03 -5.89839220... | [4.064385890960693, 1.7886152267456055] |
43248067-ae3d-4ad0-b72f-451997173ef5 | cvpr19-tracking-and-detection-challenge-how | 1906.04567 | null | https://arxiv.org/abs/1906.04567v1 | https://arxiv.org/pdf/1906.04567v1.pdf | CVPR19 Tracking and Detection Challenge: How crowded can it get? | Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research. The benchmark for Multiple Object Tracking, MOTCh... | ['Laura Leal-Taixe', 'Anton Milan', 'Javen Shi', 'Stefan Roth', 'Konrad Schindler', 'Daniel Cremers', 'Patrick Dendorfer', 'Ian Reid', 'Hamid Rezatofighi'] | 2019-06-10 | null | null | null | null | ['multiple-people-tracking'] | ['computer-vision'] | [-1.75987363e-01 -5.33019423e-01 -3.13825086e-02 -5.47250314e-03
-5.89579225e-01 -4.79529202e-01 7.39685893e-01 1.53063118e-01
-7.39525259e-01 8.69229138e-01 -1.62496741e-04 2.42563695e-01
9.89674926e-02 -1.22748390e-01 -7.04504430e-01 -5.47965169e-01
-1.51615977e-01 7.81122446e-01 9.22202229e-01 1.31501462... | [6.384876728057861, -2.0085182189941406] |
756966ee-cd20-44a3-afe5-8e9ab8b4d89a | mask-shadownet-towards-shadow-removal-via | null | null | https://ieeexplore.ieee.org/document/9408351 | https://ieeexplore.ieee.org/document/9408351 | Mask-ShadowNet: Towards Shadow Removal via Masked Adaptive Instance Normalization | Shadow removal is an important yet challenging task in image processing and computer vision. Existing methods are limited in extracting good global features due to the interference of shadow. And also, most of them ignore a fact that features inside and outside the shaded area should be treated disparately because of d... | ['Yong Du', 'Junyu Dong', 'Bing Peng', 'Shengfeng He'] | 2021-04-19 | null | null | null | null | ['shadow-removal', 'image-shadow-removal'] | ['computer-vision', 'computer-vision'] | [ 5.88666320e-01 -9.72131938e-02 3.03585202e-01 -6.46879792e-01
-1.33827049e-02 -2.07514353e-02 5.69162369e-01 -4.55857605e-01
-2.60926455e-01 6.29722893e-01 1.99179426e-01 -1.76894248e-01
2.12612525e-01 -4.34356242e-01 -3.61164868e-01 -1.15774608e+00
3.07225525e-01 6.05951212e-02 6.34925067e-01 -1.43167675... | [10.845865249633789, -4.08622932434082] |
a26b6b43-6b73-4655-8ac3-81c9eee9a8b7 | high-dimensional-smoothed-entropy-estimation | 2305.04712 | null | https://arxiv.org/abs/2305.04712v2 | https://arxiv.org/pdf/2305.04712v2.pdf | High-Dimensional Smoothed Entropy Estimation via Dimensionality Reduction | We study the problem of overcoming exponential sample complexity in differential entropy estimation under Gaussian convolutions. Specifically, we consider the estimation of the differential entropy $h(X+Z)$ via $n$ independently and identically distributed samples of $X$, where $X$ and $Z$ are independent $D$-dimension... | ['Yuancheng Yu', 'Brian Kingsbury', 'Kristjan Greenewald'] | 2023-05-08 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 8.27239081e-03 3.54232937e-01 4.05681163e-01 -3.08096170e-01
-6.59293771e-01 -4.05713469e-01 1.13815993e-01 -1.38416037e-01
-1.03796899e+00 9.55538452e-01 -3.51075172e-01 -3.38853776e-01
-5.61625957e-01 -6.86568558e-01 -7.45118141e-01 -1.17526853e+00
-7.01009929e-01 3.01840246e-01 -3.56045574e-01 3.66364777... | [7.852762222290039, 3.618507146835327] |
3c89c8b7-f413-4669-9919-f64380a32655 | deep-learning-for-punctuation-restoration-in | null | null | https://aclanthology.org/W17-2319 | https://aclanthology.org/W17-2319.pdf | Deep Learning for Punctuation Restoration in Medical Reports | In clinical dictation, speakers try to be as concise as possible to save time, often resulting in utterances without explicit punctuation commands. Since the end product of a dictated report, e.g. an out-patient letter, does require correct orthography, including exact punctuation, the latter need to be restored, prefe... | ['David Suendermann-Oeft', 'Wael Salloum', 'Greg Finley', 'Mark Miller', 'Erik Edwards'] | 2017-08-01 | null | null | null | ws-2017-8 | ['punctuation-restoration'] | ['natural-language-processing'] | [ 5.69456160e-01 5.06347835e-01 3.07035983e-01 -5.22538662e-01
-1.17210484e+00 -5.10343015e-01 2.01183945e-01 7.33426988e-01
-7.04515219e-01 7.35443234e-01 7.22576261e-01 -7.71885216e-01
-2.52940834e-01 -7.28089958e-02 -4.14884210e-01 -4.61303502e-01
2.28274137e-01 6.63935304e-01 -4.69597787e-01 -3.66666466... | [11.257030487060547, 9.725858688354492] |
8dbeac49-5092-4a3d-ab28-1bb9412fa0a0 | automated-essay-scoring-with-discourse-aware | null | null | https://aclanthology.org/W19-4450 | https://aclanthology.org/W19-4450.pdf | Automated Essay Scoring with Discourse-Aware Neural Models | Automated essay scoring systems typically rely on hand-crafted features to predict essay quality, but such systems are limited by the cost of feature engineering. Neural networks offer an alternative to feature engineering, but they typically require more annotated data. This paper explores network structures, contextu... | ['Mari Ostendorf', 'Farah Nadeem', 'Huy Nguyen', 'Yang Liu'] | 2019-08-01 | null | null | null | ws-2019-8 | ['automated-essay-scoring'] | ['natural-language-processing'] | [-1.27656505e-01 7.86587074e-02 -4.24154192e-01 -6.95657909e-01
-5.42877674e-01 -6.14825249e-01 7.28448987e-01 6.42366111e-01
-7.56507874e-01 7.55455256e-01 6.62145734e-01 -2.71721333e-01
-3.60912800e-01 -7.66655505e-01 1.03525501e-02 -8.10680017e-02
2.90445030e-01 4.00631815e-01 -5.26285022e-02 -3.82370174... | [11.305092811584473, 9.327591896057129] |
665e7eeb-397a-463d-8d59-15f02bec61c6 | audio-barlow-twins-self-supervised-audio | 2209.14345 | null | https://arxiv.org/abs/2209.14345v1 | https://arxiv.org/pdf/2209.14345v1.pdf | Audio Barlow Twins: Self-Supervised Audio Representation Learning | The Barlow Twins self-supervised learning objective requires neither negative samples or asymmetric learning updates, achieving results on a par with the current state-of-the-art within Computer Vision. As such, we present Audio Barlow Twins, a novel self-supervised audio representation learning approach, adapting Barl... | ['Bjorn W. Schuller', 'Pancham Shukla', 'Harry Coppock', 'Jonah Anton'] | 2022-09-28 | null | null | null | null | ['environmental-sound-classification'] | ['audio'] | [ 6.3489944e-01 2.1561986e-01 -6.5963879e-02 -4.8996505e-01
-1.6303165e+00 -5.0842887e-01 6.6291636e-01 3.4469047e-01
-3.0959782e-01 3.9912844e-01 6.1080468e-01 2.9051554e-01
-4.1381276e-01 -4.0473774e-01 -7.0630640e-01 -3.9345676e-01
-4.9303821e-01 6.2825894e-01 1.2062046e-01 -1.5046081e-01
-1.5449743e-01... | [15.270251274108887, 5.142383575439453] |
116879c1-1704-4e1e-9d2c-9ac3939b08ac | twina-at-semeval-2017-task-4-twitter | null | null | https://aclanthology.org/S17-2109 | https://aclanthology.org/S17-2109.pdf | TWINA at SemEval-2017 Task 4: Twitter Sentiment Analysis with Ensemble Gradient Boost Tree Classifier | This paper describes the TWINA system, with which we participated in SemEval-2017 Task 4B (Topic Based Message Polarity Classification {--} Two point scale) and 4D (two-point scale Tweet quantification). We implemented ensemble based Gradient Boost Trees classification method for both the tasks. Our system could perfor... | ['Suresh Kumar Sanampudi', 'Naveen Kumar Laskari'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-1.84112042e-01 1.67462543e-01 -2.72682130e-01 -4.11252290e-01
-8.52974176e-01 -4.47982430e-01 1.24702799e+00 7.27419972e-01
-6.31848931e-01 8.97987783e-01 3.18840116e-01 -3.88025582e-01
7.00317547e-02 -6.44330740e-01 -1.96910694e-01 -2.82211602e-01
-4.15838927e-01 7.53316998e-01 3.73548478e-01 -8.82528841... | [11.162430763244629, 7.042892932891846] |
8bb749e9-d1cc-4735-987b-539a417e9a14 | on-the-relationships-between-graph-neural | 2304.00146 | null | https://arxiv.org/abs/2304.00146v1 | https://arxiv.org/pdf/2304.00146v1.pdf | On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methods | Recent developments in Machine Learning approaches for modelling physical systems have begun to mirror the past development of numerical methods in the computational sciences. In this survey, we begin by providing an example of this with the parallels between the development trajectories of graph neural network acceler... | ['Nikolaus A. Adams', 'Andrea Panizza', 'Ludger Paehler', 'Artur P. Toshev'] | 2023-03-31 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [-1.14860147e-01 -2.28815660e-01 -3.53215188e-01 2.11035274e-02
-1.19637810e-02 9.02133062e-02 1.20603681e+00 3.99793744e-01
-3.51736277e-01 9.35536385e-01 -2.65722573e-01 -1.01345587e+00
-1.57089084e-01 -1.23825049e+00 -4.20677006e-01 -9.61201549e-01
-6.67131841e-01 7.62189984e-01 2.37987265e-01 -5.26150882... | [6.363089084625244, 3.603257417678833] |
75733754-f1ca-4b98-939d-19fb2c9961c0 | convolutional-neural-networks-for-detecting | null | null | https://spie.org/Publications/Proceedings/Paper/10.1117/12.2571111 | https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11524/115240K/Convolutional-neural-networks-for-detecting-challenging-cases-in-cloud-masking/10.1117/12.2571111.full?SSO=1 | Convolutional neural networks for detecting challenging cases in cloud masking using Sentinel-2 imagery | Cloud contamination represents a large obstacle for mapping the earth’s surface using remotely sensed data. Therefore, cloudy pixels should be identified and eliminated before any further data processing can be achieved. Although several threshold, multi-temporal and machine learning applications have been developed to... | ['Vassilia Karathanassi', 'Viktoria Kristollari'] | 2020-08-26 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 4.29148734e-01 -5.14386415e-01 4.06837285e-01 -3.70852560e-01
-6.70191348e-01 -6.72375500e-01 6.36815786e-01 1.90396413e-01
-6.21362388e-01 6.51280761e-01 -4.67345685e-01 -4.76255298e-01
-2.09241614e-01 -9.14521575e-01 -4.34988767e-01 -1.15822172e+00
-3.99951220e-01 1.08684830e-01 1.55189648e-01 -2.29788259... | [9.748889923095703, -1.7264128923416138] |
7f5e3dd3-6618-44c9-8782-46edb16b7f8b | clip2protect-protecting-facial-privacy-using-1 | 2306.10008 | null | https://arxiv.org/abs/2306.10008v2 | https://arxiv.org/pdf/2306.10008v2.pdf | CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Search | The success of deep learning based face recognition systems has given rise to serious privacy concerns due to their ability to enable unauthorized tracking of users in the digital world. Existing methods for enhancing privacy fail to generate naturalistic images that can protect facial privacy without compromising user... | ['Karthik Nandakumar', 'Muzammal Naseer', 'Fahad Shamshad'] | 2023-06-16 | clip2protect-protecting-facial-privacy-using | http://openaccess.thecvf.com//content/CVPR2023/html/Shamshad_CLIP2Protect_Protecting_Facial_Privacy_Using_Text-Guided_Makeup_via_Adversarial_Latent_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shamshad_CLIP2Protect_Protecting_Facial_Privacy_Using_Text-Guided_Makeup_via_Adversarial_Latent_CVPR_2023_paper.pdf | cvpr-2023-1 | ['face-recognition', 'face-verification'] | ['computer-vision', 'computer-vision'] | [ 3.49001974e-01 1.90530926e-01 9.51459780e-02 -5.99103391e-01
-7.32500613e-01 -8.57331753e-01 5.64011991e-01 -6.31730497e-01
-4.96347137e-02 4.33610290e-01 9.30048078e-02 -2.71910548e-01
1.66949064e-01 -6.70301020e-01 -8.41568768e-01 -1.01986861e+00
1.00584909e-01 -6.58720434e-02 -5.96213102e-01 1.16324805... | [12.78046703338623, 0.7730129957199097] |
2c835e44-aba1-4c41-bc29-19fc173ccb24 | machine-reading-fast-and-slow-when-do-models | 2209.07430 | null | https://arxiv.org/abs/2209.07430v1 | https://arxiv.org/pdf/2209.07430v1.pdf | Machine Reading, Fast and Slow: When Do Models "Understand" Language? | Two of the most fundamental challenges in Natural Language Understanding (NLU) at present are: (a) how to establish whether deep learning-based models score highly on NLU benchmarks for the 'right' reasons; and (b) to understand what those reasons would even be. We investigate the behavior of reading comprehension mode... | ['Isabelle Augenstein', 'Anna Rogers', 'Sagnik Ray Choudhury'] | 2022-09-15 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 4.41535741e-01 9.45090473e-01 -1.63556352e-01 -4.31703359e-01
-6.59346044e-01 -5.22382021e-01 8.68620455e-01 6.62283421e-01
-5.01918197e-01 6.06804669e-01 7.40206122e-01 -7.22687721e-01
-4.11063612e-01 -8.40419292e-01 -9.93384421e-01 -1.72687113e-01
1.04085788e-01 9.57064271e-01 4.59601372e-01 -6.70391619... | [9.912005424499512, 7.79224157333374] |
df8d18f6-09d4-435d-a8fe-059b04a07c0f | multi-label-restricted-boltzmann-machine-for | 1910.08149 | null | https://arxiv.org/abs/1910.08149v1 | https://arxiv.org/pdf/1910.08149v1.pdf | Multi Label Restricted Boltzmann Machine for Non-Intrusive Load Monitoring | Increasing population indicates that energy demands need to be managed in the residential sector. Prior studies have reflected that the customers tend to reduce a significant amount of energy consumption if they are provided with appliance-level feedback. This observation has increased the relevance of load monitoring ... | ['Sagar Verma', 'Shikha Singh', 'Angshul Majumdar'] | 2019-10-17 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 1.97082847e-01 1.72661468e-02 -3.99287611e-01 -5.90496063e-01
-6.42266452e-01 -1.27360955e-01 4.72585350e-01 1.71747193e-01
-4.04542357e-01 8.67862701e-01 4.48835408e-03 -4.81167100e-02
-1.60105050e-01 -7.87657559e-01 -3.34038913e-01 -1.04076815e+00
2.39691362e-01 7.87291765e-01 -1.75445154e-02 4.30398732... | [6.000402927398682, 2.5758440494537354] |
4e796be2-5e6f-40c6-bb1e-b0ee3f949146 | cross-lingual-fine-tuning-for-abstractive | null | null | https://aclanthology.org/2021.ranlp-main.74 | https://aclanthology.org/2021.ranlp-main.74.pdf | Cross-lingual Fine-tuning for Abstractive Arabic Text Summarization | While abstractive summarization in certain languages, like English, has already reached fairly good results due to the availability of trend-setting resources, like the CNN/Daily Mail dataset, and considerable progress in generative neural models, progress in abstractive summarization for Arabic, the fifth most-spoken ... | ['Attila Novák', 'Zijian Győző Yang', 'Mram Kahla'] | null | null | https://aclanthology.org/2021.ranlp-1.74 | https://aclanthology.org/2021.ranlp-1.74.pdf | ranlp-2021-9 | ['extractive-summarization'] | ['natural-language-processing'] | [-1.19530752e-01 4.29220855e-01 9.34647918e-02 -3.91216576e-01
-1.22103167e+00 -5.98437071e-01 7.75737226e-01 3.64195287e-01
-7.06728339e-01 9.39335167e-01 8.93741786e-01 -3.01417768e-01
2.53160089e-01 -5.25953412e-01 -6.33261144e-01 -2.66368866e-01
1.11784615e-01 9.95662272e-01 2.63397321e-02 -7.79680490... | [12.023859977722168, 9.543588638305664] |
b89e7411-a83c-4ffb-aa75-665d4416f7c2 | tdbscan-spatiotemporal-density-clustering | null | null | https://online-journals.org/index.php/i-joe/article/view/3881/0 | https://online-journals.org/index.php/i-joe/article/view/3881/3315 | TDBSCAN: Spatiotemporal Density Clustering | Trajectory data generated from personal or vehicle use of GPS devices can be utilized for travel analysis and traffic information service, whereas trip segmentation is a key step toward the semantic labelling of the trajectories. Two issues are difficult to deal with by the traditional density-based algorithms, i. e. m... | ['Ji M', 'Chen W', 'Wang J'] | 2014-01-01 | null | null | null | international-journal-of-online-and | ['unsupervised-spatial-clustering'] | ['time-series'] | [-3.43276799e-01 -4.71911490e-01 -4.03127611e-01 -4.61407840e-01
-6.57618225e-01 -5.61713576e-01 4.21890944e-01 4.74010438e-01
-3.59796911e-01 6.84681833e-01 2.29912847e-01 -5.49980164e-01
-7.27123618e-01 -1.20869553e+00 -4.84136343e-01 -8.65805089e-01
-2.15327278e-01 8.02582562e-01 6.01531327e-01 3.39931957... | [6.260828971862793, 1.783690094947815] |
19c04f84-af54-434c-af07-f7546ff03fb1 | crossspeech-speaker-independent-acoustic | 2302.14370 | null | https://arxiv.org/abs/2302.14370v2 | https://arxiv.org/pdf/2302.14370v2.pdf | CrossSpeech: Speaker-independent Acoustic Representation for Cross-lingual Speech Synthesis | While recent text-to-speech (TTS) systems have made remarkable strides toward human-level quality, the performance of cross-lingual TTS lags behind that of intra-lingual TTS. This gap is mainly rooted from the speaker-language entanglement problem in cross-lingual TTS. In this paper, we propose CrossSpeech which improv... | ['Byeong-Yeol Kim', 'Il-Hwan Kim', 'Yoon-Cheol Ju', 'Hong-Sun Yang', 'Ji-Hoon Kim'] | 2023-02-28 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [-1.98617175e-01 1.65144041e-01 -6.94105551e-02 -6.13299131e-01
-1.52298880e+00 -6.87239110e-01 8.20578754e-01 -2.41918027e-01
1.32281810e-01 2.67220825e-01 5.92606127e-01 -4.23765332e-01
3.04048091e-01 -2.11063281e-01 -4.74290729e-01 -8.93708408e-01
1.63411900e-01 5.58217883e-01 -2.81850636e-01 -1.80050477... | [14.816173553466797, 6.651287078857422] |
247a6194-3228-443a-b9a5-21af4ce01900 | infrared-image-super-resolution-systematic | 2212.12322 | null | https://arxiv.org/abs/2212.12322v1 | https://arxiv.org/pdf/2212.12322v1.pdf | Infrared Image Super-Resolution: Systematic Review, and Future Trends | Image Super-Resolution (SR) is essential for a wide range of computer vision and image processing tasks. Investigating infrared (IR) image (or thermal images) super-resolution is a continuing concern within the development of deep learning. This survey aims to provide a comprehensive perspective of IR image super-resol... | ['Shinichiro Omachi', 'Xiaofeng Liu', 'Tomo Miyazaki', 'Yongsong Huang'] | 2022-12-22 | null | null | null | null | ['infrared-image-super-resolution'] | ['computer-vision'] | [ 8.95087838e-01 -3.30598921e-01 -1.75317839e-01 -1.44723356e-01
-9.59324300e-01 -7.61261582e-02 1.95220247e-01 -6.80460989e-01
-2.71257609e-01 6.79177105e-01 1.40555620e-01 1.64117306e-01
-1.36912152e-01 -4.97154504e-01 -3.50665182e-01 -1.03622210e+00
1.47604495e-01 -4.14954573e-01 -1.30710155e-01 -4.04301196... | [10.571118354797363, -2.2070910930633545] |
9127abb3-54ed-41ea-b1f8-cd2c8acfb8c9 | amodal-3d-reconstruction-for-robotic | 2009.13146 | null | https://arxiv.org/abs/2009.13146v1 | https://arxiv.org/pdf/2009.13146v1.pdf | Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivity | Learning-based 3D object reconstruction enables single- or few-shot estimation of 3D object models. For robotics, this holds the potential to allow model-based methods to rapidly adapt to novel objects and scenes. Existing 3D reconstruction techniques optimize for visual reconstruction fidelity, typically measured by c... | ['Siddhartha Srinivasa', 'Caelen Wang', 'Octavian Murad', 'Aaron Walsman', 'Pedro Domingos', 'Christopher Xie', 'William Agnew'] | 2020-09-28 | null | null | null | null | ['3d-object-reconstruction'] | ['computer-vision'] | [-9.90456417e-02 -5.98656163e-02 4.87072505e-02 1.80068996e-03
-4.52962399e-01 -5.79163611e-01 5.81155121e-01 1.50582746e-01
-1.73765853e-01 3.50674719e-01 -2.30673589e-02 -1.17002688e-01
-4.26328070e-02 -5.73312104e-01 -9.69588757e-01 -2.57964015e-01
-1.64602622e-01 8.75789642e-01 6.09851241e-01 4.78061411... | [5.833354949951172, -0.8431549072265625] |
7c3feedd-96c4-4378-b9aa-6c63e3137e5f | branch-ranking-for-efficient-mixed-integer | 2207.13701 | null | https://arxiv.org/abs/2207.13701v1 | https://arxiv.org/pdf/2207.13701v1.pdf | Branch Ranking for Efficient Mixed-Integer Programming via Offline Ranking-based Policy Learning | Deriving a good variable selection strategy in branch-and-bound is essential for the efficiency of modern mixed-integer programming (MIP) solvers. With MIP branching data collected during the previous solution process, learning to branch methods have recently become superior over heuristics. As branch-and-bound is natu... | ['Jun Wang', 'Yong Yu', 'Jianye Hao', 'Mingxuan Yuan', 'Hui-Ling Zhen', 'Furui Liu', 'Chuhan Shi', 'Weinan Zhang', 'WenHao Chen', 'Zeren Huang'] | 2022-07-26 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 2.66866654e-01 1.95016086e-01 -1.27588952e+00 -4.32601452e-01
-1.13335121e+00 -7.38099933e-01 2.25804336e-02 6.96677119e-02
-3.04507792e-01 1.42745578e+00 -1.52445391e-01 -8.14437866e-01
-5.14623940e-01 -9.87559915e-01 -9.34149325e-01 -8.35514128e-01
-4.01628643e-01 1.12712681e+00 -5.22229224e-02 1.18439734... | [5.118056774139404, 2.949507236480713] |
9f3a3e69-37d9-435a-a9a8-f6c6b7050c67 | pointly-supervised-panoptic-segmentation | 2210.13950 | null | https://arxiv.org/abs/2210.13950v1 | https://arxiv.org/pdf/2210.13950v1.pdf | Pointly-Supervised Panoptic Segmentation | In this paper, we propose a new approach to applying point-level annotations for weakly-supervised panoptic segmentation. Instead of the dense pixel-level labels used by fully supervised methods, point-level labels only provide a single point for each target as supervision, significantly reducing the annotation burden.... | ['Tieniu Tan', 'Zhaoxiang Zhang', 'Junsong Fan'] | 2022-10-25 | null | null | null | null | ['weakly-supervised-panoptic-segmentation', 'panoptic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.18473780e-01 2.97883768e-02 -4.08846259e-01 -6.15677774e-01
-1.26514328e+00 -9.54976380e-01 3.96985441e-01 6.93458393e-02
-1.81877509e-01 1.00731745e-01 -2.93207765e-01 -2.73578972e-01
3.38334978e-01 -8.09043884e-01 -7.57616520e-01 -6.64537072e-01
-1.13513889e-02 5.54474175e-01 5.13456762e-01 1.93164721... | [9.459921836853027, 0.4217296540737152] |
ff9383ce-601c-4a17-a34c-9eeb753dbee4 | user-based-aggregation-for-biterm-topic-model | null | null | https://aclanthology.org/P15-2080 | https://aclanthology.org/P15-2080.pdf | User Based Aggregation for Biterm Topic Model | null | ['Jinpeng Wang', 'Yan Zhang', 'Hongfei Yan', 'Xiaoming Li', 'Weizheng Chen'] | 2015-07-01 | user-based-aggregation-for-biterm-topic-model-1 | https://aclanthology.org/P15-2080 | https://aclanthology.org/P15-2080.pdf | ijcnlp-2015-7 | ['product-recommendation'] | ['miscellaneous'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.406922340393066, 3.593177556991577] |
8a8fa4a6-2508-481e-8eba-69240fff43fa | hop-count-based-self-supervised-anomaly | 2104.07917 | null | https://arxiv.org/abs/2104.07917v4 | https://arxiv.org/pdf/2104.07917v4.pdf | Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks | Recent years have witnessed an upsurge of interest in the problem of anomaly detection on attributed networks due to its importance in both research and practice. Although various approaches have been proposed to solve this problem, two major limitations exist: (1) unsupervised approaches usually work much less efficie... | ['Mykola Pechenizkiy', 'Vlado Menkovski', 'Yulong Pei', 'Tianjin Huang'] | 2021-04-16 | null | null | null | null | ['self-supervised-anomaly-detection', 'supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 5.13678081e-02 1.05472073e-01 -2.39387751e-01 -6.63991272e-01
-1.26341060e-01 -1.14089407e-01 4.03282911e-01 6.61193311e-01
-1.56199142e-01 6.56400859e-01 -8.70016366e-02 -1.95889473e-01
-4.96184975e-01 -1.08020782e+00 -3.98467898e-01 -6.68636084e-01
-4.33189631e-01 3.62913162e-01 6.87153995e-01 -6.36194879... | [7.4605278968811035, 2.676189422607422] |
a73a1237-ce26-4143-99f2-cdb2de45cb78 | acsnet-action-context-separation-network-for | 2103.15088 | null | https://arxiv.org/abs/2103.15088v1 | https://arxiv.org/pdf/2103.15088v1.pdf | ACSNet: Action-Context Separation Network for Weakly Supervised Temporal Action Localization | The object of Weakly-supervised Temporal Action Localization (WS-TAL) is to localize all action instances in an untrimmed video with only video-level supervision. Due to the lack of frame-level annotations during training, current WS-TAL methods rely on attention mechanisms to localize the foreground snippets or frames... | ['Gang Hua', 'Nanning Zheng', 'Junsong Yuan', 'Wei Tang', 'Qilin Zhang', 'Le Wang', 'Ziyi Liu'] | 2021-03-28 | null | null | null | null | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action', 'video-polyp-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.09824401e-01 -2.19691485e-01 -7.37894356e-01 -1.05787441e-01
-6.28694355e-01 -3.83989125e-01 5.86004078e-01 -1.59414709e-01
-4.09689277e-01 5.58442295e-01 3.96182239e-01 -9.87955406e-02
1.92332551e-01 -2.78211981e-01 -5.86095929e-01 -1.11820149e+00
9.50635877e-03 -1.58098161e-01 8.36034298e-01 3.13295007... | [8.514793395996094, 0.6516478657722473] |
0119e6a1-6b37-4279-b15b-2d78fc0f9d03 | domain-expansion-in-dnn-based-acoustic-models | 1910.00565 | null | https://arxiv.org/abs/1910.00565v1 | https://arxiv.org/pdf/1910.00565v1.pdf | Domain Expansion in DNN-based Acoustic Models for Robust Speech Recognition | Training acoustic models with sequentially incoming data -- while both leveraging new data and avoiding the forgetting effect-- is an essential obstacle to achieving human intelligence level in speech recognition. An obvious approach to leverage data from a new domain (e.g., new accented speech) is to first generate a ... | ['John H. L. Hansen', 'Soheil Khorram', 'Shahram Ghorbani'] | 2019-10-01 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 3.04055184e-01 -1.00832149e-01 -2.86692777e-03 -4.87320364e-01
-5.13678610e-01 -4.37935591e-01 3.97117019e-01 -6.43709600e-02
-9.89115179e-01 8.00789475e-01 2.46179342e-01 -3.64070415e-01
-2.02179607e-02 -4.63120967e-01 -5.26148260e-01 -8.35250020e-01
2.11330965e-01 4.71988559e-01 4.96955305e-01 -1.94374233... | [14.396815299987793, 6.585726261138916] |
285338fa-f584-47b9-90fd-83bccfaa67b8 | popmag-pop-music-accompaniment-generation | 2008.07703 | null | https://arxiv.org/abs/2008.07703v1 | https://arxiv.org/pdf/2008.07703v1.pdf | PopMAG: Pop Music Accompaniment Generation | In pop music, accompaniments are usually played by multiple instruments (tracks) such as drum, bass, string and guitar, and can make a song more expressive and contagious by arranging together with its melody. Previous works usually generate multiple tracks separately and the music notes from different tracks not expli... | ['Tie-Yan Liu', 'Jinzheng He', 'Yi Ren', 'Zhou Zhao', 'Xu Tan', 'Tao Qin'] | 2020-08-18 | null | null | null | null | ['music-modeling'] | ['music'] | [-1.73262209e-01 -6.65112078e-01 -1.08760111e-01 2.54724026e-01
-5.40575743e-01 -9.49423134e-01 4.36751813e-01 -1.82644054e-01
-1.08283311e-01 6.93719745e-01 4.67333674e-01 2.89122164e-01
-3.46377194e-01 -7.52849162e-01 -5.09808421e-01 -6.07612669e-01
6.52177706e-02 4.35652852e-01 1.75518587e-01 -4.27597761... | [15.986462593078613, 5.502670764923096] |
35a3125d-73e9-4c68-b0b8-40d569fb7e11 | using-a-conditional-generative-adversarial | 2211.15807 | null | https://arxiv.org/abs/2211.15807v1 | https://arxiv.org/pdf/2211.15807v1.pdf | Using a Conditional Generative Adversarial Network to Control the Statistical Characteristics of Generated Images for IACT Data Analysis | Generative adversarial networks are a promising tool for image generation in the astronomy domain. Of particular interest are conditional generative adversarial networks (cGANs), which allow you to divide images into several classes according to the value of some property of the image, and then specify the required cla... | ['Anna Vlaskina', 'Elizaveta Gres', 'Stanislav Polyakov', 'Andrey Demichev', 'Alexander Kryukov', 'Julia Dubenskaya'] | 2022-11-28 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [ 1.45981148e-01 -2.88219657e-02 5.66500723e-01 -1.97084434e-02
-3.40555906e-01 -7.43530095e-01 7.70879388e-01 -2.61316329e-01
-5.87976515e-01 8.97638381e-01 -4.19940919e-01 -3.65460783e-01
8.96172151e-02 -1.35808659e+00 -7.87572205e-01 -1.16741455e+00
1.12419941e-01 7.79385209e-01 4.74505097e-01 -1.34038076... | [11.641218185424805, -0.4896637797355652] |
bbad3453-0748-4e9b-a74a-0077f8dde352 | home-homography-equivariant-video | 2306.01623 | null | https://arxiv.org/abs/2306.01623v1 | https://arxiv.org/pdf/2306.01623v1.pdf | HomE: Homography-Equivariant Video Representation Learning | Recent advances in self-supervised representation learning have enabled more efficient and robust model performance without relying on extensive labeled data. However, most works are still focused on images, with few working on videos and even fewer on multi-view videos, where more powerful inductive biases can be leve... | ['Ehsan Adeli', 'Li Fei-Fei', 'Juan Carlos Niebles', 'Jiajun Wu', 'Adrien Gaidon', 'Anirudh Sriram'] | 2023-06-02 | null | null | null | null | ['action-classification', 'action-recognition-in-videos'] | ['computer-vision', 'computer-vision'] | [ 2.59288818e-01 2.39658505e-02 -8.13765883e-01 -5.51247180e-01
-8.97643745e-01 -4.43074137e-01 8.30190599e-01 -1.04419664e-01
-2.64966100e-01 6.14071012e-01 6.67102575e-01 -2.99654342e-02
4.09429848e-01 -6.05516851e-01 -9.46293771e-01 -6.08195007e-01
1.30298033e-01 2.21657664e-01 1.76681265e-01 -3.70431133... | [8.584336280822754, 0.8097463250160217] |
66fa1d09-3b5b-49b5-b21c-b178c3aece31 | kss-icp-point-cloud-registration-based-on | 2211.02807 | null | https://arxiv.org/abs/2211.02807v1 | https://arxiv.org/pdf/2211.02807v1.pdf | KSS-ICP: Point Cloud Registration based on Kendall Shape Space | Point cloud registration is a popular topic which has been widely used in 3D model reconstruction, location, and retrieval. In this paper, we propose a new registration method, KSS-ICP, to address the rigid registration task in Kendall shape space (KSS) with Iterative Closest Point (ICP). The KSS is a quotient space th... | ['Baoquan Zhao', 'Weisi Lin', 'Chenlei Lv'] | 2022-11-05 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-2.96374381e-01 -5.52261531e-01 1.19348682e-01 -2.52746493e-01
-5.94685674e-01 -3.51871014e-01 5.67598343e-01 1.28873393e-01
-2.09023744e-01 4.43116240e-02 -9.63118598e-02 7.96810389e-02
-2.76674658e-01 -8.53389323e-01 -5.95737100e-01 -6.80640221e-01
3.77374560e-01 7.65289307e-01 7.09894598e-01 -4.71984386... | [7.712545394897461, -2.9065239429473877] |
3ecc5987-9430-46fc-89b9-ee2cd0e7e100 | unsupervised-detection-of-ash-dieback-disease | 2204.09041 | null | https://arxiv.org/abs/2204.09041v1 | https://arxiv.org/pdf/2204.09041v1.pdf | Unsupervised detection of ash dieback disease (Hymenoscyphus fraxineus) using diffusion-based hyperspectral image clustering | Ash dieback (Hymenoscyphus fraxineus) is an introduced fungal disease that is causing the widespread death of ash trees across Europe. Remote sensing hyperspectral images encode rich structure that has been exploited for the detection of dieback disease in ash trees using supervised machine learning techniques. However... | ['James M. Murphy', 'David A. Coomes', 'Robert J. Plemmons', 'Kangning Cui', 'Aland H. Y. Chan', 'Sam L. Polk'] | 2022-04-19 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 8.42728972e-01 -2.56077439e-01 1.14840958e-02 -2.22576894e-02
-3.72887582e-01 -5.99867284e-01 5.68286538e-01 8.68096426e-02
-1.35841042e-01 7.26670086e-01 5.40897064e-03 -5.91680229e-01
-5.67855239e-01 -9.32985604e-01 3.95487607e-01 -1.00277257e+00
-2.06771195e-01 8.36239934e-01 1.36362076e-01 7.54028708... | [9.495720863342285, -1.527036428451538] |
36b76d2a-ff85-4138-9434-cb5e1b27b163 | shape-from-tracing-towards-reconstructing-3d | 2012.03939 | null | https://arxiv.org/abs/2012.03939v1 | https://arxiv.org/pdf/2012.03939v1.pdf | Shape From Tracing: Towards Reconstructing 3D Object Geometry and SVBRDF Material from Images via Differentiable Path Tracing | Reconstructing object geometry and material from multiple views typically requires optimization. Differentiable path tracing is an appealing framework as it can reproduce complex appearance effects. However, it is difficult to use due to high computational cost. In this paper, we explore how to use differentiable ray t... | ['Daniel Ritchie', 'James Tompkin', 'Vikas Thamizharasan', 'James Guesman', 'Loudon Cohen', 'Purvi Goel'] | 2020-12-06 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 5.03760636e-01 -3.91821265e-01 8.68634224e-01 -3.44341546e-01
-7.41742551e-01 -6.45374298e-01 3.10484737e-01 -2.89289504e-01
1.51981190e-01 6.85003936e-01 -2.26983190e-01 -1.46708041e-01
1.91518262e-01 -8.14595222e-01 -8.12708080e-01 -5.11604846e-01
4.09704685e-01 5.57044804e-01 2.98781872e-01 7.82213137... | [9.659646034240723, -3.0799920558929443] |
aaeb9db3-f0d9-4cd2-adcc-fba7c54eb572 | abstract-visual-reasoning-with-tangram-shapes | 2211.16492 | null | https://arxiv.org/abs/2211.16492v1 | https://arxiv.org/pdf/2211.16492v1.pdf | Abstract Visual Reasoning with Tangram Shapes | We introduce KiloGram, a resource for studying abstract visual reasoning in humans and machines. Drawing on the history of tangram puzzles as stimuli in cognitive science, we build a richly annotated dataset that, with >1k distinct stimuli, is orders of magnitude larger and more diverse than prior resources. It is both... | ['Yoav Artzi', 'Robert D. Hawkins', 'Wai Keen Vong', 'Alane Suhr', 'Noah Rush', 'Noriyuki Kojima', 'Anya Ji'] | 2022-11-29 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 2.07255557e-02 3.56783241e-01 -8.97790939e-02 -5.43520190e-02
-6.19654238e-01 -1.14835453e+00 6.88333571e-01 4.18844432e-01
-3.04384559e-01 4.76793498e-01 6.12592936e-01 -4.06050503e-01
-4.41471040e-02 -3.64611238e-01 -6.66234791e-01 -2.99373358e-01
6.28645569e-02 7.65163541e-01 -3.09730396e-02 -1.13202959... | [10.797025680541992, 2.012969970703125] |
2c3fada0-4301-4dea-9afc-c501177449bf | an-improved-raftstereo-trained-with-a-mixed | 2210.12785 | null | https://arxiv.org/abs/2210.12785v1 | https://arxiv.org/pdf/2210.12785v1.pdf | An Improved RaftStereo Trained with A Mixed Dataset for the Robust Vision Challenge 2022 | Stereo-matching is a fundamental problem in computer vision. Despite recent progress by deep learning, improving the robustness is ineluctable when deploying stereo-matching models to real-world applications. Different from the common practices, i.e., developing an elaborate model to achieve robustness, we argue that c... | ['Wenjie Jiang', 'Rui Xu', 'Hualie Jiang'] | 2022-10-23 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 2.28354603e-01 -1.79356396e-01 7.79451989e-03 -2.88886547e-01
-7.79741883e-01 -6.20042264e-01 9.20920968e-01 -2.09655330e-01
-8.12332213e-01 3.93055975e-01 2.24046752e-01 -1.41439140e-01
8.83630887e-02 -4.21303004e-01 -9.79097486e-01 -5.33997595e-01
2.14830309e-01 4.49780136e-01 4.73326921e-01 -5.65517545... | [8.68610668182373, -2.2377936840057373] |
fa0c4f64-6bd6-4d16-9823-ee71e544ab56 | scalp-superpixels-with-contour-adherence | 1903.07149 | null | http://arxiv.org/abs/1903.07149v1 | http://arxiv.org/pdf/1903.07149v1.pdf | SCALP: Superpixels with Contour Adherence using Linear Path | Superpixel decomposition methods are generally used as a pre-processing step
to speed up image processing tasks. They group the pixels of an image into
homogeneous regions while trying to respect existing contours. For all
state-of-the-art superpixel decomposition methods, a trade-off is made between
1) computational t... | ['Vinh-Thong Ta', 'Rémi Giraud', 'Nicolas Papadakis'] | 2019-03-17 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 4.74291772e-01 1.01041786e-01 9.23443809e-02 -3.20353627e-01
-4.29006755e-01 -5.97700894e-01 3.59550685e-01 5.61071157e-01
-6.07730925e-01 4.86920267e-01 -2.79559851e-01 2.60864198e-02
7.03766523e-03 -9.01167750e-01 -4.82352465e-01 -7.70090401e-01
2.83403937e-02 4.37740922e-01 8.42239559e-01 2.69529790... | [9.265283584594727, -0.37765565514564514] |
d270d3c8-5024-4193-879b-897df97f4f59 | second-order-neural-ode-optimizer | 2109.14158 | null | https://arxiv.org/abs/2109.14158v2 | https://arxiv.org/pdf/2109.14158v2.pdf | Second-Order Neural ODE Optimizer | We propose a novel second-order optimization framework for training the emerging deep continuous-time models, specifically the Neural Ordinary Differential Equations (Neural ODEs). Since their training already involves expensive gradient computation by solving a backward ODE, deriving efficient second-order methods bec... | ['Evangelos A. Theodorou', 'Tianrong Chen', 'Guan-Horng Liu'] | 2021-09-29 | null | http://proceedings.neurips.cc/paper/2021/hash/d4c2e4a3297fe25a71d030b67eb83bfc-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/d4c2e4a3297fe25a71d030b67eb83bfc-Paper.pdf | neurips-2021-12 | ['time-series-prediction'] | ['time-series'] | [-2.20468864e-01 -6.50673499e-03 -7.18880147e-02 1.14536304e-02
-4.41687018e-01 -6.15233898e-01 5.00354230e-01 -3.28001559e-01
-5.55537105e-01 8.27548265e-01 1.10182986e-01 -6.94126964e-01
-2.82234579e-01 -4.10738438e-01 -9.74474609e-01 -7.59877026e-01
-1.62959158e-01 1.07982397e-01 -2.93441355e-01 -2.83374399... | [6.874040603637695, 3.5666449069976807] |
d59f5fb7-51d0-4c47-bcb9-aea6ed46e4d9 | a-coupled-approach-to-model-the-effect-of | 2102.05594 | null | https://arxiv.org/abs/2102.05594v1 | https://arxiv.org/pdf/2102.05594v1.pdf | A coupled approach to model the effect of wear on the dynamics of the shrouded bladed disk | This paper deals with modelling the effect of wear on the dynamics of the shrouded bladed disk with frictional contacts at the shrouds and the contact interface evolution. Prediction of fretting wear commonly occurring at the contacts of turbomachinery components, and its impact on the dynamics is increasingly research... | ['Stefano Zucca', 'Daniele Botto', 'Lakshminarayana Reddy Tamatam'] | 2021-02-10 | null | null | null | null | ['cantilever-beam'] | ['miscellaneous'] | [-4.48101044e-01 -8.02442729e-02 5.80069363e-01 6.08638465e-01
3.25360507e-01 -1.51695356e-01 4.33669835e-01 -1.99284583e-01
-1.33475721e-01 5.62612534e-01 -2.02934355e-01 1.17808349e-01
-7.31310129e-01 -3.64683747e-01 -3.44544232e-01 -9.57007289e-01
-2.16027245e-01 4.66466069e-01 6.48420990e-01 -6.63454950... | [5.919312477111816, 2.8501393795013428] |
f39058cc-2549-40d4-b200-7ea48be24b9a | deep-learning-to-improve-breast-cancer-early | 1708.09427 | null | http://arxiv.org/abs/1708.09427v5 | http://arxiv.org/pdf/1708.09427v5.pdf | Deep Learning to Improve Breast Cancer Early Detection on Screening Mammography | The rapid development of deep learning, a family of machine learning
techniques, has spurred much interest in its application to medical imaging
problems. Here, we develop a deep learning algorithm that can accurately detect
breast cancer on screening mammograms using an "end-to-end" training approach
that efficiently ... | ['Joseph H. Rothstein', 'Li Shen', 'Weiva Sieh', 'Russell B. McBride', 'Laurie R. Margolies', 'Eugene Fluder'] | 2017-08-30 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 6.79841161e-01 5.44936299e-01 -4.82939243e-01 -7.97251582e-01
-1.31226373e+00 -4.51961279e-01 1.25368059e-01 4.50105220e-01
-5.71325541e-01 2.95205802e-01 -2.41280317e-01 -9.32824612e-01
-1.15832565e-02 -8.58903408e-01 -8.66007686e-01 -5.76629460e-01
-3.01233828e-01 5.19638538e-01 4.81087357e-01 2.65157521... | [15.181408882141113, -2.522956132888794] |
8b27e878-4d1d-4d48-a9d5-784475a8cfb5 | attend-to-who-you-are-supervising-self-1 | 2111.12892 | null | https://arxiv.org/abs/2111.12892v1 | https://arxiv.org/pdf/2111.12892v1.pdf | Attend to Who You Are: Supervising Self-Attention for Keypoint Detection and Instance-Aware Association | This paper presents a new method to solve keypoint detection and instance association by using Transformer. For bottom-up multi-person pose estimation models, they need to detect keypoints and learn associative information between keypoints. We argue that these problems can be entirely solved by Transformer. Specifical... | ['Wankou Yang', 'Erjin Zhou', 'Yiping Bao', 'Shu-Tao Xia', 'Zhibin Quan', 'Shoukui Zhang', 'YanJie Li', 'Ze Chen', 'Zhicheng Wang', 'Sen yang'] | 2021-11-25 | attend-to-who-you-are-supervising-self | https://openreview.net/forum?id=ZUinrZwKnHb | https://openreview.net/pdf?id=ZUinrZwKnHb | null | ['multi-person-pose-estimation'] | ['computer-vision'] | [-6.74836859e-02 3.61638144e-02 1.30719498e-01 -4.32129145e-01
-6.86163008e-01 -5.22958815e-01 4.94581223e-01 4.69417840e-01
-6.91533685e-01 2.70966589e-01 1.50437683e-01 5.64598203e-01
-2.04409227e-01 -8.04948032e-01 -9.12929296e-01 -5.09441316e-01
2.60539223e-02 8.02945793e-01 5.47789037e-01 -1.72857657... | [7.214339256286621, -0.7632354497909546] |
c1964ab9-71ee-4615-bda9-0d0d851e0ae7 | where-does-it-exist-spatio-temporal-video | 2001.06891 | null | https://arxiv.org/abs/2001.06891v3 | https://arxiv.org/pdf/2001.06891v3.pdf | Where Does It Exist: Spatio-Temporal Video Grounding for Multi-Form Sentences | In this paper, we consider a novel task, Spatio-Temporal Video Grounding for Multi-Form Sentences (STVG). Given an untrimmed video and a declarative/interrogative sentence depicting an object, STVG aims to localize the spatio-temporal tube of the queried object. STVG has two challenging settings: (1) We need to localiz... | ['Qi. Wang', 'Huasheng Liu', 'Zhou Zhao', 'Lianli Gao', 'Zhu Zhang', 'Yang Zhao'] | 2020-01-19 | where-does-it-exist-spatio-temporal-video-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Where_Does_It_Exist_Spatio-Temporal_Video_Grounding_for_Multi-Form_Sentences_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Where_Does_It_Exist_Spatio-Temporal_Video_Grounding_for_Multi-Form_Sentences_CVPR_2020_paper.pdf | cvpr-2020-6 | ['video-grounding', 'spatio-temporal-video-grounding'] | ['computer-vision', 'computer-vision'] | [-2.39572097e-02 -1.57799631e-01 -1.69854537e-01 -2.16623440e-01
-5.59203267e-01 -6.59037232e-01 3.88778120e-01 1.25482725e-02
-1.61291227e-01 4.40613210e-01 2.07516015e-01 -9.42376405e-02
-3.17502588e-01 -8.25990677e-01 -1.01773679e+00 -4.07138288e-01
-8.78333598e-02 3.23838770e-01 7.77759731e-01 -1.34262130... | [9.833141326904297, 0.7200238108634949] |
c19c3409-c25f-4547-9ec0-1c111274ddfe | group-sparse-coding-for-image-denoising | 2212.11501 | null | https://arxiv.org/abs/2212.11501v1 | https://arxiv.org/pdf/2212.11501v1.pdf | Group Sparse Coding for Image Denoising | Group sparse representation has shown promising results in image debulrring and image inpainting in GSR [3] , the main reason that lead to the success is by exploiting Sparsity and Nonlocal self-similarity (NSS) between patches on natural images, and solve a regularized optimization problem. However, directly adapting ... | ['Fei Wu', 'Luoyu Chen'] | 2022-12-22 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 3.98971587e-01 -3.78577381e-01 1.61112756e-01 -1.38040677e-01
-5.73292732e-01 1.97008774e-02 1.71270728e-01 -5.06359696e-01
-1.12166777e-01 8.07349205e-01 7.02404320e-01 3.78931791e-01
-1.31937221e-01 -5.62229276e-01 -4.29643005e-01 -9.77838874e-01
-1.01968804e-02 -4.86080498e-01 1.33773565e-01 -5.73909044... | [11.393348693847656, -2.4571340084075928] |
fa86a5f9-e589-4375-8d29-a20b665efc45 | an-empirical-investigation-of-3d-anomaly | 2203.05550 | null | https://arxiv.org/abs/2203.05550v3 | https://arxiv.org/pdf/2203.05550v3.pdf | Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection | Despite significant advances in image anomaly detection and segmentation, few methods use 3D information. We utilize a recently introduced 3D anomaly detection dataset to evaluate whether or not using 3D information is a lost opportunity. First, we present a surprising finding: standard color-only methods outperform al... | ['Yedid Hoshen', 'Eliahu Horwitz'] | 2022-03-10 | null | null | null | null | ['rgb-3d-anomaly-detection-and-segmentation', '3d-anomaly-detection-and-segmentation', 'depth-anomaly-detection-and-segmentation', '3d-anomaly-detection', 'rgb-depth-anomaly-detection-and-segmentation'] | ['methodology', 'methodology', 'methodology', 'methodology', 'methodology'] | [ 2.07695439e-01 -2.53248870e-01 1.46361306e-01 -1.08805798e-01
-7.28223026e-01 -7.52306938e-01 6.28227115e-01 2.63552666e-01
-2.48579815e-01 1.13069631e-01 -3.77744615e-01 -7.78746963e-01
7.89691135e-02 -6.18196011e-01 -7.20073283e-01 -6.80589557e-01
-2.45017648e-01 1.57934755e-01 4.75258142e-01 -2.27248698... | [7.690794944763184, 2.095890522003174] |
1724c9cf-6701-4449-865b-2823158d593c | improving-robustness-of-jet-tagging-1 | 2303.14511 | null | https://arxiv.org/abs/2303.14511v1 | https://arxiv.org/pdf/2303.14511v1.pdf | Improving robustness of jet tagging algorithms with adversarial training: exploring the loss surface | In the field of high-energy physics, deep learning algorithms continue to gain in relevance and provide performance improvements over traditional methods, for example when identifying rare signals or finding complex patterns. From an analyst's perspective, obtaining highest possible performance is desirable, but recent... | ['Annika Stein'] | 2023-03-25 | null | null | null | null | ['jet-tagging'] | ['graphs'] | [ 1.36462510e-01 1.51606545e-01 -3.20782922e-02 -4.41303134e-01
-1.02701819e+00 -8.47274721e-01 8.62540603e-01 5.22971809e-01
-2.79176444e-01 4.29702848e-01 1.40516222e-01 -6.05043173e-01
-5.58781028e-01 -8.71803403e-01 -8.14022005e-01 -1.08957934e+00
-1.73224151e-01 3.83895785e-01 1.70380726e-01 -1.16263770... | [15.661308288574219, 2.928506374359131] |
e2028f88-3292-4546-80e4-49eb5fb9924a | mads-modulated-auto-decoding-siren-for-time | 2307.00868 | null | https://arxiv.org/abs/2307.00868v1 | https://arxiv.org/pdf/2307.00868v1.pdf | MADS: Modulated Auto-Decoding SIREN for time series imputation | Time series imputation remains a significant challenge across many fields due to the potentially significant variability in the type of data being modelled. Whilst traditional imputation methods often impose strong assumptions on the underlying data generation process, limiting their applicability, researchers have rec... | ['Svitlana Vyetrenko', 'Yousef El-Laham', 'Elizabeth Fons', 'Tom Bamford'] | 2023-07-03 | null | null | null | null | ['imputation', 'imputation', 'imputation'] | ['computer-vision', 'miscellaneous', 'time-series'] | [ 4.81205404e-01 -1.18232317e-01 -1.28630236e-01 -4.98564750e-01
-1.16506076e+00 -4.82406735e-01 8.86955082e-01 -9.26435441e-02
-3.79516870e-01 7.14884877e-01 7.02304721e-01 -2.36344784e-01
-4.02925283e-01 -5.02145708e-01 -9.80006278e-01 -3.92290860e-01
-1.60800397e-01 3.35611612e-01 -4.95688856e-01 -2.29064614... | [7.06557559967041, 3.311727285385132] |
f284f1ee-38f4-4d0f-9252-2f6418151ab6 | extracting-information-from-twitter | 2306.08236 | null | https://arxiv.org/abs/2306.08236v1 | https://arxiv.org/pdf/2306.08236v1.pdf | Extracting Information from Twitter Screenshots | Screenshots are prevalent on social media as a common approach for information sharing. Users rarely verify before sharing a screenshot whether the post it contains is fake or real. Information sharing through fake screenshots can be highly responsible for misinformation and disinformation spread on social media. Our u... | ['Michele C. Weigle', 'Michael L. Nelson', 'Tarannum Zaki'] | 2023-06-14 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [ 1.84913017e-02 3.92932117e-01 -3.77601683e-01 1.64847858e-02
-6.20731890e-01 -9.11891699e-01 9.07808483e-01 6.83596075e-01
-1.77301019e-01 8.40735674e-01 3.17438394e-01 -4.77140039e-01
7.33993530e-01 -9.56177592e-01 -5.76522708e-01 2.24582344e-01
3.18860441e-01 -3.22979629e-01 6.48441494e-01 -2.72001863... | [8.15894603729248, 10.214685440063477] |
3cfd6bc6-cd98-4a3b-a7fa-59044678563a | using-motion-history-images-with-3d | 2110.12396 | null | https://arxiv.org/abs/2110.12396v2 | https://arxiv.org/pdf/2110.12396v2.pdf | Using Motion History Images with 3D Convolutional Networks in Isolated Sign Language Recognition | Sign language recognition using computational models is a challenging problem that requires simultaneous spatio-temporal modeling of the multiple sources, i.e. faces, hands, body, etc. In this paper, we propose an isolated sign language recognition model based on a model trained using Motion History Images (MHI) that a... | ['Hacer Yalim Keles', 'Ozge Mercanoglu Sincan'] | 2021-10-24 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 1.87116303e-03 -3.46520007e-01 -6.35525063e-02 -3.54232132e-01
-7.15067565e-01 -1.51575327e-01 6.90645635e-01 -9.84899640e-01
-8.31819713e-01 3.89171988e-01 4.00600374e-01 -6.18649460e-02
1.31139606e-01 -3.28327328e-01 -6.33156776e-01 -8.40256155e-01
4.98708099e-01 1.83045313e-01 8.01860154e-01 -1.04207814... | [9.159063339233398, -6.467127323150635] |
20a4fceb-a7f9-4012-8be8-ee71c22bbaba | orthographicnet-a-deep-learning-approach-for | 1902.03057 | null | https://arxiv.org/abs/1902.03057v3 | https://arxiv.org/pdf/1902.03057v3.pdf | OrthographicNet: A Deep Transfer Learning Approach for 3D Object Recognition in Open-Ended Domains | Nowadays, service robots are appearing more and more in our daily life. For this type of robot, open-ended object category learning and recognition is necessary since no matter how extensive the training data used for batch learning, the robot might be faced with a new object when operating in a real-world environment.... | ['Hamidreza Kasaei'] | 2019-02-08 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [-2.72471607e-01 -8.50188881e-02 3.24447080e-02 -5.02594709e-01
4.58678193e-02 -5.46506107e-01 5.89472711e-01 3.29799838e-02
-5.57932198e-01 2.91222066e-01 -3.64098221e-01 -6.44433424e-02
-1.28441170e-01 -5.82251132e-01 -1.01625550e+00 -4.28039014e-01
-3.96638662e-01 8.29658091e-01 3.42676520e-01 -1.87683120... | [7.6280598640441895, -1.2136725187301636] |
14713f6d-9291-4ddd-a937-e164a8d09ebf | learning-semantic-representations-for-novel | 1811.03866 | null | http://arxiv.org/abs/1811.03866v1 | http://arxiv.org/pdf/1811.03866v1.pdf | Learning Semantic Representations for Novel Words: Leveraging Both Form and Context | Word embeddings are a key component of high-performing natural language
processing (NLP) systems, but it remains a challenge to learn good
representations for novel words on the fly, i.e., for words that did not occur
in the training data. The general problem setting is that word embeddings are
induced on an unlabeled ... | ['Hinrich Schütze', 'Timo Schick'] | 2018-11-09 | null | null | null | null | ['learning-semantic-representations'] | ['methodology'] | [ 2.50442475e-01 1.14475943e-01 -4.05081600e-01 -3.95500302e-01
-7.72099197e-01 -7.47361422e-01 7.06742883e-01 6.47948384e-01
-9.79704499e-01 3.84438306e-01 4.22070265e-01 -4.88164574e-01
3.53403360e-01 -9.50261652e-01 -6.06483757e-01 -4.33169454e-01
-4.92886603e-02 3.61003548e-01 5.72426878e-02 -3.96373600... | [10.505314826965332, 8.70571231842041] |
f7a0957b-2dcf-4038-a81c-7e273d0f030c | s-nlp-at-semeval-2021-task-5-an-analysis-of | null | null | https://aclanthology.org/2021.semeval-1.120 | https://aclanthology.org/2021.semeval-1.120.pdf | S-NLP at SemEval-2021 Task 5: An Analysis of Dual Networks for Sequence Tagging | The SemEval 2021 task 5: Toxic Spans Detection is a task of identifying considered-toxic spans in text, which provides a valuable, automatic tool for moderating online contents. This paper represents the second-place method for the task, an ensemble of two approaches. While one approach relies on combining different em... | ['Quang Huu Pham', 'Huy Quang Dao', 'Tam Minh Nguyen', 'Viet Anh Nguyen'] | 2021-08-01 | null | null | null | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [ 1.04879297e-01 1.19909830e-01 -4.06001449e-01 1.04712896e-01
-8.59447420e-01 -7.19149470e-01 9.85814273e-01 4.27061468e-01
-6.81706190e-01 6.84007525e-01 7.17355072e-01 -3.02250326e-01
-2.35117655e-02 -5.61730564e-01 -3.35873216e-01 -5.52238584e-01
-1.88439742e-01 1.36538222e-01 4.00768310e-01 -3.43453646... | [8.910737991333008, 10.61579418182373] |
00ff2dea-8739-4b9c-bd03-d8656953d515 | maniqa-multi-dimension-attention-network-for | 2204.08958 | null | https://arxiv.org/abs/2204.08958v2 | https://arxiv.org/pdf/2204.08958v2.pdf | MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment | No-Reference Image Quality Assessment (NR-IQA) aims to assess the perceptual quality of images in accordance with human subjective perception. Unfortunately, existing NR-IQA methods are far from meeting the needs of predicting accurate quality scores on GAN-based distortion images. To this end, we propose Multi-dimensi... | ['Yuan Gong', 'Shanshan Lao', 'Yujiu Yang', 'Jiahao Wang', 'Mingdeng Cao', 'Shuwei Shi', 'Tianhe Wu', 'Sidi Yang'] | 2022-04-19 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [-2.63599493e-02 -3.67742836e-01 2.35730391e-02 -3.29932094e-01
-1.10003972e+00 -1.98256016e-01 3.69294107e-01 -4.01876241e-01
2.50501744e-02 4.05524790e-01 5.56411445e-01 -1.54440328e-01
-1.84062436e-01 -8.05400014e-01 -5.60169518e-01 -6.63841546e-01
1.76541641e-01 -9.29052010e-02 5.10561503e-02 -3.33269924... | [11.834555625915527, -1.7728261947631836] |
330ca0f0-7635-4f51-bce8-61e27fa36262 | more-like-this-semantic-retrieval-with | null | null | https://aclanthology.org/2022.konvens-1.19 | https://aclanthology.org/2022.konvens-1.19.pdf | More Like This: Semantic Retrieval with Linguistic Information | null | ['Chris Biemann', 'Seid Muhie Yimam', 'Fynn Petersen-Frey', 'Saba Anwar', 'Gregor Wiedemann', 'Steffen Remus'] | null | null | null | null | konvens-ws-2022-9 | ['semantic-retrieval'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.213517665863037, 3.6255335807800293] |
e514bf0a-0082-41ee-9028-091f90b232f7 | holographic-visualisation-of-radiology-data | 1808.04929 | null | http://arxiv.org/abs/1808.04929v1 | http://arxiv.org/pdf/1808.04929v1.pdf | Holographic Visualisation of Radiology Data and Automated Machine Learning-based Medical Image Segmentation | Within this thesis we propose a platform for combining Augmented Reality (AR)
hardware with machine learning in a user-oriented pipeline, offering to the
medical staff an intuitive 3D visualization of volumetric Computed Tomography
(CT) and Magnetic Resonance Imaging (MRI) medical image segmentations inside
the AR head... | ['Lucian Trestioreanu'] | 2018-08-15 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-7.94570968e-02 5.37561357e-01 3.15443963e-01 -3.53138000e-01
-4.65537459e-01 -2.71760255e-01 1.87072605e-01 3.04521322e-01
-3.98434639e-01 6.18903823e-02 2.00867951e-02 -9.79053736e-01
1.30177271e-02 -6.33431256e-01 -3.51897299e-01 -4.44984704e-01
-2.31937692e-01 6.34074807e-01 1.31748199e-01 -1.53450429... | [14.529319763183594, -2.5239267349243164] |
6ba1eb79-2ff9-408d-8239-acb01ab44a62 | sandwiched-video-compression-efficiently | 2303.11473 | null | https://arxiv.org/abs/2303.11473v2 | https://arxiv.org/pdf/2303.11473v2.pdf | Sandwiched Video Compression: Efficiently Extending the Reach of Standard Codecs with Neural Wrappers | We propose sandwiched video compression -- a video compression system that wraps neural networks around a standard video codec. The sandwich framework consists of a neural pre- and post-processor with a standard video codec between them. The networks are trained jointly to optimize a rate-distortion loss function with ... | ['Philip A. Chou', 'Jonathan Taylor', 'Danhang Tang', 'Onur G. Guleryuz', 'Berivan Isik'] | 2023-03-20 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 5.49217522e-01 -1.22164607e-01 -2.94380128e-01 -2.97227263e-01
-8.63737822e-01 -2.86394149e-01 3.55295956e-01 -1.03357866e-01
-4.22133356e-01 1.90680146e-01 4.83634740e-01 -4.09118980e-01
-2.37629302e-02 -4.53982353e-01 -1.17128897e+00 -5.40793180e-01
-6.36250138e-01 -6.69244155e-02 -6.45227358e-02 9.51204728... | [11.351752281188965, -1.5925259590148926] |
b7fc36bc-d03c-4aab-9ed6-ab031ea21226 | scene-coordinate-and-correspondence-learning | 1805.08443 | null | http://arxiv.org/abs/1805.08443v4 | http://arxiv.org/pdf/1805.08443v4.pdf | Scene Coordinate and Correspondence Learning for Image-Based Localization | Scene coordinate regression has become an essential part of current camera
re-localization methods. Different versions, such as regression forests and
deep learning methods, have been successfully applied to estimate the
corresponding camera pose given a single input image. In this work, we propose
to regress the scene... | ['Nassir Navab', 'Mai Bui', 'Slobodan Ilic', 'Shadi Albarqouni'] | 2018-05-22 | null | null | null | null | ['image-based-localization'] | ['computer-vision'] | [ 1.90989912e-01 -4.70821559e-02 -4.47183885e-02 -4.39853787e-01
-7.05359817e-01 -4.61779237e-01 5.01367211e-01 2.24637240e-01
-7.37730086e-01 7.19072521e-01 -1.66708931e-01 8.82872716e-02
-1.24596683e-02 -6.13917887e-01 -8.13827932e-01 -5.43746173e-01
5.01484692e-01 6.61853790e-01 3.38536501e-01 3.06742731... | [7.8181986808776855, -2.299677610397339] |
532786fd-4918-4a6e-8667-be670f6ea3c3 | reinforcement-learning-based-counter | 2303.06433 | null | https://arxiv.org/abs/2303.06433v1 | https://arxiv.org/pdf/2303.06433v1.pdf | Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation | The spread of online misinformation threatens public health, democracy, and the broader society. While professional fact-checkers form the first line of defense by fact-checking popular false claims, they do not engage directly in conversations with misinformation spreaders. On the other hand, non-expert ordinary users... | ['Srijan Kumar', 'Mustaque Ahamad', 'Bing He'] | 2023-03-11 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 2.85602361e-02 7.94968367e-01 -4.02507842e-01 -1.81628093e-01
-8.33683729e-01 -8.27445626e-01 1.07176578e+00 3.96400958e-01
-1.83015108e-01 8.54562819e-01 1.04126453e+00 -6.94972456e-01
3.82794648e-01 -9.99733090e-01 -5.83935618e-01 -3.20923589e-02
7.10293889e-01 4.35475856e-01 -9.23887938e-02 -1.02536452... | [8.618947982788086, 10.149832725524902] |
f7bd6611-5f24-48ca-b4d8-9bdeb0a6b3c2 | self-supervised-multimodal-versatile-networks | 2006.16228 | null | https://arxiv.org/abs/2006.16228v2 | https://arxiv.org/pdf/2006.16228v2.pdf | Self-Supervised MultiModal Versatile Networks | Videos are a rich source of multi-modal supervision. In this work, we learn representations using self-supervision by leveraging three modalities naturally present in videos: visual, audio and language streams. To this end, we introduce the notion of a multimodal versatile network -- a network that can ingest multiple ... | ['Jeffrey De Fauw', 'Relja Arandjelović', 'Adrià Recasens', 'Jean-Baptiste Alayrac', 'Sander Dieleman', 'Rosalia Schneider', 'Lucas Smaira', 'Jason Ramapuram', 'Andrew Zisserman'] | 2020-06-29 | null | http://proceedings.neurips.cc/paper/2020/hash/0060ef47b12160b9198302ebdb144dcf-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/0060ef47b12160b9198302ebdb144dcf-Paper.pdf | neurips-2020-12 | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 3.73257875e-01 -1.28224090e-01 -4.15843874e-01 -2.13586241e-01
-8.22414637e-01 -7.16043890e-01 8.63796711e-01 -1.55725494e-01
-5.17704427e-01 6.15163743e-01 7.00265527e-01 -4.91932109e-02
2.31788859e-01 -3.97957385e-01 -9.92853820e-01 -5.41622281e-01
-1.03778042e-01 -4.03681844e-02 1.59900531e-01 -2.38800377... | [10.159567832946777, 1.1123274564743042] |
d9f8f50a-b740-49ac-aca8-06814bce6d21 | stochastic-gradient-bayesian-optimal | 2306.15731 | null | https://arxiv.org/abs/2306.15731v1 | https://arxiv.org/pdf/2306.15731v1.pdf | Stochastic Gradient Bayesian Optimal Experimental Designs for Simulation-based Inference | Simulation-based inference (SBI) methods tackle complex scientific models with challenging inverse problems. However, SBI models often face a significant hurdle due to their non-differentiable nature, which hampers the use of gradient-based optimization techniques. Bayesian Optimal Experimental Design (BOED) is a power... | ['Elliot E. Hui', 'Vincent D. Zaballa'] | 2023-06-27 | null | null | null | null | ['experimental-design'] | ['methodology'] | [ 9.52640846e-02 -4.00439113e-01 -9.34909359e-02 -2.33015478e-01
-6.98418379e-01 -4.18893516e-01 4.81555879e-01 -1.57438472e-01
-4.67799187e-01 1.15244949e+00 -1.87754452e-01 -7.12358356e-01
-7.34167874e-01 -6.00878119e-01 -9.51978564e-01 -8.49113107e-01
-8.93843397e-02 4.37825441e-01 -1.19699031e-01 1.43900849... | [6.59635066986084, 3.9344887733459473] |
21874581-6cff-4605-a860-94adf87e62b5 | task-optimized-adapters-for-an-end-to-end | 2305.02468 | null | https://arxiv.org/abs/2305.02468v3 | https://arxiv.org/pdf/2305.02468v3.pdf | Task-Optimized Adapters for an End-to-End Task-Oriented Dialogue System | Task-Oriented Dialogue (TOD) systems are designed to carry out specific tasks by tracking dialogue states and generating appropriate responses to help users achieve defined goals. Recently, end-to-end dialogue models pre-trained based on large datasets have shown promising performance in the conversational system. Howe... | ['Myoung-Wan Koo', 'Jeehyun Lee', 'Namo Bang'] | 2023-05-04 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [-1.42040253e-01 4.33975726e-01 1.32511765e-01 -6.04608536e-01
-7.65116930e-01 -6.47486985e-01 7.00459957e-01 -2.20503896e-01
-3.82458895e-01 7.61099935e-01 3.40551615e-01 -3.55330884e-01
2.25637555e-01 -5.73651671e-01 -1.04555726e-01 -3.04737210e-01
2.64501512e-01 1.03170383e+00 2.84672678e-01 -9.21222925... | [12.816556930541992, 8.047701835632324] |
55955f85-c386-43c2-b548-ed86797911f3 | meta-learning-for-code-summarization-1 | 2201.08310 | null | https://arxiv.org/abs/2201.08310v1 | https://arxiv.org/pdf/2201.08310v1.pdf | Meta Learning for Code Summarization | Source code summarization is the task of generating a high-level natural language description for a segment of programming language code. Current neural models for the task differ in their architecture and the aspects of code they consider. In this paper, we show that three SOTA models for code summarization work well ... | ['Michael Pradel', 'Sebastian Padó', 'Moiz Rauf'] | 2022-01-20 | null | null | null | null | ['code-summarization'] | ['computer-code'] | [ 3.15919340e-01 5.75180531e-01 -8.04494321e-01 -3.09689254e-01
-1.30952144e+00 -6.23213768e-01 3.34865063e-01 5.83316088e-01
4.51209173e-02 3.93534243e-01 7.88545847e-01 -4.07811821e-01
2.77215540e-01 -4.03543919e-01 -8.32596481e-01 -1.06737785e-01
-4.22045663e-02 1.27737850e-01 1.60510302e-01 -3.58716846... | [7.607944011688232, 7.954588890075684] |
fa48d6e3-4beb-45b5-b8db-8516fdb2f36b | uncertainty-informed-optimal-resource | 2307.00032 | null | https://arxiv.org/abs/2307.00032v1 | https://arxiv.org/pdf/2307.00032v1.pdf | Uncertainty Informed Optimal Resource Allocation with Gaussian Process based Bayesian Inference | We focus on the problem of uncertainty informed allocation of medical resources (vaccines) to heterogeneous populations for managing epidemic spread. We tackle two related questions: (1) For a compartmental ordinary differential equation (ODE) model of epidemic spread, how can we estimate and integrate parameter uncert... | ['Saurabh Amin', 'Samarth Gupta'] | 2023-06-30 | null | null | null | null | ['bayesian-inference', 'stochastic-optimization', 'gaussian-processes'] | ['methodology', 'methodology', 'methodology'] | [ 1.38736680e-01 -8.04349110e-02 -1.15920693e-01 1.75806969e-01
-5.26632667e-01 -6.04327977e-01 3.94820571e-01 3.25942606e-01
-5.39200544e-01 1.09166348e+00 2.59562969e-01 -7.14914203e-01
-8.81660700e-01 -6.19173527e-01 -4.02028233e-01 -5.98478675e-01
-4.20334041e-01 1.20800805e+00 4.24087569e-02 -2.14555070... | [6.0094828605651855, 4.371821880340576] |
362fbdb6-f32d-4e79-b675-6bddc4a8d553 | triggering-dark-showers-with-conditional-dual | 2306.12955 | null | https://arxiv.org/abs/2306.12955v1 | https://arxiv.org/pdf/2306.12955v1.pdf | Triggering Dark Showers with Conditional Dual Auto-Encoders | Auto-encoders (AEs) have the potential to be effective and generic tools for new physics searches at colliders, requiring little to no model-dependent assumptions. New hypothetical physics signals can be considered anomalies that deviate from the well-known background processes generally expected to describe the whole ... | ['Maurizio Pierini', 'Nadezda Chernyavskaya', 'Benedikt Maier', 'Simranjit Singh Chhibra', 'Luca Anzalone'] | 2023-06-22 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [ 3.46167475e-01 1.20718971e-01 4.09455709e-02 -7.25810111e-01
-9.19957995e-01 -4.46356207e-01 1.34346437e+00 4.31347609e-01
-4.03494745e-01 3.49299431e-01 1.67828068e-01 -6.32298291e-01
-7.09245130e-02 -5.65559626e-01 -8.99316549e-01 -8.17466974e-01
-8.87860656e-02 1.00825489e+00 1.45438641e-01 -2.71870643... | [15.656211853027344, 2.929722785949707] |
e37a560d-0740-44fb-a7b4-1f47ba79e276 | real-time-interface-control-with-motion | 2201.01755 | null | https://arxiv.org/abs/2201.01755v1 | https://arxiv.org/pdf/2201.01755v1.pdf | Real-time Interface Control with Motion Gesture Recognition based on Non-contact Capacitive Sensing | Capacitive sensing is a prominent technology that is cost-effective and low power consuming with fast recognition speed compared to existing sensing systems. On account of these advantages, Capacitive sensing has been widely studied and commercialized in the domains of touch sensing, localization, existence detection, ... | ['Yingshu Li', 'Nahom Ogbazghi', 'Jaya Krishna Mandivarapu', 'Hunmin Lee'] | 2022-01-05 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 7.96554625e-01 -7.66066551e-01 -1.35366455e-01 1.39105335e-01
-3.01392972e-01 -5.64602315e-01 1.21448383e-01 -2.15655729e-01
-6.74278259e-01 2.37155601e-01 -2.78240860e-01 -2.43375614e-01
-5.60637144e-03 -7.00720131e-01 -1.89399882e-03 -7.66840279e-01
2.67998368e-01 -1.29352808e-01 5.07874310e-01 3.32703739... | [6.4946794509887695, -0.20103038847446442] |
88cc8d0b-d938-4ca6-9f5f-5fd6af40a649 | selfvi-self-supervised-light-field-video | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Shedligeri_SeLFVi_Self-Supervised_Light-Field_Video_Reconstruction_From_Stereo_Video_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Shedligeri_SeLFVi_Self-Supervised_Light-Field_Video_Reconstruction_From_Stereo_Video_ICCV_2021_paper.pdf | SeLFVi: Self-Supervised Light-Field Video Reconstruction From Stereo Video | Light-field (LF) imaging is appealing to the mobile devices market because of its capability for intuitive post-capture processing. Acquiring LF data with high angular, spatial and temporal resolution poses significant challenges, especially with space constraints preventing bulky optics. At the same time, stereo v... | ['Kaushik Mitra', 'Oliver Cossairt', 'Sushobhan Ghosh', 'Florian Schiffers', 'Prasan Shedligeri'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['video-reconstruction'] | ['computer-vision'] | [ 6.09169185e-01 -4.13221836e-01 -1.56209007e-01 -4.54794437e-01
-9.17600989e-01 -5.57480216e-01 2.39300668e-01 -5.20489395e-01
-3.35044801e-01 8.90075564e-01 4.42761153e-01 6.09145127e-02
-1.82543710e-01 -2.71855384e-01 -1.00990462e+00 -7.11371779e-01
2.56207764e-01 7.15939403e-02 2.94289112e-01 3.16451132... | [9.692842483520508, -2.5810649394989014] |
39624cfb-dfdd-49a2-89d1-14dd1d0e5144 | low-complexity-acoustic-scene-classification-2 | 2306.02054 | null | https://arxiv.org/abs/2306.02054v1 | https://arxiv.org/pdf/2306.02054v1.pdf | Low-Complexity Acoustic Scene Classification Using Data Augmentation and Lightweight ResNet | We present a work on low-complexity acoustic scene classification (ASC) with multiple devices, namely the subtask A of Task 1 of the DCASE2021 challenge. This subtask focuses on classifying audio samples of multiple devices with a low-complexity model, where two main difficulties need to be overcome. First, the audio s... | ['Qianhua He', 'Wenfeng Pang', 'Qisheng Huang', 'Wei Xie', 'Wenchang Cao', 'Yanxiong Li'] | 2023-06-03 | null | null | null | null | ['acoustic-scene-classification', 'scene-classification', 'model-compression'] | ['audio', 'computer-vision', 'methodology'] | [ 4.54385132e-01 -1.47393748e-01 2.35739425e-01 -1.75434589e-01
-8.57603908e-01 -1.93558902e-01 -3.38370986e-02 6.70307279e-02
-6.15501165e-01 3.85167837e-01 1.74124852e-01 -3.28385681e-01
1.38492927e-01 -4.68997657e-01 -9.86730456e-01 -5.07976949e-01
9.82429311e-02 -4.13045175e-02 2.24265441e-01 -1.65338919... | [15.050661087036133, 5.313083171844482] |
d367a971-d979-42bb-87a2-d18e9e92408a | is-bert-robust-to-label-noise-a-study-on | 2204.09371 | null | https://arxiv.org/abs/2204.09371v1 | https://arxiv.org/pdf/2204.09371v1.pdf | Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text Classification | Incorrect labels in training data occur when human annotators make mistakes or when the data is generated via weak or distant supervision. It has been shown that complex noise-handling techniques - by modeling, cleaning or filtering the noisy instances - are required to prevent models from fitting this label noise. How... | ['Dietrich Klakow', 'David Ifeoluwa Adelani', 'Fangzhou Zhai', 'Michael A. Hedderich', 'Dawei Zhu'] | 2022-04-20 | null | https://aclanthology.org/2022.insights-1.8 | https://aclanthology.org/2022.insights-1.8.pdf | insights-acl-2022-5 | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 3.50369066e-01 2.99413711e-01 -1.06219448e-01 -6.53489113e-01
-6.79130495e-01 -7.72265971e-01 5.35201669e-01 5.75712264e-01
-5.96286833e-01 9.26994085e-01 -1.73500087e-02 -5.30981123e-01
-1.37730300e-01 -4.39994007e-01 -5.87774813e-01 -5.37755609e-01
4.73900855e-01 7.06993341e-01 -7.75779188e-02 -7.95027055... | [9.548697471618652, 4.395499229431152] |
6165305f-1ce8-4d2c-b853-ca465d448d15 | a-mobile-food-recognition-system-for-dietary | 2204.09432 | null | https://arxiv.org/abs/2204.09432v1 | https://arxiv.org/pdf/2204.09432v1.pdf | A Mobile Food Recognition System for Dietary Assessment | Food recognition is an important task for a variety of applications, including managing health conditions and assisting visually impaired people. Several food recognition studies have focused on generic types of food or specific cuisines, however, food recognition with respect to Middle Eastern cuisines has remained un... | ['Hazim Kemal Ekenel', 'Marwa Qaraqe', 'Şeymanur Aktı'] | 2022-04-20 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [ 1.85061708e-01 -2.84640342e-01 -2.13348255e-01 -2.31609374e-01
-2.58049935e-01 -1.51981309e-01 -5.22307381e-02 6.72300041e-01
-6.17926419e-01 4.58299577e-01 1.03695668e-01 -1.80542171e-01
1.17219679e-01 -9.55100715e-01 -5.27200222e-01 -7.08746076e-01
-6.20769821e-02 -6.87480420e-02 -1.79370835e-01 -9.73141044... | [11.563359260559082, 4.402523994445801] |
3bbc780b-14e5-44c1-a414-a7653e8fd458 | fusionmotion-multi-sensor-asynchronous-fusion | 2302.09585 | null | https://arxiv.org/abs/2302.09585v1 | https://arxiv.org/pdf/2302.09585v1.pdf | FusionMotion: Multi-Sensor Asynchronous Fusion for Continuous Occupancy Prediction via Neural-ODE | Occupancy maps are widely recognized as an efficient method for facilitating robot motion planning in static environments. However, for intelligent vehicles, occupancy of both the present and future moments is required to ensure safe driving. In the automotive industry, the accurate and continuous prediction of future ... | ['Diange Yang', 'Yunlong Wang', 'Jiusi Li', 'Ke Wang', 'Kun Jiang', 'Yining Shi'] | 2023-02-19 | null | null | null | null | ['motion-planning'] | ['robots'] | [-2.00607583e-01 -1.90899402e-01 -3.93069029e-01 -3.80427718e-01
-7.38405466e-01 -1.28197208e-01 8.07807624e-01 -5.31649776e-02
-4.24696654e-01 9.69792306e-01 6.89338967e-02 -2.51687050e-01
-2.25907639e-01 -7.61104822e-01 -6.20987236e-01 -9.65110719e-01
-1.43437818e-01 4.16363955e-01 4.38046247e-01 -3.67176980... | [5.900545597076416, 0.788245677947998] |
b05466f3-17bd-40d5-8e11-15bf5f62fd7a | efficient-automatic-punctuation-restoration | null | null | https://aclanthology.org/2020.iwslt-1.33 | https://aclanthology.org/2020.iwslt-1.33.pdf | Efficient Automatic Punctuation Restoration Using Bidirectional Transformers with Robust Inference | Though people rarely speak in complete sentences, punctuation confers many benefits to the readers of transcribed speech. Unfortunately, most ASR systems do not produce punctuated output. To address this, we propose a solution for automatic punctuation that is both cost efficient and easy to train. Our solution benefit... | [] | 2020-07-01 | null | null | null | acl-2020-7 | ['punctuation-restoration'] | ['natural-language-processing'] | [ 2.45574549e-01 2.83170938e-01 -2.10358366e-01 -4.48571175e-01
-1.34627891e+00 -8.25774431e-01 5.86050153e-01 -1.36424094e-01
-3.02720010e-01 9.99346852e-01 5.85023999e-01 -5.98924160e-01
4.78026122e-01 -3.57636899e-01 -7.11132109e-01 -3.13380331e-01
1.75457150e-01 1.97231740e-01 1.99914619e-01 -3.13401401... | [14.235575675964355, 7.060207366943359] |
a1b515cc-f502-4a78-9517-fbd8c14e844a | all-in-one-multi-task-learning-for-rumour | 1806.03713 | null | http://arxiv.org/abs/1806.03713v1 | http://arxiv.org/pdf/1806.03713v1.pdf | All-in-one: Multi-task Learning for Rumour Verification | Automatic resolution of rumours is a challenging task that can be broken down
into smaller components that make up a pipeline, including rumour detection,
rumour tracking and stance classification, leading to the final outcome of
determining the veracity of a rumour. In previous work, these steps in the
process of rumo... | ['Arkaitz Zubiaga', 'Maria Liakata', 'Elena Kochkina'] | 2018-06-10 | all-in-one-multi-task-learning-for-rumour-1 | https://aclanthology.org/C18-1288 | https://aclanthology.org/C18-1288.pdf | coling-2018-8 | ['rumour-detection'] | ['natural-language-processing'] | [-1.23948045e-03 2.62443960e-01 -1.12774521e-01 -2.79689938e-01
-5.86711109e-01 -3.81090045e-01 1.10663402e+00 5.29692292e-01
-1.22447178e-01 5.94911933e-01 4.67270255e-01 -3.88316691e-01
4.75012958e-02 -4.79701459e-01 -5.66955984e-01 -3.43371391e-01
-1.56736970e-01 6.44418955e-01 5.36813736e-01 -2.81377614... | [8.224037170410156, 10.125519752502441] |
1b936b02-1bad-45d9-bd84-d1b69c3d3185 | tarn-temporal-attentive-relation-network-for | 1907.09021 | null | https://arxiv.org/abs/1907.09021v1 | https://arxiv.org/pdf/1907.09021v1.pdf | TARN: Temporal Attentive Relation Network for Few-Shot and Zero-Shot Action Recognition | In this paper we propose a novel Temporal Attentive Relation Network (TARN) for the problems of few-shot and zero-shot action recognition. At the heart of our network is a meta-learning approach that learns to compare representations of variable temporal length, that is, either two videos of different length (in the ca... | ['Georgios Zoumpourlis', 'Mina Bishay', 'Ioannis Patras'] | 2019-07-21 | null | null | null | null | ['zero-shot-action-recognition', 'few-shot-action-recognition'] | ['computer-vision', 'computer-vision'] | [ 5.37092090e-01 -1.53259650e-01 -4.54366505e-01 -2.94330746e-01
-7.29480445e-01 7.22615346e-02 8.50785375e-01 -7.88935497e-02
-6.73902869e-01 3.41514349e-01 4.37772125e-01 1.81448199e-02
-2.84602821e-01 -6.54495835e-01 -6.43022478e-01 -6.83858335e-01
-1.17924869e-01 2.90260047e-01 4.01802719e-01 -7.00637773... | [8.339781761169434, 0.8017737865447998] |
122d15b9-a6c1-4f82-998c-279c776bf5d4 | active-class-selection-for-few-shot-class | 2307.02641 | null | https://arxiv.org/abs/2307.02641v1 | https://arxiv.org/pdf/2307.02641v1.pdf | Active Class Selection for Few-Shot Class-Incremental Learning | For real-world applications, robots will need to continually learn in their environments through limited interactions with their users. Toward this, previous works in few-shot class incremental learning (FSCIL) and active class selection (ACS) have achieved promising results but were tested in constrained setups. There... | ['Alan R. Wagner', 'Sarah M. Rajtmajer', 'Harsh Tyagi', 'Ali Ayub', 'Christopher McClurg'] | 2023-07-05 | null | null | null | null | ['class-incremental-learning', 'few-shot-class-incremental-learning', 'incremental-learning', 'navigate'] | ['computer-vision', 'methodology', 'methodology', 'reasoning'] | [ 2.35850155e-01 2.70519525e-01 1.38331940e-02 -3.78812850e-01
-2.74453789e-01 -5.66782892e-01 7.62603343e-01 2.68633604e-01
-7.03618407e-01 8.59578371e-01 -2.72275656e-01 1.87686339e-01
-4.99334097e-01 -9.67026889e-01 -7.47847795e-01 -6.84720218e-01
-4.41288203e-01 9.32204187e-01 1.12421691e+00 -5.93455672... | [4.712668418884277, 0.6207226514816284] |
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