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h5 指数:53
h5 中位数:96

h5 指数是指在过去整整 5 年中所发表文章的 h 指数。h 指在 2018-2022 年间发表的 h 篇文章每篇至少都被引用过 h 次的最大值。隐藏

出版物的 h5 中位数，是指出版物的 h5 指数所涵盖的所有文章获得的引用次数的中位值。隐藏

#15 Computer Vision & Pattern Recognition

标题/作者	引用次数	年份
Pros and cons of GAN evaluation measures A Borji Computer Vision and Image Understanding 179, 41-65	606	2019
VideoLSTM convolves, attends and flows for action recognition Z Li, K Gavrilyuk, E Gavves, M Jain, CGM Snoek Computer Vision and Image Understanding 166, 41-50	407	2018
Superpixels: An evaluation of the state-of-the-art D Stutz, A Hermans, B Leibe Computer Vision and Image Understanding 166, 1-27	393	2018
Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes M Sabokrou, M Fayyaz, M Fathy, Z Moayed, R Klette Computer Vision and Image Understanding 172, 88-97	352	2018
Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound F Milletari, SA Ahmadi, C Kroll, A Plate, V Rozanski, J Maiostre, J Levin, ... Computer Vision and Image Understanding 164, 92-102	327	2017
Systematic evaluation of convolution neural network advances on the Imagenet D Mishkin, N Sergievskiy, J Matas Computer Vision and Image Understanding 161, 11-19	321	2017
Detecting anomalous events in videos by learning deep representations of appearance and motion D Xu, Y Yan, E Ricci, N Sebe Computer Vision and Image Understanding 156, 117-127	306	2017
The THUMOS challenge on action recognition for videos “in the wild” H Idrees, AR Zamir, YG Jiang, A Gorban, I Laptev, R Sukthankar, M Shah Computer Vision and Image Understanding 155, 1-23	306	2017
Learning a no-reference quality metric for single-image super-resolution C Ma, CY Yang, X Yang, MH Yang Computer Vision and Image Understanding 158, 1-16	294	2017
Visual question answering: A survey of methods and datasets Q Wu, D Teney, P Wang, C Shen, A Dick, A van den Hengel Computer Vision and Image Understanding 163, 21-40	293	2017
Space-time representation of people based on 3D skeletal data: A review F Han, B Reily, W Hoff, H Zhang Computer Vision and Image Understanding 158, 85-105	291	2017
RGB-D-based human motion recognition with deep learning: A survey P Wang, W Li, P Ogunbona, J Wan, S Escalera Computer Vision and Image Understanding 171, 118-139	289	2018
Instance-level salient object segmentation G Li, P Yan, Y Xie, G Wang, L Lin, Y Yu Computer Vision and Image Understanding 207, 103207	245	2021
A survey on deep learning based face recognition G Guo, N Zhang Computer Vision and Image Understanding 189, 102805	225	2019
Computer vision for assistive technologies M Leo, G Medioni, M Trivedi, T Kanade, GM Farinella Computer Vision and Image Understanding 154, 1-15	204	2017
A two-streamed network for estimating fine-scaled depth maps from single RGB images J Li, C Yuce, R Klein, A Yao Computer Vision and Image Understanding 186, 25-36	195	2019
Monocular human pose estimation: A survey of deep learning-based methods Y Chen, Y Tian, M He Computer Vision and Image Understanding 192, 102897	195	2020
Looking beyond appearances: Synthetic training data for deep CNNs in re-identification IB Barbosa, M Cristani, B Caputo, A Rognhaugen, T Theoharis Computer Vision and Image Understanding 167, 50-62	189	2018
Getting to know low-light images with the Exclusively Dark dataset YP Loh, CS Chan Computer Vision and Image Understanding 178, 30-42	175	2019
Visual question answering: Datasets, algorithms, and future challenges K Kafle, C Kanan Computer Vision and Image Understanding 163, 3-20	168	2017

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