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h5 指数:110
h5 中位数:161

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 2017-2021 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

#6 Computer Vision & Pattern Recognition

标题/作者	引用次数	年份
Recent advances in convolutional neural networks J Gu, Z Wang, J Kuen, L Ma, A Shahroudy, B Shuai, T Liu, X Wang, ... Pattern Recognition 77, 354-377	2778	2018
Explaining nonlinear classification decisions with deep Taylor decomposition G Montavon, S Lapuschkin, A Binder, W Samek, KR Müller Pattern Recognition 65, 211-222	975	2017
Wild patterns: Ten years after the rise of adversarial machine learning B Biggio, F Roli Pattern Recognition 84, 317-331	936	2018
Wider or Deeper: Revisiting the ResNet Model for Visual Recognition Z Wu, C Shen, A van den Hengel Pattern Recognition 90, 119-133	893	2019
Towards better exploiting convolutional neural networks for remote sensing scene classification K Nogueira, OAB Penatti, JA dos Santos Pattern Recognition 61, 539-556	766	2017
LLNet: A deep autoencoder approach to natural low-light image enhancement KG Lore, A Akintayo, S Sarkar Pattern Recognition 61, 650-662	641	2017
Improving person re-identification by attribute and identity learning Y Lin, L Zheng, Z Zheng, Y Wu, Z Hu, C Yan, Y Yang Pattern Recognition 95, 151-161	636	2019
Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order AT Lopes, E de Aguiar, AF De Souza, T Oliveira-Santos Pattern Recognition 61, 610-628	633	2017
Enhanced skeleton visualization for view invariant human action recognition M Liu, H Liu, C Chen Pattern Recognition 68, 346-362	515	2017
The impact of class imbalance in classification performance metrics based on the binary confusion matrix A Luque, A Carrasco, A Martín, A de las Heras Pattern Recognition 91, 216-231	432	2019
Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification W Shen, M Zhou, F Yang, D Yu, D Dong, C Yang, Y Zang, J Tian Pattern Recognition 61, 663-673	431	2017
Deep visual tracking: Review and experimental comparison P Li, D Wang, L Wang, H Lu Pattern Recognition 76, 323-338	392	2018
How deep learning extracts and learns leaf features for plant classification SH Lee, CS Chan, SJ Mayo, P Remagnino Pattern Recognition 71, 1-13	363	2017
Multiple instance learning: A survey of problem characteristics and applications MA Carbonneau, V Cheplygina, E Granger, G Gagnon Pattern Recognition 77, 329-353	357	2018
A Survey on semi-supervised feature selection methods R Sheikhpour, MA Sarram, S Gharaghani, MAZ Chahooki Pattern Recognition 64, 141-158	341	2017
A comparative evaluation of outlier detection algorithms: Experiments and analyses R Domingues, M Filippone, P Michiardi, J Zouaoui Pattern Recognition 74, 406-421	338	2018
Local binary features for texture classification: Taxonomy and experimental study L Liu, P Fieguth, Y Guo, X Wang, M Pietikäinen Pattern Recognition 62, 135-160	337	2017
Deep learning for image-based cancer detection and diagnosis− A survey Z Hu, J Tang, Z Wang, K Zhang, L Zhang, Q Sun Pattern Recognition 83, 134-149	323	2018
Handcrafted vs. non-handcrafted features for computer vision classification L Nanni, S Ghidoni, S Brahnam Pattern Recognition 71, 158-172	319	2017
A deep convolutional neural network for video sequence background subtraction M Babaee, DT Dinh, G Rigoll Pattern Recognition 76, 635-649	317	2018

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