Shaohua Kevin Zhou

Computer Vision and Pattern Recognition · 2002–2018

A researcher in the Science corpus: 7 papers; trusts 513 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 105 citations into it; #4,916 of 21,575 by trust here, #7,584 by citations
• Artificial Intelligence — 59 citations into it; #11,269 of 35,345 by trust here, #17,776 by citations
• Model Architectures — 20 citations into it
• Optimization & Training — 19 citations into it
• Efficiency & Compression — 17 citations into it; #633 of 1,264 by trust here, #1,140 by citations
• Experimental and Cognitive Psychology — 13 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Media Technology — 9 citations into it

Whom Shaohua Kevin Zhou trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Diederik P. Kingma
5. Yoshua Bengio
6. Baback Moghaddam
7. Yann LeCun
8. Karen Simonyan
9. Vladimir Naumovich Vapni
10. Andrew Zisserman

Who trusts Shaohua Kevin Zhou overall

1. Xue Mei
2. Haibin Ling
3. Tianzhu Zhang
4. Ha Quang Minh
5. Mehrtash Harandi
6. Luis Gómez-Chova
7. Jiahui Yu
8. Minchul Kim
9. Baiyang Liu
10. Wen Zhao

Papers from Shaohua Kevin Zhou's perspective

Ordered by how strongly the researchers Shaohua Kevin Zhou trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Probabilistic recognition of human faces from video (2002) — 344 citations
• Visual tracking and recognition using appearance-adaptive models in particle filters (2004) — 743 citations
• Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation (2018) — 434 citations
• Learning to Prune Filters in Convolutional Neural Networks (2018) — 186 citations
• From sample similarity to ensemble similarity: probabilistic distance measures in reproducing kernel Hilbert space (2006) — 195 citations
• 3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes (2017) — 158 citations
• FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition (2016) — 386 citations