Jing Zhang

Computer Vision and Pattern Recognition · with Li · 2015–2018

A researcher in the Science corpus: 5 papers; trusts 438 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 168 citations into it; #5,180 of 21,575 by trust here, #4,584 by citations
• Artificial Intelligence — 90 citations into it; #8,701 of 35,345 by trust here, #7,172 by citations
• Biomedical Engineering — 46 citations into it; #1,854 of 10,734 by trust here, #4,472 by citations
• Human-Computer Interaction — 42 citations into it; #954 of 2,253 by trust here, #714 by citations
• Representation & Self-Supervised Learning — 30 citations into it
• 3D Vision & Pose — 16 citations into it; #535 of 689 by trust here, #504 by citations
• Evaluation & Benchmarks — 12 citations into it; #1,791 of 3,147 by trust here, #2,009 by citations
• Cancer Research — 8 citations into it; #998 of 1,780 by trust here, #1,426 by citations

Whom Jing Zhang trusts overall

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

1. Mingsheng Long
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Kaiming He
6. Ilya Sutskever
7. Yann LeCun
8. Diederik P. Kingma
9. Ross B. Girshick
10. Karen Simonyan

Who trusts Jing Zhang overall

1. Jian Liang
2. Pichao Wang
3. Jun Liu
4. Wanqing Li
5. Dapeng Hu
6. G. Wang
7. Wen Zhang
8. Amir Shahroudy
9. Jhair Gallardo
10. Yen-Cheng Liu

Papers from Jing Zhang's perspective

Ordered by how strongly the researchers Jing Zhang 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Importance Weighted Adversarial Nets for Partial Domain Adaptation (2018) — 450 citations
• Joint Geometrical and Statistical Alignment for Visual Domain Adaptation (2017) — 536 citations
• Action Recognition From Depth Maps Using Deep Convolutional Neural Networks (2016) — 293 citations
• ConvNets-Based Action Recognition from Depth Maps through Virtual Cameras and Pseudocoloring (2015) — 103 citations
• RGB-D-based action recognition datasets: A survey (2016) — 264 citations