Xiaohang Zhan

Computer Vision and Pattern Recognition · 2019–2021

A researcher in the Science corpus: 7 papers; trusts 884 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 238 citations into it; #13,841 of 21,575 by trust here, #4,317 by citations
• Artificial Intelligence — 145 citations into it; #33,654 of 35,345 by trust here, #8,706 by citations
• Representation & Self-Supervised Learning — 71 citations into it
• Generative Models — 49 citations into it
• Model Architectures — 19 citations into it
• Optimization & Training — 14 citations into it
• Radiology, Nuclear Medicine and Imaging — 14 citations into it; #2,927 of 3,265 by trust here, #780 by citations
• Meta & Continual Learning — 8 citations into it

Whom Xiaohang Zhan trusts overall

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

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

Who trusts Xiaohang Zhan overall

1. Ziwei Liu
2. Guodong Xu
3. Lei Ke
4. Jie Gui
5. Chenlin Meng
6. Xintao Wang
7. Weihao Xia
8. Bingyi Kang
9. Zongze Wu
10. Andrey Voynov

Papers from Xiaohang Zhan's perspective

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

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

• Large-Scale Long-Tailed Recognition in an Open World (2019) — 1,434 citations
• Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation (2020) — 448 citations
• Open Compound Domain Adaptation (2019) — 144 citations
• DetCo: Unsupervised Contrastive Learning for Object Detection (2021) — 369 citations
• Self-Supervised Learning via Conditional Motion Propagation (2019) — 53 citations
• Self-Supervised Scene De-Occlusion (2020) — 186 citations
• Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective (2020)