Hong Chang

Computer Vision and Pattern Recognition · European Conference on Computer Vision · 2004–2023

A researcher in the Science corpus: 9 papers; trusts 880 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 208 citations into it; #20,617 of 21,575 by trust here, #9,722 by citations
• Artificial Intelligence — 155 citations into it; #31,635 of 35,345 by trust here, #10,798 by citations
• Safety, Robustness & Interpretability — 38 citations into it
• Optimization & Training — 37 citations into it
• Model Architectures — 33 citations into it
• Representation & Self-Supervised Learning — 29 citations into it
• Privacy & Security — 10 citations into it
• Signal Processing — 8 citations into it

Whom Hong Chang trusts overall

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

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

Who trusts Hong Chang overall

1. Radu Timofte
2. Chao Dong
3. Chih-Yuan Yang
4. Jia-Bin Huang
5. Jiwon Kim
6. Ming-Hsuan Yang
7. Luc Van Gool
8. Wei-Sheng Lai
9. Zongben Xu
10. Xinbo Gao

Papers from Hong Chang's perspective

Ordered by how strongly the researchers Hong Chang 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. 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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Interaction-And-Aggregation Network for Person Re-Identification (2019) — 392 citations
• Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training (2020) — 618 citations
• On The Impact of Machine Learning Randomness on Group Fairness (2023) — 43 citations
• Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework (2022) — 973 citations
• Cross Attention Network for Few-shot Classification (2019) — 806 citations
• Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer (2019) — 209 citations
• Modeling Video Dynamics with Deep Dynencoder (2014) — 47 citations
• Super-resolution through neighbor embedding (2004) — 2,173 citations
• SVC2004: First International Signature Verification Competition (2004) — 500 citations