Chang-Bin Zhang

Computer Vision and Pattern Recognition · 2020–2023

A researcher in the Science corpus: 4 papers; trusts 861 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 236 citations into it
• Artificial Intelligence — 182 citations into it
• Meta & Continual Learning — 76 citations into it
• Model Architectures — 36 citations into it
• Representation & Self-Supervised Learning — 32 citations into it
• Optimization & Training — 21 citations into it
• Evaluation & Benchmarks — 15 citations into it
• Safety, Robustness & Interpretability — 12 citations into it

Whom Chang-Bin Zhang trusts overall

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

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

Who trusts Chang-Bin Zhang overall

1. Da-Wei Zhou
2. Beomyoung Kim
3. Fabio Cermelli
4. Zekang Zhang
5. Qiwen Wang
6. Matthieu Cord
7. Joonsang Yu
8. Huyong Wang

Papers from Chang-Bin Zhang's perspective

Ordered by how strongly the researchers Chang-Bin Zhang 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Representation Compensation Networks for Continual Semantic Segmentation (2022) — 143 citations
• Endpoints Weight Fusion for Class Incremental Semantic Segmentation (2023) — 77 citations
• LayerCAM: Exploring Hierarchical Class Activation Maps for Localization (2021) — 871 citations
• Delving Deep Into Label Smoothing (2020) — 285 citations