Can Peng

Computer Vision and Pattern Recognition · 2020–2022

A researcher in the Science corpus: 3 papers; trusts 401 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 61 citations into it; #9,950 of 21,575 by trust here, #14,478 by citations
• Artificial Intelligence — 54 citations into it; #15,401 of 35,345 by trust here, #18,586 by citations
• Meta & Continual Learning — 25 citations into it; #479 of 1,041 by trust here, #681 by citations
• Representation & Self-Supervised Learning — 11 citations into it
• Efficiency & Compression — 10 citations into it
• Model Architectures — 10 citations into it
• Optimization & Training — 8 citations into it
• Radiology, Nuclear Medicine and Imaging — 7 citations into it

Whom Can Peng 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. Shaoqing Ren
6. Tsung-Yi Lin
7. Yoshua Bengio
8. Yann LeCun
9. X. Zhang
10. Diederik P. Kingma

Who trusts Can Peng overall

1. K. J. Joseph
2. Liyuan Wang
3. Salman Hameed Khan
4. Xingxing Zhang
5. Keon-Hee Park
6. Xinyu Zhang
7. Akshita Gupta
8. Fahad Shahbaz Khan
9. Junsu Kim
10. Yibo Yang

Papers from Can Peng's perspective

Ordered by how strongly the researchers Can Peng 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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

• Few-Shot Class-Incremental Learning from an Open-Set Perspective (2022) — 151 citations
• Faster ILOD: Incremental Learning for Object Detectors based on Faster RCNN (2020) — 148 citations
• SID: Incremental Learning for Anchor-Free Object Detection via Selective and Inter-Related Distillation (2020) — 68 citations