Kaiyang Zhou

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 14 papers; trusts 1,933 researchers and is trusted by 88 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 726 citations into it; #5,309 of 35,345 by trust here, #3,624 by citations
• Computer Vision and Pattern Recognition — 716 citations into it; #3,302 of 21,575 by trust here, #2,836 by citations
• Representation & Self-Supervised Learning — 244 citations into it; #2,868 of 3,145 by trust here, #2,178 by citations
• Safety, Robustness & Interpretability — 91 citations into it
• Evaluation & Benchmarks — 79 citations into it
• Radiology, Nuclear Medicine and Imaging — 70 citations into it
• Optimization & Training — 64 citations into it
• Model Architectures — 58 citations into it

Whom Kaiyang Zhou trusts overall

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

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

Who trusts Kaiyang Zhou overall

1. Renrui Zhang
2. Yu Qiao
3. Jingkang Yang
4. Ziwei Liu
5. Yongxin Yang
6. Tao A. Xiang
7. Muhammad Uzair Khattak
8. Chen Change Loy
9. Siyuan Li
10. Jindong Wang

Papers from Kaiyang Zhou's perspective

Ordered by how strongly the researchers Kaiyang Zhou 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. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
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

• Learning to Prompt for Vision-Language Models (2021) — 4,174 citations
• Conditional Prompt Learning for Vision-Language Models (2022) — 2,313 citations
• Open-Vocabulary DETR with Conditional Matching (2022) — 319 citations
• Generalized Out-of-Distribution Detection: A Survey (2021) — 1,452 citations
• Domain Generalization with MixStyle (2021) — 1,040 citations
• Omni-Scale Feature Learning for Person Re-Identification (2019) — 1,032 citations
• Learning to Generate Novel Domains for Domain Generalization (2020) — 557 citations
• Domain Generalization in Vision: A Survey (2021)
• Domain Adaptive Ensemble Learning (2020) — 366 citations
• Domain Generalization: A Survey (2021) — 1,606 citations
• OpenOOD: Benchmarking Generalized Out-of-Distribution Detection (2022) — 381 citations
• Energy-Based Open-World Uncertainty Modeling for Confidence Calibration (2021) — 80 citations
• Deep Domain-Adversarial Image Generation for Domain Generalisation (2020) — 500 citations
• Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward (2017) — 509 citations