Kai Han

Computer Vision and Pattern Recognition · Neural Information Processing Systems · 2018–2023

A researcher in the Science corpus: 14 papers; trusts 1,689 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 660 citations into it; #6,405 of 21,575 by trust here, #2,525 by citations
• Artificial Intelligence — 458 citations into it; #20,301 of 35,345 by trust here, #9,557 by citations
• Model Architectures — 195 citations into it; #1,649 of 2,278 by trust here, #686 by citations
• Efficiency & Compression — 186 citations into it
• Optimization & Training — 56 citations into it
• Evaluation & Benchmarks — 34 citations into it
• Representation & Self-Supervised Learning — 34 citations into it
• Natural Language Processing — 33 citations into it

Whom Kai Han trusts overall

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

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

Who trusts Kai Han overall

1. Yunhe Wang
2. Jianyuan Guo
3. Yehui Tang
4. Weihao Yu
5. Chien-Yao Wang
6. Wenhai Wang
7. Yulin Wang
8. Alexey Bochkovskiy
9. Yunhao Zhang
10. Zilong Huang

Papers from Kai Han's perspective

Ordered by how strongly the researchers Kai Han 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Transformer in Transformer (2021) — 2,223 citations
• GhostNet: More Features From Cheap Operations (2019) — 4,183 citations
• CMT: Convolutional Neural Networks Meet Vision Transformers (2021) — 951 citations
• Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets (2020) — 111 citations
• Co-Evolutionary Compression for Unpaired Image Translation (2019) — 93 citations
• A Survey on Vision Transformer (2020) — 3,751 citations
• Post-Training Quantization for Vision Transformer (2021) — 505 citations
• Learning Instance-wise Sparsity for Accelerating Deep Models (2019) — 30 citations
• Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism (2023) — 614 citations
• Vision GNN: An Image is Worth Graph of Nodes (2022) — 633 citations
• GhostNetV2: Enhance Cheap Operation with Long-Range Attention (2022) — 579 citations
• Distilling Object Detectors via Decoupled Features (2021) — 258 citations
• Low-resolution Visual Recognition via Deep Feature Distillation (2019) — 42 citations
• Attribute-Aware Attention Model for Fine-grained Representation Learning (2018) — 132 citations