Chun-Fu Chen
Computer Vision and Pattern Recognition · with Panda · 2021
A researcher in the Science corpus: 4 papers; trusts 1,029 researchers and is trusted by 22 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 187 citations into it; #6,025 of 21,575 by trust here, #6,206 by citations
- Artificial Intelligence — 135 citations into it; #8,542 of 35,345 by trust here, #8,893 by citations
- Model Architectures — 68 citations into it; #986 of 2,278 by trust here, #994 by citations
- Efficiency & Compression — 46 citations into it
- Optimization & Training — 18 citations into it
- Evaluation & Benchmarks — 16 citations into it
- Multimodal Learning — 9 citations into it
- Reinforcement Learning — 5 citations into it
Whom Chun-Fu Chen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Ross B. Girshick
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Andrew Zisserman
- Ilya Sutskever
- Karen Simonyan
- Yann LeCun
Who trusts Chun-Fu Chen overall
- Yanyu Li
- Yuda Song
- Yulin Wang
- Jianwei Yang
- Wenxiao Wang
- Yunhao Zhang
- Yang Wen
- Youngwan Lee
- Salman Hameed Khan
- Zhuqing He
Papers from Chun-Fu Chen's perspective
Ordered by how strongly the researchers Chun-Fu Chen trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification (2021) — 2,175 citations
- RegionViT: Regional-to-Local Attention for Vision Transformers (2021) — 255 citations
- AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition (2021) — 67 citations
- Dynamic Network Quantization for Efficient Video Inference (2021) — 57 citations