Minghao Chen

Computer Vision and Pattern Recognition · IEEE International Conference on Computer Vision · 2019–2023

A researcher in the Science corpus: 6 papers; trusts 930 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 225 citations into it; #12,506 of 21,575 by trust here, #8,612 by citations
• Artificial Intelligence — 151 citations into it; #21,575 of 35,345 by trust here, #15,866 by citations
• Model Architectures — 59 citations into it
• Generative Models — 46 citations into it
• Representation & Self-Supervised Learning — 42 citations into it
• Multimodal Learning — 36 citations into it
• Optimization & Training — 26 citations into it
• Evaluation & Benchmarks — 18 citations into it

Whom Minghao Chen 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. Yoshua Bengio
6. Diederik P. Kingma
7. Ilya Sutskever
8. Yann LeCun
9. Jia Deng
10. Karen Simonyan

Who trusts Minghao Chen overall

1. Luke Melas-Kyriazi
2. Yanyu Li
3. Yuxi Wang
4. Zeyu Han
5. Yahao Liu
6. D. Epstein
7. Kan Wu
8. Xi-Wei Hu
9. Weixi Feng
10. Jian Liang

Papers from Minghao Chen's perspective

Ordered by how strongly the researchers Minghao Chen 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. 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

• Rethinking and Improving Relative Position Encoding for Vision Transformer (2021) — 450 citations
• AutoFormer: Searching Transformers for Visual Recognition (2021) — 350 citations
• Domain Adaptation for Semantic Segmentation With Maximum Squares Loss (2019) — 320 citations
• Training-Free Layout Control with Cross-Attention Guidance (2023) — 373 citations
• Expanding Language-Image Pretrained Models for General Video Recognition (2022) — 522 citations
• SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations (2020) — 29 citations