Yuheng Li

Computer Vision and Pattern Recognition · with Lee · 2019–2023

A researcher in the Science corpus: 6 papers; trusts 969 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 264 citations into it; #10,362 of 21,575 by trust here, #4,088 by citations
• Generative Models — 126 citations into it; #1,878 of 1,956 by trust here, #1,252 by citations
• Artificial Intelligence — 98 citations into it; #17,544 of 35,345 by trust here, #6,014 by citations
• Multimodal Learning — 70 citations into it; #639 of 1,211 by trust here, #385 by citations
• Evaluation & Benchmarks — 31 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Computer Graphics and Computer-Aided Design — 22 citations into it
• Model Architectures — 16 citations into it

Whom Yuheng Li trusts overall

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

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

Who trusts Yuheng Li overall

1. Haotian Liu
2. Robin Rombach
3. Minghao Chen
4. Jing Shi
5. Yong Jae Lee
6. Lin Chen
7. Junsu Kim
8. Zhen Li
9. Yanwei Li
10. Bo Li

Papers from Yuheng Li's perspective

Ordered by how strongly the researchers Yuheng Li 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

• Improved Baselines with Visual Instruction Tuning (2023) — 5,499 citations
• GLIGEN: Open-Set Grounded Text-to-Image Generation (2023) — 965 citations
• MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation (2019) — 84 citations
• Collaging Class-specific GANs for Semantic Image Synthesis (2021) — 36 citations
• Towards Universal Fake Image Detectors that Generalize Across Generative Models (2023) — 644 citations
• Contrastive Learning for Diverse Disentangled Foreground Generation (2022) — 8 citations