Minfeng Zhu

Computer Vision and Pattern Recognition · with Chen · 2019–2025

A researcher in the Science corpus: 3 papers; trusts 178 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 50 citations into it; #5,540 of 21,575 by trust here, #6,671 by citations
• Artificial Intelligence — 47 citations into it
• Generative Models — 24 citations into it; #532 of 1,956 by trust here, #759 by citations
• Representation & Self-Supervised Learning — 19 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Optimization & Training — 9 citations into it
• Natural Language Processing — 7 citations into it
• Large Language Models — 6 citations into it

Whom Minfeng Zhu trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Christian Szegedy
5. Alex Krizhevsky
6. Yoshua Bengio
7. Yann LeCun
8. Ruslan Salakhutdinov
9. Tao Xu
10. Ilya Sutskever

Who trusts Minfeng Zhu overall

1. Jaemin Cho
2. Weihao Xia
3. Yufan Zhou
4. Ming Tao
5. Tobias Hinz
6. Zhu Zhang
7. Dong Huk Park
8. Jiadong Liang
9. Fengda Zhu
10. Douglas M. Souza

Papers from Minfeng Zhu's perspective

Ordered by how strongly the researchers Minfeng Zhu 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. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-To-Image Synthesis (2019) — 672 citations
• R1-Onevision: Advancing Generalized Multimodal Reasoning Through Cross-Modal Formalization (2025) — 387 citations
• SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations (2020) — 29 citations