Rongyao Fang

Computer Vision and Pattern Recognition · 2021–2025

A researcher in the Science corpus: 5 papers; trusts 422 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 209 citations into it; #14,570 of 21,575 by trust here, #6,089 by citations
• Artificial Intelligence — 168 citations into it; #23,658 of 35,345 by trust here, #8,525 by citations
• Representation & Self-Supervised Learning — 51 citations into it
• Model Architectures — 37 citations into it
• Large Language Models — 31 citations into it
• Natural Language Processing — 29 citations into it
• Computational Mechanics — 23 citations into it
• Aerospace Engineering — 16 citations into it

Whom Rongyao Fang trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yoshua Bengio
6. Yann LeCun
7. Ross B. Girshick
8. Ilya Sutskever
9. Andrew Zisserman
10. Jimmy Ba

Who trusts Rongyao Fang overall

1. Renrui Zhang
2. Muhammad Uzair Khattak
3. Lvmin Zhang
4. Yi-Lin Sung
5. Zeyu Han

Papers from Rongyao Fang's perspective

Ordered by how strongly the researchers Rongyao Fang 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• CLIP-Adapter: Better Vision-Language Models with Feature Adapters (2021) — 1,792 citations
• Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling (2021) — 591 citations
• Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training (2022) — 417 citations
• Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classification (2022) — 616 citations
• Qwen3-VL Technical Report (2025) — 1,662 citations