Fangyun Wei

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

A researcher in the Science corpus: 7 papers; trusts 1,158 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 241 citations into it; #6,962 of 21,575 by trust here, #4,245 by citations
• Artificial Intelligence — 166 citations into it; #11,890 of 35,345 by trust here, #6,193 by citations
• Multimodal Learning — 43 citations into it
• Representation & Self-Supervised Learning — 40 citations into it
• Model Architectures — 39 citations into it; #1,734 of 2,278 by trust here, #969 by citations
• Evaluation & Benchmarks — 30 citations into it
• Natural Language Processing — 20 citations into it
• Optimization & Training — 11 citations into it

Whom Fangyun Wei trusts overall

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

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

Who trusts Fangyun Wei overall

1. Ze Liu
2. Jingyi Zhang
3. Zhiqing Sun
4. Hao Huang
5. Jiaxing Huang
6. Zheng Zhang
7. Qing-Long Zhang
8. Yue Cao
9. Manli Shu

Papers from Fangyun Wei's perspective

Ordered by how strongly the researchers Fangyun Wei 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

• GCNet: Non-Local Networks Meet Squeeze-Excitation Networks and Beyond (2019) — 1,934 citations
• End-to-End Semi-Supervised Object Detection with Soft Teacher (2021) — 662 citations
• Side Adapter Network for Open-Vocabulary Semantic Segmentation (2023) — 461 citations
• Unsupervised Prompt Learning for Vision-Language Models (2022) — 182 citations
• A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-Language Model (2021) — 346 citations
• RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder (2020) — 73 citations
• Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model (2022) — 465 citations