Quan-Sen Sun

Computer Vision and Pattern Recognition · 2022–2023

A researcher in the Science corpus: 3 papers; trusts 543 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 228 citations into it
• Artificial Intelligence — 172 citations into it
• Model Architectures — 54 citations into it
• Evaluation & Benchmarks — 53 citations into it
• Optimization & Training — 31 citations into it
• Representation & Self-Supervised Learning — 28 citations into it
• Efficiency & Compression — 15 citations into it
• Hardware and Architecture — 10 citations into it

Whom Quan-Sen Sun trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. João Carreira
5. Diederik P. Kingma
6. Ross B. Girshick
7. Yoshua Bengio
8. Andrew Zisserman
9. Yann LeCun
10. Jia Deng

Who trusts Quan-Sen Sun overall

1. Kunchang Li
2. Yi Wang
3. Shengbang Tong
4. Weihan Wang
5. Yinan He
6. Yichen Gong
7. Shuhuai Ren
8. Wenyi Hong
9. Xingyu Fu
10. Yunqing Zhao

Papers from Quan-Sen Sun's perspective

Ordered by how strongly the researchers Quan-Sen Sun trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. 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

• EVA: Exploring the Limits of Masked Visual Representation Learning at Scale (2022) — 1,015 citations
• EVA-CLIP: Improved Training Techniques for CLIP at Scale (2023) — 929 citations
• EVA-02: A Visual Representation for Neon Genesis (2023) — 551 citations