Xiang Yu

Computer Vision and Pattern Recognition · with Chandraker · 2017–2022

A researcher in the Science corpus: 7 papers; trusts 761 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 178 citations into it; #9,937 of 21,575 by trust here, #4,584 by citations
• Artificial Intelligence — 116 citations into it
• Representation & Self-Supervised Learning — 50 citations into it
• Signal Processing — 44 citations into it; #4,789 of 7,572 by trust here, #4,051 by citations
• Efficiency & Compression — 17 citations into it
• Evaluation & Benchmarks — 17 citations into it
• Radiology, Nuclear Medicine and Imaging — 16 citations into it
• Privacy & Security — 14 citations into it

Whom Xiang Yu trusts overall

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

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

Who trusts Xiang Yu overall

1. Yujun Shen
2. Xi Yin
3. Mei Wang
4. Jingwen Ye
5. Luan Tran
6. Zachary Bessinger
7. Xiaoming Liu
8. Xiang Wang
9. Weihong Deng
10. Adam Dziedzic

Papers from Xiang Yu's perspective

Ordered by how strongly the researchers Xiang Yu 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Learning Efficient Object Detection Models with Knowledge Distillation (2017) — 1,151 citations
• Feature Transfer Learning for Face Recognition With Under-Represented Data (2019) — 326 citations
• Towards Large-Pose Face Frontalization in the Wild (2017) — 334 citations
• On Generalizing Beyond Domains in Cross-Domain Continual Learning (2022) — 44 citations
• Cross-Domain Similarity Learning for Face Recognition in Unseen Domains (2021) — 28 citations
• Private-kNN: Practical Differential Privacy for Computer Vision (2020) — 81 citations
• Feature Transfer Learning for Deep Face Recognition with Under-Represented Data (2018) — 45 citations