Xiang Wu

Computer Vision and Pattern Recognition · with He · 2015–2020

A researcher in the Science corpus: 3 papers; trusts 597 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 127 citations into it; #11,122 of 21,575 by trust here, #9,305 by citations
• Signal Processing — 43 citations into it; #3,593 of 7,572 by trust here, #2,114 by citations
• Artificial Intelligence — 34 citations into it
• Model Architectures — 16 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Optimization & Training — 10 citations into it
• Generative Models — 5 citations into it

Whom Xiang Wu trusts overall

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

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

Who trusts Xiang Wu overall

1. Sergey I. Nikolenko
2. Anjith George
3. Görkem Algan
4. Yichun Shi
5. Jiankang Deng
6. Ran He
7. Xi Yin
8. Mang Ye
9. Sheng Chen
10. Rui Huang

Papers from Xiang Wu's perspective

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

Their papers

• A Light CNN for Deep Face Representation With Noisy Labels (2015) — 1,144 citations
• Learning an Evolutionary Embedding via Massive Knowledge Distillation (2020) — 35 citations
• Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition (2017) — 297 citations