Weijian Xu

Computer Vision and Pattern Recognition · with Tu · 2017–2021

A researcher in the Science corpus: 4 papers; trusts 599 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 136 citations into it; #10,512 of 21,575 by trust here, #6,671 by citations
• Artificial Intelligence — 88 citations into it; #16,998 of 35,345 by trust here, #10,430 by citations
• Model Architectures — 41 citations into it
• Generative Models — 39 citations into it
• Representation & Self-Supervised Learning — 23 citations into it
• Optimization & Training — 17 citations into it
• Probabilistic & Bayesian Methods — 14 citations into it; #994 of 1,525 by trust here, #1,136 by citations
• Signal Processing — 10 citations into it

Whom Weijian Xu trusts overall

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

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

Who trusts Weijian Xu overall

1. Zhen Wang
2. Erik Nijkamp
3. Wenhai Wang
4. Enze Xie
5. Jianwen Xie
6. Youngwan Lee
7. Liu Liu
8. Omid Nejati Manzari
9. Youwei Liang
10. Salman Hameed Khan

Papers from Weijian Xu's perspective

Ordered by how strongly the researchers Weijian Xu 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. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Co-Scale Conv-Attentional Image Transformers (2021) — 466 citations
• Wasserstein Introspective Neural Networks (2017) — 61 citations
• Pose Recognition with Cascade Transformers (2021) — 269 citations
• Guided Variational Autoencoder for Disentanglement Learning (2020) — 126 citations