Shixiang Tang

Computer Vision and Pattern Recognition · 2020–2021

A researcher in the Science corpus: 6 papers; trusts 633 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 229 citations into it; #12,357 of 21,575 by trust here, #10,154 by citations
• Artificial Intelligence — 203 citations into it; #19,635 of 35,345 by trust here, #13,760 by citations
• Representation & Self-Supervised Learning — 83 citations into it
• Meta & Continual Learning — 59 citations into it; #754 of 1,041 by trust here, #692 by citations
• Optimization & Training — 33 citations into it; #1,969 of 2,601 by trust here, #1,986 by citations
• Model Architectures — 21 citations into it
• Radiology, Nuclear Medicine and Imaging — 21 citations into it
• Efficiency & Compression — 18 citations into it

Whom Shixiang Tang trusts overall

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

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

Who trusts Shixiang Tang overall

1. Zhen Wang
2. Yajing Kong
3. Liu Liu
4. Fei Ye
5. Yabin Wang
6. Liyuan Wang
7. Zhiwu Huang
8. Jinguo Zhu
9. Dapeng Chen
10. Rakib Hyder

Papers from Shixiang Tang's perspective

Ordered by how strongly the researchers Shixiang Tang 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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

• Adapting Object Detectors with Conditional Domain Normalization (2020) — 100 citations
• Layerwise Optimization by Gradient Decomposition for Continual Learning (2021) — 94 citations
• Continual Representation Learning for Biometric Identification (2020) — 50 citations
• Mutual CRF-GNN for Few-shot Learning (2021) — 60 citations
• Complementary Relation Contrastive Distillation (2021) — 101 citations
• Gradient Regularized Contrastive Learning for Continual Domain Adaptation (2020) — 72 citations