Xinting Hu

Computer Vision and Pattern Recognition · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 288 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 90 citations into it; #18,385 of 21,575 by trust here, #7,092 by citations
• Artificial Intelligence — 75 citations into it; #29,978 of 35,345 by trust here, #14,718 by citations
• Meta & Continual Learning — 29 citations into it; #921 of 1,041 by trust here, #649 by citations
• Representation & Self-Supervised Learning — 16 citations into it
• Optimization & Training — 12 citations into it
• Model Architectures — 9 citations into it

Whom Xinting Hu trusts overall

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

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

Who trusts Xinting Hu overall

1. Yaoyao Liu
2. Liyuan Wang
3. Marco Toldo
4. Hichem Sahbi
5. Bernt Schiele
6. Minsoo Kang
7. Yujin Tang
8. Aishwarya Agarwal
9. Guangyuan Shi
10. Da-Wei Zhou

Papers from Xinting Hu's perspective

Ordered by how strongly the researchers Xinting Hu 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• TransTrack: Multiple-Object Tracking with Transformer (2020) — 798 citations
• Learning to Segment the Tail (2020) — 93 citations
• Distilling Causal Effect of Data in Class-Incremental Learning (2021) — 213 citations