Tianhao Li

Computer Vision and Pattern Recognition · 2020

A researcher in the Science corpus: 2 papers; trusts 836 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 72 citations into it; #10,527 of 21,575 by trust here, #15,529 by citations
• Artificial Intelligence — 51 citations into it; #19,023 of 35,345 by trust here, #21,878 by citations
• Representation & Self-Supervised Learning — 27 citations into it; #2,282 of 3,145 by trust here, #2,558 by citations
• Natural Language Processing — 21 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Model Architectures — 7 citations into it
• Signal Processing — 5 citations into it

Whom Tianhao Li trusts overall

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

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

Who trusts Tianhao Li overall

1. Yi Wang
2. Christoph Feichtenhofer
3. Kunchang Li
4. Mandela Patrick
5. Yinan He
6. Yonglong Tian
7. Haoqi Fan
8. Yizhuo Li
9. Yuki M. Asano
10. Limin Wang

Papers from Tianhao Li's perspective

Ordered by how strongly the researchers Tianhao Li 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. 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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Learning Spatiotemporal Features via Video and Text Pair Discrimination (2020) — 64 citations
• Dictionary-based Data Augmentation for Cross-Domain Neural Machine Translation (2020) — 27 citations