Kaihua Tang

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 7 papers; trusts 934 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 233 citations into it; #5,621 of 21,575 by trust here, #3,955 by citations
• Artificial Intelligence — 194 citations into it; #10,587 of 35,345 by trust here, #6,614 by citations
• Multimodal Learning — 54 citations into it; #512 of 1,211 by trust here, #714 by citations
• Natural Language Processing — 40 citations into it; #2,663 of 5,688 by trust here, #2,695 by citations
• Meta & Continual Learning — 33 citations into it; #922 of 1,041 by trust here, #649 by citations
• Evaluation & Benchmarks — 26 citations into it
• Causal Inference & Discovery — 25 citations into it; #240 of 364 by trust here, #114 by citations
• Model Architectures — 24 citations into it

Whom Kaihua Tang trusts overall

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

1. Kaiming He
2. Ross B. Girshick
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yoshua Bengio
6. Diederik P. Kingma
7. Judea Pearl
8. Tsung-Yi Lin
9. Ilya Sutskever
10. Piotr Doll'ar

Who trusts Kaihua Tang overall

1. Hanwang Zhang
2. Xu Yang
3. Xinting Hu
4. Boris Knyazev
5. Feihong Shen
6. Xiaojun Chang
7. Marcella Cornia
8. Yulei Niu
9. Bo Wu
10. Yaoyao Liu

Papers from Kaihua Tang's perspective

Ordered by how strongly the researchers Kaihua 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
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. Going deeper with convolutions

Their papers

• Auto-Encoding Scene Graphs for Image Captioning (2018) — 801 citations
• Unbiased Scene Graph Generation From Biased Training (2020) — 853 citations
• Learning to Compose Dynamic Tree Structures for Visual Contexts (2018) — 612 citations
• Learning to Segment the Tail (2020) — 93 citations
• Distilling Causal Effect of Data in Class-Incremental Learning (2021) — 213 citations
• Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect (2020) — 525 citations
• Counterfactual VQA: A Cause-Effect Look at Language Bias (2020) — 539 citations