Tengda Han

Computer Vision and Pattern Recognition · 2019–2023

A researcher in the Science corpus: 5 papers; trusts 667 researchers and is trusted by 168 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 265 citations into it; #2,830 of 21,575 by trust here, #6,146 by citations
• Artificial Intelligence — 232 citations into it; #10,384 of 35,345 by trust here, #8,525 by citations
• Representation & Self-Supervised Learning — 96 citations into it
• Evaluation & Benchmarks — 34 citations into it
• Model Architectures — 32 citations into it
• Natural Language Processing — 32 citations into it
• Optimization & Training — 11 citations into it
• Generative Models — 10 citations into it

Whom Tengda Han trusts overall

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

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

Who trusts Tengda Han overall

1. Rohit Girdhar
2. Christoph Feichtenhofer
3. Tianhao Li
4. Pedro Morgado
5. Mandela Patrick
6. Chengxu Zhuang
7. Jiangliu Wang
8. Kristen Grauman
9. Ishan Misra
10. Limin Wang

Papers from Tengda Han's perspective

Ordered by how strongly the researchers Tengda Han 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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

• Video Representation Learning by Dense Predictive Coding (2019) — 397 citations
• Prompting Visual-Language Models for Efficient Video Understanding (2021) — 522 citations
• WhisperX: Time-Accurate Speech Transcription of Long-Form Audio (2023) — 572 citations
• Flamingo: a Visual Language Model for Few-Shot Learning (2022) — 6,220 citations
• Self-supervised Co-training for Video Representation Learning (2020) — 398 citations