Zhan Tong

Computer Vision and Pattern Recognition · Neural Information Processing Systems · 2020–2023

A researcher in the Science corpus: 5 papers; trusts 1,039 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 309 citations into it; #10,441 of 21,575 by trust here, #6,795 by citations
• Artificial Intelligence — 227 citations into it
• Model Architectures — 81 citations into it
• Representation & Self-Supervised Learning — 59 citations into it
• Evaluation & Benchmarks — 31 citations into it
• Optimization & Training — 25 citations into it
• Endocrinology, Diabetes and Metabolism — 10 citations into it
• Computational Mechanics — 9 citations into it

Whom Zhan Tong trusts overall

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

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

Who trusts Zhan Tong overall

1. Kunchang Li
2. Yi Wang
3. Christoph Feichtenhofer
4. Adrien Bardes
5. Yinan He
6. Shoufa Chen
7. Ilija Radosavovic
8. Chaoning Zhang
9. Xiang Wang
10. Jie Gui

Papers from Zhan Tong's perspective

Ordered by how strongly the researchers Zhan Tong 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

• VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training (2022) — 2,096 citations
• VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking (2023) — 754 citations
• Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations (2022) — 457 citations
• TDN: Temporal Difference Networks for Efficient Action Recognition (2020) — 493 citations
• AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition (2022) — 1,174 citations