Yansong Tang

Computer Vision and Pattern Recognition · 2017–2022

A researcher in the Science corpus: 7 papers; trusts 1,069 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 307 citations into it; #12,546 of 21,575 by trust here, #5,116 by citations
• Artificial Intelligence — 174 citations into it; #27,212 of 35,345 by trust here, #10,566 by citations
• Model Architectures — 71 citations into it
• Multimodal Learning — 33 citations into it
• Representation & Self-Supervised Learning — 29 citations into it
• Evaluation & Benchmarks — 26 citations into it
• Human-Computer Interaction — 21 citations into it
• Natural Language Processing — 14 citations into it

Whom Yansong Tang trusts overall

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

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

Who trusts Yansong Tang overall

1. Lei Shi
2. Antoine Miech
3. Francesco Ragusa
4. Yifan Zhang
5. Jianwei Yang
6. Linchao Zhu
7. Wei Peng
8. Paul Hongsuck Seo
9. Lingzhi Zhang
10. Georgios Kapidis

Papers from Yansong Tang's perspective

Ordered by how strongly the researchers Yansong Tang 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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

• COIN: A Large-Scale Dataset for Comprehensive Instructional Video Analysis (2019) — 448 citations
• DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting (2021) — 830 citations
• LAVT: Language-Aware Vision Transformer for Referring Image Segmentation (2021) — 526 citations
• Action recognition in RGB-D egocentric videos (2017) — 51 citations
• HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions (2022) — 401 citations
• Multi-Stream Deep Neural Networks for RGB-D Egocentric Action Recognition (2019) — 60 citations
• Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition (2018) — 380 citations