Jiangliu Wang

Computer Vision and Pattern Recognition · 2019–2022

A researcher in the Science corpus: 3 papers; trusts 719 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 159 citations into it; #15,920 of 21,575 by trust here, #9,305 by citations
• Artificial Intelligence — 111 citations into it
• Representation & Self-Supervised Learning — 49 citations into it
• Model Architectures — 24 citations into it
• Evaluation & Benchmarks — 21 citations into it
• Optimization & Training — 10 citations into it
• Electrical and Electronic Engineering — 8 citations into it
• Cognitive Neuroscience — 6 citations into it

Whom Jiangliu Wang 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. Ross B. Girshick
6. Karen Simonyan
7. Yoshua Bengio
8. Diederik P. Kingma
9. Yann LeCun
10. Ilya Sutskever

Who trusts Jiangliu Wang overall

1. Senthil Purushwalkam
2. Tianhao Li
3. Pritam Sarkar
4. Hanwen Liang
5. Jie Gui
6. Bruno Korbar
7. Pedro Morgado
8. Limin Wang
9. Peihao Chen
10. Mandela Patrick

Papers from Jiangliu Wang's perspective

Ordered by how strongly the researchers Jiangliu Wang 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. 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

• Self-Supervised Spatio-Temporal Representation Learning for Videos by Predicting Motion and Appearance Statistics (2019) — 223 citations
• Self-supervised Video Representation Learning by Pace Prediction (2020) — 263 citations
• AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition (2022) — 1,174 citations