Haiping Wu

Computer Vision and Pattern Recognition · with Xiao · 2018–2023

A researcher in the Science corpus: 3 papers; trusts 526 researchers and is trusted by 114 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 134 citations into it; #3,071 of 21,575 by trust here, #3,166 by citations
• Artificial Intelligence — 94 citations into it
• Model Architectures — 35 citations into it; #1,563 of 2,278 by trust here, #595 by citations
• Evaluation & Benchmarks — 18 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Optimization & Training — 9 citations into it
• Human-Computer Interaction — 6 citations into it; #147 of 2,253 by trust here, #290 by citations

Whom Haiping Wu trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Ross B. Girshick
5. Yann LeCun
6. Ilya Sutskever
7. Yoshua Bengio
8. Christian Szegedy
9. Diederik P. Kingma
10. Jia Deng

Who trusts Haiping Wu overall

1. Jianwei Yang
2. Xingyi Zhou
3. Gyeongsik Moon
4. Bin Xiao
5. Luke Melas-Kyriazi
6. Sachin Mehta
7. Zhen Wang
8. Lu Yuan
9. Wenhai Wang
10. Yufei Xu

Papers from Haiping Wu's perspective

Ordered by how strongly the researchers Haiping Wu 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• CvT: Introducing Convolutions to Vision Transformers (2021) — 2,478 citations
• Simple Baselines for Human Pose Estimation and Tracking (2018) — 2,084 citations
• Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks (2023) — 565 citations