Pichao Wang

Computer Vision and Pattern Recognition · 2014–2021

A researcher in the Science corpus: 20 papers; trusts 1,771 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 875 citations into it; #3,664 of 21,575 by trust here, #3,006 by citations
• Artificial Intelligence — 425 citations into it; #10,470 of 35,345 by trust here, #9,968 by citations
• Biomedical Engineering — 255 citations into it; #383 of 10,734 by trust here, #1,731 by citations
• Human-Computer Interaction — 226 citations into it; #126 of 2,253 by trust here, #122 by citations
• Model Architectures — 113 citations into it
• 3D Vision & Pose — 90 citations into it; #545 of 689 by trust here, #430 by citations
• Representation & Self-Supervised Learning — 50 citations into it
• Evaluation & Benchmarks — 39 citations into it; #1,888 of 3,147 by trust here, #2,009 by citations

Whom Pichao Wang trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Karen Simonyan
4. Ross B. Girshick
5. Xiaodong Yang
6. Kaiming He
7. Wanqing Li
8. Ilya Sutskever
9. Andrew Zisserman
10. Yoshua Bengio

Who trusts Pichao Wang overall

1. Wanqing Li
2. Jun Liu
3. Yonghong Hou
4. Zhimin Gao
5. Mengyuan Liu
6. Philip O. Ogunbona
7. Zehua Sun
8. Chuankun Li
9. Amir Shahroudy
10. Hong Liu

Papers from Pichao Wang's perspective

Ordered by how strongly the researchers Pichao Wang 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Exploiting Temporal Contexts With Strided Transformer for 3D Human Pose Estimation (2021) — 302 citations
• Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks (2016) — 386 citations
• Action Recognition From Depth Maps Using Deep Convolutional Neural Networks (2016) — 293 citations
• Mining Mid-Level Features for Action Recognition Based on Effective Skeleton Representation (2014) — 45 citations
• ConvNets-Based Action Recognition from Depth Maps through Virtual Cameras and Pseudocoloring (2015) — 103 citations
• Large-scale Isolated Gesture Recognition using Convolutional Neural Networks (2016) — 98 citations
• Skeleton Optical Spectra-Based Action Recognition Using Convolutional Neural Networks (2018) — 326 citations
• Joint Distance Maps Based Action Recognition With Convolutional Neural Networks (2017) — 302 citations
• Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks (2016) — 54 citations
• Action Recognition Based on Joint Trajectory Maps with Convolutional Neural Networks (2016) — 234 citations
• TransReID: Transformer-based Object Re-Identification (2021) — 1,247 citations
• MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation (2021) — 408 citations
• CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation (2021) — 303 citations
• Spatially and Temporally Structured Global to Local Aggregation of Dynamic Depth Information for Action Recognition (2018) — 18 citations
• Depth Pooling Based Large-Scale 3-D Action Recognition With Convolutional Neural Networks (2018) — 157 citations
• Learning Approximate Stochastic Transition Models (2017) — 25 citations
• RGB-D-based Human Motion Recognition with Deep Learning: A Survey (2017) — 384 citations
• Structured Images for RGB-D Action Recognition (2017) — 57 citations
• Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition with Convolutional Neural Networks (2017) — 136 citations
• RGB-D-based action recognition datasets: A survey (2016) — 264 citations