Ping Hu

Computer Vision and Pattern Recognition · with Liu · 2017–2021

A researcher in the Science corpus: 5 papers; trusts 981 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 220 citations into it; #6,976 of 21,575 by trust here, #7,092 by citations
• Artificial Intelligence — 118 citations into it; #19,567 of 35,345 by trust here, #15,004 by citations
• Biomedical Engineering — 28 citations into it; #3,984 of 10,734 by trust here, #5,112 by citations
• Representation & Self-Supervised Learning — 26 citations into it
• Human-Computer Interaction — 22 citations into it
• Model Architectures — 19 citations into it; #1,374 of 2,278 by trust here, #1,421 by citations
• Natural Language Processing — 13 citations into it
• 3D Vision & Pose — 12 citations into it

Whom Ping Hu trusts overall

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

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

Who trusts Ping Hu overall

1. Golnaz Ghiasi
2. Wenguan Wang
3. Mateusz Michalkiewicz
4. David Acuna
5. Jiuxiang Gu
6. Jun Liu
7. Nian Liu
8. S. Gadre
9. Xiuye Gu
10. Dat T. Huynh

Papers from Ping Hu's perspective

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

Their papers

• Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation (2020) — 79 citations
• ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes (2021) — 90 citations
• Deep Level Sets for Salient Object Detection (2017) — 192 citations
• Global Context-Aware Attention LSTM Networks for 3D Action Recognition (2017) — 655 citations
• Conterfactual Generative Zero-Shot Semantic Segmentation (2021) — 11 citations