Shenghao Zhou

Computer Vision and Pattern Recognition · 2019–2022

A researcher in the Science corpus: 3 papers; trusts 480 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 152 citations into it
• Artificial Intelligence — 89 citations into it
• Human-Computer Interaction — 35 citations into it
• 3D Vision & Pose — 32 citations into it
• Representation & Self-Supervised Learning — 21 citations into it
• Robotics & Control — 18 citations into it
• Reinforcement Learning — 14 citations into it
• Model Architectures — 11 citations into it

Whom Shenghao Zhou trusts overall

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

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

Papers from Shenghao Zhou's perspective

Ordered by how strongly the researchers Shenghao Zhou 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
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

• Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and Applications (2022) — 106 citations
• Keyframing the Future: Keyframe Discovery for Visual Prediction and Planning (2019) — 32 citations
• Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification (2022) — 386 citations