Yang Zhang

Computer Vision and Pattern Recognition · with David · 2017–2022

A researcher in the Science corpus: 5 papers; trusts 1,151 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 221 citations into it; #5,025 of 21,575 by trust here, #5,806 by citations
• Artificial Intelligence — 155 citations into it; #9,944 of 35,345 by trust here, #12,083 by citations
• Representation & Self-Supervised Learning — 52 citations into it; #2,176 of 3,145 by trust here, #2,066 by citations
• Evaluation & Benchmarks — 30 citations into it
• Generative Models — 25 citations into it
• Model Architectures — 19 citations into it
• Meta & Continual Learning — 17 citations into it
• Optimization & Training — 16 citations into it

Whom Yang Zhang trusts overall

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

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

Who trusts Yang Zhang overall

1. Yuhua Chen
2. Yi-Hsuan Tsai
3. Jiahua Dong
4. Christos Sakaridis
5. Umberto Michieli
6. Xinge Zhu
7. Matteo Biasetton
8. Luc Van Gool
9. Serban Stan
10. Dengxin Dai

Papers from Yang Zhang's perspective

Ordered by how strongly the researchers Yang Zhang 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes (2017) — 360 citations
• Domain Adaptation for Semantic Segmentation of Urban Scenes (2017)
• PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation (2020) — 610 citations
• Few-Shot Incremental Learning for Label-to-Image Translation (2022) — 11 citations
• Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization Without Accessing Target Domain Data (2019) — 472 citations