Long Zhao

Computer Vision and Pattern Recognition · 2015–2023

A researcher in the Science corpus: 9 papers; trusts 1,263 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 359 citations into it; #11,509 of 21,575 by trust here, #8,205 by citations
• Artificial Intelligence — 250 citations into it; #35,168 of 35,345 by trust here, #15,004 by citations
• Model Architectures — 91 citations into it
• Representation & Self-Supervised Learning — 81 citations into it
• Generative Models — 69 citations into it
• Optimization & Training — 55 citations into it
• Human-Computer Interaction — 25 citations into it
• Control and Systems Engineering — 23 citations into it

Whom Long Zhao trusts overall

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

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

Who trusts Long Zhao overall

1. Jiaxing Huang
2. Zhengzhong Tu
3. Fengchun Qiao
4. Shiyu Chang
5. Mengmeng Ma
6. Hongsuk Choi
7. Han Zhang
8. Xi Peng
9. Ce Zheng
10. Jinlu Zhang

Papers from Long Zhao's perspective

Ordered by how strongly the researchers Long Zhao 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

• Improved Transformer for High-Resolution GANs (2021) — 114 citations
• Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding (2021) — 227 citations
• Learning to Learn Single Domain Generalization (2020) — 556 citations
• Semantic Graph Convolutional Networks for 3D Human Pose Regression (2019) — 621 citations
• Object proposal by multi-branch hierarchical segmentation (2015) — 40 citations
• Steering Prototypes with Prompt-tuning for Rehearsal-free Continual Learning (2023) — 43 citations
• Aggregating Nested Transformers (2021) — 83 citations
• SMIL: Multimodal Learning with Severely Missing Modality (2021) — 436 citations
• Knowledge As Priors: Cross-Modal Knowledge Generalization for Datasets Without Superior Knowledge (2020) — 73 citations