Aojun Zhou

Computer Vision and Pattern Recognition · 2017–2023

A researcher in the Science corpus: 9 papers; trusts 1,045 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 359 citations into it; #9,967 of 21,575 by trust here, #2,177 by citations
• Artificial Intelligence — 341 citations into it; #17,340 of 35,345 by trust here, #2,899 by citations
• Efficiency & Compression — 113 citations into it
• Model Architectures — 80 citations into it
• Evaluation & Benchmarks — 43 citations into it
• Natural Language Processing — 33 citations into it
• Optimization & Training — 29 citations into it
• Representation & Self-Supervised Learning — 15 citations into it

Whom Aojun Zhou trusts overall

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

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

Who trusts Aojun Zhou overall

1. Zhen Dong
2. Zi-Mei Yao
3. Asit K. Mishra
4. Elias Frantar
5. Huiqiang Xie
6. Zhi Gang Liu
7. Linfeng Zhang
8. Kuan Wang
9. Vasileios Belagiannis
10. Michaela Blott

Papers from Aojun Zhou's perspective

Ordered by how strongly the researchers Aojun 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. 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. Going deeper with convolutions

Their papers

• Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights (2017) — 1,135 citations
• Incorporating Convolution Designs into Visual Transformers (2021) — 600 citations
• LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention (2023) — 1,040 citations
• LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model (2023) — 758 citations
• Explicit Loss-Error-Aware Quantization for Low-Bit Deep Neural Networks (2018) — 93 citations
• Group Fisher Pruning for Practical Network Compression (2021) — 218 citations
• Omni-Dimensional Dynamic Convolution (2022) — 447 citations
• Learning N: M Fine-grained Structured Sparse Neural Networks From Scratch (2021) — 313 citations
• Adversarial Robustness vs. Model Compression, or Both? (2019) — 181 citations