Peizhao Zhang

Computer Vision and Pattern Recognition · 2018–2023

A researcher in the Science corpus: 12 papers; trusts 751 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 356 citations into it; #8,174 of 21,575 by trust here, #2,016 by citations
• Artificial Intelligence — 285 citations into it; #20,261 of 35,345 by trust here, #5,682 by citations
• Efficiency & Compression — 118 citations into it; #578 of 1,264 by trust here, #194 by citations
• Model Architectures — 87 citations into it
• Representation & Self-Supervised Learning — 52 citations into it
• Optimization & Training — 43 citations into it
• Evaluation & Benchmarks — 31 citations into it
• Generative Models — 27 citations into it

Whom Peizhao Zhang 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. Yoshua Bengio
6. Ross B. Girshick
7. Jia Deng
8. Karen Simonyan
9. Yann LeCun
10. Ilya Sutskever

Who trusts Peizhao Zhang overall

1. Xuanyi Dong
2. Xiaoliang Dai
3. Dimitrios Stamoulis
4. Zi-Mei Yao
5. Zhen Dong
6. Bichen Wu
7. Mingxing Tan
8. Zechun Liu
9. Wuyang Chen
10. Changlin Li

Papers from Peizhao Zhang's perspective

Ordered by how strongly the researchers Peizhao 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. 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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search (2018) — 1,467 citations
• Visual Transformers: Token-based Image Representation and Processing for Computer Vision (2020) — 755 citations
• ChamNet: Towards Efficient Network Design Through Platform-Aware Model Adaptation (2018) — 294 citations
• Unbiased Teacher for Semi-Supervised Object Detection (2021) — 634 citations
• Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP (2022) — 767 citations
• FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions (2020) — 334 citations
• FBNetV3: Joint Architecture-Recipe Search using Neural Acquisition Function (2020) — 77 citations
• Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack (2023) — 312 citations
• Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search (2018) — 313 citations
• Data-Efficient Language-Supervised Zero-Shot Learning with Self-Distillation (2021) — 35 citations
• Token Merging: Your ViT But Faster (2022) — 1,128 citations
• Machine Learning at Facebook: Understanding Inference at the Edge (2019) — 490 citations