Peng Zhou

Computer Vision and Pattern Recognition · with Davis · 2017–2020

A researcher in the Science corpus: 7 papers; trusts 522 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 150 citations into it; #4,943 of 21,575 by trust here, #5,236 by citations
• Artificial Intelligence — 99 citations into it; #18,110 of 35,345 by trust here, #8,283 by citations
• Generative Models — 35 citations into it
• Natural Language Processing — 27 citations into it
• Large Language Models — 20 citations into it; #608 of 799 by trust here, #621 by citations
• Model Architectures — 16 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Evaluation & Benchmarks — 15 citations into it

Whom Peng 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. Diederik P. Kingma
7. Yann LeCun
8. Ilya Sutskever
9. Christian Szegedy
10. Shaoqing Ren

Who trusts Peng Zhou overall

1. Luisa Verdoliva
2. Yuezun Li
3. Eden Belouadah
4. Davide Cozzolino
5. Huy Hoang Le Nguyen
6. Yue Wu
7. Yucheng Luo
8. Sheng-Yu Wang
9. Yinglin Zheng
10. Thanh Thi Nguyen

Papers from Peng Zhou's perspective

Ordered by how strongly the researchers Peng Zhou trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
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

• Two-Stream Neural Networks for Tampered Face Detection (2017) — 635 citations
• Learning Rich Features for Image Manipulation Detection (2018) — 759 citations
• K-BERT: Enabling Language Representation with Knowledge Graph (2019) — 930 citations
• Inclusive GAN: Improving Data and Minority Coverage in Generative Models (2020) — 69 citations
• M2KD: Multi-model and Multi-level Knowledge Distillation for Incremental Learning (2019) — 58 citations
• FastBERT: a Self-distilling BERT with Adaptive Inference Time (2020) — 436 citations
• Deepstrip: High-Resolution Boundary Refinement (2020) — 25 citations