Peicheng Zhou

Computer Vision and Pattern Recognition · 2013–2019

A researcher in the Science corpus: 11 papers; trusts 999 researchers and is trusted by 58 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 262 citations into it; #3,500 of 21,575 by trust here, #2,729 by citations
• Artificial Intelligence — 78 citations into it
• Media Technology — 77 citations into it; #236 of 3,621 by trust here, #147 by citations
• Remote Sensing — 37 citations into it
• Aerospace Engineering — 21 citations into it; #608 of 6,421 by trust here, #922 by citations
• Model Architectures — 18 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Management, Monitoring, Policy and Law — 10 citations into it

Whom Peicheng Zhou trusts overall

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

1. Gong Cheng
2. Ross B. Girshick
3. Junwei Han
4. Alex Krizhevsky
5. David G. Lowe
6. Geoffrey E. Hinton
7. Kaiming He
8. Karen Simonyan
9. Ilya Sutskever
10. Andrew Zisserman

Who trusts Peicheng Zhou overall

1. Gong Cheng
2. Junwei Han
3. Ke Li
4. Jian Ding
5. Zhenwei Shi
6. Aharon Azulay
7. Yuan Yuan
8. Xiwen Yao
9. Souleyman Chaib
10. Xiaoxiang Zhu

Papers from Peicheng Zhou's perspective

Ordered by how strongly the researchers Peicheng 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
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

• RIFD-CNN: Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection (2016) — 171 citations
• Learning Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection (2019) — 371 citations
• Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images (2016) — 1,611 citations
• Scene classification of high resolution remote sensing images using convolutional neural networks (2016) — 80 citations
• Object detection in VHR optical remote sensing images via learning rotation-invariant HOG feature (2016) — 33 citations
• Auto-encoder-based shared mid-level visual dictionary learning for scene classification using very high resolution remote sensing images (2015) — 63 citations
• Weakly supervised target detection in remote sensing images based on transferred deep features and negative bootstrapping (2015) — 91 citations
• Multi-class geospatial object detection and geographic image classification based on collection of part detectors (2014) — 928 citations
• Scalable multi-class geospatial object detection in high-spatial-resolution remote sensing images (2014) — 24 citations
• Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding (2014) — 215 citations
• Object detection in remote sensing imagery using a discriminatively trained mixture model (2013) — 164 citations