Wenzhi Zhao

Computer Vision and Pattern Recognition · 2015–2022

A researcher in the Science corpus: 5 papers; trusts 358 researchers and is trusted by 71 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 71 citations into it; #3,883 of 21,575 by trust here, #9,722 by citations
• Media Technology — 44 citations into it; #157 of 3,621 by trust here, #442 by citations
• Artificial Intelligence — 37 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Atmospheric Science — 24 citations into it; #69 of 4,804 by trust here, #618 by citations
• Ecology — 9 citations into it; #1,294 of 10,717 by trust here, #6,072 by citations
• Analytical Chemistry — 8 citations into it
• Meta & Continual Learning — 8 citations into it

Whom Wenzhi Zhao trusts overall

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

1. Geoffrey E. Hinton
2. David G. Lowe
3. Alex Krizhevsky
4. Yann LeCun
5. Yoshua Bengio
6. Yi Yang
7. Ilya Sutskever
8. Kaiming He
9. Ross B. Girshick
10. Navneet Dalal

Who trusts Wenzhi Zhao overall

1. Shutao Li
2. Nicolas Audebert
3. Jingxiang Yang
4. Alberto Signoroni
5. Ying Li
6. Shihong Du
7. Weiwei Song
8. Pedram Ghamisi
9. Liangpei Zhang
10. Guangrui Zhao

Papers from Wenzhi Zhao's perspective

Ordered by how strongly the researchers Wenzhi Zhao 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• Spectral–spatial classification of hyperspectral images using deep convolutional neural networks (2015) — 465 citations
• Continual Contrastive Learning for Cross-Dataset Scene Classification (2022) — 8 citations
• Learning multiscale and deep representations for classifying remotely sensed imagery (2016) — 375 citations
• Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach (2016) — 1,136 citations
• Scene classification using multi-scale deeply described visual words (2016) — 55 citations