Chao Tao

Computer Vision and Pattern Recognition · 2015–2022

A researcher in the Science corpus: 8 papers; trusts 718 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 230 citations into it
• Artificial Intelligence — 135 citations into it
• Representation & Self-Supervised Learning — 102 citations into it
• Media Technology — 96 citations into it; #3,019 of 3,621 by trust here, #633 by citations
• Atmospheric Science — 27 citations into it
• Evaluation & Benchmarks — 26 citations into it
• Model Architectures — 15 citations into it
• Ocean Engineering — 13 citations into it

Whom Chao Tao trusts overall

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

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

Who trusts Chao Tao overall

1. Nicolas Audebert
2. Jingxiang Yang
3. Ronald Kemker
4. Shutao Li
5. Alberto Signoroni
6. Valerio Marsocci
7. Rui Li
8. Ayesha Shafique
9. Grant J. Scott
10. Xiaoxiang Zhu

Papers from Chao Tao's perspective

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

• Remote Sensing Image Scene Classification With Self-Supervised Paradigm Under Limited Labeled Samples (2020) — 112 citations
• FALSE: False Negative Samples Aware Contrastive Learning for Semantic Segmentation of High-Resolution Remote Sensing Image (2022) — 25 citations
• Self-Supervised Remote Sensing Feature Learning: Learning Paradigms, Challenges, and Future Works (2022) — 91 citations
• Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images (2021) — 153 citations
• CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification (2020) — 66 citations
• Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion (2016) — 71 citations
• Unsupervised Multilayer Feature Learning for Satellite Image Scene Classification (2016) — 138 citations
• Unsupervised Spectral–Spatial Feature Learning With Stacked Sparse Autoencoder for Hyperspectral Imagery Classification (2015) — 377 citations