Chu-Song Chen

Computer Vision and Pattern Recognition · 2009–2019

A researcher in the Science corpus: 9 papers; trusts 585 researchers and is trusted by 50 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 174 citations into it; #4,140 of 21,575 by trust here, #3,747 by citations
• Artificial Intelligence — 82 citations into it; #19,807 of 35,345 by trust here, #9,641 by citations
• Representation & Self-Supervised Learning — 35 citations into it
• Signal Processing — 22 citations into it; #3,553 of 7,572 by trust here, #3,673 by citations
• Evaluation & Benchmarks — 14 citations into it
• Aerospace Engineering — 13 citations into it; #4,167 of 6,421 by trust here, #4,570 by citations
• Media Technology — 12 citations into it
• Efficiency & Compression — 8 citations into it

Whom Chu-Song Chen trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Paul A. Viola
4. David G. Lowe
5. Ross B. Girshick
6. Kaiming He
7. Yann LeCun
8. Yoshua Bengio
9. Andrew Zisserman
10. Andreas Lanitis

Who trusts Chu-Song Chen overall

1. Fei Ye
2. Wenzhi Cao
3. Shiv Ram Dubey
4. Yandong Wen
5. Seong Joon Oh
6. Jiankang Deng
7. Stylianos Moschoglou
8. Lacey Best-Rowden
9. Adrian G. Bors
10. Jingkuan Song

Papers from Chu-Song Chen's perspective

Ordered by how strongly the researchers Chu-Song Chen 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval (2014) — 425 citations
• Compacting, Picking and Growing for Unforgetting Continual Learning (2019) — 379 citations
• Deep learning of binary hash codes for fast image retrieval (2015) — 607 citations
• Ordinal hyperplanes ranker with cost sensitivities for age estimation (2011) — 362 citations
• Face Recognition and Retrieval Using Cross-Age Reference Coding With Cross-Age Celebrity Dataset (2015) — 301 citations
• Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong Learning (2019) — 47 citations
• Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks (2016) — 348 citations
• Multiple Kernel Fuzzy Clustering (2012) — 404 citations
• Fighting Phishing with Discriminative Keypoint Features (2009) — 115 citations