Jun-Cheng Chen

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 5 papers; trusts 688 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 108 citations into it; #7,977 of 21,575 by trust here, #6,795 by citations
• Signal Processing — 28 citations into it; #2,237 of 7,572 by trust here, #1,388 by citations
• Artificial Intelligence — 19 citations into it
• Media Technology — 13 citations into it
• Generative Models — 10 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Representation & Self-Supervised Learning — 7 citations into it
• Model Architectures — 6 citations into it

Whom Jun-Cheng Chen trusts overall

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

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

Who trusts Jun-Cheng Chen overall

1. Mei Wang
2. Xi Yin
3. Iacopo Masi
4. Andras Rozsa
5. Rajeev Ranjan
6. Carlos Domingo Castillo
7. Weihong Deng
8. Soumyadip Sengupta
9. Ramalingam Chellappa
10. Xiaoming Liu

Papers from Jun-Cheng Chen's perspective

Ordered by how strongly the researchers Jun-Cheng 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. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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

• Frontal to profile face verification in the wild (2016) — 842 citations
• Unconstrained face verification using deep CNN features (2015) — 302 citations
• Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms (2018) — 361 citations
• Unsupervised Domain-Specific Deblurring via Disentangled Representations (2019) — 153 citations
• The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification (2020) — 160 citations