Chengcheng Jia

Computer Vision and Pattern Recognition · 2014–2016

A researcher in the Science corpus: 2 papers; trusts 189 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 25 citations into it; #11,172 of 21,575 by trust here, #19,766 by citations
• Artificial Intelligence — 15 citations into it
• Computational Mathematics — 12 citations into it; #410 of 612 by trust here, #569 by citations
• Human-Computer Interaction — 10 citations into it
• Biomedical Engineering — 9 citations into it; #5,773 of 10,734 by trust here, #10,484 by citations
• Signal Processing — 6 citations into it
• Computational Mechanics — 5 citations into it; #6,646 of 10,001 by trust here, #9,276 by citations

Whom Chengcheng Jia trusts overall

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

1. Tamara G. Kolda
2. Guangcan Liu
3. Omar Oreifej
4. Xiaodong Yang
5. Shuicheng Yan
6. Zicheng Liu
7. Tae-Kyun Kim
8. Karen Simonyan
9. Xiaofei He
10. Geoffrey E. Hinton

Who trusts Chengcheng Jia overall

1. Zhiyuan Shi
2. Shutao Li
3. Yun Raymond Fu
4. Tae-Kyun Kim
5. Renwei Dian
6. Leyuan Fang

Papers from Chengcheng Jia's perspective

Ordered by how strongly the researchers Chengcheng Jia 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
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

• Low-Rank Tensor Subspace Learning for RGB-D Action Recognition (2016) — 45 citations
• Low-Rank Tensor Learning with Discriminant Analysis for Action Classification and Image Recovery (2014) — 35 citations