Chunhui Jiang

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 416 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 64 citations into it
• Artificial Intelligence — 25 citations into it
• Efficiency & Compression — 13 citations into it
• Model Architectures — 12 citations into it
• Aerospace Engineering — 6 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Optimization & Training — 5 citations into it

Whom Chunhui Jiang trusts overall

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

1. Ross B. Girshick
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Kaiming He
5. Yann LeCun
6. Sergey Ioffe
7. Karen Simonyan
8. Christian Szegedy
9. Yoshua Bengio
10. Ilya Sutskever

Who trusts Chunhui Jiang overall

1. Ting-Wu Chin
2. Jing Liu
3. Cha Zhang

Papers from Chunhui Jiang's perspective

Ordered by how strongly the researchers Chunhui Jiang 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Efficient DNN Neuron Pruning by Minimizing Layer-wise Nonlinear Reconstruction Error (2018) — 47 citations
• Relief R-CNN: Utilizing Convolutional Features for Fast Object Detection (2016) — 7 citations
• Relief Impression Image Detection : Unsupervised Extracting Objects Directly From Feature Arrangements of Deep CNN (2016)