Changming Sun

Computer Vision and Pattern Recognition · 2015–2019

A researcher in the Science corpus: 4 papers; trusts 270 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 97 citations into it; #20,103 of 21,575 by trust here, #9,435 by citations
• Artificial Intelligence — 41 citations into it; #32,834 of 35,345 by trust here, #13,760 by citations
• Model Architectures — 19 citations into it
• Media Technology — 14 citations into it
• Biophysics — 10 citations into it

Whom Changming Sun trusts overall

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

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

Who trusts Changming Sun overall

1. Chunhua Shen

Papers from Changming Sun's perspective

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

Their papers

• An End-to-End TextSpotter with Explicit Alignment and Attention (2018) — 226 citations
• Knowledge Adaptation for Efficient Semantic Segmentation (2019) — 250 citations
• Crowdsourcing the creation of image segmentation algorithms for connectomics (2015) — 371 citations
• RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans (2018) — 439 citations