Changxin Gao

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 7 papers; trusts 986 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 279 citations into it; #7,078 of 21,575 by trust here, #3,361 by citations
• Artificial Intelligence — 127 citations into it; #27,319 of 35,345 by trust here, #8,007 by citations
• Model Architectures — 43 citations into it
• Evaluation & Benchmarks — 20 citations into it
• Media Technology — 18 citations into it
• Efficiency & Compression — 16 citations into it
• Optimization & Training — 10 citations into it
• Biophysics — 6 citations into it

Whom Changxin Gao trusts overall

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

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

Who trusts Changxin Gao overall

1. Changqian Yu
2. Huihui Pan
3. Yuanduo Hong
4. Yuval Nirkin
5. Jiacong Xu
6. Xiangtai Li
7. Hanchao Li
8. Zilong Huang
9. Shihua Huang

Papers from Changxin Gao's perspective

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

Their papers

• BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation (2018) — 2,729 citations
• Learning a Discriminative Feature Network for Semantic Segmentation (2018) — 818 citations
• BiSeNet V2: Bilateral Network with Guided Aggregation for Real-Time Semantic Segmentation (2020) — 1,654 citations
• Lite-HRNet: A Lightweight High-Resolution Network (2021) — 426 citations
• FGN: Fully Guided Network for Few-Shot Instance Segmentation (2020) — 83 citations
• Domain Adaptation for Image Dehazing (2020) — 401 citations
• Hard sample mining makes person re-identification more efficient and accurate (2020) — 38 citations