Changqian Yu

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 5 papers; trusts 1,001 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 233 citations into it; #6,576 of 21,575 by trust here, #3,191 by citations
• Artificial Intelligence — 113 citations into it; #28,454 of 35,345 by trust here, #8,525 by citations
• Model Architectures — 44 citations into it
• Evaluation & Benchmarks — 23 citations into it
• Efficiency & Compression — 16 citations into it
• Media Technology — 15 citations into it
• Biophysics — 8 citations into it
• Optimization & Training — 8 citations into it

Whom Changqian Yu 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. Christian Szegedy
7. Yoshua Bengio
8. Liang-Chieh Chen
9. Karen Simonyan
10. Yann LeCun

Who trusts Changqian Yu overall

1. Huihui Pan
2. Yuval Nirkin
3. Yuanduo Hong
4. Jiacong Xu
5. Xiangtai Li
6. Hanchao Li
7. Zilong Huang
8. Junjun He
9. Yanwei Li
10. Tianyi Wu

Papers from Changqian Yu's perspective

Ordered by how strongly the researchers Changqian Yu trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Long Short-Term Memory
8. Gradient-based learning applied to document recognition
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
• An End-To-End Network for Panoptic Segmentation (2019) — 150 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