Xin Yang

Computer Vision and Pattern Recognition · European Conference on Computer Vision · 2017–2020

A researcher in the Science corpus: 7 papers; trusts 414 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 129 citations into it; #9,851 of 21,575 by trust here, #7,657 by citations
• Artificial Intelligence — 92 citations into it; #17,270 of 35,345 by trust here, #11,624 by citations
• Radiology, Nuclear Medicine and Imaging — 51 citations into it; #1,500 of 3,265 by trust here, #2,073 by citations
• Efficiency & Compression — 39 citations into it
• Ophthalmology — 37 citations into it; #131 of 222 by trust here, #156 by citations
• Model Architectures — 28 citations into it
• Optimization & Training — 8 citations into it
• Representation & Self-Supervised Learning — 8 citations into it

Whom Xin Yang trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Evan Shelhamer
4. Ross B. Girshick
5. Geoffrey E. Hinton
6. Christian Szegedy
7. Karen Simonyan
8. Yoshua Bengio
9. Yann LeCun
10. Trevor Darrell

Who trusts Xin Yang overall

1. Zechun Liu
2. Arvid Lundervold
3. Yao Zhou
4. Zhiqiang Shen

Papers from Xin Yang's perspective

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

Their papers

• Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm (2018) — 648 citations
• MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning (2019) — 640 citations
• Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks (2017) — 154 citations
• Enabling a Single Deep Learning Model for Accurate Gland Instance Segmentation: A Shape-Aware Adversarial Learning Framework (2020) — 44 citations
• A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation (2019) — 305 citations
• Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation (2018) — 385 citations
• Co‐trained convolutional neural networks for automated detection of prostate cancer in multi‐parametric MRI (2017) — 143 citations