Yinan Yu

Computer Vision and Pattern Recognition · 2011–2019

A researcher in the Science corpus: 8 papers; trusts 1,102 researchers and is trusted by 283 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 247 citations into it; #2,353 of 21,575 by trust here, #2,949 by citations
• Artificial Intelligence — 91 citations into it; #10,905 of 35,345 by trust here, #8,439 by citations
• Aerospace Engineering — 22 citations into it; #5,837 of 6,421 by trust here, #5,612 by citations
• Model Architectures — 22 citations into it
• Evaluation & Benchmarks — 17 citations into it
• Optimization & Training — 16 citations into it
• Media Technology — 13 citations into it; #3,300 of 3,621 by trust here, #3,192 by citations
• 3D Vision & Pose — 5 citations into it

Whom Yinan Yu trusts overall

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

1. Ross B. Girshick
2. Kaiming He
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Christian Szegedy
6. Karen Simonyan
7. Yann LeCun
8. Ilya Sutskever
9. David G. Lowe
10. Andrew Zisserman

Who trusts Yinan Yu overall

1. Wanli Ouyang
2. Suo Qiu
3. Bo Li
4. Jan Hendrik Hosang
5. Nantheera Anantrasirichai
6. De-An Huang
7. Xinggang Wang
8. Alexander Richard
9. Xiaogang Wang
10. Liang-Chieh Chen

Papers from Yinan Yu's perspective

Ordered by how strongly the researchers Yinan Yu 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. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• DenseBox: Unifying Landmark Localization with End to End Object Detection (2015) — 484 citations
• Look and Think Twice: Capturing Top-Down Visual Attention with Feedback Convolutional Neural Networks (2015) — 398 citations
• SSAP: Single-Shot Instance Segmentation With Affinity Pyramid (2019) — 248 citations
• Object detection by labeling superpixels (2015) — 109 citations
• Deep multiple instance learning for image classification and auto-annotation (2015) — 429 citations
• Boosted local structured HOG-LBP for object localization (2011) — 170 citations
• High-Level Semantic Feature Detection: A New Perspective for Pedestrian Detection (2019) — 395 citations
• Parse geometry from a line: Monocular depth estimation with partial laser observation (2016) — 108 citations