Yun Liu

Computer Vision and Pattern Recognition · with Cheng · 2016–2021

A researcher in the Science corpus: 6 papers; trusts 1,240 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 334 citations into it; #5,885 of 21,575 by trust here, #5,082 by citations
• Artificial Intelligence — 109 citations into it
• Model Architectures — 74 citations into it
• Cognitive Neuroscience — 37 citations into it
• Media Technology — 22 citations into it
• Efficiency & Compression — 16 citations into it
• Optimization & Training — 15 citations into it
• Aerospace Engineering — 13 citations into it

Whom Yun Liu trusts overall

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

1. Kaiming He
2. Ross B. Girshick
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Karen Simonyan
6. Piotr Doll'ar
7. Diederik P. Kingma
8. Yoshua Bengio
9. Christian Szegedy
10. Andrew Zisserman

Who trusts Yun Liu overall

1. Deng-Ping Fan
2. Ruoxi Deng
3. Ming-Ming Cheng
4. Jing Zhang
5. Yu-Huan Wu
6. Sijie Li
7. Ge-Peng Ji
8. Fan Yang
9. Xueyan Zou
10. Jiangjiang Liu

Papers from Yun Liu's perspective

Ordered by how strongly the researchers Yun Liu 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. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Richer Convolutional Features for Edge Detection (2016) — 1,215 citations
• Structure-Measure: A New Way to Evaluate Foreground Maps (2017) — 1,869 citations
• P2T: Pyramid Pooling Transformer for Scene Understanding (2021) — 313 citations
• Transformer in Convolutional Neural Networks (2021) — 71 citations
• Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks (2019) — 640 citations
• Semantic Edge Detection with Diverse Deep Supervision (2018) — 112 citations