Minghui Liao

Computer Vision and Pattern Recognition · 2016–2022

A researcher in the Science corpus: 8 papers; trusts 1,245 researchers and is trusted by 46 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 324 citations into it; #3,739 of 21,575 by trust here, #4,212 by citations
• Media Technology — 97 citations into it; #429 of 3,621 by trust here, #447 by citations
• Artificial Intelligence — 74 citations into it; #13,014 of 35,345 by trust here, #15,542 by citations
• Natural Language Processing — 38 citations into it
• Model Architectures — 32 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Optimization & Training — 9 citations into it
• Signal Processing — 9 citations into it; #2,160 of 7,572 by trust here, #2,114 by citations

Whom Minghui Liao trusts overall

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

1. Ross B. Girshick
2. Kaiming He
3. Baoguang Shi
4. Xiang Bai
5. Cong Yao
6. Geoffrey E. Hinton
7. Karen Simonyan
8. Alex Krizhevsky
9. Diederik P. Kingma
10. Andrew Zisserman

Who trusts Minghui Liao overall

1. Xue Yang
2. Shangbang Long
3. Cong Yao
4. Pengyuan Lyu
5. Xiang Bai
6. Xiaobing Wang
7. Chengquan Zhang
8. Zhaoyi Wan
9. Junchi Yan
10. Jian Ding

Papers from Minghui Liao's perspective

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

Their papers

• Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes (2018) — 642 citations
• TextBoxes: A Fast Text Detector with a Single Deep Neural Network (2016) — 923 citations
• TextBoxes++: A Single-Shot Oriented Scene Text Detector (2018) — 800 citations
• Rotation-Sensitive Regression for Oriented Scene Text Detection (2018) — 487 citations
• Real-Time Scene Text Detection With Differentiable Binarization and Adaptive Scale Fusion (2022) — 370 citations
• Real-time Scene Text Detection with Differentiable Binarization (2019) — 850 citations
• Symmetry-Constrained Rectification Network for Scene Text Recognition (2019) — 137 citations
• Cascaded Segmentation-Detection Networks for Text-Based Traffic Sign Detection (2018) — 97 citations