Attributed Graph Mining and Matching: An Attempt to Define and Extract Soft Attributed Patterns

By Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki. 2014

19 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Song-Chun Zhu
2. Quanshi Zhang
3. Ying Nian Wu
4. Ruiming Cao
5. Yu Yang
6. Erik Nijkamp
7. Mitch Hill
8. Jianwen Xie

In Computer Vision and Pattern Recognition

1. Song-Chun Zhu
2. Quanshi Zhang
3. Ying Nian Wu
4. Ruiming Cao
5. Alan L. Yuille
6. Erik Nijkamp
7. Luc Van Gool
8. Jianwen Xie

In Signal Processing

1. Song-Chun Zhu
2. Ying Nian Wu
3. Ruiming Cao
4. Quanshi Zhang
5. Jianwen Xie
6. Yang Lu
7. Myle Ott
8. Arthur Szlam

Citations in the corpus

• Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning (2016, methodology): “Inspired by graph mining (Zhang, Wu, and Zhu 2015; Zhang et al. 2016; Zhang et al. 2014), we mine common patterns from CNN activation maps in conv-layers to explain the part.”