Object Discovery: Soft Attributed Graph Mining

By Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki. 2016

9 citations recorded by the source corpus.

Subfields: 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 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; 2014), we mine common patterns from CNN activation maps in conv-layers to explain the part.”
• Mining And-Or Graphs for Graph Matching and Object Discovery (2015, methodology): “Experiment 1, Mining from RGB-D images: We applied our method to five categories in the Kinect RGBD image database [40]—notebook PC, drink box, basket, bucket, and dustpan, which is widely used [40, 39, 41, 42] as a benchmark dataset to evaluate graph matching performance.”