Weakly-Supervised Dual Clustering for Image Semantic Segmentation

By Yang Liu, J. Liu, Zechao Li, Jinhui Tang, Hanqing Lu. 2013

76 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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. Junwei Han
2. Gong Cheng
3. Luc Van Gool
4. Brian L. Price
5. Scott D. Cohen
6. Dacheng Tao
7. Ming-Hsuan Yang
8. Jiashi Feng

In Media Technology

1. Gong Cheng
2. Junwei Han
3. Xiwen Yao
4. Lei Guo
5. Peicheng Zhou
6. Mahmood Fathy
7. Ning Xu
8. Thomas S. Huang

Citations in the corpus

• Deep Interactive Object Selection (2016, methodology): “Many algorithms have been proposed such as superpixel-based [28, 24, 25] and clusteringbased methods [23, 15, 14].”
• STFCN: Spatio-Temporal FCN for Semantic Video Segmentation (2016, methodology): “This field includes clustering algorithms such as k-means and mean-shift [31], or graph-based algorithms [12,18,23,47].”
• Semantic Segmentation based on Stacked Discriminative Autoencoders and Context-Constrained Weakly Supervised Learning (2015, background): “Table 1 reports the average accuracy over all 21 object classes in comparison with state-of-the-art semantic segmentation methods, including both fully supervised (FS) [10, 11] and weakly supervised (WS) [4, 5].”