Semantic Segmentation based on Stacked Discriminative Autoencoders and Context-Constrained Weakly Supervised Learning

By Xiwen Yao, Junwei Han, Gong Cheng, Lei Guo. 2015

11 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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. Liqing Zhang
2. Li Niu
3. Zhangxuan Gu
4. Zihan Zhao
5. Siyuan Zhou
6. Chen Change Loy
7. Bo Dai
8. Chong Zhou

In Computer Vision and Pattern Recognition

1. Liqing Zhang
2. Li Niu
3. Zhangxuan Gu
4. Zihan Zhao
5. Siyuan Zhou
6. Chen Change Loy
7. Bo Dai
8. Chong Zhou

Citations in the corpus

• Context-aware Feature Generation For Zero-shot Semantic Segmentation (2020, background): “To reduce the annotation pressure, leveraging weak annotations like image-level [34, 35, 47], box-level [18, 36], or scribble-level [24] annotations for semantic segmentation recently gained the interest of researchers.”