1. Learning from Weak and Noisy Labels for Semantic Segmentation.
- Author
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Lu, Zhiwu, Fu, Zhenyong, Xiang, Tao, Han, Peng, Wang, Liwei, and Gao, Xin
- Subjects
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IMAGE segmentation , *SEMANTIC networks (Information theory) , *ARTIFICIAL neural networks , *MACHINE learning - Abstract
A weakly supervised semantic segmentation (WSSS) method aims to learn a segmentation model from weak (image-level) as opposed to strong (pixel-level) labels. By avoiding the tedious pixel-level annotation process, it can exploit the unlimited supply of user-tagged images from media-sharing sites such as Flickr for large scale applications. However, these ‘free’ tags/labels are often noisy and few existing works address the problem of learning with both weak and noisy labels. In this work, we cast the WSSS problem into a label noise reduction problem. Specifically, after segmenting each image into a set of superpixels, the weak and potentially noisy image-level labels are propagated to the superpixel level resulting in highly noisy labels; the key to semantic segmentation is thus to identify and correct the superpixel noisy labels. To this end, a novel $L_1$
-optimisation based sparse learning model is formulated to directly and explicitly detect noisy labels. To solve the $L_1$- Published
- 2017
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