Learning from Weak and Noisy Labels for Semantic Segmentation

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 suc...

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Vydané v:IEEE transactions on pattern analysis and machine intelligence Ročník 39; číslo 3; s. 486 - 500
Hlavní autori: Lu, Zhiwu, Fu, Zhenyong, Xiang, Tao, Han, Peng, Wang, Liwei, Gao, Xin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.03.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Computational modeling
Image annotation
Image segmentation
label noise reduction
Labeling
Labels
Machine learning
Noise measurement
Noise reduction
Optimization
Pixels
Semantic segmentation
Semantics
sparse learning
Training
weakly supervised learning
ISSN:0162-8828, 2160-9292, 1939-3539
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Shrnutí: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 -optimisation problem, we further develop an efficient learning algorithm by introducing an intermediate labelling variable. Extensive experiments on three benchmark datasets show that our method yields state-of-the-art results given noise-free labels, whilst significantly outperforming the existing methods when the weak labels are also noisy.
Bibliografia:ObjectType-Article-1
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ISSN:0162-8828
2160-9292
1939-3539
DOI:10.1109/TPAMI.2016.2552172