Weakly-Supervised Semantic Segmentation by Iterative Affinity Learning

Xiang Wang, Sifei Liu, Huimin Ma, Ming Hsuan Yang

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)


Weakly-supervised semantic segmentation is a challenging task as no pixel-wise label information is provided for training. Recent methods have exploited classification networks to localize objects by selecting regions with strong response. While such response map provides sparse information, however, there exist strong pairwise relations between pixels in natural images, which can be utilized to propagate the sparse map to a much denser one. In this paper, we propose an iterative algorithm to learn such pairwise relations, which consists of two branches, a unary segmentation network which learns the label probabilities for each pixel, and a pairwise affinity network which learns affinity matrix and refines the probability map generated from the unary network. The refined results by the pairwise network are then used as supervision to train the unary network, and the procedures are conducted iteratively to obtain better segmentation progressively. To learn reliable pixel affinity without accurate annotation, we also propose to mine confident regions. We show that iteratively training this framework is equivalent to optimizing an energy function with convergence to a local minimum. Experimental results on the PASCAL VOC 2012 and COCO datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.

Original languageEnglish
Pages (from-to)1736-1749
Number of pages14
JournalInternational Journal of Computer Vision
Issue number6
Publication statusPublished - 2020 Jun 1

Bibliographical note

Funding Information:
This work is supported by National Key Basic Research Program of China (No. 2016YFB0100900), Beijing Science and Technology Planning Project (No. Z191100007419001), National Natural Science Foundation of China (No. 61773231), and National Science Foundation (CAREER No. 1149783).

Publisher Copyright:
© 2020, Springer Science+Business Media, LLC, part of Springer Nature.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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