Adversarial Learning of Object-Aware Activation Map for Weakly-Supervised Semantic Segmentation

Junliang Chen, Weizeng Lu, Yuexiang Li, Linlin Shen, Jinming Duan

Research output: Journal PublicationArticlepeer-review

2 Citations (Scopus)


Recent years have witnessed impressive advances in the area of weakly-supervised semantic segmentation (WSSS). However, most of existing approaches are based on class activation maps (CAMs), which suffer from the under-segmentation problem (i.e., objects of interest are segmented partially). Although a number of literature works have been proposed to tackle this under-segmentation problem, we argue that these solutions built on CAMs may not be optimal for the WSSS task. Instead, in this paper we propose a network based on the object-aware activation map (OAM). The proposed network, termed OAM-Net, consists of four loss functions (foreground loss, background loss, average pixel and consistency loss) which ensure exactness, completeness, compactness and consistency of segmented objects via adversarial training. Compared to conventional CAM-based methods, our OAM-Net overcomes the under-segmentation drawback and significantly improves segmentation accuracy with negligible computational cost. A thorough comparison between OAM-Net and CAM-based approaches is carried out on the PASCAL VOC2012 dataset, and experimental results show that our network outperforms state-of-the-art approaches by a large margin. The code will be available soon.

Original languageEnglish
Pages (from-to)3935-3946
Number of pages12
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number8
Publication statusPublished - 1 Aug 2023
Externally publishedYes


  • Weakly-supervised semantic segmentation
  • class activation map
  • object-aware activation map

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Media Technology


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