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


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 (<italic>i.e</italic>., 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)1
Number of pages1
JournalIEEE Transactions on Circuits and Systems for Video Technology
Publication statusAccepted/In press - 2023
Externally publishedYes


  • Cams
  • class activation map
  • Object segmentation
  • object-aware activation map
  • Semantic segmentation
  • Semantics
  • Streaming media
  • Task analysis
  • Training
  • weakly-supervised semantic segmentation

ASJC Scopus subject areas

  • Media Technology
  • Electrical and Electronic Engineering


Dive into the research topics of 'Adversarial Learning of Object-Aware Activation Map for Weakly-Supervised Semantic Segmentation'. Together they form a unique fingerprint.

Cite this