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- Title
AAR:Attention Remodulation for Weakly Supervised Semantic Segmentation.
- Authors
Lin, Yu-e; Li, Houguo; Liang, Xingzhu; Li, Mengfan; Liu, Huilin
- Abstract
Weakly Supervised Semantic Segmentation is a crucial task in computer vision. However, existing methods that utilize Class Activation Maps (CAMs) with classification tasks can only identify a small part of the region. To address this limitation, we propose a novel Attention Activation Remodulation (AAR) scheme that leverages traditional CAMs and the remodulation branch to obtain weighted CAMs for recalibrated supervision. The AAR scheme re-arranges important features' distribution from the channel and space perspectives, which regulates segmentation-oriented activation responses. In addition, we propose a Feature Pixel Extraction Module (FPEM) that utilizes contextual information to improve pixel prediction. Furthermore, the proposed scheme can be combined with other methods to improve overall performance. Extensive experiments on the PASCAL VOC 2012 dataset demonstrate the effectiveness of the AAR mechanism and FPEM module.
- Subjects
FEATURE extraction; MARKETING channels
- Publication
Journal of Supercomputing, 2024, Vol 80, Issue 7, p9096
- ISSN
0920-8542
- Publication type
Article
- DOI
10.1007/s11227-023-05786-z