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The Journal of Supercomputing
Erschienen in: The Journal of Supercomputing 7/2024

02.12.2023

AAR:Attention Remodulation for Weakly Supervised Semantic Segmentation

verfasst von: Yu-e Lin, Houguo Li, Xingzhu Liang, Mengfan Li, Huilin Liu

Erschienen in: The Journal of Supercomputing | Ausgabe 7/2024

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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.
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Metadaten
Titel
AAR:Attention Remodulation for Weakly Supervised Semantic Segmentation
verfasst von
Yu-e Lin
Houguo Li
Xingzhu Liang
Mengfan Li
Huilin Liu
Publikationsdatum
02.12.2023
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 7/2024
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-023-05786-z

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