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Weakly-Supervised Semantic Segmentation Based on Improved CAM

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Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 89))

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Abstract

When image-level labels are used for weakly-supervised semantic segmentation, there is a problem of missing target position and shape. Using classification networks to generate class activation maps has become a common method. Since the location map generated by Class Activation Mapping (CAM) tends to focus only on a small part of the significant area of the target, the generated class activation map is sparse and rough. To solve this common problem, an improved method based on CAM is proposed, which suppresses the classifier’s attention to salient regions, guides and spreads to adjacent non-salient regions, and generates denser segmentation labels. The generated segmentation label is enhanced by an improved VGG-16 network, and the high-quality pseudo-segment label will be used as the supervision information to supervise the training of the segmentation network. The comparison of the results on the PASCAL VOC 2012 proves the effectiveness of the method and excellent segmentation performance.

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Author information

Authors and Affiliations

  1. Xi’an University of Posts and Telecommunications, Xi’an, 710121, China

    Xingya Yan, Ying Gao & Gaihua Wang

Authors
  1. Xingya Yan
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  2. Ying Gao
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  3. Gaihua Wang
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Corresponding author

Correspondence to Ying Gao .

Editor information

Editors and Affiliations

  1. Guizhou University, Guiyang, China

    Quan Xie

  2. Computer Science and Mathematics, University of São Paulo (USP), Ribeirão Preto, São Paulo, Brazil

    Liang Zhao

  3. College of Electrical and Information, Hunan University, Changsha, China

    Kenli Li

  4. Department of Mathematics, NIT Jalandhar, Jalandhar, India

    Anupam Yadav

  5. Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

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Yan, X., Gao, Y., Wang, G. (2022). Weakly-Supervised Semantic Segmentation Based on Improved CAM. In: Xie, Q., Zhao, L., Li, K., Yadav, A., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 89. Springer, Cham. https://doi.org/10.1007/978-3-030-89698-0_60

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