Multi-scale capsule attention-based salient object detection with multi-crossed layer connections

Qi Qi, Sanyuan Zhao, Jianbing Shen, Kin Man Lam

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

12 Citations (Scopus)


With the popularization of convolutional networks being used for saliency models, saliency detection performance has achieved significant improvement. However, how to integrate accurate and crucial features for modeling saliency is still underexplored. In this paper, we present CapSalNet, which includes a multi-scale Capsule attention module and multi-crossed layer connections for Salient object detection. We first propose a novel capsule attention model, which integrates multi-scale contextual information with dynamic routing. Then, our model adaptively learns to aggregate multi-level features by using multi-crossed skip-layer connections. Finally, the predicted results are efficiently fused to generate the final saliency map in a coarse-to-fine manner. Comprehensive experiments on four benchmark datasets demonstrate that our proposed algorithm outperforms existing state-of-the-art approaches.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Multimedia and Expo, ICME 2019
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9781538695524
Publication statusPublished - 8 Jul 2019
Event2019 IEEE International Conference on Multimedia and Expo, ICME 2019 - Shanghai, China
Duration: 8 Jul 201912 Jul 2019

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X


Conference2019 IEEE International Conference on Multimedia and Expo, ICME 2019


  • Capsule attention
  • Multi-crossed layer connections
  • Salient object detection

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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