Saliency detection with multi-contextual models and spatially coherent loss function

Po Sheng Huang, Chin Han Shen, Hsu-Feng Hsiao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We have proposed a multi-contextual model architecture with color and depth information considered independently in this work. To utilize the feature maps of different levels better, short connection structures are used to integrate the knowledge from color and depth data separately. A novel loss function considering three criteria is proposed to improve the detection accuracy and spatial coherence of the detected results. The training process of the proposed network is divided into two stages, a pre-training phase and a refinement phase to increase the efficiency of the network.

Original languageEnglish
Title of host publication2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728103976
DOIs
StatePublished - 1 Jan 2019
Event2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Sapporo, Japan
Duration: 26 May 201929 May 2019

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2019-May
ISSN (Print)0271-4310

Conference

Conference2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019
CountryJapan
CitySapporo
Period26/05/1929/05/19

Keywords

  • Deep learning
  • Multi-contextual model
  • Saliency detection

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  • Cite this

    Huang, P. S., Shen, C. H., & Hsiao, H-F. (2019). Saliency detection with multi-contextual models and spatially coherent loss function. In 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings [8702378] (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAS.2019.8702378