Ransp: Ranking Attention Network for Saliency Prediction on Omnidirectional Images

Dandan Zhu, Yongqing Chen, Tian Han, Defang Zhao, Yucheng Zhu, Qiangqiang Zhou, Guangtao Zhai, Xiaokang Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Various convolutional neural network (CNN)-based methods have shown the ability to boost the performance of saliency prediction on omnidirectional images (ODIs). However, these methods are limited by sub-optimal accuracy, because not all the features extracted by the CNN model are not useful for the final fine-grained saliency prediction. Features are redundant and have negative impact on the final fine-grained saliency prediction. To tackle this problem, we propose a novel Ranking Attention Network for saliency prediction (RANSP) of head fixations on ODIs. Specifically, the part-guided attention (PA) module and channel-wise feature (CF) extraction module are integrated in a unified framework and are trained in an end-to-end manner for fine-grained saliency prediction. To better utilize the channel-wise feature map, we further propose a new Ranking Attention Module (RAM), which automatically ranks and selects these maps based on scores for fine-grained saliency prediction. Extensive experiments are conducted to show the effectiveness of our method for saliency prediction of ODIs.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Multimedia and Expo, ICME 2020
ISBN (Electronic)9781728113319
DOIs
StatePublished - Jul 2020
Event2020 IEEE International Conference on Multimedia and Expo, ICME 2020 - London, United Kingdom
Duration: 6 Jul 202010 Jul 2020

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
Volume2020-July
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Conference

Conference2020 IEEE International Conference on Multimedia and Expo, ICME 2020
Country/TerritoryUnited Kingdom
CityLondon
Period6/07/2010/07/20

Keywords

  • Channel-wise feature map
  • Omnidirectional images
  • Part-guided attention
  • Ranking attention
  • Saliency prediction

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