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Additionally, very few investigations have been undertaken concerning RGB-D-saliency prediction. The proposed study presents a method based on a hierarchical multimodal adaptive fusion (HMAF) network to facilitate end-to-end prediction of RGB-D saliency. In the proposed method, hierarchical (multilevel) multimodal features are first extracted from an RGB image and depth map using a VGG-16-based two-stream network. Subsequently, the most significant hierarchical features of the said RGB image and depth map are predicted using three two-input attention modules. Furthermore, adaptive fusion of saliencies concerning the above-mentioned fused saliency features of different levels (hierarchical fusion saliency features) can be accomplished using a three-input attention module to facilitate high-accuracy RGB-D visual saliency prediction. Comparisons based on the application of the proposed HMAF-based approach against those of other state-of-the-art techniques on two challenging RGB-D datasets demonstrate that the proposed method outperforms other competing approaches consistently by a considerable margin.<\/jats:p>","DOI":"10.1155\/2020\/8841681","type":"journal-article","created":{"date-parts":[[2020,11,21]],"date-time":"2020-11-21T20:20:07Z","timestamp":1605990007000},"page":"1-9","source":"Crossref","is-referenced-by-count":3,"title":["Hierarchical Multimodal Adaptive Fusion (HMAF) Network for Prediction of RGB-D Saliency"],"prefix":"10.1155","volume":"2020","author":[{"given":"Ying","family":"Lv","sequence":"first","affiliation":[{"name":"School of Information and Electronic Engineering, Zhejiang University of Science & Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3055-2493","authenticated-orcid":true,"given":"Wujie","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information and Electronic Engineering, Zhejiang University of Science & Technology, Hangzhou 310023, China"},{"name":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/7496735"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/7942501"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1109\/tmag.2009.2012691"},{"issue":"3","key":"4","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1007\/s10921-014-0232-1","article-title":"Image fusion for improved detection of near-surface defects in NDT\u2013CE using unsupervised clustering methods","volume":"33","author":"P. 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