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In each layer, the features in both modules are refined and guided by the spatial information obtained from the other module through ISGMs. In the RGB module, before sending the depth-guided feature map to the decoder, a convolutional gated recurrent unit (ConvGRU)-based block is introduced to handle temporal information. Thinking about the clear movement information in RGB features, the block also guides temporal information in DRM. By merging the results from both the DRM and RGB modules, a segmentation map with distinct boundaries is generated. Considering the lack of depth images in popular public datasets, we utilize a depth estimation network that incorporates manual postprocessing-based correction to generate depth images on the DAVIS and UVSD datasets. The state-of-the-art performance achieved on both the original and new datasets illustrates the advantage of our RGBD feature fusion strategy, with a real-time speed of 19 fps on a single GPU.<\/jats:p>","DOI":"10.1007\/s40747-023-01072-w","type":"journal-article","created":{"date-parts":[[2023,5,6]],"date-time":"2023-05-06T09:02:17Z","timestamp":1683363737000},"page":"6343-6358","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Salient object detection for RGBD video via spatial interaction and depth-based boundary refinement"],"prefix":"10.1007","volume":"9","author":[{"given":"Yujian","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Ziyan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Ping","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Mengnan","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,6]]},"reference":[{"issue":"5","key":"1072_CR1","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1109\/TCSVT.2013.2280096","volume":"24","author":"Z Ren","year":"2013","unstructured":"Ren Z, Gao S, Chia L, Tsang IW (2013) Region-based saliency detection and its application in object recognition. 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