Year: 2024
Type: article
Abstract: The salient object detection (SOD) task aims to automatically detect the most prominent areas observed by the human eye in an image. Since RGB images and depth images contain different information, how to effectively integrate cross-modal features in the RGB-D SOD task remains a major challenge. Therefore, this paper proposes a cross-modal adaptive interaction network (CMANet) for the RGB-D salient object detection task, which consists of a cross-modal feature integration module (CMF) and an adaptive feature fusion module (AFFM). These modules are designed to integrate and enhance multi-scale features from both modalities, improve the effect of integrating cross-modal complementary information of RGB and depth images, enhance feature information, and generate richer and more representative feature maps. Extensive experiments were conducted on four RGB-D datasets to verify the effectiveness of CMANet. Compared with 17 RGB-D SOD methods, our model accurately detects salient regions in images and achieves state-of-the-art performance across four evaluation metrics.
Source: Applied Sciences
DOI: 10.3390/app14177440
Institution: Changchun University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,728
Award: G7485320778
OpenAlex ID: W4401894600