Assisting RGB and depth salient object detection with nonconvolutional encoder: an improvement approach

Abstract

RGB-D salient object detection is a challenging task in computer vision, and deep architectures have been widely adopted in the previous studies. However, current convolutional neural network (CNN)-based models struggle with capturing global long-distance features efficiently, whereas transformer-based methods are computationally intensive. To address these limitations, we propose a nonconvolutional feature encoder. This encoder captures long-distance dependencies while reducing computation costs, making it a potential alternative to CNNs and transformers. Additionally, we introduce a spatial info enhancing mechanism to overcome weakened local information while capturing long-range dependencies. This mechanism balances local and global information at different expansion rates by exploring multiscale feature fusion in the feature maps. Furthermore, we introduce a spatial info sensing module to enhance the compatibility of multimodal features in longrange dependencies and extract informative cues from depth features. Through comprehensive experiments on four widely used datasets, we demonstrate that our proposed involution encoder significantly outperforms previous state-of-the-art RGB-D salient object detection methods based on CNNs in four key metrics. Compared to transformer-based methods, our approach balances speed and efficiency favorably.