Position-Aware Relation Learning for RGB-Thermal Salient Object Detection

Abstract

Salient object detection (SOD) is an important task in computer vision that aims to identify visually conspicuous regions in images. RGB-Thermal SOD combines two spectra to achieve better segmentation results. However, most existing methods for RGB-T SOD use boundary maps to learn sharp boundaries, which lead to sub-optimal performance as they ignore the interactions between isolated boundary pixels and other confident pixels. To address this issue, we propose a novel position-aware relation learning network (PRLNet) for RGB-T SOD. PRLNet explores the distance and direction relationships between pixels by designing an auxiliary task and optimizing the feature structure to strengthen intra-class compactness and inter-class separation. Our method consists of two main components: A signed distance map auxiliary module (SDMAM), and a feature refinement approach with direction field (FRDF). SDMAM improves the encoder feature representation by considering the distance relationship between foreground-background pixels and boundaries, which increases the inter-class separation between foreground and background features. FRDF rectifies the features of boundary neighborhoods by exploiting the features inside salient objects. It utilizes the direction relationship of object pixels to enhance the intra-class compactness of salient features. In addition, we constitute a transformer-based decoder to decode multispectral feature representation. Experimental results on three public RGB-T SOD datasets demonstrate that our proposed method not only outperforms the state-of-the-art methods, but also can be integrated with different backbone networks in a plug-and-play manner. Ablation study and visualizations further prove the validity and interpretability of our method.