S2A:Semantic-to-Spatial Alignment for Alignment-Free RGB-T Salient Object Detection

2026-09-23

AI Summary

本文提出S2A框架，通过语义到空间的对齐方法解决未对齐RGB-T图像中显著物体检测时的空间错位问题。

Abstract

Alignment-free RGB-T salient object detection (RGB-T SOD) aims to identify salient objects from unregistered RGB and thermal image pairs without costly pre-alignment. However, spatial misalignment breaks pixel-wise correspondence and causes feature contamination during cross-modal fusion. To address this issue, we propose S2A, a semantic-to-spatial alignment framework for alignment-free RGB-T SOD. Specifically, a global-guided hierarchical fusion module (GGHF) first exploits global semantic guidance to suppress background interference and refine hierarchical intra-modal features. Subsequently, the alignment-free cross-modal channel attention module (AFCA) globally exchanges complementary semantic information through channel-wise interaction, effectively overcoming the interference caused by local spatial misalignments. Finally, a spatial deformable cross-attention module (SDCA) predicts adaptive sampling offsets to recover local cross-modal spatial correspondence. Through this semantic-to-spatial paradigm, S2A first enables reliable cross-modal semantic interaction and subsequently performs local spatial calibration, effectively reducing misalignment-induced feature contamination. Without bells and whistles, S2A achieves highly competitive performance on multiple public alignment-free RGB-T benchmarks, demonstrating its effectiveness in alleviating misalignment-induced feature contamination.

Problem

• alignment-free
• RGB-T SOD
• spatial misalignment
• feature contamination
• cross-modal fusion

Innovation

• Semantic-to-spatial alignment
• Global-guided hierarchical fusion
• Alignment-free cross-modal channel attention
• Spatial deformable cross-attention