DiMSOD: A Diffusion-Based Framework for Multi-Modal Salient Object Detection
Shuo Zhang, Jiaming Huang, Wenbing Tang, Yan Wu, Terrence Hu, Xiaogang Xu, Jing Liu
Abstract
Multi-modal salient object detection (SOD) through the integration of additional data such as depth or thermal information has become a significant task in computer vision during recent years. Traditionally, the challenges of identifying salient objects in RGB, RGB-D (Depth), and RGB-T (Thermal) images are tackled separately. However, without intricate cross-modal fusion strategies, such approaches struggle to effectively integrate multi-modal information, often resulting in poorly defined object edges or overconfident inaccurate predictions. Recent studies have shown that designing a unified end-to-end framework to handle all three types of SOD tasks simultaneously is both necessary and difficult. To address this need, we propose a novel approach that treats multi-modal SOD as a conditional mask generation task utilizing diffusion models. We introduce DiMSOD, which enables the concurrent use of local (depth maps, thermal maps) and global controls (original images) within a unified model for progressive denoising and refined prediction. DiMSOD is efficient, only requiring fine-tuning of our newly introduced modules on the existing stable diffusion, which not only reduces the fine-tuning cost, making it more viable for practical use, but also enhances the integration of multi-modal conditional controls. Specifically, we have developed modules including SOD-ControlNet, Feature Adaptive Network (FAN), and Feature Injection Attention Network (FIAN) to enhance the model's performance. Extensive experiments demonstrate that DiMSOD efficiently detects salient objects across RGB, RGB-D, and RGB-T datasets, achieving superior performance compared to previous well-established methods.
BibTeX
@article{Zhang_Huang_Tang_Wu_Hu_Xu_Liu_2025, title={DiMSOD: A Diffusion-Based Framework for Multi-Modal Salient Object Detection}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/33096}, DOI={10.1609/aaai.v39i10.33096}, abstractNote={Multi-modal salient object detection (SOD) through the integration of additional data such as depth or thermal information has become a significant task in computer vision during recent years. Traditionally, the challenges of identifying salient objects in RGB, RGB-D (Depth), and RGB-T (Thermal) images are tackled separately. However, without intricate cross-modal fusion strategies, such approaches struggle to effectively integrate multi-modal information, often resulting in poorly defined object edges or overconfident inaccurate predictions.
Recent studies have shown that designing a unified end-to-end framework to handle all three types of SOD tasks simultaneously is both necessary and difficult. To address this need, we propose a novel approach that treats multi-modal SOD as a conditional mask generation task utilizing diffusion models. We introduce DiMSOD, which enables the concurrent use of local (depth maps, thermal maps) and global controls (original images) within a unified model for progressive denoising and refined prediction. DiMSOD is efficient, only requiring fine-tuning of our newly introduced modules on the existing stable diffusion, which not only reduces the fine-tuning cost, making it more viable for practical use, but also enhances the integration of multi-modal conditional controls. Specifically, we have developed modules including SOD-ControlNet, Feature Adaptive Network (FAN), and Feature Injection Attention Network (FIAN) to enhance the model’s performance. Extensive experiments demonstrate that DiMSOD efficiently detects salient objects across RGB, RGB-D, and RGB-T datasets, achieving superior performance compared to previous well-established methods.}, number={10}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Zhang, Shuo and Huang, Jiaming and Tang, Wenbing and Wu, Yan and Hu, Terrence and Xu, Xiaogang and Liu, Jing}, year={2025}, month={Apr.}, pages={10103-10111} }