SOD-diffusion: Salient Object Detection via Diffusion-Based Image Generators

Abstract

Salient Object Detection (SOD) is a challenging task that aims to precisely identify and segment the salient objects. However, existing SOD methods still face challenges in making explicit predictions near the edges and often lack end-to-end training capabilities. To alleviate these problems, we propose SOD-diffusion, a novel framework that formulates salient object detection as a denoising diffusion process from noisy masks to object masks. Specifically, object masks diffuse from ground-truth masks to random distribution in latent space, and the model learns to reverse this noising process to reconstruct object masks. To enhance the denoising learning process, we design an attention feature interaction module (AFIM) and a specific fine-tuning protocol to integrate conditional semantic features from the input image with diffusion noise embedding. Extensive experiments on five widely used SOD benchmark datasets demonstrate that our proposed SOD-diffusion achieves favorable performance compared to previous well-established methods. Furthermore, leveraging the outstanding generalization capability of SOD-diffusion, we applied it to publicly available images, generating high-quality masks that serve as an additional SOD benchmark testset.