Rethinking Conditional Generation for Underwater Salient Object Detection

• URL: http://arxiv.org/abs/2607.01825v1
• Date: Thu, 02 Jul 2026 07:48:06 GMT
• Title: Rethinking Conditional Generation for Underwater Salient Object Detection
• Abstract summary: A Conditional Generation Network (DCGNet) is designed to construct reliable conditional features for underwater saliency generation.<n>DCGNet significantly outperforms existing state-of-the-art methods, verifying its potential for complex underwater visual applications.
• Score: 69.38806804258452
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Salient Object Detection in underwater images remains challenging due to low contrast, uneven illumination, and color distortion caused by scattering and absorption effects, which limit the effectiveness of conventional SOD methods in underwater environments. To address these challenges, we propose a Degradation-aware Conditional Generation Network (DCGNet), specifically designed to construct reliable conditional features for underwater saliency generation. First, we design a Dynamic Multi-Granularity module (DMG) grounded in the human visual system to robustly detect salient objects of varying scales with blurred boundaries. Then, we develop an Underwater Physics-Prior module (UPP), which utilizes pseudo-depth guidance to estimate underwater light attenuation and backscatter, thereby restoring degradation-aware RGB features and mitigating color distortion and boundary ambiguity. Based on the physics-guided representation, we introduce an Underwater Spatial Gaussian module (USG), which constructs a spatial Gaussian saliency prior from the strongest guided response to enhance object-centered salient regions and suppress cluttered underwater backgrounds. In addition, a lightweight timestep-adaptive Diffusion Transformer (DiT) bottleneck is inserted into the denoising decoder to refine fused features at different diffusion timesteps. Comprehensive experiments on USOD10K, USOD, CSOD10K, MAS3K, and RMAS demonstrate that DCGNet significantly outperforms existing state-of-the-art methods, verifying its potential for complex underwater visual applications.

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