Marine Saliency Segmenter: Object-Focused Conditional Diffusion with Region-Level Semantic Knowledge Distillation.

Abstract

Marine Saliency Segmentation (MSS) plays a pivotal role in a wide range of vision-based marine exploration tasks. However, existing techniques often face the dilemma of imprecise boundaries due to the interference-rich nature of underwater environments, where suspended particles, low contrast, and color distortion hinder accurate segmentation. Although diffusion models have shown impressive performance in visual tasks, their potential to incorporate contextual semantics for enhancing feature learning of region-level salient objects remains under-explored, thereby hindering segmentation outcomes. Building on this insight, we propose DiffMSS, a novel marine saliency segmenter based on the diffusion model, which utilizes semantic knowledge distillation to guide the detection of marine salient objects. Specifically, we design the Word-level Semantic Saliency Extraction module that identifies salient terms at the word level from the captions by computing region-word similarity. These high-level semantic features are distilled into the Conditional Feature Learning Network to generate accurate and semantically informed diffusion conditions. The Object-Focused Conditional Diffusion module then leverages these conditions to iteratively generate fine-grained segmentation masks of marine instances, while a Consensus Deterministic Sampling scheme is further employed to suppress overconfident mis-segmentations and enhance structural fidelity. Extensive experiments demonstrate the superior performance of DiffMSS over state-of-the-art methods in both quantitative and qualitative evaluations. Our code and pre-trained models will be released on GitHub.