UniV2D: Bridging Visual Restoration and Semantic Perception for Underwater Salient Object Detection

• URL: http://arxiv.org/abs/2605.07146v1
• Date: Fri, 08 May 2026 02:31:45 GMT
• Title: UniV2D: Bridging Visual Restoration and Semantic Perception for Underwater Salient Object Detection
• Abstract summary: We propose a Unified Vision-to-Detection Network (UniV2D) to jointly optimize visual restoration and salient object detection.<n>UniV2D introduces a semantic-driven learning paradigm: high-level saliency semantics actively guide the restoration process.<n>It significantly outperforms state-of-the-art methods in both quantitative and qualitative evaluations.
• Score: 71.83097731030254
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Underwater salient object detection (USOD) plays a vital role in marine vision tasks but remains fundamentally challenging due to severe visual degradation, such as selective absorption and medium scattering. Conventional pipelines typically adopt a sequential "enhance-then-detect" paradigm. However, isolating low-level visual restoration from high-level semantic perception often leads to semantic inconsistency, where the restored images may not be optimal for detection and can even introduce task-irrelevant noise. To break this sequential bottleneck, we propose UniV2D, a Unified Vision-to-Detection Network that jointly optimizes visual restoration and salient object detection within a mutually beneficial framework. Unlike traditional methods that rely on disjointed pipelines or rigid physical priors, UniV2D introduces a semantic-driven learning paradigm: high-level saliency semantics actively guide the restoration process, while the restored visual cues reciprocally enhance saliency perception. Specifically, UniV2D features a hierarchical dual-branch architecture. It first employs a self-calibrated decoder to predict initial saliency masks alongside a mask-aware restoration module to reconstruct image content. Subsequently, a saliency-guided refinement module equipped with cross-level modulation is utilized to align structural fidelity with semantic consistency. Extensive experiments across multiple benchmarks demonstrate that UniV2D significantly outperforms state-of-the-art methods in both quantitative and qualitative evaluations, establishing a new standard for joint underwater perception.

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