USIS-PGM: Photometric Gaussian Mixtures for Underwater Salient Instance Segmentation

Underwater instance segmentation supports marine surveys, aquaculture, and underwater robotics. Haze, colour casts, and low contrast blur object boundaries, and recent benchmarks distinguish up to 158 fine-grained categories. The strongest published methods mostly have 219 to 632 million parameters. This paper proposes CoRef-DETR, a compact detection transformer that targets three error sources of a real-time detector: context, boundaries, and categories. First, a decoupled global context (DGC) detector, with the base detector's 33.4 M parameters, applies global self-attention to a 384-pixel token grid but supervises and decodes masks in a 576-pixel frame. Second, a posterior-conditioned boundary refiner with 47.7 k parameters corrects mask logits on predicted boundaries, and a quality score on the corrected masks re-ranks the detections. Third, a crop-probe category branch on frozen DINOv2 features revises the categories of at most three masks per image, where a rule fixed on validation data chooses soft fusion or a confident-disagreement gate. On five protocols of UIIS, UIIS10K, USIS10K, and USIS16K, CoRef-DETR achieves state-of-the-art mask average precision (AP), exceeding the strongest published results by 2.3 to 5.2 points, and outperforms three reproduced recent methods on the same test images (on USIS16K, with its category branch). In half precision with TensorRT, it processes an image in a median of 34 to 40 ms, JPEG decoding included, on a T4 GPU, 1.4 to 11 times faster than these methods. Ablations attribute most of DGC's gain to the larger frame; an error analysis locates the remaining error in crowds of small objects.

Underwater salient object detection (USOD) has attracted increasing attention for underwater visual scene understanding and vision-guided robotic applications. However, existing USOD methods still struggle with underwater image degradations, which often lead to inaccurate object localization, fragmented salient regions, and coarse boundary prediction. To address these challenges, this paper proposes DSS-USOD, a novel RGB-based USOD method built upon dynamic structural specialization. DSS-USOD extracts a shared base representation from a single underwater image, decomposes it into boundary-sensitive and region-coherent structural features, and dynamically coordinates their contributions according to local structural context. Specifically, the extracted shared base representation is decomposed into a boundary-sensitive branch for modeling fine-grained boundary details and a region-coherent branch for capturing region-level structural consistency. A spatial coordination module is then introduced to adaptively regulate the relative contributions of the two branches according to local structural context. Moreover, cooperative structural supervision is introduced to promote branch specialization and stabilize spatial coordination, enabling DSS-USOD to better balance boundary precision and region coherence under degraded underwater conditions. Extensive experiments show that DSS-USOD achieves superior performance on benchmark datasets. Finally, real-world deployment on an underwater robot validates the practical effectiveness of DSS-USOD for underwater object inspection.

Open-vocabulary instance segmentation (OVIS) provides a promising solution for marine perception by recognizing and segmenting categories beyond predefined label spaces. Existing OVIS methods usually rely on vision-language alignment, where visual region features are matched with textual category embeddings to recognize both seen and unseen classes. However, this alignment assumption becomes fragile in underwater environments. Color attenuation, scattering, turbidity, and low contrast distort visual appearance, causing region features to deviate from their corresponding semantic concepts. As a result, the similarity between degraded visual features and textual classifiers becomes unreliable, which particularly weakens the recognition of unseen underwater categories that cannot be corrected by category-specific supervision. To address these issues, we propose GeoPrompt, a geometry-conditioned prompt learning framework for underwater open-vocabulary instance segmentation. Our key insight is that geometric structure is more stable than visual appearance in degraded underwater scenes. Specifically, GeoPrompt introduces a geometry-guided visual adaptation module (GVAM) to extract depth-derived structural priors and inject them into multi-scale visual features. It further develops a geometry-conditioned prompt learning (GCPL) that generates geometry-aware textual classifiers from structural representations. These geometry-aware classifiers are integrated with fixed and image-conditioned textual classifiers to improve open-vocabulary semantic alignment. Experiments on the MARIS-OpenIS benchmark show that GeoPrompt achieves state-of-the-art performance and brings consistent improvements on underwater open-vocabulary instance segmentation.