USIS-PGM: Photometric Gaussian Mixtures for Underwater Salient Instance Segmentation
This addresses underwater image degradation for marine robotic systems, but appears incremental as it builds on existing segmentation methods with specific adaptations.
The paper tackles underwater salient instance segmentation (USIS) by proposing USIS-PGM, a single-stage framework that enhances boundary cues and uses Photometric Gaussian Mixture supervision, achieving superior results in experiments.
Underwater salient instance segmentation (USIS) is crucial for marine robotic systems, as it enables both underwater salient object detection and instance-level mask prediction for visual scene understanding. Compared with its terrestrial counterpart, USIS is more challenging due to the underwater image degradation. To address this issue, this paper proposes USIS-PGM, a single-stage framework for USIS. Specifically, the encoder enhances boundary cues through a frequency-aware module and performs content-adaptive feature reweighting via a dynamic weighting module. The decoder incorporates a Transformer-based instance activation module to better distinguish salient instances. In addition, USIS-PGM employs multi-scale Gaussian heatmaps generated from ground-truth masks through Photometric Gaussian Mixture (PGM) to supervise intermediate decoder features, thereby improving salient instance localization and producing more structurally coherent mask predictions. Experimental results demonstrate the superiority and practical applicability of the proposed USIS-PGM model.