Empowering DINO Representations for Underwater Instance Segmentation via Aligner and Prompter
This work addresses underwater instance segmentation for marine resource exploration and ecological protection, presenting an incremental improvement by adapting existing foundation models to a specific domain.
The paper tackles underwater instance segmentation by adapting DINO pretrained models with a framework called DiveSeg, which includes an AquaStyle Aligner for domain adaptation and an ObjectPrior Prompter for object-level guidance, achieving state-of-the-art performance on UIIS and USIS10K datasets.
Underwater instance segmentation (UIS), integrating pixel-level understanding and instance-level discrimination, is a pivotal technology in marine resource exploration and ecological protection. In recent years, large-scale pretrained visual foundation models, exemplified by DINO, have advanced rapidly and demonstrated remarkable performance on complex downstream tasks. In this paper, we demonstrate that DINO can serve as an effective feature learner for UIS, and we introduce DiveSeg, a novel framework built upon two insightful components: (1) The AquaStyle Aligner, designed to embed underwater color style features into the DINO fine-tuning process, facilitating better adaptation to the underwater domain. (2) The ObjectPrior Prompter, which incorporates binary segmentation-based prompts to deliver object-level priors, provides essential guidance for instance segmentation task that requires both object- and instance-level reasoning. We conduct thorough experiments on the popular UIIS and USIS10K datasets, and the results show that DiveSeg achieves the state-of-the-art performance. Code: https://github.com/ettof/Diveseg.