用风格对齐与先验提示增强DINO，提升水下实例分割精度
Empowering DINO Representations for Underwater Instance Segmentation via Aligner and Prompter
- 引入水下风格对齐器，融合水色特征优化DINO微调
- 设计物体先验提示器，通过二值分割提供实例级引导
- 在UIIS和USIS10K上达最优性能，适合水下视觉任务研究者
水下实例分割（UIS）融合像素级理解与实例级区分，是海洋资源勘探与生态保护的关键技术。近年来，以DINO为代表的大型预训练视觉基础模型快速发展，在复杂下游任务中表现卓越。本文证明DINO可作为有效的特征提取器用于UIS，提出DiveSeg框架，包含两个关键组件：(1) AquaStyle Aligner，将水下色彩风格特征嵌入DINO微调过程，提升对水下域的适应性；(2) ObjectPrior Prompter，利用二值分割生成的提示提供对象级先验，为需同时进行对象与实例推理的任务提供关键指导。在主流数据集UIIS和USIS10K上开展全面实验，结果表明DiveSeg达到当前最优性能。代码已开源：https://github.com/ettof/Diveseg。
原文摘要 · Abstract (English)
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.
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