构建1.6万张水下显著实例分割数据集，推动水下视觉理解研究
USIS16K: High-Quality Dataset for Underwater Salient Instance Segmentation
- 基于生物视觉注意力机制，统一处理水下目标显著性与实例分割
- 涵盖158类物体的16,151张高清图像，标注了精细的实例级显著区域
- 首次提供水下目标检测与显著实例分割的基准评测，开放数据集
受生物视觉系统选择性关注显著目标的启发，水下显著实例分割（USIS）旨在同步解决‘看哪里’（显著性预测）和‘是什么’（实例分割）的问题。然而，由于水下环境难访问、动态性强，且缺乏大规模高质量标注数据，该任务仍处于探索阶段。本文提出USIS16K，一个包含16,151张高分辨率水下图像的大规模数据集，覆盖158种水下物体类别，采自多样环境。每张图像均配有高质量实例级显著对象掩码，显著提升数据多样性、复杂性与可扩展性。同时，我们基于USIS16K提供了水下目标检测与USIS任务的基准评测。为促进该领域研究，数据集及基准模型已公开。
原文摘要 · Abstract (English)
Inspired by the biological visual system that selectively allocates attention to efficiently identify salient objects or regions, underwater salient instance segmentation (USIS) aims to jointly address the problems of where to look (saliency prediction) and what is there (instance segmentation) in underwater scenarios. However, USIS remains an underexplored challenge due to the inaccessibility and dynamic nature of underwater environments, as well as the scarcity of large-scale, high-quality annotated datasets. In this paper, we introduce USIS16K, a large-scale dataset comprising 16,151 high-resolution underwater images collected from diverse environmental settings and covering 158 categories of underwater objects. Each image is annotated with high-quality instance-level salient object masks, representing a significant advance in terms of diversity, complexity, and scalability. Furthermore, we provide benchmark evaluations on underwater object detection and USIS tasks using USIS16K. To facilitate future research in this domain, the dataset and benchmark models are publicly available.
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