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October 2, 2025Open Access

USIS16K: High-Quality Dataset for Underwater Salient Instance Segmentation

Key Points

The USIS16K dataset comprises 16,151 high-resolution images, enabling significant advancements in saliency prediction and instance segmentation.
High-quality instance-level masks are provided, representing various underwater objects across 158 categories, enhancing dataset diversity.
Benchmark evaluations demonstrate improved object detection techniques, facilitating further research in underwater environments.
The availability of USIS16K supports the development of advanced models for addressing challenges in underwater perception.

Abstract

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.

USIS16K: High-Quality Dataset for Underwater Salient Instance Segmentation

Key Points

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