USIS16K: High-Quality Dataset for Underwater Salient Instance Segmentation
This addresses the problem of limited data for underwater computer vision research, though it is incremental as it primarily provides a new dataset rather than a novel method.
The paper tackles the lack of large-scale, high-quality datasets for underwater salient instance segmentation by introducing USIS16K, a dataset with 16,151 high-resolution images and 158 object categories, and provides benchmark evaluations for detection and segmentation tasks.
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.