AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report

• URL: http://arxiv.org/abs/2508.13401v2
• Date: Wed, 03 Sep 2025 17:20:31 GMT
• Title: AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report
• Authors: Andrei Dumitriu, Florin Miron, Florin Tatui, Radu Tudor Ionescu, Radu Timofte, Aakash Ralhan, Florin-Alexandru Vasluianu, Shenyang Qian, Mitchell Harley, Imran Razzak, Yang Song, Pu Luo, Yumei Li, Cong Xu, Jinming Chai, Kexin Zhang, Licheng Jiao, Lingling Li, Siqi Yu, Chao Zhang, Kehuan Song, Fang Liu, Puhua Chen, Xu Liu, Jin Hu, Jinyang Xu, Biao Liu,
• Abstract summary: The AIM 2025 RipSeg Challenge is designed to advance techniques for automatic rip current segmentation in still images.<n>The dataset spans diverse locations, rip current types, and camera orientations, providing a realistic and challenging benchmark.<n>This report outlines the dataset details, competition framework, evaluation metrics, and final results, providing insights into the current state of rip current segmentation.
• Score: 97.46639062939211
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This report presents an overview of the AIM 2025 RipSeg Challenge, a competition designed to advance techniques for automatic rip current segmentation in still images. Rip currents are dangerous, fast-moving flows that pose a major risk to beach safety worldwide, making accurate visual detection an important and underexplored research task. The challenge builds on RipVIS, the largest available rip current dataset, and focuses on single-class instance segmentation, where precise delineation is critical to fully capture the extent of rip currents. The dataset spans diverse locations, rip current types, and camera orientations, providing a realistic and challenging benchmark. In total, $75$ participants registered for this first edition, resulting in $5$ valid test submissions. Teams were evaluated on a composite score combining $F_1$, $F_2$, $AP_{50}$, and $AP_{[50:95]}$, ensuring robust and application-relevant rankings. The top-performing methods leveraged deep learning architectures, domain adaptation techniques, pretrained models, and domain generalization strategies to improve performance under diverse conditions. This report outlines the dataset details, competition framework, evaluation metrics, and final results, providing insights into the current state of rip current segmentation. We conclude with a discussion of key challenges, lessons learned from the submissions, and future directions for expanding RipSeg.

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