AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report
Dumitriu, Andrei Miron, Florin Tatui, Florin Ionescu, Radu Tudor Timofte, Radu Ralhan, Aakash Vasluianu, Florin-Alexandru Qian, Shenyang Harley, Mitchell Razzak, Imran
Dumitriu, Andrei
Miron, Florin
Tatui, Florin
Ionescu, Radu Tudor
Timofte, Radu
Ralhan, Aakash
Vasluianu, Florin-Alexandru
Qian, Shenyang
Harley, Mitchell
Razzak, Imran
Author
Dumitriu, Andrei
Miron, Florin
Tatui, Florin
Ionescu, Radu Tudor
Timofte, Radu
Ralhan, Aakash
Vasluianu, Florin-Alexandru
Qian, Shenyang
Harley, Mitchell
Razzak, Imran
Song, Yang
Luo, Pu
Li, Yumei
Xu, Cong
Chai, Jinming
Zhang, Kexin
Jiao, Licheng
Li, Lingling
Yu, Siqi
Zhang, Chao
Song, Kehuan
Liu, Fang
Chen, Puhua
Liu, Xu
Hu, Jin
Xu, Jinyang
Liu, Biao
Miron, Florin
Tatui, Florin
Ionescu, Radu Tudor
Timofte, Radu
Ralhan, Aakash
Vasluianu, Florin-Alexandru
Qian, Shenyang
Harley, Mitchell
Razzak, Imran
Song, Yang
Luo, Pu
Li, Yumei
Xu, Cong
Chai, Jinming
Zhang, Kexin
Jiao, Licheng
Li, Lingling
Yu, Siqi
Zhang, Chao
Song, Kehuan
Liu, Fang
Chen, Puhua
Liu, Xu
Hu, Jin
Xu, Jinyang
Liu, Biao
Supervisor
Department
Computational Biology
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Conference proceeding
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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.
Citation
A. Dumitriu, F. Miron, F. Tatui, R.T. Ionescu, R. Timofte, A. Ralhan , et al., "AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report," 2026, pp. 5610-5619.
Source
2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
Conference
2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
Keywords
46 Information and Computing Sciences, 4607 Graphics, Augmented Reality and Games
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Source
2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
Publisher
IEEE