# AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report

arXiv: https://arxiv.org/abs/2508.13401 · HTML: https://mlsanity.com/paper/2508.13401/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 53 references that are in the map.
- First posted on arXiv 18 Aug 2025.

## Builds on (most cited first, 18 of 18)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://mlsanity.com/paper/2012.07177/) (2020)
- [YOLOv11: An Overview of the Key Architectural Enhancements](https://mlsanity.com/paper/2410.17725/) (2024)
- [Video Instance Segmentation](https://mlsanity.com/paper/1905.04804/) (2019)
- [DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation](https://mlsanity.com/paper/2111.14887/) (2021)
- [RTMDet: An Empirical Study of Designing Real-Time Object Detectors](https://mlsanity.com/paper/2212.07784/) (2022)
- [Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation](https://mlsanity.com/paper/2204.02548/) (2022)
- [Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking Platform](https://mlsanity.com/paper/2110.05802/) (2021)
- [Sparse Instance Activation for Real-Time Instance Segmentation](https://mlsanity.com/paper/2203.12827/) (2022)
- [Automated Rip Current Detection with Region based Convolutional Neural Networks](https://mlsanity.com/paper/2102.02902/) (2021)
- [RipViz: Finding Rip Currents by Learning Pathline Behavior](https://mlsanity.com/paper/2302.12983/) (2023)
- [Rip Current Segmentation: A Novel Benchmark and YOLOv8 Baseline Results](https://mlsanity.com/paper/2504.02558/) (2025)
- [RipVIS: Rip Currents Video Instance Segmentation Benchmark for Beach Monitoring and Safety](https://mlsanity.com/paper/2504.01128/) (2025)

## Built on by (most cited first, 7 of 7)

- [AIM 2025 Low-light RAW Video Denoising Challenge: Dataset, Methods and Results](https://mlsanity.com/paper/2508.16830/) (2025)
- [NTIRE 2026 Rip Current Detection and Segmentation (RipDetSeg) Challenge Report](https://mlsanity.com/paper/2604.17070/) (2026)
- [AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results](https://mlsanity.com/paper/2509.06793/) (2025)
- [AIM 2025 Challenge on Real-World RAW Image Denoising](https://mlsanity.com/paper/2510.06601/) (2025)
- [Efficient Real-World Deblurring using Single Images: AIM 2025 Challenge Report](https://mlsanity.com/paper/2510.12788/) (2025)
- [Efficient Perceptual Image Super Resolution: AIM 2025 Study and Benchmark](https://mlsanity.com/paper/2510.12765/) (2025)
- [UAV-Based Environmental Monitoring of Rip-Current Indicators Using Wavelet-Derived Texture Features](https://mlsanity.com/paper/2608.02448/) (2026)

## Abstract (arXiv)

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 F1, F2, AP50, 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.