We present an overview of the Spatio-temporal Instance Segmentation (SIS) challenge held in conjunction with the CVPR 2025 Event-based Vision Workshop. The task is to predict accurate pixel-level segmentation masks of defined object classes from spatio-temporally aligned event camera and grayscale camera data. We provide an overview of the task, dataset, challenge details and results. Furthermore, we describe the methods used by the top-5 ranking teams in the challenge. More resources and code of the participants' methods are available here: https://github.com/tub-rip/MouseSIS/blob/main/docs/challenge_results.md
@article{arxiv.2508.12813,
title = {SIS-Challenge: Event-based Spatio-temporal Instance Segmentation Challenge at the CVPR 2025 Event-based Vision Workshop},
author = {Friedhelm Hamann and Emil Mededovic and Fabian Gülhan and Yuli Wu and Johannes Stegmaier and Jing He and Yiqing Wang and Kexin Zhang and Lingling Li and Licheng Jiao and Mengru Ma and Hongxiang Huang and Yuhao Yan and Hongwei Ren and Xiaopeng Lin and Yulong Huang and Bojun Cheng and Se Hyun Lee and Gyu Sung Ham and Kanghan Oh and Gi Hyun Lim and Boxuan Yang and Bowen Du and Guillermo Gallego},
journal= {arXiv preprint arXiv:2508.12813},
year = {2025}
}