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SIS-Challenge: Event-based Spatio-temporal Instance Segmentation Challenge at the CVPR 2025 Event-based Vision Workshop

Fuente: arXiv

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Bibliographic Details
Main Authors:	Hamann, Friedhelm, Mededovic, Emil, Gülhan, Fabian, Wu, Yuli, Stegmaier, Johannes, He, Jing, Wang, Yiqing, Zhang, Kexin, Li, Lingling, Jiao, Licheng, Ma, Mengru, Huang, Hongxiang, Yan, Yuhao, Ren, Hongwei, Lin, Xiaopeng, Huang, Yulong, Cheng, Bojun, Lee, Se Hyun, Ham, Gyu Sung, Oh, Kanghan, Lim, Gi Hyun, Yang, Boxuan, Du, Bowen, Gallego, Guillermo
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition Machine Learning
Online Access:	Acceder al recurso
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_version_	1866918126548942848
author	Hamann, Friedhelm Mededovic, Emil Gülhan, Fabian Wu, Yuli Stegmaier, Johannes He, Jing Wang, Yiqing Zhang, Kexin Li, Lingling Jiao, Licheng Ma, Mengru Huang, Hongxiang Yan, Yuhao Ren, Hongwei Lin, Xiaopeng Huang, Yulong Cheng, Bojun Lee, Se Hyun Ham, Gyu Sung Oh, Kanghan Lim, Gi Hyun Yang, Boxuan Du, Bowen Gallego, Guillermo
author_facet	Hamann, Friedhelm Mededovic, Emil Gülhan, Fabian Wu, Yuli Stegmaier, Johannes He, Jing Wang, Yiqing Zhang, Kexin Li, Lingling Jiao, Licheng Ma, Mengru Huang, Hongxiang Yan, Yuhao Ren, Hongwei Lin, Xiaopeng Huang, Yulong Cheng, Bojun Lee, Se Hyun Ham, Gyu Sung Oh, Kanghan Lim, Gi Hyun Yang, Boxuan Du, Bowen Gallego, Guillermo
contents	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
format	Preprint
id	arxiv_https___arxiv_org_abs_2508_12813
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	SIS-Challenge: Event-based Spatio-temporal Instance Segmentation Challenge at the CVPR 2025 Event-based Vision Workshop Hamann, Friedhelm Mededovic, Emil Gülhan, Fabian Wu, Yuli Stegmaier, Johannes He, Jing Wang, Yiqing Zhang, Kexin Li, Lingling Jiao, Licheng Ma, Mengru Huang, Hongxiang Yan, Yuhao Ren, Hongwei Lin, Xiaopeng Huang, Yulong Cheng, Bojun Lee, Se Hyun Ham, Gyu Sung Oh, Kanghan Lim, Gi Hyun Yang, Boxuan Du, Bowen Gallego, Guillermo Computer Vision and Pattern Recognition Machine Learning 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
title	SIS-Challenge: Event-based Spatio-temporal Instance Segmentation Challenge at the CVPR 2025 Event-based Vision Workshop
topic	Computer Vision and Pattern Recognition Machine Learning
url	https://arxiv.org/abs/2508.12813