SIS-Challenge: Event-Based Spatio-Temporal Instance Segmentation Challenge at the CVPR 2025 Event-Based Vision Workshop
IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 4734–4742
Abstract
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/tubrip/MouseSIS/blob/main/docs/challengeesults.md
Authors 24
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Einstein Center Digital Future · Technische Universität Berlin
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TU Berlin, SCIoI, ECDF
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Emil Mededovic Aachen
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RWTH Aachen
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Fabian Gülhan Aachen
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RWTH Aachen
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Yuli Wu Aachen
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RWTH Aachen
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Johannes Stegmaier Aachen
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RWTH Aachen
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Xidian University
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Xidian University
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Xidian University
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Xidian University
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Xidian University
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Xidian University
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Hong Kong University of Science and Technology
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Hong Kong University of Science and Technology
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Sun Yat-sen University
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Hong Kong University of Science and Technology
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Hong Kong University of Science and Technology
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Hong Kong University of Science and Technology
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Hong Kong University of Science and Technology
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Hong Kong University of Science and Technology
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Hong Kong University of Science and Technology
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Hong Kong University of Science and Technology
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Hong Kong University of Science and Technology
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Wonkwang University
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Wonkwang University
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Wonkwang University
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Wonkwang University
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Tongji University
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Affiliation as printed
Tongji University
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Einstein Center Digital Future · Technische Universität Berlin
Affiliation as printed
TU Berlin, SCIoI, ECDF
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Cited by patents worldwide 1 (Lens.org)
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Mobile instance segmentation method and systemCN121861558A 2026-04-14 Active