SIS-Challenge: Event-based Spatio-temporal Instance Segmentation Challenge at the CVPR 2025 Event-based Vision Workshop
This is an incremental effort to benchmark methods for event-based vision, targeting researchers in computer vision and robotics.
The paper presents the Spatio-temporal Instance Segmentation (SIS) challenge at the CVPR 2025 Event-based Vision Workshop, focusing on predicting pixel-level segmentation masks from event and grayscale camera data, with results from top-5 teams detailed.
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