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

• URL: http://arxiv.org/abs/2508.12813v1
• Date: Mon, 18 Aug 2025 10:49:06 GMT
• Title: SIS-Challenge: Event-based Spatio-temporal Instance Segmentation Challenge at the CVPR 2025 Event-based Vision Workshop
• Authors: Friedhelm Hamann, Emil Mededovic, Fabian Gülhan, Yuli Wu, Johannes Stegmaier, Jing He, Yiqing Wang, Kexin Zhang, Lingling Li, Licheng Jiao, Mengru Ma, Hongxiang Huang, Yuhao Yan, Hongwei Ren, Xiaopeng Lin, Yulong Huang, Bojun Cheng, Se Hyun Lee, Gyu Sung Ham, Kanghan Oh, Gi Hyun Lim, Boxuan Yang, Bowen Du, Guillermo Gallego,
• Abstract summary: We present an overview of the S-temporal Instance (SIS) challenge held in conjunction with the CVPR 2025 Event-based Vision Workshop.<n>We provide an overview of the task, challenge details and results.
• Score: 35.087783646406955
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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/tub-rip/MouseSIS/blob/main/docs/challenge_results.md

Related papers

• Event-Based Eye Tracking. 2025 Event-based Vision Workshop [47.79620756734669]
  This survey serves as a review for the 2025 Event-Based Eye Tracking Challenge organized as part of the 2025 CVPR event-based vision workshop.<n>We review and summarize the innovative methods from teams rank the top in the challenge to advance future event-based eye tracking research.
  arXiv  Detail & Related papers  (2025-04-25T10:50:14Z)
• PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of Complex Videos in the Wild [164.8093566483583]
  This report provides a comprehensive overview of the 4th Pixel-level Video Understanding in the Wild (PVUW) Challenge, held in conjunction with CVPR 2025.<n>The challenge features two tracks: MOSE, which focuses on complex scene video object segmentation, and MeViS, which targets motion-guided, language-based video segmentation.
  arXiv  Detail & Related papers  (2025-04-15T16:02:47Z)
• The 2nd Solution for LSVOS Challenge RVOS Track: Spatial-temporal Refinement for Consistent Semantic Segmentation [0.0]
  We propose a method to enhance the temporal consistency of the referring object segmentation model. Our method placed 2nd in the final ranking of the RVOS Track at the ECCV 2024 LSVOS Challenge.
  arXiv  Detail & Related papers  (2024-08-22T14:43:02Z)
• Finding Meaning in Points: Weakly Supervised Semantic Segmentation for Event Cameras [45.063747874243276]
  We present EV-WSSS: a novel weakly supervised approach for event-based semantic segmentation. The proposed framework performs asymmetric dual-student learning between 1) the original forward event data and 2) the longer reversed event data. We show that the proposed method achieves substantial segmentation results even without relying on pixel-level dense ground truths.
  arXiv  Detail & Related papers  (2024-07-15T20:00:50Z)
• 1st Place Solution for MeViS Track in CVPR 2024 PVUW Workshop: Motion Expression guided Video Segmentation [81.50620771207329]
  We investigate the effectiveness of static-dominant data and frame sampling on referring video object segmentation (RVOS) Our solution achieves a J&F score of 0.5447 in the competition phase and ranks 1st in the MeViS track of the PVUW Challenge.
  arXiv  Detail & Related papers  (2024-06-11T08:05:26Z)
• OpenSUN3D: 1st Workshop Challenge on Open-Vocabulary 3D Scene Understanding [96.69806736025248]
  This report provides an overview of the challenge hosted at the OpenSUN3D Workshop on Open-Vocabulary 3D Scene Understanding held in conjunction with ICCV 2023.
  arXiv  Detail & Related papers  (2024-02-23T13:39:59Z)
• Task-Specific Data Augmentation and Inference Processing for VIPriors Instance Segmentation Challenge [9.43662534739698]
  We develop task-specific data augmentation strategy and task-specific inference processing strategy. We demonstrate the applicability of proposed method on VIPriors Instance Challenge. Experimental results demonstrate that proposed method can achieve a competitive result on the test set of 2022 VIPriors Instance Challenge.
  arXiv  Detail & Related papers  (2022-11-21T09:15:30Z)
• LID 2020: The Learning from Imperfect Data Challenge Results [242.86700551532272]
  Learning from Imperfect Data workshop aims to inspire and facilitate the research in developing novel approaches. We organize three challenges to find the state-of-the-art approaches in weakly supervised learning setting. This technical report summarizes the highlights from the challenge.
  arXiv  Detail & Related papers  (2020-10-17T13:06:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.