1st Place Solution for MeViS Track in CVPR 2024 PVUW Workshop: Motion Expression guided Video Segmentation
Mingqi Gao, Jingnan Luo, Jinyu Yang, Jungong Han, Feng, Zheng
TL;DR
This paper presents the winning solution for the MeViS track at CVPR 2024, demonstrating effective use of static-dominant data and frame sampling to improve motion and expression-guided video segmentation.
Contribution
The paper introduces a novel approach leveraging static-dominant data and frame sampling techniques for motion and expression-guided video segmentation.
Findings
Achieved a J&F score of 0.5447 in the competition
Ranked 1st in the MeViS track of PVUW Challenge
Validated effectiveness of static-dominant data and frame sampling
Abstract
Motion Expression guided Video Segmentation (MeViS), as an emerging task, poses many new challenges to the field of referring video object segmentation (RVOS). In this technical report, we investigated and validated the effectiveness of static-dominant data and frame sampling on this challenging setting. 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. The code is available at: https://github.com/Tapall-AI/MeViS_Track_Solution_2024.
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Taxonomy
TopicsImage Processing Techniques and Applications