Semi-supervised Video Object Segmentation aims to segment a specified target throughout a video sequence, initialized by a first-frame mask. Previous methods rely heavily on appearance-based pattern matching and thus exhibit limited robustness against challenges such as drastic visual changes, occlusions, and scene shifts. This failure is often attributed to a lack of high-level conceptual understanding of the target. The recently proposed Segment Concept (SeC) framework mitigated this limitation by using a Large Vision-Language Model (LVLM) to establish a deep semantic understanding of the object for more persistent segmentation. In this work, we evaluate its zero-shot performance on the challenging coMplex video Object SEgmentation v2 (MOSEv2) dataset. Without any fine-tuning on the training set, SeC achieved 39.7 \JFn on the test set and ranked 2nd place in the Complex VOS track of the 7th Large-scale Video Object Segmentation Challenge.
@article{arxiv.2509.23838,
title = {2nd Place Report of MOSEv2 Challenge 2025: Concept Guided Video Object Segmentation via SeC},
author = {Zhixiong Zhang and Shuangrui Ding and Xiaoyi Dong and Yuhang Zang and Yuhang Cao and Jiaqi Wang},
journal= {arXiv preprint arXiv:2509.23838},
year = {2025}
}