Advancing Complex Video Object Segmentation via Progressive Concept Construction

Zhixiong Zhang; Shuangrui Ding; Xiaoyi Dong; Songxin He; Jianfan Lin; Junsong Tang; Yuhang Zang; Yuhang Cao; Dahua Lin; Jiaqi Wang

arXiv · 2507.15852

Advancing Complex Video Object Segmentation via Progressive Concept Construction

Zhixiong Zhang·Shuangrui Ding·Xiaoyi Dong·Songxin He·Jianfan Lin·Junsong Tang·Yuhang Zang·Yuhang Cao·Dahua Lin·Jiaqi Wang

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Abstract

We propose Segment Concept (SeC), a concept-driven video object segmentation (VOS) framework that shifts from conventional feature matching to the progressive construction and utilization of high-level, object-centric representations. SeC employs Large Vision-Language Models (LVLMs) to integrate visual cues across diverse frames, constructing robust conceptual priors. To balance semantic reasoning with computational overhead, SeC forwards the LVLMs only when a new scene appears, injecting concept-level features at those points. To rigorously assess VOS methods in scenarios demanding high-level conceptual reasoning and robust semantic understanding, we introduce the Semantic Complex Scenarios Video Object Segmentation benchmark (SeCVOS). SeCVOS comprises 160 manually annotated multi-scenario videos designed to challenge models with substantial appearance variations and dynamic scene transformations. Empirical evaluations demonstrate that SeC substantially outperforms state-of-the-art approaches, including SAM 2 and its advanced variants, on both SeCVOS and standard VOS benchmarks. In particular, SeC achieves an 11.8-point improvement over SAM 2.1 on SeCVOS, establishing a new state-of-the-art in concept-aware VOS.

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Zhixiong Zhang, Shuangrui Ding, Xiaoyi Dong, Songxin He, Jianfan Lin, Junsong Tang, Yuhang Zang, Yuhang Cao, Dahua Lin, Jiaqi Wang. 2025-07-21. Advancing Complex Video Object Segmentation via Progressive Concept Construction. https://arxiv.org/abs/2507.15852

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