SeC: Advancing Complex Video Object Segmentation via Progressive Concept
  Construction
Authors: name: Zhixiong Zhang; name: Shuangrui Ding; name: Xiaoyi Dong; name: Songxin He; name: Jianfan Lin; name: Junsong Tang; name: Yuhang Zang; name: Yuhang Cao; name: Dahua Lin; name: Jiaqi Wang
Published: 2025-07-21 17:59:02

Abstract: Video Object Segmentation (VOS) is a core task in computer vision, requiring
models to track and segment target objects across video frames. Despite notable
advances with recent efforts, current techniques still lag behind human
capabilities in handling drastic visual variations, occlusions, and complex
scene changes. This limitation arises from their reliance on appearance
matching, neglecting the human-like conceptual understanding of objects that
enables robust identification across temporal dynamics. Motivated by this gap,
we propose Segment Concept (SeC), a concept-driven segmentation 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. During inference, SeC forms a
comprehensive semantic representation of the target based on processed frames,
realizing robust segmentation of follow-up frames. Furthermore, SeC adaptively
balances LVLM-based semantic reasoning with enhanced feature matching,
dynamically adjusting computational efforts based on scene complexity. 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. 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 video object segmentation.