SeC: 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
2025-07-22
Summary
This paper talks about SeC, a new system that helps computers better understand and track objects in videos by using advanced reasoning and breaking down the task into smaller ideas.
What's the problem?
The problem is that video object segmentation, which means separating and following objects in a video, is really hard because videos have complex scenes with moving objects and changing backgrounds, and current methods struggle to handle this complexity well.
What's the solution?
The authors created SeC, which uses large AI models that understand both vision and language to build and use concepts progressively. This helps the system think about the meaning of objects and scenes at different levels, improving how it separates objects over time even in complicated videos.
Why it matters?
This matters because better video object segmentation technology can improve things like video editing, autonomous driving, and surveillance by allowing computers to see and understand the world more clearly and accurately.
Abstract
A concept-driven segmentation framework using Large Vision-Language Models improves video object segmentation by integrating high-level semantic reasoning and adapting to scene complexity.