SeC: Advancing Complex Video Object Segmentation via Progressive Concept Construction

SeC: Advancing Complex Video Object Segmentation via Progressive Concept Construction is a new method in computer vision focused on Video Object Segmentation (VOS), which is the task of tracking and segmenting target objects across video frames. Unlike traditional methods that rely mainly on feature or appearance matching, SeC introduces a concept-driven framework that progressively builds high-level, object-centric representations or “concepts” of the target object.

Here is the key points about SeC:

• Concept-driven approach: It moves beyond pixel-level matching to construct a semantic “concept” of the object by integrating visual cues across multiple video frames using Large Vision-Language Models (LVLMs). This allows more human-like understanding of objects.
• Progressive construction: The object concept is built progressively and used to robustly identify and segment the target even across drastic visual changes, occlusions, and complex scene transformations.
• Adaptive inference: SeC dynamically balances semantic reasoning via LVLMs with enhanced traditional feature matching, adjusting computational resources based on scene complexity to improve efficiency.
• Benchmarking: To evaluate performance in conceptually challenging video scenarios, the authors introduced the Semantic Complex Scenarios Video Object Segmentation benchmark (SeCVOS), including 160 videos with significant appearance and scene variations.
• Performance: SeC achieved state-of-the-art results, showing an 11.8-point improvement over the prior best method (SAM 2.1) on the SeCVOS benchmark, highlighting its superior capability in handling complex videos.

In simpler terms, SeC works like a “smart detective” that learns and refines a rich mental image or concept of the object being tracked over time, similar to how humans recognize objects by understanding their characteristics beyond just appearance. This approach significantly advances video object segmentation, especially in challenging conditions where objects undergo drastic changes or are partially obscured.