VideoTemp-o3: AI Framework for Long-Video Understanding
Researchers propose VideoTemp-o3, a unified agentic framework for long-video understanding that jointly models video grounding and question answering. It addresses inefficiencies in existing methods by offering strong localization, on-demand clipping, and refinement of inaccurate localizations. The framework uses a supervised fine-tuning stage with a unified masking mechanism to encourage exploration. This work is published on arXiv with ID 2602.07801.
Key facts
- VideoTemp-o3 is a unified agentic thinking-with-videos framework.
- It jointly models video grounding and question answering.
- It exhibits strong localization capability.
- It supports on-demand clipping.
- It can refine inaccurate localizations.
- The supervised fine-tuning stage uses a unified masking mechanism.
- The paper is on arXiv with ID 2602.07801.
- It addresses inefficiencies in existing agentic thinking-with-videos paradigms.
Entities
Institutions
- arXiv