VideoZoomer: Dynamic Temporal Focusing for Long Video Understanding
Published:Dec 26, 2025 11:43
•1 min read
•ArXiv
Analysis
This paper introduces VideoZoomer, a novel framework that addresses the limitations of MLLMs in long video understanding. By enabling dynamic temporal focusing through a reinforcement-learned agent, VideoZoomer overcomes the constraints of limited context windows and static frame selection. The two-stage training strategy, combining supervised fine-tuning and reinforcement learning, is a key aspect of the approach. The results demonstrate significant performance improvements over existing models, highlighting the effectiveness of the proposed method.
Key Takeaways
- •Addresses the context window limitations of MLLMs in long video understanding.
- •Proposes VideoZoomer, a framework for dynamic temporal focusing.
- •Employs a two-stage training strategy: supervised fine-tuning and reinforcement learning.
- •Achieves strong performance improvements over existing models on long video understanding benchmarks.
- •Demonstrates superior efficiency under reduced frame budgets.
Reference
“VideoZoomer invokes a temporal zoom tool to obtain high-frame-rate clips at autonomously chosen moments, thereby progressively gathering fine-grained evidence in a multi-turn interactive manner.”