MemDreamer: Decoupling Perception and Reasoning for Long Video Understanding via Hierarchical Graph Memory and Agentic Retrieval Mechanism
📰 ArXiv cs.AI
Learn how MemDreamer decouples perception and reasoning for long video understanding using hierarchical graph memory and agentic retrieval mechanism
Action Steps
- Build a Hierarchical Graph Memory using a top-down three-tier architecture to store video information
- Implement an agentic retrieval mechanism to incrementally stream videos and update the graph memory
- Decouple perception and reasoning using MemDreamer's framework to reduce token explosion and attention dilution
- Apply the MemDreamer framework to hours-long videos to improve understanding and reduce computational costs
- Test the framework using various video datasets to evaluate its performance and effectiveness
Who Needs to Know This
Computer vision engineers and researchers can benefit from this framework to improve long video understanding, while AI engineers can apply the agentic exploration process to similar problems
Key Insight
💡 Decoupling perception and reasoning using hierarchical graph memory and agentic retrieval mechanism can improve long video understanding
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Introducing MemDreamer: a framework for long video understanding via hierarchical graph memory and agentic retrieval mechanism #AI #ComputerVision
Key Takeaways
Learn how MemDreamer decouples perception and reasoning for long video understanding using hierarchical graph memory and agentic retrieval mechanism
Full Article
Title: MemDreamer: Decoupling Perception and Reasoning for Long Video Understanding via Hierarchical Graph Memory and Agentic Retrieval Mechanism
Abstract:
arXiv:2606.07512v1 Announce Type: cross Abstract: Current Vision-Language Models struggle with hours-long videos because processing full-length visual sequences induces prohibitive token explosion and attention dilution. To overcome this, we introduce MemDreamer to decouple perception and reasoning, shifting long-video understanding into an agentic exploration process. As a plug-and-play framework, it incrementally streams videos to construct a Hierarchical Graph Memory, a top-down three-tier ar
Abstract:
arXiv:2606.07512v1 Announce Type: cross Abstract: Current Vision-Language Models struggle with hours-long videos because processing full-length visual sequences induces prohibitive token explosion and attention dilution. To overcome this, we introduce MemDreamer to decouple perception and reasoning, shifting long-video understanding into an agentic exploration process. As a plug-and-play framework, it incrementally streams videos to construct a Hierarchical Graph Memory, a top-down three-tier ar
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