Hierarchical Memory System for Long-Form Video Understanding
Overview
Understanding long-form videos requires more than just analyzing individual frames. Our research focuses on developing a hierarchical memory system that enables AI to reason about events, relationships, and narratives across extended video sequences.
The Challenge
Traditional video understanding models struggle with:
- Limited context: Can only process short clips
- Temporal dependencies: Missing long-range relationships between events
- Memory constraints: Cannot efficiently store information from long videos
- Reasoning capability: Difficulty answering questions that require understanding the full narrative
Our Solution
We developed a hierarchical memory architecture that:
- Frame-level Processing: Extracts visual features from individual frames
- Short-term Memory: Aggregates information from video segments (30-60 seconds)
- Long-term Memory: Maintains key events and entities throughout the entire video
- Query-based Retrieval: Efficiently accesses relevant information for answering questions
Technical Components
- Vision Encoder: Pre-trained CLIP model for frame features
- Temporal Transformer: Captures temporal dependencies
- Hierarchical Memory Banks: Multi-level storage system
- Reasoning Module: Combines retrieved information for question answering
Key Technologies
- PyTorch: Deep learning framework
- Transformers: Attention-based architecture
- Vision-Language Models: CLIP, BLIP for multimodal understanding
- Python: Implementation and data processing
Evaluation
Tested on multiple video understanding benchmarks:
- NExT-QA: Question answering on diverse videos
- LVU: Long video understanding tasks
- VideoQA: Complex reasoning questions
Results
- Improved accuracy on long-form videos (>10 minutes) by 15%
- Efficient memory usage through hierarchical compression
- Strong performance on causal and temporal reasoning questions
Architecture Highlights
The hierarchical structure allows the model to:
- Process videos of any length without memory overflow
- Maintain fine-grained details while keeping long-term context
- Adaptively focus on relevant moments based on the query
- Scale to hours of video content
Applications
This technology can be applied to:
- Video search and retrieval
- Content summarization
- Automated video editing
- Educational video analysis
- Surveillance and security
Publication
Submitted to AAAI 2026: “Hierarchical Memory Networks for Long-Form Video Question Answering”
Future Directions
- Multi-modal integration (audio + vision + text)
- Real-time processing for live streams
- Interactive video understanding systems
- Cross-video reasoning (understanding relationships between multiple videos)