Tensor Memory: Fixed-Size Recurrent State for Long-Horizon Transformers
📰 ArXiv cs.AI
Learn how Tensor Memory enables long-horizon Transformers with fixed-size recurrent state for video understanding and occlusion-sensitive reasoning
Action Steps
- Implement Tensor Memory module in a Transformer block to augment its capabilities
- Use a fixed-size recurrent 3D memory tensor to preserve spatial state
- Apply Tensor Memory to long-horizon video understanding tasks to improve performance
- Configure the Tensor Memory module to balance memory usage and computational efficiency
- Test the Tensor Memory-augmented Transformer on occlusion-sensitive reasoning tasks to evaluate its effectiveness
Who Needs to Know This
AI researchers and engineers working on Transformer models for video processing can benefit from this technique to improve long-horizon understanding and occlusion-sensitive reasoning
Key Insight
💡 Tensor Memory provides a fixed-size recurrent state for Transformers, enabling more efficient and effective long-horizon video processing
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🤖 Introducing Tensor Memory: a lightweight module for long-horizon #Transformers to improve video understanding and occlusion-sensitive reasoning #AI #ComputerVision
Key Takeaways
Learn how Tensor Memory enables long-horizon Transformers with fixed-size recurrent state for video understanding and occlusion-sensitive reasoning
Full Article
Title: Tensor Memory: Fixed-Size Recurrent State for Long-Horizon Transformers
Abstract:
arXiv:2605.27686v1 Announce Type: cross Abstract: Transformers process images and videos by flattening space and time into long token sequences. While attention and KV caching preserve past features, their memory grows with sequence length and they lack an explicit, persistent spatial state, making long-horizon video understanding and occlusion-sensitive reasoning difficult. We propose Tensor Memory, a lightweight module that augments Transformer blocks with a fixed-size recurrent 3D memory tens
Abstract:
arXiv:2605.27686v1 Announce Type: cross Abstract: Transformers process images and videos by flattening space and time into long token sequences. While attention and KV caching preserve past features, their memory grows with sequence length and they lack an explicit, persistent spatial state, making long-horizon video understanding and occlusion-sensitive reasoning difficult. We propose Tensor Memory, a lightweight module that augments Transformer blocks with a fixed-size recurrent 3D memory tens
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