PARCEL: Pool-Anchored Resampling with Conditioned Elastic Queries for Efficient Vision-Language Understanding
📰 ArXiv cs.AI
Learn how PARCEL improves vision-language understanding by efficiently compressing visual tokens, reducing computational bottlenecks
Action Steps
- Implement pool-anchored resampling to reduce spatial dimensions
- Apply conditioned elastic queries to compress visual tokens
- Train a single model to run at multiple visual-token budgets
- Evaluate the performance of PARCEL under aggressive compression
- Compare PARCEL with existing compression approaches like nested pooling
Who Needs to Know This
Computer vision and natural language processing teams can benefit from PARCEL to improve the efficiency of their vision-language models, especially when dealing with large amounts of visual data
Key Insight
💡 PARCEL improves vision-language understanding by efficiently compressing visual tokens, reducing computational bottlenecks
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🚀 PARCEL: Efficient vision-language understanding with pool-anchored resampling and conditioned elastic queries! 💡
Key Takeaways
Learn how PARCEL improves vision-language understanding by efficiently compressing visual tokens, reducing computational bottlenecks
Full Article
Title: PARCEL: Pool-Anchored Resampling with Conditioned Elastic Queries for Efficient Vision-Language Understanding
Abstract:
arXiv:2605.30126v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) map visual inputs into dense token sequences, imposing a quadratic computational bottleneck for inference. Elastic visual-token compression addresses this by training a single model that can run at multiple visual-token budgets. However, existing approaches struggle under aggressive compression. Spatial-only compression, as in nested pooling, behaves as an imperfect low-pass filter and induces spectral aliasin
Abstract:
arXiv:2605.30126v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) map visual inputs into dense token sequences, imposing a quadratic computational bottleneck for inference. Elastic visual-token compression addresses this by training a single model that can run at multiple visual-token budgets. However, existing approaches struggle under aggressive compression. Spatial-only compression, as in nested pooling, behaves as an imperfect low-pass filter and induces spectral aliasin
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