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AdaptVision: Efficient Vision-Language Models via Adaptive Visual Acquisition
🤖AI Summary
Researchers introduce AdaptVision, a new Vision-Language Model that reduces computational overhead by adaptively determining the minimum visual tokens needed per sample. The model uses a coarse-to-fine approach with reinforcement learning to balance accuracy and efficiency, achieving superior performance while consuming fewer visual tokens than existing methods.
Key Takeaways
- →AdaptVision introduces adaptive visual token acquisition to reduce computational overhead in Vision-Language Models.
- →The model uses a coarse-to-fine approach, starting with compressed visual tokens and selectively acquiring more detail when needed.
- →Decoupled Turn Policy Optimization (DTPO) separates tool learning from accuracy improvement for better optimization.
- →The approach is inspired by human active vision mechanisms for more efficient visual processing.
- →Experiments show superior performance across VQA benchmarks while using substantially fewer visual tokens.
#vision-language-models#computational-efficiency#reinforcement-learning#visual-processing#machine-learning#ai-optimization#computer-vision
Read Original →via arXiv – CS AI
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