Cropping and compression are complementary strategies for reducing VLM inference cost—use cropping for detail-focused questions and compression for context-heavy ones, with a learned safety mechanism to protect accuracy.
This paper proposes C²-Routing, a method that adaptively chooses between cropping (for local details) and compression (for context) to reduce computational costs in vision-language models for VQA tasks. It includes a safety gate to prevent accuracy loss and achieves 36.53% FLOP reduction with minimal performance drop.