Input-Adaptive Visual Preprocessing for Efficient Fast Vision-Language Model Inference

This paper presents a compelling approach to improving the efficiency of Vision-Language Models (VLMs) by introducing input-adaptive visual preprocessing. The core idea of dynamically adjusting input resolution and spatial coverage based on image content is innovative and addresses a key bottleneck in VLM deployment: high computational cost. The fact that the method integrates seamlessly with FastVLM without requiring retraining is a significant advantage. The experimental results, demonstrating a substantial reduction in inference time and visual token count, are promising and highlight the practical benefits of this approach. The focus on efficiency-oriented metrics and the inference-only setting further strengthens the relevance of the findings for real-world deployment scenarios.