Optimizing Vision-Language Model Inference with Input-Adaptive Preprocessing

This research paper explores a method for optimizing the inference of Vision-Language Models (VLMs), focusing on input-adaptive visual preprocessing. The proposed approach likely aims to improve efficiency by tailoring the preprocessing steps to the specific input data.