AdaptInfer: Adaptive Token Pruning for Vision-Language Model Inference via Dynamical Text Guidance

Vision-language models (VLMs) have achieved impressive performance on multimodal inference tasks, but the cost remains a significant challenge due to the large number of vision tokens processed during the prefill stage. Existing token pruning methods often rely on utilizing the static attention patt