Yun-Chen Wu

Not All Error Yields to Scale: Where Scaling Stops in Vision-Language Inference

Vision-language models (VLMs) face a fixed-budget trade-off between processing more visual information for fine-grained perception and using a larger language backbone for complex reasoning. Existing studies do not tell us which combination of backbone size and input resolution to deploy, especially in high-resolution...