Multimodal Large Language Models (LLMs) have recently achieved promising zero-shot accuracy on visual question answering (VQA) -- a fundamental task affecting various downstream applications and domains. Given the great potential for the broad use of these models, it is important to investigate their limitations in dealing with different image and question properties. In this work, we investigate whether multimodal LLMs can perceive small details as well as large details in images. In particular, we show that their zero-shot accuracy in answering visual questions is very sensitive to the size of the visual subject of the question, declining up to $46\%$ with size. Furthermore, we show that this effect is causal by observing that human visual cropping can significantly mitigate their sensitivity to size. Inspired by the usefulness of human cropping, we then propose three automatic visual cropping methods as inference time mechanisms to improve the zero-shot performance of multimodal LLMs. We study their effectiveness on four popular VQA datasets, and a subset of the VQAv2 dataset tailored towards fine visual details. Our findings suggest that multimodal LLMs should be used with caution in detail-sensitive VQA applications, and that visual cropping is a promising direction to improve their zero-shot performance. Our code and data are publicly available.

这项研究探讨了多模态大型语言模型在处理不同图像和问题属性时是否能够感知图像的细节，并表明它们在回答视觉问题的零样本准确性受到问题中视觉主体大小的敏感性，但通过人工裁剪可以显著缓解这种敏感性影响，并提出了三种自动图像裁剪方法用于改进多模态大型语言模型的零样本性能。

视觉裁剪提升多模态大型语言模型的零指导问题回答