标题： VisionTrap：视觉数据上的无法回答的问题 显示英文标题

标题： VisionTrap: Unanswerable Questions On Visual Data

摘要： 视觉问答（VQA）已成为一个广泛研究的主题，大量研究集中在如何让视觉语言模型根据现实世界的图像回答可回答的问题。 然而，对于这些模型如何处理无法回答的问题，特别是当它们应该避免提供回答时，研究仍然有限。 本研究调查了在不切实际生成的图像或提出无法回答的问题情况下，VQA的表现，评估模型是否能认识到自身知识的局限性，或者试图生成错误的答案。 我们引入了一个数据集，VisionTrap，包含三种类型的无法回答的问题，涵盖多种图像类型：（1）融合物体和动物的混合实体，（2）以非常规或不可能场景描绘的物体，（3）虚构或不存在的图形。 提出的问题在逻辑上结构清晰，但本质上无法回答，测试模型是否能正确识别自身的局限性。 我们的研究结果强调了将此类问题纳入VQA基准的重要性，以评估模型在应避免回答时是否倾向于回答。 显示英文摘要

摘要： Visual Question Answering (VQA) has been a widely studied topic, with extensive research focusing on how VLMs respond to answerable questions based on real-world images. However, there has been limited exploration of how these models handle unanswerable questions, particularly in cases where they should abstain from providing a response. This research investigates VQA performance on unrealistically generated images or asking unanswerable questions, assessing whether models recognize the limitations of their knowledge or attempt to generate incorrect answers. We introduced a dataset, VisionTrap, comprising three categories of unanswerable questions across diverse image types: (1) hybrid entities that fuse objects and animals, (2) objects depicted in unconventional or impossible scenarios, and (3) fictional or non-existent figures. The questions posed are logically structured yet inherently unanswerable, testing whether models can correctly recognize their limitations. Our findings highlight the importance of incorporating such questions into VQA benchmarks to evaluate whether models tend to answer, even when they should abstain.