隐藏于表象之中：评估视觉语言模型中的抽象形状识别
Hidden in Plain Sight: Evaluating Abstract Shape Recognition in Vision-Language Models
AI总结：
本研究提出IllusionBench数据集，评估视觉语言模型对抽象形状的识别能力，发现模型虽依赖形状但仍难识别人类易察觉的排列形状，为改进视觉感知系统指明方向。
摘要：
尽管形状感知在人类视觉中具有重要意义，早期的神经图像分类器在物体识别中对形状信息的依赖程度低于其他（往往是虚假的）特征。虽然近期研究表明当前的大型视觉语言模型（VLMs）对形状的依赖有所增强，但我们发现它们在这方面仍然存在严重局限。为量化这些局限，我们引入了IllusionBench，一个挑战当前尖端视觉语言模型在场景中视觉元素排列所表示的形状信息进行解读的数据集。我们的广泛评估显示，尽管这些形状容易被人类标注者检测到，当前的视觉语言模型却难以识别它们，这为未来开发更稳健的视觉感知系统指明了重要方向。完整数据集和代码库可在以下网址获取：\url{https://arshiahemmat.github.io/illusionbench/}
英文摘要：
Despite the importance of shape perception in human vision, early neural image classifiers relied less on shape information for object recognition than other (often spurious) features. While recent research suggests that current large Vision-Language Models (VLMs) exhibit more reliance on shape, we find them to still be seriously limited in this regard. To quantify such limitations, we introduce IllusionBench, a dataset that challenges current cutting-edge VLMs to decipher shape information when the shape is represented by an arrangement of visual elements in a scene. Our extensive evaluations reveal that, while these shapes are easily detectable by human annotators, current VLMs struggle to recognize them, indicating important avenues for future work in developing more robust visual perception systems. The full dataset and codebase are available at: \url{https://arshiahemmat.github.io/illusionbench/}
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
