视觉计数仍然是多式联通生成AI的挑战
Alberto Testolin1, Kuinan Hou2, Marco Zorzi3,4
1Department of General Psychology and Department of Mathematics, University of Padova, Padova, Italy.
PloS one
|September 12, 2025
概括
当前的人工智能模型在视觉计数方面扎,无法准确地计算图像中的对象或生成特定数量的图像. 这表明,仅仅增加人工智能模型大小并不能发展出强大的计数技能.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 计算机视觉 计算机视觉
背景情况:
- 人类和动物具有天生的数值能力,而当前的AI系统表现出有限的视觉计数技能.
- 评估AI的数值感对于推进多式联络基础模型至关重要.
研究的目的:
- 引入两个认知科学启发的基准任务,用于精确评估人工智能模型中的视觉计数能力.
- 提供对AI的数感和计数能力的客观测量.
主要方法:
- 评估了流行的视觉问答 (VQA),图像到文本和文本到图像的人工智能模型.
- 利用基准任务来评估AI命名对象数量和生成具有特定数量的图像的能力.
主要成果:
- 先进的AI模型在命名对象计数和生成具有目标数字的图像方面都表现出较低的准确性.
- 对于超出分类范围的数字,模型性能显著降低,错误通常取决于对象类别.
- 人工智能模型表现出明显的错误,即使是小数量,与人类行为不同.
结论:
- 开发直观的视觉数字理解仍然是人工智能面临的重大挑战.
- 仅仅增加人工智能模型大小不太可能培养系统的计数技能.
- 该研究发布了基准代码,以帮助未来的人工智能计数技能评估.
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