提出多模态零样本方法，提升艺术裸体内容识别准确率
An Art-centric perspective on AI-based content moderation of nudity
- 结合视觉与文本信息的多模态零样本分类
- 发现算法对性别和风格存在明显识别偏差
- 适合关注AI伦理与艺术内容审核的研究者
当生成式人工智能对视觉艺术的影响备受争议时，我们关注一个更微妙的现象：在线艺术裸体内容的算法审查。我们分析了三种「不适合工作场所」图像分类器在艺术裸体识别上的表现，实证发现其存在性别与风格偏差，且仅依赖视觉信息时存在显著技术局限。为此，我们提出一种多模态零样本分类方法，显著提升艺术裸体识别性能。研究揭示若干启示，期望为该领域未来研究提供参考。
原文摘要 · Abstract (English)
At a time when the influence of generative Artificial Intelligence on visual arts is a highly debated topic, we raise the attention towards a more subtle phenomenon: the algorithmic censorship of artistic nudity online. We analyze the performance of three "Not-Safe-For-Work'' image classifiers on artistic nudity, and empirically uncover the existence of a gender and a stylistic bias, as well as evident technical limitations, especially when only considering visual information. Hence, we propose a multi-modal zero-shot classification approach that improves artistic nudity classification. From our research, we draw several implications that we hope will inform future research on this topic.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。