抽象标签在隐私分类中更有效，尤其标签少时。
The impact of abstract and object tags on image privacy classification
- 对比抽象与实体标签对图像隐私判断的影响。
- 标签数量少时，抽象标签准确率更高；数量多时两者相当。
- 为隐私模型设计提供标签类型与数量的指导依据。
实体标签表示具体对象，广泛用于计算机视觉任务；抽象标签捕捉高层语义信息，适用于需要上下文或主观理解的任务。从图像中提取的标签有助于提升可解释性。本文研究哪种标签类型更适合具有情境依赖性和主观性的图像隐私分类。尽管通常使用实体标签进行隐私判断，我们发现当标签数量受限时，抽象标签表现更优；而当每张图可使用更多标签时，实体信息同样有效。这些发现将推动未来更精准图像隐私分类器的研发，强调标签类型与数量的作用。
原文摘要 · Abstract (English)
Object tags denote concrete entities and are central to many computer vision tasks, whereas abstract tags capture higher-level information, which is relevant for tasks that require a contextual, potentially subjective scene understanding. Object and abstract tags extracted from images also facilitate interpretability. In this paper, we explore which type of tags is more suitable for the context-dependent and inherently subjective task of image privacy. While object tags are generally used for privacy classification, we show that abstract tags are more effective when the tag budget is limited. Conversely, when a larger number of tags per image is available, object-related information is as useful. We believe that these findings will guide future research in developing more accurate image privacy classifiers, informed by the role of tag types and quantity.
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