CONF Baranouskaya_ICASSP_2026/IDIAP The impact of abstract and object tags on image privacy classification Baranouskaya, Darya Cavallaro, Andrea abstractness Image classification privacy tags EXTERNAL http://publications.idiap.ch/attachments/papers/2026/Baranouskaya_ICASSP_2026.pdf PUBLIC Proceedings of the 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2026 https://arxiv.org/abs/2510.07976 URL https://doi.org/10.48550/arXiv.2510.07976 doi 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.