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@INPROCEEDINGS{Baia_ECCVW_2024,
                      author = {Baia, Alina Elena and Cavallaro, Andrea},
                    keywords = {Interpretability, topic modeling, Vision language models},
                    projects = {Idiap},
                       title = {Image-guided topic modeling for interpretable privacy classification},
                   booktitle = {Proceedings of the European Conference on Computer Vision (ECCV) Workshops},
                        year = {2024},
                    abstract = {Predicting and explaining the private information contained in an image 
in human-understandable terms is a complex and contextual task. This task is challenging even for large language models. To facilitate the understanding of privacy decisions, we propose to predict image privacy based on a set of natural language content descriptors. These content descriptors are associated with privacy scores that reflect how people perceive image content. We generate descriptors with our novel Image-guided Topic Modeling (ITM) approach. ITM leverages, via multimodality alignment, both vision information and image textual descriptions from a vision language model.
We use the ITM-generated descriptors to learn a privacy predictor, Priv×ITM, whose decisions are interpretable by design. Our Priv×ITM, classifier outperforms the reference interpretable method by 5 percentage points in accuracy and performs comparably to the current non-interpretable state-of-the-art model.},
                         pdf = {https://publications.idiap.ch/attachments/papers/2024/Baia_ECCVW_2024.pdf}
}