ProtoMask: Segmentation-Guided Prototype Learning

• URL: http://arxiv.org/abs/2510.00683v1
• Date: Wed, 01 Oct 2025 09:07:24 GMT
• Title: ProtoMask: Segmentation-Guided Prototype Learning
• Authors: Steffen Meinert, Philipp Schlinge, Nils Strodthoff, Martin Atzmueller,
• Abstract summary: We study the use of prominent image segmentation foundation models to improve the truthfulness of the mapping between embedding and input space.<n>To perceive the information of a prototypical image, we use the bounding box from each generated segmentation mask to crop the image.<n>We conduct experiments on three popular fine-grained classification datasets with a wide set of metrics.
• Score: 2.1823636275559575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: XAI gained considerable importance in recent years. Methods based on prototypical case-based reasoning have shown a promising improvement in explainability. However, these methods typically rely on additional post-hoc saliency techniques to explain the semantics of learned prototypes. Multiple critiques have been raised about the reliability and quality of such techniques. For this reason, we study the use of prominent image segmentation foundation models to improve the truthfulness of the mapping between embedding and input space. We aim to restrict the computation area of the saliency map to a predefined semantic image patch to reduce the uncertainty of such visualizations. To perceive the information of an entire image, we use the bounding box from each generated segmentation mask to crop the image. Each mask results in an individual input in our novel model architecture named ProtoMask. We conduct experiments on three popular fine-grained classification datasets with a wide set of metrics, providing a detailed overview on explainability characteristics. The comparison with other popular models demonstrates competitive performance and unique explainability features of our model. https://github.com/uos-sis/quanproto

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