Semantic Segmentation using Foundation Models for Cultural Heritage: an Experimental Study on Notre-Dame de Paris
Résumé
Vision foundation models have already had a major impact on several computer vision tasks. This work aims to study their usefulness in the context of cultural heritage. By utilizing the Segment Anything Model (SAM) we could perform segmentation on Notre-Dame de Paris images. Additionally, we have developed a pipeline that combines various foundation models (GroundingDINO and CLIP) to demonstrate their abilities for semantic segmentation of cultural heritage data.
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