Semantic Segmentation in Art Paintings

نویسندگان

چکیده

Semantic segmentation is a difficult task even when trained in supervised manner on photographs. In this paper, we tackle the problem of semantic artistic paintings, an more challenging because much larger diversity colors, textures, and shapes there are no ground truth annotations available for segmentation. We propose unsupervised method paintings using domain adaptation. Our approach creates training set pseudo-paintings specific styles by style-transfer PASCAL VOC 2012 dataset, then applies confusion between real paintings. These two steps build new dataset gathered called DRAM (Diverse Realism Art Movements) composed figurative art from four movements, which highly diverse pattern, color, geometry. To segment present composite multi-domain adaptation that trains each sub-domain separately composes their solutions during inference time. provides better results not only movements DRAM, but also other, unseen ones. compare our to alternative methods show applications The code models publicly at: https://github.com/Nadavc220/SemanticSegmentationInArtPaintings.

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ژورنال

عنوان ژورنال: Computer Graphics Forum

سال: 2022

ISSN: ['1467-8659', '0167-7055']

DOI: https://doi.org/10.1111/cgf.14473