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                <front>
                    <journal-meta>
                    <journal-id journal-id-type="publisher-id">ei</journal-id>
                    <journal-title>Electronic Imaging</journal-title>
                    <issn pub-type="ppub">2470-1173</issn><issn pub-type="epub">2470-1173</issn>
                    <publisher>
                        <publisher-name>Society for Imaging Science and Technology</publisher-name>
                        <publisher-loc>IS&amp;T 7003 Kilworth Lane, Springfield, VA 22151 USA</publisher-loc>
                    </publisher>
                    </journal-meta>
                    <article-meta>
                    <article-id pub-id-type="doi">10.2352/EI.2024.36.14.CVAA-177</article-id>
                    <article-id pub-id-type="publisher-id">CVAA-177</article-id>
                    <article-categories>
                        <subj-group>
                        <subject>Proceedings</subject>
                        </subj-group>
                    </article-categories>
                    <title-group>
                        <article-title>Towards Artist Recognition Based on Material Rendering. A Case Study for Recognition of Rembrandt and Van Dyck</article-title>
                    </title-group><contrib-group content-type="all"><contrib contrib-type="author"><name>
                            <surname>Huang</surname>
                            <given-names>Jing </given-names>
                           </name> <xref ref-type="aff" rid="aff1author1"/></contrib><aff id="aff1author1">Chongqing University, China</aff></contrib-group><contrib-group content-type="all"><contrib contrib-type="author"><name>
                            <surname>Elgammal</surname>
                            <given-names>Ahmed </given-names>
                           </name> <xref ref-type="aff" rid="aff2author2"/></contrib><aff id="aff2author2"> Rutgers University,  US</aff></contrib-group><abstract>
                    <title>Abstract</title>
                    <p>Can artists be recognized from the way they render certain materials, such as fabric, skin, or hair? In this paper, we study this problem with a focus on recognizing works by Rembrandt, Van Dyck, and other Dutch and Flemish artists from the same era. This paper proposes a novel material-based approach based on Swin Transformer and Cascade Mask R-CNN to address artist recognition task. We report the performance on a dataset of 644 images. Additionally, the models robustness to image variations is studied.</p>
                    </abstract><pub-date>
                        <day>21</day>
                        <month>01</month>
                        <year>2024</year>
                        </pub-date><volume>36</volume>
                    <issue-acronym>CVAA</issue-acronym>
                    <issue-title>Computer Vision and Image Analysis of Art 2024</issue-title>
                    <issue seq="177">14</issue>
                    <fpage>177-1</fpage>
                    <lpage>177-7</lpage>
                    <permissions>
                         <copyright-statement>© 2024, Society for Imaging Science and Technology</copyright-statement>
                        <copyright-year>2024</copyright-year>
                    </permissions><kwd-group><kwd>Artist Recognition</kwd><kwd>Instance Segmentation</kwd><kwd>Painting Material Recognition</kwd><kwd>Swin Transformer</kwd></kwd-group></article-meta>
                </front>
                </article>