[{"command":"insert","method":null,"selector":".course-modal-ajax-placeholder-959633","data":"\u003Cdiv class=\u0022container content-rte\u0022\u003E\n \u003Cdiv class=\u0022row\u0022\u003E\n \u003Cdiv class=\u0022box col-md-6 bg-primary text-white\u0022\u003E\n \u003Ch2 class=\u0022h1 text-left\u0022\u003E AI and Computer vision for cultural heritage\n\u003C\/h2\u003E\n \u003Cdiv class=\u0022field-spacing\u0022\u003E\n \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EECTS :\n 2.5\n \u003C\/div\u003E\n \u003C\/div\u003E\n \u003Cdiv class=\u0022field-spacing\u0022\u003E\n \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ESemester:\n Semestre 2\n \u003C\/div\u003E\n \u003C\/div\u003E\n \u003Cdiv class=\u0022field-spacing\u0022\u003E\n \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ED\u00e9tail du volume horaire\u003C\/div\u003E\n \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n \u003Cspan class=\u0022field__label h4 mt-5 \u0022\u003ELecture:\u003C\/span\u003E\n \u003Cspan\u003E12\u003C\/span\u003E\n \u003C\/div\u003E\n \u003C\/div\u003E\n \u003Cdiv class=\u0022field-spacing\u0022\u003E\n \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ELangue d\u0026#039;enseignement\u003C\/div\u003E\n \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n \u003Cspan\u003EAnglais\u003C\/span\u003E\n \u003C\/div\u003E\n \u003C\/div\u003E\n \u003Cdiv class=\u0022field-spacing\u0022\u003E\n \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EEnseignement \u00e0 distance\u003C\/div\u003E\n \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n \u003Cspan\u003Enon\u003C\/span\u003E\n \u003C\/div\u003E\n \u003C\/div\u003E\n \u003C\/div\u003E\n \u003Cdiv class=\u0022box col-md-6\u0022\u003E\n \u003Cdiv class=\u0022field-spacing\u0022\u003E\n \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EPr\u00e9requis\u003C\/div\u003E\n \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n \u003Cspan\u003E\u003Cp\u003EPython with numpy proficiency is required. Some familiarity with scipy, opencv,\u003Cbr\u003E\nscikit-image, pandas, and one of the major neural network frameworks (pytorch, tensorflow,\u003Cbr\u003E\njax) is recommended. Jupyter will be used for interactive teaching and for evaluation.\u003C\/p\u003E\u003C\/span\u003E\n \u003C\/div\u003E\n \u003C\/div\u003E\n \u003Cdiv class=\u0022field-spacing\u0022\u003E\n \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EProgramme \/ plan \/ contenus\u003C\/div\u003E\n \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n \u003Cspan\u003E\u003Col\u003E\n\u003Cli\u003EOverview of technical and chemical imaging; relation to materiality\u003C\/li\u003E\n\u003Cli\u003ECross-modal image registration\u003C\/li\u003E\n\u003Cli\u003EModeling conditional expectation in multimodal imaging of oil paintings\u003C\/li\u003E\n\u003Cli\u003EDirect microstructural analysis of paintings\u003C\/li\u003E\n\u003Cli\u003EText and image embeddings with pre-trained networks\u003C\/li\u003E\n\u003Cli\u003EImage-to-image translation with CNNs\u003C\/li\u003E\n\u003C\/ol\u003E\u003C\/span\u003E\n \u003C\/div\u003E\n \u003C\/div\u003E\n \u003Cdiv class=\u0022field-spacing\u0022\u003E\n \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EObjectifs d\u0026#039;apprentissage\u003C\/div\u003E\n \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n \u003Cspan\u003E\u003Cp\u003EArt history and cultural heritage science are undergoing rapid advances due to the\u003Cbr\u003E\nsimultaneous advent of new and inexpensive imaging modalities, fast computation, and ubiquitous high-speed internet. We can easily collect gigabytes or terabytes of multimodal imaging data about an artwork such as a painting, offering the ability to dramatically improve our understanding of who made it, how it was made, its state of conservation, and its relation to other artworks. Additionally, cultural heritage institutions are digitizing millions of works and making them freely available, presenting opportunities for researchers to investigate and visualize central art-historical questions such as attribution, style, and iconography in new\u003Cbr\u003E\nways.\u003Cbr\u003E\nIn all cases, extracting the full value from these data requires the extensive use of\u003Cbr\u003E\nsophisticated computational tools. The aims of this course are two-fold: first, to familiarize students with the rich and little-known world of cultural heritage imaging and its many fascinating and challenging problems; and second, to provide an introduction to a selection of the most important recent developments in computer vision and deep learning to solve these\u003Cbr\u003E\nproblems.\u003C\/p\u003E\u003C\/span\u003E\n \u003C\/div\u003E\n \u003C\/div\u003E\n \u003Cdiv class=\u0022field-spacing\u0022\u003E\n \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EOrganisation g\u00e9n\u00e9rale et modalit\u00e9s p\u00e9dagogiques\u003C\/div\u003E\n \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n \u003Cspan\u003E\u003Cp\u003EA considerable portion of each session will involve hands-on work with real data from across the instructor\u2019s 19 years of experience in the field of cultural heritage imaging. Course\u003Cbr\u003E\nreadings will include important papers from the computer vision and deep learning fields (KeyNet, MAGSAC++, Neural Style Transfer, U-Net, CLIP, DINOv2, etc.)\u003C\/p\u003E\u003C\/span\u003E\n \u003C\/div\u003E\n \u003C\/div\u003E\n \u003Cdiv class=\u0022field-spacing\u0022\u003E\n \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ENature de l\u0026#039;\u00e9valuation\u003C\/div\u003E\n \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n \u003Cspan\u003EEvaluation Continue non Int\u00e9grale\u003C\/span\u003E\n \u003C\/div\u003E\n \u003C\/div\u003E\n \u003C\/div\u003E\n \u003C\/div\u003E\n\u003C\/div\u003E\n","settings":null}]