Please use this identifier to cite or link to this item: https://dspace.univ-ghardaia.edu.dz/xmlui/handle/123456789/1055
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dc.contributor.authorkorichi, Safa batoul-
dc.contributor.authoraimene, Karim-
dc.date.accessioned2022-05-29T11:12:42Z-
dc.date.available2022-05-29T11:12:42Z-
dc.date.issued2021-
dc.identifier.urihttps://dspace.univ-ghardaia.edu.dz/xmlui/handle/123456789/1055-
dc.description.abstractArtificial Intelligence (AI) is currently moving increasingly towards multimodal learning which involve build system that can process information from multiple sources, such as text, images or audio. Image captioning is one of the main visual-linguistic tasks that requires generating captions to a specific image. The challenge is to create a unified Deep Learning (DL) model, suitable to describe an image in a correct sentence. To do so, we need to understand the proper way to visualize the text in a certain space. We used the new term of Transformer that brings a new concept into a sequence to sequence mechanism, we also include the power of modern GPU in processing data in an efficient and faster manner. In this path, we have experimented with a Transformer-based approach and applied it to the image captioning problem using MS COCO dataset.EN_en
dc.publisheruniversité GhardaiaEN_en
dc.subjectMultimodal Learning, Image captioning, Deep Learning (DL), Transformer, Sequence to sequence, MS-COCOEN_en
dc.titleAutomatic Image Caption Generation: study and implementationEN_en
dc.typeThesisEN_en
Appears in Collections:Mémoires de Master

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