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Automatic Image Caption Generation: study and implementation

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dc.contributor.author korichi, Safa batoul
dc.contributor.author aimene, Karim
dc.date.accessioned 2022-05-29T11:12:42Z
dc.date.available 2022-05-29T11:12:42Z
dc.date.issued 2021
dc.identifier.uri http://dspace.univ-ghardaia.dz:8080/xmlui/handle/123456789/1055
dc.description.abstract Artificial 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.publisher université Ghardaia EN_en
dc.subject Multimodal Learning, Image captioning, Deep Learning (DL), Transformer, Sequence to sequence, MS-COCO EN_en
dc.title Automatic Image Caption Generation: study and implementation EN_en
dc.type Thesis EN_en


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