Image Captioning with Attention Based Model

dc.contributor.authorYv, S.S.
dc.contributor.authorChoubey, Y.
dc.contributor.authorNaik, D.
dc.date.accessioned2026-02-06T06:35:56Z
dc.date.issued2021
dc.description.abstractDefining the content of an image automatically in Artificial Intelligence is basically a rudimentary problem that connects computer vision and NLP (Natural Language Processing). In the proposed work, a generative model is presented by combining the recent developments in machine learning and computer vision based on a deep recurrent architecture that describes the image using natural language phrases. By integrating the training picture, the trained model maximizes the likelihood of the target description sentence. The efficiency of the model, its accuracy and the language it learns is only dependent on the image descriptions, which was demonstrated by experiments performed on several datasets. © 2021 IEEE.
dc.identifier.citationProceedings - 5th International Conference on Computing Methodologies and Communication, ICCMC 2021, 2021, Vol., , p. 1051-1055
dc.identifier.urihttps://doi.org/10.1109/ICCMC51019.2021.9418347
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/30149
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectAttention
dc.subjectCaption
dc.subjectComponent
dc.subjectDecoder
dc.subjectDense
dc.subjectEncoder
dc.subjectFormatting
dc.subjectGated Recurrent Unit(GRU)
dc.titleImage Captioning with Attention Based Model

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