The Optimal Choice of the Encoder-Decoder Model Components for Image Captioning

Mateusz Bartosiewicz; Marcin Iwanowski

doi:10.20944/preprints202408.1045.v1

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 13 August 2024 / Approved: 14 August 2024 / Online: 14 August 2024 (16:36:18 CEST)

Image captioning aims at generating meaningful verbal descriptions of a digital image. Our paper focuses on the classic encoder-decoder deep learning model that consists of several components – sub-networks, each performing a separate task that, combined, form an effective caption generator. We investigate image feature extractors, recurrent neural networks, word embedding models, and word generation layers and discuss how each component influences the captioning model’s overall performance. Our experiments are performed on the MS COCO 2014 dataset. The results help design efficient models with optimal combinations of their components.

image captioning; image processing; image analysis; computer vision; recurrent neural network

Computer Science and Mathematics, Computer Vision and Graphics

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The Optimal Choice of the Encoder-Decoder Model Components for Image Captioning

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