Exploring the Relationship between Dataset Size and Image Captioning Model Performance

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43969948" target="_blank" >RIV/49777513:23520/23:43969948 - isvavai.cz</a>
Result on the web
<a href="http://svk.fav.zcu.cz/download/proceedings_svk_2023.pdf" target="_blank" >http://svk.fav.zcu.cz/download/proceedings_svk_2023.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Exploring the Relationship between Dataset Size and Image Captioning Model Performance
Original language description
Image captioning is a deep learning task, which goal is to automatically generate textual description of an input image. It is a complex task that requires identifying and interpreting visual information and generating grammatically correct and fluent sentences. Because different individuals may consider various aspects of an image important, there isn’t any single correct caption. This means that there is no ideal evaluation metric for measuring caption quality, as different metrics may better evaluate different attributes of the caption. Image captioning models, just like other deep learning models, need a large amount of training data and require a long time to train. In this work, we investigate the impact of using a smaller amount of training data on the performance of the standard image captioning model Oscar.
Czech name
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Czech description
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Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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