Learning multimodal entity representations and their ensembles, with applications in a data-driven advisory framework for video game players

Janusz, Andrzej; Kałuża, Daniel; Matraszek, Maciej; Grad, Lukasz; ̧zak, Dominik Śle

doi:10.1016/j.ins.2022.10.097

Cited by 5 publications

(1 citation statement)

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“…Image captioning methods combining text and visual data belong to the multi-modal machine-learning approaches [22], [40], [59]. Captioning models can be divided into traditional and deep-learning-based.…”

Section: Previous Workmentioning

confidence: 99%

On combining image features and word embeddings for image captioning

Bartosiewicz,

Iwanowski,

Wiszniewska

et al. 2023

Annals of Computer Science and Information Systems

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Image captioning is the task of generating semantically and grammatically correct caption for a given image. Captioning model usually has an encoder-decoder structure where encoded image is decoded as list of words being a consecutive elements of the descriptive sentence. In this work, we investigate how encoding of the input image and way of coding words affects the result of the training of the encoderdecoder captioning model. We performed experiments with image encoding using 10 all-purpose popular backbones and 2 types of word embeddings. We compared those models using most popular image captioning evaluation metrics. Our research shows that the model's performance highly depends on the optimal combination of the neural image feature extractor and language processing model. The outcome of our research are applicable in all the research works that lead to the developing the optimal encoderdecoder image captioning model.

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Section: Previous Workmentioning

confidence: 99%