Text Augmentation Using BERT for Image Captioning

By Viktar Atliha, Dmitrij Sesok. 2020

37 citations recorded by the source corpus.

Image captioning is an important task for improving human-computer interaction as well as for a deeper understanding of the mechanisms underlying the image description by human. In recent years, this research field has rapidly developed and a number of impressive results have been achieved. The typical models are based on a neural networks, including convolutional ones for encoding images and recurrent ones for decoding them into text. More than that, attention mechanism and transformers are actively used for boosting performance. However, even the best models have a limit in their quality with a lack of data. In order to generate a variety of descriptions of objects in different situations you need a large training set. The current commonly used datasets although rather large in terms of number of images are quite small in terms of the number of different captions per one image. We expanded the training dataset using text augmentation methods. Methods include augmentation with synonyms as a baseline and the state-of-the-art language model called Bidirectional Encoder Representations from Transformers (BERT). As a result, models that were trained on a datasets augmented show better results than that models trained on a dataset without augmentation.

Subfields: Computer Vision and Pattern Recognition, Natural Language Processing, Multimodal Learning

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Citations in the corpus

A Survey of Data Augmentation Approaches for NLP (2021, background): “Atliha and Šešok (2020) explore DA meth-ods such as synonym replacement and contextualized word embeddings augmentation using BERT for image captioning .”