Generating Rich Image Descriptions from Localized Attention

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Date

2023-08

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University of the Witwatersrand, Johannesburg

Abstract

The field of image captioning is constantly growing with swathes of new methodologies, performance leaps, datasets, and challenges. One new challenge is the task of long-text image description. While the vast majority of research has focused on short captions for images with only short phrases or sentences, new research and the recently released Localized Narratives dataset have pushed this to rich, paragraph length descriptions. In this work we perform additional research to grow the sub-field of long-text image descriptions and determine the viability of our new methods. We experiment with a variety of progressively more complex LSTM and Transformer-based approaches, utilising human-generated localised attention traces and image data to generate suitable captions, and evaluate these methods on a suite of common language evaluation metrics. We find that LSTM-based approaches are not well suited to the task, and under-perform Transformer-based implementations on our metric suite while also proving substantially more demanding to train. On the other hand, we find that our Transformer-based methods are well capable of generating captions with rich focus over all regions of the image and in a grammatically sound manner, with our most complex model outperforming existing approaches on our metric suite.

Description

A dissertation submitted in fulfilment of the requirements for the degree of Master of Science in Computer Science, to the Faculty of Science, School of Computer Science & Applied Mathematics, University of the Witwatersrand, Johannesburg, 2023.

Keywords

Computer vision, Natural language processing, Machine learning, Deep learning, Data fusion, Multi-modal models, UCTD

Citation

Poulton, David. (2023). Generating Rich Image Descriptions from Localized Attention. [Master's dissertation, University of the Witwatersrand, Johannesburg]. https://hdl.handle.net/10539/41976

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