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arXiv · 1812.08126

Generating Diverse and Meaningful Captions

Annika Lindh·Robert J. Ross·Abhijit Mahalunkar·Giancarlo Salton·John D. Kelleher
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Abstract

Image Captioning is a task that requires models to acquire a multi-modal understanding of the world and to express this understanding in natural language text. While the state-of-the-art for this task has rapidly improved in terms of n-gram metrics, these models tend to output the same generic captions for similar images. In this work, we address this limitation and train a model that generates more diverse and specific captions through an unsupervised training approach that incorporates a learning signal from an Image Retrieval model. We summarize previous results and improve the state-of-the-art on caption diversity and novelty. We make our source code publicly available online.

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Annika Lindh, Robert J. Ross, Abhijit Mahalunkar, Giancarlo Salton, John D. Kelleher. 2018-12-19. Generating Diverse and Meaningful Captions. https://doi.org/10.1007/978-3-030-01418-6_18

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