Large-Scale Bidirectional Training for Zero-Shot Image Captioning

• URL: http://arxiv.org/abs/2211.06774v3
• Date: Sun, 1 Oct 2023 13:59:25 GMT
• Title: Large-Scale Bidirectional Training for Zero-Shot Image Captioning
• Authors: Taehoon Kim, Mark Marsden, Pyunghwan Ahn, Sangyun Kim, Sihaeng Lee, Alessandra Sala, Seung Hwan Kim
• Abstract summary: We introduce Bidirectional Image Text Training in largER Scale, BITTERS, an efficient training and inference framework for zero-shot image captioning. We show that careful selection of large-scale training set and model architecture is the key to achieving zero-shot image captioning.
• Score: 44.17587735943739
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: When trained on large-scale datasets, image captioning models can understand the content of images from a general domain but often fail to generate accurate, detailed captions. To improve performance, pretraining-and-finetuning has been a key strategy for image captioning. However, we find that large-scale bidirectional training between image and text enables zero-shot image captioning. In this paper, we introduce Bidirectional Image Text Training in largER Scale, BITTERS, an efficient training and inference framework for zero-shot image captioning. We also propose a new evaluation benchmark which comprises of high quality datasets and an extensive set of metrics to properly evaluate zero-shot captioning accuracy and societal bias. We additionally provide an efficient finetuning approach for keyword extraction. We show that careful selection of large-scale training set and model architecture is the key to achieving zero-shot image captioning.

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