Story Visualization by Online Text Augmentation with Context Memory

• URL: http://arxiv.org/abs/2308.07575v2
• Date: Sat, 19 Aug 2023 07:30:52 GMT
• Title: Story Visualization by Online Text Augmentation with Context Memory
• Authors: Daechul Ahn, Daneul Kim, Gwangmo Song, Seung Hwan Kim, Honglak Lee, Dongyeop Kang, Jonghyun Choi
• Abstract summary: We propose a novel memory architecture for the Bi-directional Transformer framework with an online text augmentation. The proposed method significantly outperforms the state of the arts in various metrics including FID, character F1, frame accuracy, BLEU-2/3, and R-precision.
• Score: 64.86944645907771
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Story visualization (SV) is a challenging text-to-image generation task for the difficulty of not only rendering visual details from the text descriptions but also encoding a long-term context across multiple sentences. While prior efforts mostly focus on generating a semantically relevant image for each sentence, encoding a context spread across the given paragraph to generate contextually convincing images (e.g., with a correct character or with a proper background of the scene) remains a challenge. To this end, we propose a novel memory architecture for the Bi-directional Transformer framework with an online text augmentation that generates multiple pseudo-descriptions as supplementary supervision during training for better generalization to the language variation at inference. In extensive experiments on the two popular SV benchmarks, i.e., the Pororo-SV and Flintstones-SV, the proposed method significantly outperforms the state of the arts in various metrics including FID, character F1, frame accuracy, BLEU-2/3, and R-precision with similar or less computational complexity.

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