CIGLI: Conditional Image Generation from Language & Image

• URL: http://arxiv.org/abs/2108.08955v1
• Date: Fri, 20 Aug 2021 00:58:42 GMT
• Title: CIGLI: Conditional Image Generation from Language & Image
• Authors: Xiaopeng Lu, Lynnette Ng, Jared Fernandez, Hao Zhu
• Abstract summary: We propose a new task called CIGLI: Conditional Image Generation from Language and Image. Instead of generating an image based on text as in text-image generation, this task requires the generation of an image from a textual description and an image prompt.
• Score: 5.159265382427163
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multi-modal generation has been widely explored in recent years. Current research directions involve generating text based on an image or vice versa. In this paper, we propose a new task called CIGLI: Conditional Image Generation from Language and Image. Instead of generating an image based on text as in text-image generation, this task requires the generation of an image from a textual description and an image prompt. We designed a new dataset to ensure that the text description describes information from both images, and that solely analyzing the description is insufficient to generate an image. We then propose a novel language-image fusion model which improves the performance over two established baseline methods, as evaluated by quantitative (automatic) and qualitative (human) evaluations. The code and dataset is available at https://github.com/vincentlux/CIGLI.

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