Towards Better Text-Image Consistency in Text-to-Image Generation

• URL: http://arxiv.org/abs/2210.15235v1
• Date: Thu, 27 Oct 2022 07:47:47 GMT
• Title: Towards Better Text-Image Consistency in Text-to-Image Generation
• Authors: Zhaorui Tan, Zihan Ye, Xi Yang, Qiufeng Wang, Yuyao Yan, Kaizhu Huang
• Abstract summary: We develop a novel CLIP-based metric termed as Semantic Similarity Distance (SSD) We further design the Parallel Deep Fusion Generative Adversarial Networks (PDF-GAN), which can fuse semantic information at different granularities. Our PDF-GAN can lead to significantly better text-image consistency while maintaining decent image quality on the CUB and COCO datasets.
• Score: 15.735515302139335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating consistent and high-quality images from given texts is essential for visual-language understanding. Although impressive results have been achieved in generating high-quality images, text-image consistency is still a major concern in existing GAN-based methods. Particularly, the most popular metric $R$-precision may not accurately reflect the text-image consistency, often resulting in very misleading semantics in the generated images. Albeit its significance, how to design a better text-image consistency metric surprisingly remains under-explored in the community. In this paper, we make a further step forward to develop a novel CLIP-based metric termed as Semantic Similarity Distance (SSD), which is both theoretically founded from a distributional viewpoint and empirically verified on benchmark datasets. Benefiting from the proposed metric, we further design the Parallel Deep Fusion Generative Adversarial Networks (PDF-GAN), which can fuse semantic information at different granularities and capture accurate semantics. Equipped with two novel plug-and-play components: Hard-Negative Sentence Constructor and Semantic Projection, the proposed PDF-GAN can mitigate inconsistent semantics and bridge the text-image semantic gap. A series of experiments show that, as opposed to current state-of-the-art methods, our PDF-GAN can lead to significantly better text-image consistency while maintaining decent image quality on the CUB and COCO datasets.

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