Despite the recent impressive breakthroughs in text-to-image generation, generative models have difficulty in capturing the data distribution of underrepresented attribute compositions while over-memorizing overrepresented attribute compositions, which raises public concerns about their robustness and fairness. To tackle this challenge, we propose ACTIG, an attribute-centric compositional text-to-image generation framework. We present an attribute-centric feature augmentation and a novel image-free training scheme, which greatly improves model's ability to generate images with underrepresented attributes. We further propose an attribute-centric contrastive loss to avoid overfitting to overrepresented attribute compositions. We validate our framework on the CelebA-HQ and CUB datasets. Extensive experiments show that the compositional generalization of ACTIG is outstanding, and our framework outperforms previous works in terms of image quality and text-image consistency.
@article{arxiv.2301.01413,
title = {Attribute-Centric Compositional Text-to-Image Generation},
author = {Yuren Cong and Martin Renqiang Min and Li Erran Li and Bodo Rosenhahn and Michael Ying Yang},
journal= {arXiv preprint arXiv:2301.01413},
year = {2023}
}