Object-Attribute Binding in Text-to-Image Generation: Evaluation and Control
<|reference_start|>Object-Attribute Binding in Text-to-Image Generation: Evaluation and Control: Current diffusion models create photorealistic images given a text prompt as input but struggle to correctly bind attributes mentioned in the text to the right objects in the image. This is evidenced by our novel image-graph alignment model called EPViT (Edge Prediction Vision Transformer) for the evaluation of image-text alignment. To alleviate the above problem, we propose focused cross-attention (FCA) that controls the visual attention maps by syntactic constraints found in the input sentence. Additionally, the syntax structure of the prompt helps to disentangle the multimodal CLIP embeddings that are commonly used in T2I generation. The resulting DisCLIP embeddings and FCA are easily integrated in state-of-the-art diffusion models without additional training of these models. We show substantial improvements in T2I generation and especially its attribute-object binding on several datasets.\footnote{Code and data will be made available upon acceptance.<|reference_end|>
@article{trusca2024object-attribute,
    title={Object-Attribute Binding in Text-to-Image Generation: Evaluation and
  Control},
    author={Maria Mihaela Trusca, Wolf Nuyts, Jonathan Thomm, Robert Honig, Thomas
  Hofmann, Tinne Tuytelaars, Marie-Francine Moens},
    journal={arXiv preprint arXiv:2404.13766},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13766},
    primaryClass={cs.CV}
}