From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion Models

• URL: http://arxiv.org/abs/2309.04109v1
• Date: Fri, 8 Sep 2023 04:10:01 GMT
• Title: From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion Models
• Authors: Changming Xiao, Qi Yang, Feng Zhou, Changshui Zhang
• Abstract summary: We propose a method to utilize the attention mechanism in the denoising network of text-to-image diffusion models. We evaluate our method on Pascal VOC 2012 and Microsoft COCO 2014 under weakly-supervised semantic segmentation setting. Our work reveals a novel way to extract the rich multi-modal knowledge hidden in diffusion models for segmentation.
• Score: 41.66656119637025
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models have revolted the field of text-to-image generation recently. The unique way of fusing text and image information contributes to their remarkable capability of generating highly text-related images. From another perspective, these generative models imply clues about the precise correlation between words and pixels. In this work, a simple but effective method is proposed to utilize the attention mechanism in the denoising network of text-to-image diffusion models. Without re-training nor inference-time optimization, the semantic grounding of phrases can be attained directly. We evaluate our method on Pascal VOC 2012 and Microsoft COCO 2014 under weakly-supervised semantic segmentation setting and our method achieves superior performance to prior methods. In addition, the acquired word-pixel correlation is found to be generalizable for the learned text embedding of customized generation methods, requiring only a few modifications. To validate our discovery, we introduce a new practical task called "personalized referring image segmentation" with a new dataset. Experiments in various situations demonstrate the advantages of our method compared to strong baselines on this task. In summary, our work reveals a novel way to extract the rich multi-modal knowledge hidden in diffusion models for segmentation.

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