Teaching Text-to-Image Models to Communicate in Dialog
This addresses the problem of enabling conversational agents to generate effective image responses, though it is incremental as it builds on existing text-to-image models.
The paper tackles the dialog-to-image generation task by fine-tuning text-to-image models to produce high-resolution images aligned with dialog context, achieving consistent and remarkable improvements across three state-of-the-art backbones on PhotoChat and MMDialog Corpus.
A picture is worth a thousand words, thus, it is crucial for conversational agents to understand, perceive, and effectively respond with pictures. However, we find that directly employing conventional image generation techniques is inadequate for conversational agents to produce image responses effectively. In this paper, we focus on the innovative dialog-to-image generation task, where the model synthesizes a high-resolution image aligned with the given dialog context as a response. To tackle this problem, we design a tailored fine-tuning approach on the top of state-of-the-art text-to-image generation models to fully exploit the structural and semantic features in dialog context during image generation. Concretely, we linearize the dialog context with specific indicators to maintain the dialog structure, and employ in-domain data to alleviate the style mismatch between dialog-to-image and conventional image generation tasks. Empirical results on PhotoChat and MMDialog Corpus show that our approach brings consistent and remarkable improvement with 3 state-of-the-art pre-trained text-to-image generation backbones.