Transferring Visual Attributes from Natural Language to Verified Image Generation

Text to image generation methods (T2I) are widely popular in generating art and other creative artifacts. While hallucination can be a positive factor in scenarios where creativity is appreciated, such artifacts are poorly suited for tasks where the generated image needs to be grounded in a strict manner, e.g. as an illustration of a task, an action or in the context of a story. In this paper, we propose to strengthen the factual consistency properties of T2I methods in the presence of natural prompts. First, we cast the problem as an MT problem that translates natural prompts into visual prompts. Then we filter the image with a VQA approach where we answer a set of questions in the visual domain (the image) and in the natural language domain (the natural prompt). Finally, to measure the alignment of answers, we depart from the recent literature that do string matching, and compare answers in an embedding space that assesses the semantic and entailment associations between a natural prompt and its generated image.

• Machine Perception

• Natural Language Processing