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Localized Narratives is a dataset with detailed natural language descriptionsof images paired with mouse traces that provide a sparse, fine-grained visualgrounding for phrases. We propose TReCS, a sequential model that exploits thisgrounding to generate images. TReCS uses descriptions to retrieve segmentationmasks and predict object labels aligned with mouse traces. These alignments areused to select and position masks to generate a fully covered segmentationcanvas; the final image is produced by a segmentation-to-image generator usingthis canvas. This multi-step, retrieval-based approach outperforms existingdirect text-to-image generation models on both automatic metrics and humanevaluations: overall, its generated images are more photo-realistic and bettermatch descriptions.

Text-to-Image Generation Grounded by Fine-Grained User Attention