Generative Text-to-Image Retrieval via Hierarchical Identifiers and Semantic Internalization

摘要

Generative Retrieval (GR) has emerged as a promising text-to-image paradigm, yet it suffers from limited semantic discriminability, alignment bias, and closed-set restrictions. To address these challenges, we propose SIGMA, a novel framework for Semantic Internalization for Generative Multimodal Alignment. SIGMA constructs multi-granularity hierarchical identifiers to ensure unique, semantically consistent image representations. We further introduce a progressive semantic internalization training strategy augmented with semantic soft labels, which captures fine-grained text-image affinities and enables inductive identifier assignment for unseen samples realizing open-set dynamic indexing capabilities. Experiments on the Flickr30K and MS-COCO datasets demonstrate that SIGMA outperforms state-of-the-art baselines, achieving average Recall@1, Recall@5, and Recall@10 improvements of 10.65%, 8.50%, and 7.00%, respectively.