GARRET: Generative Feedback for Region Retrieval in Visual Documents
Multimodal retrieval-augmented generation over visually rich documents requires evidence at a finer granularity than entire pages. Existing visual retrievers typically index or return full pages, whereas training region-level retrievers often requires costly query-to-region annotations or noisy model-generated labels. We introduce GARRET, a region-retrieval framework trained from (page, question, answer) triplets through differentiable generative feedback. A shared context-preserving retriever produces page and region multi-vector representations. During training, their retrieval scores define a soft routing distribution over candidate region crops. An auxiliary vision-language model atte...
| Authors | Weijian Jian, Siyang Chen, Yuguang Yang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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