CONCAP: Seeing Beyond English with Retrieval-Augmented Captioning
Abstract
Multilingual vision-language models have made significant strides in image captioning, yet they still lag behind their English counterparts due to limited multilingual training data and costly large-scale model parameterization. Retrieval-augmented generation (RAG) offers a promising alternative by conditioning caption generation on retrieved examples in the target language, reducing the need for extensive multilingual training. However, multilingual RAG captioning models often depend on retrieved captions translated from English, which can introduce mismatches and linguistic biases relative to the source language. We introduce CONCAP, a multilingual image captioning model that integrates retrieved captions with image-specific concepts, enhancing the contextualization of the input image and grounding the captioning process across different languages. Experiments on the XM3600 dataset indicate that CONCAP enables strong performance with significantly reduced data requirements. Additionally, evaluations on a culturally focused Arabic dataset show that conceptaware captioning improves cultural relevance, though models trained directly on cultural data still maintain an edge. Our findings highlight the effectiveness of concept aware retrieval augmentation in bridging multilingual performance gaps and emphasize the need for culturally informed modeling in broader multilingual applications.
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