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Contrastive Language-Image Pre-training for the Italian Language

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arxiv 2108.08688 v1 pith:KLTCSYDM submitted 2021-08-19 cs.CL cs.CV

Federico Bianchi , Giuseppe Attanasio , Raphael Pisoni , Silvia Terragni , Gabriele Sarti , Sri Lakshmi This is my paper · ORCID
classification cs.CLcs.CV
keywords modelcliplanguageclassificationclip-italiancontrastivedataitalian
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CLIP (Contrastive Language-Image Pre-training) is a very recent multi-modal model that jointly learns representations of images and texts. The model is trained on a massive amount of English data and shows impressive performance on zero-shot classification tasks. Training the same model on a different language is not trivial, since data in other languages might be not enough and the model needs high-quality translations of the texts to guarantee a good performance. In this paper, we present the first CLIP model for the Italian Language (CLIP-Italian), trained on more than 1.4 million image-text pairs. Results show that CLIP-Italian outperforms the multilingual CLIP model on the tasks of image retrieval and zero-shot classification.

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  1. Cross-Lingual Representation Alignment Through Contrastive Image-Caption Tuning

    cs.CL 2025-05 conditional novelty 5.0 of 10

    Contrastive fine-tuning on multilingual image-caption pairs, without bitexts, improves cross-lingual sentence alignment and partially incorporates a previously unseen language.

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