Stop Pre-Training: Adapt Visual-Language Models to Unseen Languages
Yasmine Karoui, Rémi Lebret, Negar Foroutan Eghlidi, Karl Aberer · 2023
Vision-Language Pre-training (VLP) has advanced the performance of many visionlanguage tasks, such as image-text retrieval, visual entailment, and visual reasoning.The pre-training mostly utilizes lexical databases and image queries in English.Previous work has demonstrated that the pre-training in English does not transfer well to other languages in a zero-shot setting.However, multilingual pre-trained language models (MPLM) have excelled at a variety of single-modal language tasks.In this paper, we propose a simple yet efficient approach to adapt VLP to unseen languages using MPLM.We utilize a cross-lingual contextualized token embeddings alignment approach to train text encoders for non-English languages.Our approach does not require image input and primarily uses machine translation, eliminating the need for target language data.Our evaluation across three distinct tasks (image-text retrieval, visual entailment, and natural language visual reasoning) demonstrates that this approach outperforms the state-of-the-art multilingual vision-language models without requiring large parallel corpora.Our code is available at https://github.com/Yasminekaroui/CliCoTea.