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Register a new userCross-lingual Cross-modal Pretraining for Multimodal Retrieval
تبادل اللغات التعليب الوسائط الاحتجاج لاسترجاع الوسائط المتعددة
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حققت نماذج لغة الرؤية المحددة الأخيرة أداء مثير للإعجاب على مهام الاسترجاع عبر مشروط باللغة الإنجليزية. ومع ذلك، تعتمد نجاحهم بشكل كبير على توافر العديد من مجموعات بيانات التعليق المشروح على الصورة لإحاطاء، حيث لا تكون النصوص بالضرورة باللغة الإنجليزية. على الرغم من أنه يمكننا استخدام أدوات الترجمة الآلية (MT) لترجمة النص غير الإنجليزي إلى اللغة الإنجليزية، فإن الأداء لا يزال يعتمد إلى حد كبير على جودة MT وقد يعاني من مشاكل عالية من الكمون في تطبيقات العالم الحقيقي. تقترح هذه الورقة نهجا جديدا لتعلم تمثيلات متعددة الوسائط عبر اللغات لمطابقة الصور وإياراتها ذات الصلة بلغات متعددة. نجمع بسلاسة بسلاسة أهداف محالمانية عبر اللغات وأهداف محالم الاحتجاج بالعدوان في إطار موحد لتعلم الصور والنص في مساحة تضمين مشتركة من بيانات التسمية التوضيحية باللغة الإنجليزية المتاحة، مونولينغيا ومتوازي Corpus. نظظ أن نهجنا يحقق أداء SOTA في مهام استرجاع على معايير التسمية التوضيحية متعددة اللغات متعددة اللغات متعددة اللغات: multi30k مع التسميات التوضيحية الألمانية و mscoco مع التسميات التوضيحية اليابانية.
Recent pretrained vision-language models have achieved impressive performance on cross-modal retrieval tasks in English. Their success, however, heavily depends on the availability of many annotated image-caption datasets for pretraining, where the texts are not necessarily in English. Although we can utilize machine translation (MT) tools to translate non-English text to English, the performance still largely relies on MT's quality and may suffer from high latency problems in real-world applications. This paper proposes a new approach to learn cross-lingual cross-modal representations for matching images and their relevant captions in multiple languages. We seamlessly combine cross-lingual pretraining objectives and cross-modal pretraining objectives in a unified framework to learn image and text in a joint embedding space from available English image-caption data, monolingual and parallel corpus. We show that our approach achieves SOTA performance in retrieval tasks on two multimodal multilingual image caption benchmarks: Multi30k with German captions and MSCOCO with Japanese captions.
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