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• W3139243190 abstract "This paper studies zero-shot cross-lingual transfer of vision-language models. Specifically, we focus on multilingual text-to-video search and propose a Transformer-based model that learns contextualized multilingual multimodal embeddings. Under a zero-shot setting, we empirically demonstrate that performance degrades significantly when we query the multilingual text-video model with non-English sentences. To address this problem, we introduce a multilingual multimodal pre-training strategy, and collect a new multilingual instructional video dataset (MultiHowTo100M) for pre-training. Experiments on VTT show that our method significantly improves video search in non-English languages without additional annotations. Furthermore, when multilingual annotations are available, our method outperforms recent baselines by a large margin in multilingual text-to-video search on VTT and VATEX; as well as in multilingual text-to-image search on Multi30K. Our model and Multi-HowTo100M is available at http://github.com/berniebear/Multi-HT100M." @default.
• W3139243190 created "2021-03-29" @default.
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• W3139243190 date "2021-03-16" @default.
• W3139243190 modified "2023-10-03" @default.
• W3139243190 title "Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models" @default.
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• W3139243190 doi "https://doi.org/10.48550/arxiv.2103.08849" @default.
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