Efficient Multilingual Multi-modal Pre-training through Triple Contrastive Loss (2022.coling-1)
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| Challenge: | Existing approaches to learn visual and textual representations from web-scale image-text pairs are limited due to labeling cost and limited scalability. |
| Approach: | They propose to use web-scale image-text pairs to learn visual and textual representations in the shared space. |
| Outcome: | The proposed enhancement scheme improves multilingual vision-and-language tasks by minimizing a triplet contrastive loss on images and two different language texts with the same meaning. |
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| Challenge: | Existing V&L pre-training methods rely on strictly-aligned multilingual image-text pairs generated from English-centric datasets. |
| Approach: | They propose a regularized cross-lingual visual contrastive learning objective that constrains representation proximity of weakly-aligned multilingual image-text pairs. |
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Cross-lingual Cross-modal Pretraining for Multimodal Retrieval (2021.naacl-main)
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| Challenge: | Recent pretrained vision-language models have achieved impressive performance on cross-modal retrieval tasks in English. |
| Approach: | They propose a new approach to learn cross-lingual cross-modal representations for matching images and captions in multiple languages using an annotated corpus. |
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Cross-Lingual Representation Alignment Through Contrastive Image-Caption Tuning (2025.acl-short)
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| Challenge: | Multilingual alignment of sentence representations has mostly required bitexts to bridge the gap between languages. |
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Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models (2021.naacl-main)
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| Challenge: | a new study examines zero-shot cross-lingual transfer of vision-language models . we study multilingual text-to-video search in non-English languages without annotations . |
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Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task (D19-64)
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| Challenge: | Existing methods to learn multimodal multilingual embeddings for text and image retrieval tasks are limited to English. |
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Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task (D19-66)
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| Challenge: | Existing methods to learn multimodal multilingual embeddings for text and image retrieval tasks are limited to English. |
| Approach: | They propose a new approach to learn multimodal multilingual embeddings for matching images and captions in two languages by combing two existing objective functions and adapting alignment between existing languages. |
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Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-training (2023.acl-long)
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| Challenge: | Empirical results show that CCLM significantly outperforms the prior state-of-the-art with an average absolute improvement of over 10%. |
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Improving In-context Learning of Multilingual Generative Language Models with Cross-lingual Alignment (2024.naacl-long)
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| Challenge: | Existing studies show that multilingual generative models exhibit a strong language bias toward high-resource languages. |
| Approach: | They propose a cross-lingual alignment framework exploiting pairs of translation sentences to improve cross-linguistic abilities. |
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Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval (2023.acl-long)
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| Challenge: | Contrastive learning is the dominant paradigm for learning text representations from parallel text, but finding negative examples can be expensive in terms of compute or manual effort. |
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Translation-Enhanced Multilingual Text-to-Image Generation (2023.acl-long)
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| Challenge: | Existing models for text-to-image generation are mostly based on the English language due to the lack of annotated image-caption data in other languages. |
| Approach: | They propose to use a multilingual multi-modal encoder to bootstrap mTTI systems that can be translated into other languages. |
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