Quantifying the Gaps Between Translation and Native Perception in Training for Multimodal, Multilingual Retrieval (2024.emnlp-main)
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| Challenge: | Existing models that account for perceptual differences in image captions are limited to use in English . culture-based tasks such as recognition, detection, and image retrieval are hindered by relying on English supervision. |
| Approach: | They propose and evaluate caption augmentation strategies to address these gaps . they use captions from german perception and captions that have been machine-translated or human-transcribed from English into german . |
| Outcome: | The proposed models achieve a mean recall improvement of +1.3, but still lack flexibility . cultural differences present in language with respect to object specificity and importance . |
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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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| Challenge: | Recent pretrained vision-language models have achieved impressive performance on cross-modal retrieval tasks in English. |
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Cross-lingual Visual Pre-training for Multimodal Machine Translation (2021.eacl-main)
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Ozan Caglayan, Menekse Kuyu, Mustafa Sercan Amac, Pranava Madhyastha, Erkut Erdem, Aykut Erdem, Lucia Specia
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