Towards Zero-shot Cross-lingual Image Retrieval and Tagging

• URL: http://arxiv.org/abs/2109.07622v1
• Date: Wed, 15 Sep 2021 23:39:15 GMT
• Title: Towards Zero-shot Cross-lingual Image Retrieval and Tagging
• Authors: Pranav Aggarwal, Ritiz Tambi, Ajinkya Kale
• Abstract summary: We present a zero-shot approach for learning multi-modal representations using cross-lingual pre-training on the text side. We introduce a new 1K multi-lingual MSCOCO2014 caption test dataset (XTD10) in 7 languages that we collected using a crowdsourcing platform. We also demonstrate how a cross-lingual model can be used for downstream tasks like multi-lingual image tagging in a zero shot manner.
• Score: 1.4425878137951236
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: There has been a recent spike in interest in multi-modal Language and Vision problems. On the language side, most of these models primarily focus on English since most multi-modal datasets are monolingual. We try to bridge this gap with a zero-shot approach for learning multi-modal representations using cross-lingual pre-training on the text side. We present a simple yet practical approach for building a cross-lingual image retrieval model which trains on a monolingual training dataset but can be used in a zero-shot cross-lingual fashion during inference. We also introduce a new objective function which tightens the text embedding clusters by pushing dissimilar texts away from each other. For evaluation, we introduce a new 1K multi-lingual MSCOCO2014 caption test dataset (XTD10) in 7 languages that we collected using a crowdsourcing platform. We use this as the test set for zero-shot model performance across languages. We also demonstrate how a cross-lingual model can be used for downstream tasks like multi-lingual image tagging in a zero shot manner. XTD10 dataset is made publicly available here: https://github.com/adobe-research/Cross-lingual-Test-Dataset-XTD10.

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