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:
this https URL

本论文探讨采用跨语言预训练的零样本方法来学习多模态表示，提出建立跨语言图像检索模型的简单实用方法，并引入了一种新的目标函数来测试多语言 MSCOCO2014 字幕测试数据集（XTD10）的零样本模型性能，证明跨语言模型可用于零样本的下游任务， 如多语言图像标记。

零样本跨语言图像检索与标记

Towards Zero-shot Cross-lingual Image Retrieval and Tagging

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 from each
other. Finally, 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 evaluating zero-shot model
performance across languages. XTD10 dataset is made publicly available here:
this https URL

该论文介绍了一种使用跨语言预训练进行零 - shot 学习的多模式语言和视觉问题解决方法，它可以用于构建跨语言图像检索模型和改进文本嵌入聚类，并在多语言环境下进行评估。