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Multilingual (or cross-lingual) embeddings represent several languages in aunique vector space. Using a common embedding space enables for a sharedsemantic between words from different languages. In this paper, we propose toembed images and texts into a unique distributional vector space, enabling tosearch images by using text queries expressing information needs related to the(visual) content of images, as well as using image similarity. Our frameworkforces the representation of an image to be similar to the representation ofthe text that describes it. Moreover, by using multilingual embeddings weensure that words from two different languages have close descriptors and thusare attached to similar images. We provide experimental evidence of theefficiency of our approach by experimenting it on two datasets: Common Objectsin COntext (COCO) [19] and Multi30K [7].

Image search using multilingual texts: a cross-modal learning approach between image and text