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This paper considers the task of matching images and sentences by learning avisual-textual embedding space for cross-modal retrieval. Finding such a spaceis a challenging task since the features and representations of text and imageare not comparable. In this work, we introduce an end-to-end deep multimodalconvolutional-recurrent network for learning both vision and languagerepresentations simultaneously to infer image-text similarity. The model learnswhich pairs are a match (positive) and which ones are a mismatch (negative)using a hinge-based triplet ranking. To learn about the joint representations,we leverage our newly extracted collection of tweets from Twitter. The maincharacteristic of our dataset is that the images and tweets are notstandardized the same as the benchmarks. Furthermore, there can be a highersemantic correlation between the pictures and tweets contrary to benchmarks inwhich the descriptions are well-organized. Experimental results on MS-COCObenchmark dataset show that our model outperforms certain methods presentedpreviously and has competitive performance compared to the state-of-the-art.The code and dataset have been made available publicly.

Deep Multimodal Image-Text Embeddings for Automatic Cross-Media Retrieval