  This paper considers the task of matching images and sentences by learning a
visual-textual embedding space for cross-modal retrieval. Finding such a space
is a challenging task since the features and representations of text and image
are not comparable. In this work, we introduce an end-to-end deep multimodal
convolutional-recurrent network for learning both vision and language
representations simultaneously to infer image-text similarity. The model learns
which 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 main
characteristic of our dataset is that the images and tweets are not
standardized the same as the benchmarks. Furthermore, there can be a higher
semantic correlation between the pictures and tweets contrary to benchmarks in
which the descriptions are well-organized. Experimental results on MS-COCO
benchmark dataset show that our model outperforms certain methods presented
previously and has competitive performance compared to the state-of-the-art.
The code and dataset have been made available publicly.
