  With the novel and fast advances in the area of deep neural networks, several
challenging image-based tasks have been recently approached by researchers in
pattern recognition and computer vision. In this paper, we address one of these
tasks, which is to match image content with natural language descriptions,
sometimes referred as multimodal content retrieval. Such a task is particularly
challenging considering that we must find a semantic correspondence between
captions and the respective image, a challenge for both computer vision and
natural language processing areas. For such, we propose a novel multimodal
approach based solely on convolutional neural networks for aligning images with
their captions by directly convolving raw characters. Our proposed
character-based textual embeddings allow the replacement of both
word-embeddings and recurrent neural networks for text understanding, saving
processing time and requiring fewer learnable parameters. Our method is based
on the idea of projecting both visual and textual information into a common
embedding space. For training such embeddings we optimize a contrastive loss
function that is computed to minimize order-violations between images and their
respective descriptions. We achieve state-of-the-art performance in the largest
and most well-known image-text alignment dataset, namely Microsoft COCO, with a
method that is conceptually much simpler and that possesses considerably fewer
parameters than current approaches.
