  The current state-of-the-art for image annotation and image retrieval tasks
is obtained through deep neural networks, which combine an image representation
and a text representation into a shared embedding space. In this paper we
evaluate the impact of using the Full-Network embedding in this setting,
replacing the original image representation in a competitive multimodal
embedding generation scheme. Unlike the one-layer image embeddings typically
used by most approaches, the Full-Network embedding provides a multi-scale
representation of images, which results in richer characterizations. To measure
the influence of the Full-Network embedding, we evaluate its performance on
three different datasets, and compare the results with the original multimodal
embedding generation scheme when using a one-layer image embedding, and with
the rest of the state-of-the-art. Results for image annotation and image
retrieval tasks indicate that the Full-Network embedding is consistently
superior to the one-layer embedding. These results motivate the integration of
the Full-Network embedding on any multimodal embedding generation scheme,
something feasible thanks to the flexibility of the approach.
