  We propose a new and fully end-to-end approach for multimodal translation
where the source text encoder modulates the entire visual input processing
using conditional batch normalization, in order to compute the most informative
image features for our task. Additionally, we propose a new attention mechanism
derived from this original idea, where the attention model for the visual input
is conditioned on the source text encoder representations. In the paper, we
detail our models as well as the image analysis pipeline. Finally, we report
experimental results. They are, as far as we know, the new state of the art on
three different test sets.
