  We address the problem of segmenting an object given a natural language
expression that describes it. Current techniques tackle this task by either
(\textit{i}) directly or recursively merging linguistic and visual information
in the channel dimension and then performing convolutions; or by (\textit{ii})
mapping the expression to a space in which it can be thought of as a filter,
whose response is directly related to the presence of the object at a given
spatial coordinate in the image, so that a convolution can be applied to look
for the object. We propose a novel method that integrates these two insights in
order to fully exploit the recursive nature of language. Additionally, during
the upsampling process, we take advantage of the intermediate information
generated when downsampling the image, so that detailed segmentations can be
obtained. We compare our method against the state-of-the-art approaches in four
standard datasets, in which it surpasses all previous methods in six of eight
of the splits for this task.
