  How to best integrate linguistic and perceptual processing in multi-modal
tasks that involve language and vision is an important open problem. In this
work, we argue that the common practice of using language in a top-down manner,
to direct visual attention over high-level visual features, may not be optimal.
We hypothesize that the use of language to also condition the bottom-up
processing from pixels to high-level features can provide benefits to the
overall performance. To support our claim, we propose a U-Net-based model and
perform experiments on two language-vision dense-prediction tasks: referring
expression segmentation and language-guided image colorization. We compare
results where either one or both of the top-down and bottom-up visual branches
are conditioned on language. Our experiments reveal that using language to
control the filters for bottom-up visual processing in addition to top-down
attention leads to better results on both tasks and achieves competitive
performance. Our linguistic analysis suggests that bottom-up conditioning
improves segmentation of objects especially when input text refers to low-level
visual concepts. Code is available at https://github.com/ilkerkesen/bvpr.
