  We introduce UViM, a unified approach capable of modeling a wide range of
computer vision tasks. In contrast to previous models, UViM has the same
functional form for all tasks; it requires no task-specific modifications which
require extensive human expertise. The approach involves two components: (I) a
base model (feed-forward) which is trained to directly predict raw vision
outputs, guided by a learned discrete code and (II) a language model
(autoregressive) that is trained to generate the guiding code. These components
complement each other: the language model is well-suited to modeling structured
interdependent data, while the base model is efficient at dealing with
high-dimensional outputs. We demonstrate the effectiveness of UViM on three
diverse and challenging vision tasks: panoptic segmentation, depth prediction
and image colorization, where we achieve competitive and near state-of-the-art
results. Our experimental results suggest that UViM is a promising candidate
for a unified modeling approach in computer vision.
