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

UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes