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.
@article{arxiv.2205.10337,
title = {UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes},
author = {Alexander Kolesnikov and André Susano Pinto and Lucas Beyer and Xiaohua Zhai and Jeremiah Harmsen and Neil Houlsby},
journal= {arXiv preprint arXiv:2205.10337},
year = {2022}
}