• Corpus ID: 233423345

Exploring Relational Context for Multi-Task Dense Prediction

@article{Bruggemann2021ExploringRC,
  title={Exploring Relational Context for Multi-Task Dense Prediction},
  author={David Bruggemann and Menelaos Kanakis and Anton Obukhov and Stamatios Georgoulis and Luc Van Gool},
  journal={ArXiv},
  year={2021},
  volume={abs/2104.13874}
}
The timeline of computer vision research is marked with advances in learning and utilizing efficient contextual representations. Most of them, however, are targeted at improving model performance on a single downstream task. We consider a multi-task environment for dense prediction tasks, represented by a common backbone and independent task-specific heads. Our goal is to find the most efficient way to refine each task prediction by capturing cross-task contexts dependent on tasks’ relations… 
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