Search
Results
-
Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)We present an approach for encoding visual task relationships to improve model performance in an Unsupervised Domain Adaptation (UDA) setting. Semantic segmentation and monocular depth estimation are shown to be complementary tasks; in a multi-task learning setting, a proper encoding of their relationships can further improve performance on both tasks. Motivated by this observation, we propose a novel Cross-Task Relation Layer (CTRL), ...Conference Paper