  Multi-task learning (MTL) paradigm focuses on jointly learning two or more
tasks, aiming for significant improvement w.r.t model's generalizability,
performance, and training/inference memory footprint. The aforementioned
benefits become ever so indispensable in the case of joint training for
vision-related {\bf dense} prediction tasks. In this work, we tackle the MTL
problem of two dense tasks, i.e., semantic segmentation and depth estimation,
and present a novel attention module called Cross-Channel Attention Module
({CCAM}), which facilitates effective feature sharing along each channel
between the two tasks, leading to mutual performance gain with a negligible
increase in trainable parameters. In a true symbiotic spirit, we then formulate
a novel data augmentation for the semantic segmentation task using predicted
depth called {AffineMix}, and a simple depth augmentation using predicted
semantics called {ColorAug}. Finally, we validate the performance gain of the
proposed method on the Cityscapes and ScanNet dataset, which helps us achieve
state-of-the-art results for a semi-supervised joint model based on depth and
semantic segmentation.
