SwinMTL: A Shared Architecture for Simultaneous Depth Estimation and
  Semantic Segmentation from Monocular Camera Images
Pardis Taghavi Reza Langari Gaurav Pandey
This research paper presents an innovative multi-task learning framework that
allows concurrent depth estimation and semantic segmentation using a single
camera. The proposed approach is based on a shared encoder-decoder
architecture, which integrates various techniques to improve the accuracy of
the depth estimation and semantic segmentation task without compromising
computational efficiency. Additionally, the paper incorporates an adversarial
training component, employing a Wasserstein GAN framework with a critic
network, to refine model's predictions. The framework is thoroughly evaluated
on two datasets - the outdoor Cityscapes dataset and the indoor NYU Depth V2
dataset - and it outperforms existing state-of-the-art methods in both
segmentation and depth estimation tasks. We also conducted ablation studies to
analyze the contributions of different components, including pre-training
strategies, the inclusion of critics, the use of logarithmic depth scaling, and
advanced image augmentations, to provide a better understanding of the proposed
framework. The accompanying source code is accessible at
\url{https://github.com/PardisTaghavi/SwinMTL}.