  This paper addresses the task of semantic segmentation in computer vision,
aiming to achieve precise pixel-wise classification. We investigate the joint
training of models for semantic edge detection and semantic segmentation, which
has shown promise. However, implicit cross-task consistency learning in
multi-task networks is limited. To address this, we propose a novel "decoupled
cross-task consistency loss" that explicitly enhances cross-task consistency.
Our semantic segmentation network, TriangleNet, achieves a substantial 2.88\%
improvement over the Baseline in mean Intersection over Union (mIoU) on the
Cityscapes test set. Notably, TriangleNet operates at 77.4\% mIoU/46.2 FPS on
Cityscapes, showcasing real-time inference capabilities at full resolution.
With multi-scale inference, performance is further enhanced to 77.8\%.
Furthermore, TriangleNet consistently outperforms the Baseline on the FloodNet
dataset, demonstrating its robust generalization capabilities. The proposed
method underscores the significance of multi-task learning and explicit
cross-task consistency enhancement for advancing semantic segmentation and
highlights the potential of multitasking in real-time semantic segmentation.
