This repository provides implementations of multi-task learning models and training strategies for computer vision. It includes MTI-Net and other architectures designed to jointly solve multiple dense prediction tasks such as semantic segmentation and depth estimation. The code supports standard benchmarks like NYU-Depth-V2 and PASCAL Context, enabling researchers to experiment with multi-task learning approaches for scene understanding.