  Thanks to breakthroughs in AI and Deep learning methodology, Computer vision
techniques are rapidly improving. Most computer vision applications require
sophisticated image segmentation to comprehend what is image and to make an
analysis of each section easier. Training deep learning networks for semantic
segmentation required a large amount of annotated data, which presents a major
challenge in practice as it is expensive and labor-intensive to produce such
data. The paper presents 1. Self-supervised techniques to boost semantic
segmentation performance using multi-task learning with Depth prediction and
Surface Normalization . 2. Performance evaluation of the different types of
weighing techniques (UW, Nash-MTL) used for Multi-task learning. NY2D dataset
was used for performance evaluation. According to our evaluation, the Nash-MTL
method outperforms single task learning(Semantic Segmentation).
