Multi-Task Self-Supervised Learning for Image Segmentation Task
This addresses the problem of expensive and labor-intensive data annotation for semantic segmentation in computer vision, though it appears incremental as it builds on existing multi-task learning techniques.
The paper tackled the challenge of requiring large annotated datasets for semantic segmentation by using multi-task self-supervised learning with depth prediction and surface normalization, finding that the Nash-MTL method outperformed single-task learning on the NY2D dataset.
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).