Semantic Segmentation Alternative Technique: Segmentation Domain Generation
This is an incremental approach for computer vision researchers, as it applies existing GAN methods to semantic segmentation without demonstrating clear advancements.
The authors tackled semantic segmentation by framing it as a domain transfer problem, using a feed-forward network with Generative Adversarial Networks to generate segmentation masks from input images, but no concrete results or numbers are provided.
Detecting objects of interest in images was always a compelling task to automate. In recent years this task was more and more explored using deep learning techniques, mostly using region-based convolutional networks. In this project we propose an alternative semantic segmentation technique making use of Generative Adversarial Networks. We consider semantic segmentation to be a domain transfer problem. Thus, we train a feed forward network (FFNN) to receive as input a seed real image and generate as output its segmentation mask.