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.
@article{arxiv.2107.02525,
title = {Semantic Segmentation Alternative Technique: Segmentation Domain Generation},
author = {Ana-Cristina Rogoz and Radu Muntean and Stefan Cobeli},
journal= {arXiv preprint arXiv:2107.02525},
year = {2021}
}
Comments
Accepted contribution at EEML2021 with poster presentation