Multi-View Graph Neural Network for Semantic Image Segmentation

Semantic image segmentation is a fundamental task in computer vision, frequently addressed using deep learning techniques. Nevertheless, these methods often struggle to fully capture the structural details and semantic relationships present within an image. We propose a new approach, based on a multiview graph neural network, allowing to exploit various kinds of structural information, each one being related to a particular view. We perform experiments on both a synthetic dataset and a real-world one and demonstrate that our model is superior to conventional graph neural network and resilient to small training datasets. Subsequently, our method outperforms other classic methods when considering a few training data. Additionally, the integration of views appears to improve convergence in training. Our findings highlight the potential of multi-view representations in enhancing image segmentation tasks, paving the way for more advanced and accurate computer vision systems.