Hypergraph Convolutional Networks for Weakly-Supervised Semantic Segmentation

Autor:	Giraldo, Jhony H., Scarrica, Vincenzo, Staiano, Antonino, Camastra, Francesco, Bouwmans, Thierry
Rok vydání:	2022
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Semantic segmentation is a fundamental topic in computer vision. Several deep learning methods have been proposed for semantic segmentation with outstanding results. However, these models require a lot of densely annotated images. To address this problem, we propose a new algorithm that uses HyperGraph Convolutional Networks for Weakly-supervised Semantic Segmentation (HyperGCN-WSS). Our algorithm constructs spatial and k-Nearest Neighbor (k-NN) graphs from the images in the dataset to generate the hypergraphs. Then, we train a specialized HyperGraph Convolutional Network (HyperGCN) architecture using some weak signals. The outputs of the HyperGCN are denominated pseudo-labels, which are later used to train a DeepLab model for semantic segmentation. HyperGCN-WSS is evaluated on the PASCAL VOC 2012 dataset for semantic segmentation, using scribbles or clicks as weak signals. Our algorithm shows competitive performance against previous methods. Comment: Accepted in IEEE International Conference on Image Processing 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2210.05564 Zobrazit plný text záznamu View this record from Arxiv