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arXiv · 2210.05564

Hypergraph Convolutional Networks for Weakly-Supervised Semantic Segmentation

Jhony H. Giraldo·Vincenzo Scarrica·Antonino Staiano·Francesco Camastra·Thierry Bouwmans
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Abstract

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.

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Jhony H. Giraldo, Vincenzo Scarrica, Antonino Staiano, Francesco Camastra, Thierry Bouwmans. 2022-10-11. Hypergraph Convolutional Networks for Weakly-Supervised Semantic Segmentation. https://arxiv.org/abs/2210.05564

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