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		<doi>10.1109/SIBGRAPI51738.2020.00035</doi>
		<citationkey>AvelarTavSilJunLam:2020:SuImCl</citationkey>
		<title>Superpixel Image Classification with Graph Attention Networks</title>
		<format>On-line</format>
		<year>2020</year>
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		<author>Avelar, Pedro Henrique da Costa,</author>
		<author>Tavares, Anderson Rocha,</author>
		<author>Silveira, Thiago Lopes Trugillo da,</author>
		<author>Jung, Cláudio Rosito,</author>
		<author>Lamb, Luís da Cunha,</author>
		<affiliation>Federal University of Rio Grande do Sul</affiliation>
		<affiliation>Federal University of Rio Grande do Sul</affiliation>
		<affiliation>University of Rio Grande</affiliation>
		<affiliation>Federal University of Rio Grande do Sul</affiliation>
		<affiliation>Federal University of Rio Grande do Sul</affiliation>
		<editor>Musse, Soraia Raupp,</editor>
		<editor>Cesar Junior, Roberto Marcondes,</editor>
		<editor>Pelechano, Nuria,</editor>
		<editor>Wang, Zhangyang (Atlas),</editor>
		<e-mailaddress>phcavelar@inf.ufrgs.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 33 (SIBGRAPI)</conferencename>
		<conferencelocation>Porto de Galinhas (virtual)</conferencelocation>
		<date>7-10 Nov. 2020</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
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		<versiontype>finaldraft</versiontype>
		<keywords>superpixel,graph attention networks,graph neural networks.</keywords>
		<abstract>This paper presents a methodology for image classification using Graph Neural Network (GNN) models. We transform the input images into region adjacency graphs (RAGs), in which regions are superpixels and edges connect neighboring superpixels. Our experiments suggest that Graph Attention Networks (GATs), which combine graph convolutions with self-attention mechanisms, outperforms other GNN models. Although raw image classifiers perform better than GATs due to information loss during the RAG generation, our methodology opens an interesting avenue of research on deep learning beyond rectangular-gridded images, such as 360-degree field of view panoramas. Traditional convolutional kernels of current state-of-the-art methods cannot handle panoramas, whereas the adapted superpixel algorithms and the resulting region adjacency graphs can naturally feed a GNN, without topology issues.</abstract>
		<language>en</language>
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