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		<doi>10.1109/SIBGRAPI54419.2021.00016</doi>
		<citationkey>VieiraOliv:2021:GaEsVi</citationkey>
		<title>Gaze estimation via self-attention augmented convolutions</title>
		<format>On-line</format>
		<year>2021</year>
		<numberoffiles>1</numberoffiles>
		<size>1497 KiB</size>
		<author>Vieira, Gabriel Lefundes,</author>
		<author>Oliveira, Luciano,</author>
		<affiliation>Federal University of Bahia </affiliation>
		<affiliation>Federal University of Bahia</affiliation>
		<editor>Paiva, Afonso ,</editor>
		<editor>Menotti, David ,</editor>
		<editor>Baranoski, Gladimir V. G. ,</editor>
		<editor>Proença, Hugo Pedro ,</editor>
		<editor>Junior, Antonio Lopes Apolinario ,</editor>
		<editor>Papa, João Paulo ,</editor>
		<editor>Pagliosa, Paulo ,</editor>
		<editor>dos Santos, Thiago Oliveira ,</editor>
		<editor>e Sá, Asla Medeiros ,</editor>
		<editor>da Silveira, Thiago Lopes Trugillo ,</editor>
		<editor>Brazil, Emilio Vital ,</editor>
		<editor>Ponti, Moacir A. ,</editor>
		<editor>Fernandes, Leandro A. F. ,</editor>
		<editor>Avila, Sandra,</editor>
		<e-mailaddress>lefundes.gabriel@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 34 (SIBGRAPI)</conferencename>
		<conferencelocation>Gramado, RS, Brazil (virtual)</conferencelocation>
		<date>18-22 Oct. 2021</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<versiontype>finaldraft</versiontype>
		<keywords>deep learning, gaze estimation, attention-augmented convolutions.</keywords>
		<abstract>Although recently deep learning methods have boosted the accuracy of appearance-based gaze estimation, there is still room for improvement in the network architectures for this particular task. Hence we propose here a novel network architecture grounded on self-attention augmented convolutions to improve the quality of the learned features during the training of a shallower residual network. The rationale is that self-attention mechanism can help outperform deeper architectures by learning dependencies between distant regions in full-face images. This mechanism can also create better and more spatially-aware feature representations derived from the face and eye images before gaze regression. We dubbed our framework ARes-gaze, which explores our Attention-augmented ResNet (ARes-14) as twin convolutional backbones. In our experiments, results showed a decrease of the average angular error by 2.38% when compared to state-of-the-art methods on the MPIIFaceGaze data set, while achieving a second-place on the EyeDiap data set. It is noteworthy that our proposed framework was the only one to reach high accuracy simultaneously on both data sets.</abstract>
		<language>en</language>
		<targetfile>gaze_attention_sibgrapi_2021_CAMERA_READY(1).pdf</targetfile>
		<usergroup>lefundes.gabriel@gmail.com</usergroup>
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