%0 Conference Proceedings
%T Shoot less and Sketch more: An Efficient Sketch Classification via Joining Graph Neural Networks and Few-shot Learning
%A Asma Bensalah
%A Pau Riba
%A Alicia Fornes
%A Josep Llados
%B 13th IAPR International Workshop on Graphics Recognition
%D 2019
%F Asma Bensalah2019
%O DAG; 600.140; 601.302; 600.121
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3354), last updated on Tue, 24 Nov 2020 12:27:52 +0100
%X With the emergence of the touchpad devices and drawing tablets, a new era of sketching started afresh. However, the recognition of sketches is still a tough task due to the variability of the drawing styles. Moreover, in some application scenarios there is few labelled data available for training,which imposes a limitation for deep learning architectures. In addition, in many cases there is a need to generate models able to adapt to new classes. In order to cope with these limitations, we propose a method based on few-shot learning and graph neural networks for classifying sketches aiming for an efficient neural model. We test our approach with several databases ofsketches, showing promising results.
%K Sketch classification
%K Convolutional Neural Network
%K Graph Neural Network
%K Few-shot learning
%U http://refbase.cvc.uab.es/files/BRF2019.pdf
%P 80-85