PT Unknown
AU Asma Bensalah
   Pau Riba
   Alicia Fornes
   Josep Llados
TI Shoot less and Sketch more: An Efficient Sketch Classification via Joining Graph Neural Networks and Few-shot Learning
BT 13th IAPR International Workshop on Graphics Recognition
PY 2019
BP 80
EP 85
DE Sketch classification; Convolutional Neural Network; Graph Neural Network; Few-shot learning
AB 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.
ER