Sketch Recognition Based on Deformable Convolutional Network - shi, yao; wang, ke (IJRDO -Journal of Computer Science Engineering, 2020)
Sketch contains various poses of objects and exaggerated strokes due to various painting styles of artists. Traditional method could not achieve high accuracy on recognizing sketch. To solve this problem, this method proposed deformable convolutional neutral network to enhance the ability of the model on recognizing transforms on sketch objects. We proposed a improved deformable convolutional neural network for recognizing sketch. First, we add convolutional kernel on original convolutional layer to construct deformable convolutional layer, which can learn offset and fine-tune factor. Second, we replace the first layer of the ResNet18 network to learn the detailed features of the sketch. Third, we replace the last layer of the ResNet18 with the full connection layer which has an output of 250 dimensions. We verify our method on the TU-Berlin sketch dataset and achieve accuracy of 79.1%.