  Freehand sketching is a dynamic process where points are sequentially sampled
and grouped as strokes for sketch acquisition on electronic devices. To
recognize a sketched object, most existing methods discard such important
temporal ordering and grouping information from human and simply rasterize
sketches into binary images for classification. In this paper, we propose a
novel single-branch attentive network architecture RNN-Rasterization-CNN
(Sketch-R2CNN for short) to fully leverage the dynamics in sketches for
recognition. Sketch-R2CNN takes as input only a vector sketch with grouped
sequences of points, and uses an RNN for stroke attention estimation in the
vector space and a CNN for 2D feature extraction in the pixel space
respectively. To bridge the gap between these two spaces in neural networks, we
propose a neural line rasterization module to convert the vector sketch along
with the attention estimated by RNN into a bitmap image, which is subsequently
consumed by CNN. The neural line rasterization module is designed in a
differentiable way to yield a unified pipeline for end-to-end learning. We
perform experiments on existing large-scale sketch recognition benchmarks and
show that by exploiting the sketch dynamics with the attention mechanism, our
method is more robust and achieves better performance than the state-of-the-art
methods.
