11 papers with code • 0 benchmarks • 3 datasets
These leaderboards are used to track progress in Sketch Recognition
Online sketches provide significantly more information than paper sketches, but they still do not provide the flexibility, naturalness, and simplicity of a simple piece of paper.
In our work, we propose a recurrent neural network architecture for sketch object recognition which exploits the long-term sequential and structural regularities in stroke data in a scalable manner.
Key to our network design is the embedding of unique characteristics of human sketch, where (i) a two-branch CNN-RNN architecture is adapted to explore the temporal ordering of strokes, and (ii) a novel hashing loss is specifically designed to accommodate both the temporal and abstract traits of sketches.
We present a theoretical analysis of the technique to show the effective representational power of the resulting layers, and explore the forms of data they model best.
Sketch-BERT: Learning Sketch Bidirectional Encoder Representation from Transformers by Self-supervised Learning of Sketch Gestalt
Unfortunately, the representation learned by SketchRNN is primarily for the generation tasks, rather than the other tasks of recognition and retrieval of sketches.
To bridge the domain gap we present a novel augmentation technique that is tailored to the task of learning sketch recognition from a training set of natural images.