Sketch-a-Net that Beats Humans

Qian Yu, Yongxin Yang, Yi-Zhe Song, Tao Xiang, Timothy Hospedales

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a multi-scale multi-channel deep neural network framework that, for the first time, yields sketch recognition performance surpassing that of humans. Our superior performance is a result of explicitly embedding the unique characteristics of sketches in our model: (i) a network architecture designed for sketch rather than natural photo statistics, (ii) a multi-channel generalisation that encodes sequential ordering in the sketching process, and (iii) a multi-scale network ensemble with joint Bayesian fusion that accounts for the different levels of abstraction exhibited in free-hand sketches. We show that state-of-the art deep networks specifically engineered for photos of natural objects fail to perform well on sketch recognition, regardless whether they are trained using photo or sketch. Our network on the other hand not only delivers the best performance on the largest human sketch dataset to date, but also is small in size making efficient training possible using just CPUs.
Original languageEnglish
Title of host publicationProceedings of the British Machine Vision Conference (BMVC)
EditorsMark W. Jones Xianghua Xie, Gary K. L. Tam
PublisherBMVA Press
Pages1-12
Number of pages12
ISBN (Print)1-901725-53-7
DOIs
Publication statusPublished - 1 Sep 2015

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