Augmenting the 6-3-5 method with design information

This paper describes a comparative study between the 6-3-5 Method and the ICR Grid. The ICR Grid is an evolved variant of 6-3-5 intended to better integrate information into the concept generation process. Unlike a conventional 6-3-5 process where participants continually sketch concepts, using the ICR Grid (the name derived from its Inform, Create, Reflect activities and structured, grid-like output) participants are additionally required to undertake information search tasks, use specific information items for concept development, and reflect on the merit of concepts as the session progresses. The results indicate that although the quantity of concepts was lower, the use of information had a positive effect in a number of areas, principally the quality and variety of output. Although grounded in the area of product development, this research is applicable to any organisation undertaking idea generation and problem solving. As well as providing insights on the transference of information to concepts, it holds additional interest for studies on the composition and use of digital libraries

Sketch-a-Net that Beats Humans

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.Comment: Accepted to BMVC 2015 (oral