Deep Learning for Identifying Potential Conceptual Shifts for Co-creative Drawing
This addresses the challenge of enhancing creativity in drawing for users, though it appears incremental as it builds on existing sketch recognition and matching techniques.
The paper tackles the problem of identifying conceptual shifts between visual categories to support co-creative drawing, resulting in a system that recognizes human sketches and matches them to structurally similar sketches from different categories to generate ambiguous blends.
We present a system for identifying conceptual shifts between visual categories, which will form the basis for a co-creative drawing system to help users draw more creative sketches. The system recognizes human sketches and matches them to structurally similar sketches from categories to which they do not belong. This would allow a co-creative drawing system to produce an ambiguous sketch that blends features from both categories.