Humans can envision a realistic photo given a free-hand sketch that is not only spatially
imprecise and geometrically distorted but also without colors and visual details. We study …

Sketch-based image retrieval (SBIR) is a cross-modal matching problem which is typically
solved by learning a joint embedding space where the semantic content shared between …

Sketching is a powerful tool for creating abstract images that are sparse but meaningful.
Sketch understanding poses fundamental challenges for general-purpose vision algorithms …

Sketch-based image retrieval (SBIR) is a challenging task due to the large cross-domain
gap between sketches and natural images. How to align abstract sketches and natural …

We propose and evaluate several deep network architectures for measuring the similarity
between sketches and photographs, within the context of the sketch based image retrieval …

We present the Sketchy database, the first large-scale collection of sketch-photo pairs. We
ask crowd workers to sketch particular photographic objects sampled from 125 categories …

In this paper, we investigate the problem of zero-shot sketch-based image retrieval (ZS-
SBIR), where human sketches are used as queries to conduct retrieval of photos from …

Zero-shot sketch-based image retrieval typically asks for a trained model to be applied as is
to unseen categories. In this paper, we question to argue that this setup by definition is not …

We present an efficient representation for sketch based image retrieval (SBIR) derived from
a triplet loss convolutional neural network (CNN). We treat SBIR as a cross-domain …

© 2014. The copyright of this document resides with its authors. An important characteristic
of sketches, compared with text, rests with their ability to intrinsically capture object …