We investigate the problem of fine-grained sketch-based image retrieval (SBIR), where free-
hand human sketches are used as queries to perform instance-level retrieval of images. This …

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 …

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 …

Sketch-based image retrieval (SBIR) is a challenging task due to the ambiguity inherent in
sketches when compared with photos. In this paper, we propose a novel convolutional …

Free-hand sketches are highly illustrative, and have been widely used by humans to depict
objects or stories from ancient times to the present. The recent prevalence of touchscreen …

This paper, for the very first time, introduces human sketches to the landscape of XAI
(Explainable Artificial Intelligence). We argue that sketch as a" human-centred" data form …

Abstract Development of region proposal algorithms has rapidly become one of the most
critical research areas over recent years. The perfect accuracy of region-based recognition …

Sketch as an image search query is an ideal alternative to text in capturing the fine-grained
visual details. Prior successes on fine-grained sketch-based image retrieval (FG-SBIR) have …

We cast shape matching as metric learning with convolutional networks. We break the end-
to-end process of image representation into two parts. Firstly, well established efficient …

We contribute the rst large-scale dataset of scene sketches, SketchyScene, with the goal of
advancing research on sketch understanding at both the object and scene level. The dataset …