In this paper, we leverage CLIP for zero-shot sketch based image retrieval (ZS-SBIR). We
are largely inspired by recent advances on foundation models and the unparalleled …

Drawing is a cognitive tool that makes the invisible contents of mental life visible. Humans
use this tool to produce a remarkable variety of pictures, from realistic portraits to schematic …

Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by
learning increasingly complex representations of object shapes. Some recent studies …

We present a simple and general framework for feature learning from point cloud. The key to
the success of CNNs is the convolution operator that is capable of leveraging spatially-local …

The problem of domain generalization is to learn from multiple training domains, and extract
a domain-agnostic model that can then be applied to an unseen domain. Domain …

This paper presents an unpaired method for creating line drawings from photographs.
Current methods often rely on high quality paired datasets to generate line drawings …

Sketching enables many exciting applications, notably, image retrieval. The fear-to-sketch
problem (ie," I can't sketch") has however proven to be fatal for its widespread adoption. This …

Can a user create a deep generative model by sketching a single example? Traditionally,
creating a GAN model has required the collection of a large-scale dataset of exemplars and …

This paper advances the fine-grained sketch-based image retrieval (FG-SBIR) literature by
putting forward a strong baseline that overshoots prior state-of-the art by 11%. This is not via …

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 …