This paper for the first time explores text-to-image diffusion models for Zero-Shot Sketch-
based Image Retrieval (ZS-SBIR). We highlight a pivotal discovery: the capacity of text-to …

Two primary input modalities prevail in image retrieval: sketch and text. While text is widely
used for inter-category retrieval tasks sketches have been established as the sole preferred …

This paper unravels the potential of sketches for diffusion models addressing the deceptive
promise of direct sketch control in generative AI. We importantly democratise the process …

In this paper we democratise 3D content creation enabling precise generation of 3D shapes
from abstract sketches while overcoming limitations tied to drawing skills. We introduce a …

Image Retrieval aims to retrieve corresponding images based on a given query. In
application scenarios, users intend to express their retrieval intent through various query …

Abstract 3D shape modeling is labor-intensive, time-consuming, and requires years of
expertise. To facilitate 3D shape modeling, we propose a 3D shape generation network that …

In this paper, we extend scene understanding to include that of human sketch. The result is a
complete trilogy of scene representation from three diverse and complementary modalities …

Nowadays, many people frequently have to search for new accommodation options.
Searching for a suitable apartment is a time-consuming process, especially because visiting …

Semantic typographic logos harmoniously blend typeface and imagery to represent
semantic concepts while maintaining legibility. Conventional methods using spatial …

This paper presents Universal Vision-Language Dense Retrieval (UniVL-DR), which builds
a unified model for multi-modal retrieval. UniVL-DR encodes queries and multi-modality …