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 …

Sketches are highly expressive, inherently capturing subjective and fine-grained visual
cues. The exploration of such innate properties of human sketches has, however, been …

This work focuses on the potential of Vision LLMs (VLLMs) in visual reasoning. Different
from prior studies, we shift our focus from evaluating standard performance to introducing a …

In this paper we propose a novel abstraction-aware sketch-based image retrieval framework
capable of handling sketch abstraction at varied levels. Prior works had mainly focused on …

Human sketch has already proved its worth in various visual understanding tasks (eg,
retrieval, segmentation, image-captioning, etc). In this paper, we reveal a new trait of …

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 …

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 …

We propose SketchINR to advance the representation of vector sketches with implicit neural
models. A variable length vector sketch is compressed into a latent space of fixed dimension …