GAN inversion aims to invert a given image back into the latent space of a pretrained GAN
model so that the image can be faithfully reconstructed from the inverted code by the …

The content based image retrieval aims to find the similar images from a large scale dataset
against a query image. Generally, the similarity between the representative features of the …

The challenge of learning a new concept, object, or a new medical disease recognition
without receiving any examples beforehand is called Zero-Shot Learning (ZSL). One of the …

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 …

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 …

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 …

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 …

A fundamental challenge faced by existing Fine-Grained Sketch-Based Image Retrieval (FG-
SBIR) models is the data scarcity--model performances are largely bottlenecked by the lack …

Cross-modal retrieval has recently attracted growing attention, which aims to match
instances captured from different modalities. The performance of cross-modal retrieval …