Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer
vision and pattern recognition, and underpins a diverse set of real-world applications. The …

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 …

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 …

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 …

Medical datasets and especially biobanks, often contain extensive tabular data with rich
clinical information in addition to images. In practice, clinicians typically have less data, both …

Whether what you see in Figure 1 is a" flamingo" or a" bird", is the question we ask in this
paper. While fine-grained visual classification (FGVC) strives to arrive at the former, for the …

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 …

Fine-grained image recognition is challenging because discriminative clues are usually
fragmented, whether from a single image or multiple images. Despite their significant …

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 …