Large language models (LLMs) has been effectively used for many computer vision tasks,
including image classification. In this paper, we present a simple yet effective approach for …

Recent works have shown that unstructured text (documents) from online sources can serve
as useful auxiliary information for zero-shot image classification. However, these methods …

Zero-shot image classification is a challenging task aiming to classify real images without
real training examples. Recent research has employed synthetic training images generated …

The zero-shot performance of existing vision-language models (VLMs) such as CLIP is
limited by the availability of large-scale aligned image and text datasets in specific domains …

Natural language supervision in the form of image captions was recently shown to be an
effective way of training zero-shot image classification models. In this work, we focus on …

Abstract Vision-Language Models (VLMs) have demonstrated impressive performance on
zero-shot classification ie classification when provided merely with a list of class names. In …

The application of zero-shot learning in computer vision has been revolutionized by the use
of image-text matching models. The most notable example, CLIP, has been widely used for …

Using natural language as a supervision for training visual recognition models holds great
promise. Recent works have shown that if such supervision is used in the form of alignment …

The synergy of language and vision models has given rise to Large Language and Vision
Assistant models (LLVAs), designed to engage users in rich conversational experiences …

Despite the tremendous progress in zero-shot learning (ZSL), the majority of existing
methods still rely on human-annotated attributes, which are difficult to annotate and scale …