Conditional similarity-based image identification and matching with reduced labels

Abstract

Systems and methods are provided for matching one or more images using conditional similarity pseudo-labels, including analyzing an unlabeled dataset of images, accessing a foundational vision-language model trained on a plurality of image-text pairs, and defining a set of attributes each comprising multiple possible values for generating pseudo-labels based on notions of similarity (NoS). Text prompts are generated for each attribute value using a prompt template and encoding the text prompts using a text encoder of the foundational model. Each image in the dataset of images is processed through a vision encoder of the foundational model to obtain visual features, the visual features are compared against encoded text prompts to assign a pseudo-label for each attribute for each image, and a conditional similarity network (CSN) is trained with the pseudo-labeled images to generate a conditional similarity model.