Research on unsupervised image retrieval methods based on contrastive learning

Abstract

In the convergence of fashion and artificial intelligence (AI), significant strides have been made in areas such as clothing recognition, retrieval, and classification, enabled by advanced AI technologies and expansive annotated datasets. As the AI in Fashion market continues to surge, the future of the fashion industry promises to be redefined by intelligent, efficient, and more accessible solutions. Image retrieval, one of the important parts in AI, has experienced remarkable growth, empowered by advanced algorithms and vast annotated datasets, making it a crucial component in various domains such as digital libraries, online marketing. Therefore, this report mainly provides an extensive review of image retrieval methods and the emerging paradigm of contrastive learning, underscoring their relevance and applications in the realm of artificial intelligence. This paper primarily reviews the technologies in the amalgamation of the image retrieval field and contrastive learning. It elucidates the history and progression of image retrieval, offers a methodical analysis of the two primary approachestext-based image retrieval and content-based image retrievaland examines how contrastive learning is employed in image retrieval systems.