Unsupervised Image Retrieval

Unsupervised image retrieval focuses on developing methods to find visually similar images without relying on labeled training data, aiming to learn effective visual representations and similarity metrics automatically. Current research emphasizes the use of transformer-based architectures, contrastive learning, and quantization techniques to improve retrieval accuracy, often incorporating strategies to address issues like redundancy and false negatives in similarity comparisons. These advancements are significant for various applications, including large-scale image search, video retrieval, and question answering systems, by enabling efficient and scalable retrieval from massive unlabeled datasets.