Unsupervised Image

Unsupervised image analysis focuses on extracting meaningful information from images without relying on labeled training data, aiming to automate tasks like image clustering, object recognition, and image enhancement. Current research emphasizes leveraging powerful architectures like transformers and generative adversarial networks (GANs), often incorporating techniques such as contrastive learning and prompt engineering to improve performance. These advancements are driving progress in diverse applications, including low-light image enhancement, open-world object detection, and cross-spectrum depth estimation, ultimately contributing to more robust and adaptable computer vision systems.