Universal Image

Universal image embedding research aims to create single models capable of representing and processing images across diverse domains and tasks, overcoming the limitations of domain-specific models. Current efforts focus on developing robust and efficient embedding models, often leveraging large language models (LLMs) and contrastive learning frameworks, to achieve high performance on various downstream applications like image retrieval, segmentation, and generation. This pursuit of universality is significant because it promises more efficient and adaptable AI systems, impacting fields ranging from medical image analysis to large-scale visual search.