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.
Papers
Conversational AI Multi-Agent Interoperability, Universal Open APIs for Agentic Natural Language Multimodal Communications
Diego Gosmar, Deborah A. Dahl, Emmett Coin
UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in Node Classification Tasks
Xiaotang Wang, Yun Zhu, Haizhou Shi, Yongchao Liu, Chuntao Hong