# Hard Negative Sample Enhancement

UniME is a general embedding learning framework based on multimodal large models, significantly enhancing multimodal embedding capabilities through text discriminative knowledge distillation and hard negative sample-enhanced instruction tuning strategies.

UniME is a general embedding learning model based on a multimodal large model, focusing on breaking down modal barriers to achieve cross-modal retrieval and embedding learning.