Preprint Open access
ResComEmb: Effective and Efficient Multimodal Embedding via Residual Homogeneity Compression
Multimodal large language models (MLLMs) have shown strong potential for universal multimodal representation learning. However, existing methods either compress each input into a single vector, limiting fine-grained expressiveness, or retain long sequences of visual-token vectors, incurring substantial storage and inte …