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 substant