E5-V: Universal Embeddings with Multimodal Large Language Models
Authors: name: Ting Jiang, name: Minghui Song, name: Zihan Zhang, name: Haizhen Huang, name: Weiwei Deng, name: Feng Sun, name: Qi Zhang, name: Deqing Wang, name: Fuzhen Zhuang

Multimodal large language models (MLLMs) have shown promising advancements in
general visual and language understanding. However, the representation of
multimodal information using MLLMs remains largely unexplored. In this work, we
introduce a new framework, E5-V, designed to adapt MLLMs for achieving
universal multimodal embeddings. Our findings highlight the significant
potential of MLLMs in representing multimodal inputs compared to previous
approaches. By leveraging MLLMs with prompts, E5-V effectively bridges the
modality gap between different types of inputs, demonstrating strong
performance in multimodal embeddings even without fine-tuning. We propose a
single modality training approach for E5-V, where the model is trained
exclusively on text pairs. This method demonstrates significant improvements
over traditional multimodal training on image-text pairs, while reducing
training costs by approximately 95%. Additionally, this approach eliminates the
need for costly multimodal training data collection. Extensive experiments
across four types of tasks demonstrate the effectiveness of E5-V. As a
universal multimodal model, E5-V not only achieves but often surpasses
state-of-the-art performance in each task, despite being trained on a single
modality.