  Unified multi-model representation spaces are the foundation of multimodal
understanding and generation. However, the billions of model parameters and
catastrophic forgetting problems make it challenging to further enhance
pre-trained unified spaces. In this work, we propose FreeBind, an idea that
treats multimodal representation spaces as basic units, and freely augments
pre-trained unified space by integrating knowledge from extra expert spaces via
"space bonds". Specifically, we introduce two kinds of basic space bonds: 1)
Space Displacement Bond and 2) Space Combination Bond. Based on these basic
bonds, we design Complex Sequential & Parallel Bonds to effectively integrate
multiple spaces simultaneously. Benefiting from the modularization concept, we
further propose a coarse-to-fine customized inference strategy to flexibly
adjust the enhanced unified space for different purposes. Experimentally, we
bind ImageBind with extra image-text and audio-text expert spaces, resulting in
three main variants: ImageBind++, InternVL_IB, and InternVL_IB++. These
resulting spaces outperform ImageBind on 5 audio-image-text downstream tasks
across 9 datasets. Moreover, via customized inference, it even surpasses the
advanced audio-text and image-text expert spaces.
