4.4. Analysis and Applications
Multimodal embedding space arithmetic. We study
whether IMAGEBIND’s embeddings can be used to com-
pose information across modalities. In Figure 4, we show
image retrievals obtained by adding together image and au-
dio embeddings. The joint embedding space allows for us to
compose two embeddings: e.g., image of fruits on a table +
sound of chirping birds and retrieve an image that contains
both these concepts, i.e., fruits on trees with birds. Such
emergent compositionality whereby semantic content from
different modalities can be composed will likely enable a
rich variety of compositional tasks.

Without re-training, we can ‘upgrade’ existing vision
models that use CLIP embeddings to use IMAGEBIND em-
beddings from other modalities such as audio.
Upgrading text-based detectors to audio-based. We use a
IMAGEBIND- One Embedding Space To Bind Them A