  We present ImageBind, an approach to learn a joint embedding across six
different modalities - images, text, audio, depth, thermal, and IMU data. We
show that all combinations of paired data are not necessary to train such a
joint embedding, and only image-paired data is sufficient to bind the
modalities together. ImageBind can leverage recent large scale vision-language
models, and extends their zero-shot capabilities to new modalities just by
using their natural pairing with images. It enables novel emergent applications
'out-of-the-box' including cross-modal retrieval, composing modalities with
arithmetic, cross-modal detection and generation. The emergent capabilities
improve with the strength of the image encoder and we set a new
state-of-the-art on emergent zero-shot recognition tasks across modalities,
outperforming specialist supervised models. Finally, we show strong few-shot
recognition results outperforming prior work, and that ImageBind serves as a
new way to evaluate vision models for visual and non-visual tasks.
