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

ImageBind: One Embedding Space To Bind Them All