ImageBind One Embedding Space to Bind Them All

We present ImageBind, an approach to learn a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data. The emergent capabilities improve with the strength of the image encoder and we set a new advanced on emergent zero-shot recognition tasks across modalities, outperforming specialist supervised models.

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• 1 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.
• 2 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.
• 3 It enables novel emergent applications ‘out-of-the-box’ including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection and generation.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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