ImageBind: One Embedding Space To Bind Them All

• URL: http://arxiv.org/abs/2305.05665v2
• Date: Wed, 31 May 2023 04:57:12 GMT
• Title: ImageBind: One Embedding Space To Bind Them All
• Authors: Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, Ishan Misra
• Abstract summary: ImageBind is an approach to learn a joint embedding across six different modalities. We show that only image-paired data is sufficient to bind the modalities together.
• Score: 41.46167013891263
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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