IMAGEBIND: One Embedding Space To Bind Them All

PubDate: Apr 2023

Teams: FAIR, Meta AI

Writers: Rohit Girdhar;Alaaeldin El-Nouby;Zhuang Liu;Mannat Singh;Kalyan Vasudev Alwala; Armand Joulin;Ishan Misra

PDF: IMAGEBIND: One Embedding Space To Bind Them All

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 zeroshot 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-theart 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.