# 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  
**arXiv:** [2305.05665](https://arxiv.org/abs/2305.05665)  
**Date:** 2023-05-09  
**Summary page:** https://www.summarizepaper.com/en/paper/2305.05665v1/

## Key points

- ImageBind is a new approach to learning a joint embedding across six different modalities: images, text, audio, depth, thermal and IMU data.
- Only image-paired data is necessary to bind the modalities together.
- The framework leverages recent large-scale vision-language models to extend their zero-shot capabilities to new modalities.
- Emergent applications 'out-of-the-box' are enabled by this framework, including cross-modal retrieval, composing modalities with arithmetic operations, cross-modal detection and generation.
- ImageBind sets a new state-of-the-art on emergent zero-shot recognition tasks across modalities outperforming specialist supervised models as well as strong few shot recognition results compared to prior work.
- Code/models for reproducibility purposes are provided by ImageBind.

## Summary

ImageBind is a novel approach to learning a joint embedding across six different modalities: images, text, audio, depth, thermal and IMU data. The authors demonstrate that only image-paired data is necessary to bind the modalities together and leverage recent large-scale vision-language models to extend their zero-shot capabilities to new modalities. This framework enables emergent applications 'out-of-the-box', including cross-modal retrieval, composing modalities with arithmetic operations, cross-modal detection and generation. It sets a new state-of-the art on emergent zero-shot recognition tasks across modalities outperforming specialist supervised models as well as strong few shot recognition results compared to prior work. Furthermore, ImageBind provides code/models for reproducibility purposes and serves as a powerful tool for exploring multi-modal datasets.