Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev, Alwala Armand Joulin, Ishan Misra, Meta Fair, Ai, Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev, Alwala Armand Joulin, Ishan Misra, Meta Fair, Ai, Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev, Alwala Armand Joulin, Ishan Misra, Meta Fair, Ai (2023)
The paper introduces IMAGEBIND, a novel approach for learning a joint embedding space across six modalities: images, text, audio, depth, thermal, and IMU data. The authors demonstrate that IMAGEBIND can effectively bind these modalities together using minimal image-paired data, facilitating cross-modal retrieval and enabling zero-shot recognition capabilities. This approach alleviates the need for extensive multimodal datasets where all modalities are simultaneously present. The main contributions include impressive performance in emergent zero-shot tasks, few-shot recognition success, and enhanced capabilities for generating and detecting through a unified embedding model. The paper highlights the potential for extensive applications across various domains without requiring extensive retraining or specific tasks. Additionally, it addresses limitations, emphasizing the need for further research to improve upon their model and explore novel evaluation benchmarks.
This paper employs the following methods:
The following datasets were used in this research:
The authors identified the following limitations: