Multimodal Foundation

Multimodal foundation models integrate information from diverse sources like images and text to create robust and generalizable AI systems. Current research focuses on applying these models to challenging tasks such as autonomous driving, human-object interaction understanding, and mitigating bias in computer vision, often employing transformer-based architectures and exploring training-free or data-augmentation techniques to improve performance and generalization. This work is significant because it addresses limitations of traditional unimodal approaches, leading to more adaptable and reliable AI systems across various applications, while also highlighting the crucial need for fairness and robustness in these powerful models.