MUltimodal RObustness

Multimodal robustness research focuses on improving the reliability and resilience of models that process information from multiple sources (e.g., images, text, audio). Current efforts concentrate on identifying vulnerabilities in these models, particularly to adversarial attacks and data corruptions, using benchmarks that evaluate performance under various perturbations and developing techniques to enhance robustness, such as improved training procedures and modified loss functions. This work is crucial for deploying reliable multimodal systems in real-world applications, where noisy or manipulated data is common, and for advancing our understanding of how these models integrate and reason across different modalities.