Based Model

Foundation models (FMs) are transforming various fields by integrating multiple modalities like vision, audio, and language, aiming to create more robust and versatile AI systems. Current research focuses on improving the robustness of these models, particularly addressing issues like hallucination and out-of-distribution detection, often employing transformer-based architectures and contrastive learning techniques. These advancements have significant implications for diverse applications, including medical image analysis, autonomous driving, and audio forensics, by enabling more reliable and efficient processing of complex, real-world data.