Foundational Vision Model

Foundational vision models are large, pre-trained neural networks designed to learn generalizable visual representations from massive datasets, serving as a basis for various downstream vision tasks. Current research emphasizes improving their performance on specialized tasks with limited data, often through adaptor modules or by leveraging multi-modal information (e.g., combining image and text data). These models are significantly impacting computer vision, enabling advancements in diverse applications such as medical image analysis, robotic navigation, and scene understanding, particularly in scenarios with limited labeled data. Furthermore, research is actively exploring the robustness and vulnerabilities of these models to adversarial attacks.