Vision Language Foundation Model

Vision-language foundation models (VLMs) integrate visual and textual information to achieve robust multimodal understanding, aiming to bridge the gap between computer vision and natural language processing. Current research emphasizes improving VLM performance on diverse downstream tasks through techniques like prompt engineering, test-time adaptation, and efficient fine-tuning methods, often leveraging architectures based on CLIP and incorporating large language models. These advancements are significantly impacting various fields, including medical image analysis, autonomous driving, and robotics, by enabling more accurate, efficient, and generalizable solutions for complex tasks.