Image Text

Image-text research focuses on developing models that understand and generate relationships between visual and textual information, aiming to bridge the gap between these modalities. Current research emphasizes improving the robustness and efficiency of vision-language models (VLMs) like CLIP, often through techniques such as prompt engineering, contrastive learning, and specialized datasets for domains like medicine and agriculture. This work is significant because it enables advancements in various applications, including medical image analysis, agricultural monitoring, and improved multimodal large language models (MLLMs), ultimately leading to more accurate and efficient AI systems.