Pre Trained Vision Language Model

Pre-trained vision-language models (VLMs) integrate visual and textual information, aiming to improve multimodal understanding and enable zero-shot or few-shot learning across diverse tasks. Current research focuses on enhancing VLMs' compositional reasoning, adapting them to specialized domains (e.g., agriculture, healthcare), and improving efficiency through quantization and parameter-efficient fine-tuning techniques like prompt learning and adapter modules. These advancements are significant because they enable more robust and efficient applications of VLMs in various fields, ranging from robotics and medical image analysis to open-vocabulary object detection and long-tailed image classification.