Vision Encoders

Vision encoders are the core components of multimodal models, tasked with transforming images into numerical representations that can be understood by language models. Current research focuses on improving these encoders, exploring architectures like Vision Transformers (ViTs) and incorporating techniques such as knowledge distillation and multimodal contrastive learning to enhance performance on various tasks, including image captioning, visual question answering, and object detection. This research is significant because advancements in vision encoders directly impact the capabilities of larger vision-language models, leading to improvements in applications ranging from autonomous driving to medical image analysis.