Prompt Encoder

Prompt encoders are components within larger models that transform various input prompts—text, points, boxes, or even other images—into a format usable by a downstream model for tasks like image segmentation, object detection, or text-to-speech synthesis. Current research focuses on improving the efficiency and adaptability of prompt encoders, particularly through techniques like lightweight architectures (e.g., CNN-Transformer hybrids), early vision-language fusion, and disentangled representation learning, often within the context of existing models like SAM. These advancements are significant because they enable more effective zero-shot and few-shot learning across diverse domains, leading to improved performance in applications ranging from medical image analysis to multilingual speech recognition.