Zero Shot Detection

Zero-shot detection aims to identify objects or text belonging to classes unseen during model training, leveraging existing knowledge to generalize to novel categories. Current research focuses on improving the accuracy and efficiency of zero-shot detectors, exploring techniques like embedding spaces (e.g., CLIP), language-vision models, and the incorporation of semantic knowledge or contextual information (e.g., prompts, attributes). This field is significant because it addresses the limitations of traditional supervised learning, enabling applications in diverse areas such as anomaly detection in robotics, AI-generated content identification, and medical image analysis where labeled data is scarce or expensive to obtain.