Zero Shot Retrieval

Zero-shot retrieval aims to retrieve relevant information without any task-specific training data, relying instead on pre-trained models' ability to generalize. Current research focuses on improving cross-modal alignment in models like CLIP, enhancing the diversity of representations, and developing efficient retrieval methods using large language models (LLMs) or learning-to-hash techniques. These advancements are significant because they enable more robust and efficient information retrieval across diverse domains and modalities, impacting applications ranging from personalized language learning to medical image analysis.