Unseen Category

Unseen category research focuses on enabling machine learning models to recognize and process data from categories not encountered during training. Current efforts concentrate on developing robust methods for zero-shot and few-shot learning, employing architectures like diffusion models, transformers, and neural descriptor fields to transfer knowledge from seen to unseen categories. This work is crucial for advancing artificial intelligence's generalizability and applicability to real-world scenarios where exhaustive training data is unavailable, impacting fields like robotics, object recognition, and natural language processing. Significant progress is being made through techniques like prompt tuning, contrastive learning, and multi-modal integration.