Semantic Interactive Learning

Semantic Interactive Learning (SIL) focuses on improving machine learning models by incorporating human feedback, particularly semantic information, to enhance learning efficiency and generalization. Current research explores various approaches, including multi-branch architectures that leverage interactions between different learning tasks (e.g., image fusion and segmentation, object detection components) and frameworks that integrate large language models to learn symbolic representations from human-provided language feedback for tasks like robot planning. This field is significant because it bridges the gap between human expertise and machine learning capabilities, leading to more robust, adaptable, and explainable AI systems across diverse applications such as robotics, computer vision, and natural language processing.