MAML-RAL: Learning Domain-Invariant HOI Rules for Real-Time Video Matting

Real-time video matting is essential for applications like online video conferencing but faces challenges in human-object interaction (HOI) scenarios, known as the HOI-matting problem. This problem is challenging due to its open-recognition nature, where no dataset can cover the wide range of potential HOI cases, making it difficult for feature-learning-based methods to generalize effectively. To address this issue, we present an HOI-matting dataset and introduce a Model-Agnostic Meta-Learning-based rule-aware learning approach (MAML-RAL). MAML-RAL combines transfer learning and meta-learning to capture domain-invariant HOI rules, complemented by a fast local adaptation strategy to counter domain shifts and background interference. Our method achieves a mean intersection-over-union (mIoU) of 92.3%, outperforming current algorithms, with local adaptation further boosting performance to a remarkable mIoU of 95.84%.