Semi-Supervised Video Classification

Semi-Supervised Video Classification is a video classification technique that combines supervised and unsupervised learning methods, aiming to achieve efficient and accurate video content classification using a small amount of labeled data and a large amount of unlabeled data. This method enhances the model's generalization and robustness by mining the potential structural information in the unlabeled data, thereby improving classification performance. In the field of computer vision, this technology has significant application value, especially in large-scale video data processing and analysis, where it can significantly reduce the cost of manual annotation and improve automation levels.