Video Classification

Video classification aims to automatically categorize video content based on its visual and/or audio features, facilitating efficient indexing, retrieval, and analysis. Current research emphasizes improving efficiency and robustness, focusing on architectures like transformers and convolutional neural networks, often incorporating multi-modal fusion (audio-visual) and techniques like knowledge distillation and active learning to optimize training with limited labeled data. These advancements are crucial for applications ranging from content moderation and surveillance to medical image analysis and industrial automation, driving progress in both computer vision and related fields.