Video Vision Transformer

Video Vision Transformers (ViViTs) are a class of deep learning models applying the transformer architecture to video analysis, aiming to improve upon the capabilities of convolutional neural networks (CNNs) for tasks like action recognition, facial expression analysis, and violence detection. Current research focuses on optimizing ViViT training efficiency, addressing issues like high computational cost and memory consumption, and exploring variations like multi-branch classifiers to enhance performance on imbalanced datasets. The effectiveness of ViViTs, particularly in low-data regimes, is demonstrating their potential to revolutionize video understanding across diverse applications, from healthcare (e.g., MCI detection) to public safety.