Video Data Augmentation

Video data augmentation aims to enhance the performance and robustness of video analysis models by artificially expanding training datasets. Current research focuses on developing novel augmentation techniques tailored to specific video characteristics, such as addressing long-tail distributions in trajectory data or incorporating temporal dynamics, often within the context of deep learning architectures like transformers and convolutional neural networks. These advancements are crucial for improving the accuracy and generalizability of video-based applications across diverse domains, including robotics, human-computer interaction, and action recognition, particularly when labeled data is scarce or expensive to obtain.