Video Prior

Video priors leverage the inherent structure and statistical properties of video data to improve various computer vision tasks. Current research focuses on developing self-supervised and weakly-supervised methods that learn these priors from limited or synthetic data, employing architectures like diffusion models and convolutional neural networks, often incorporating temporal information through techniques such as pixel shuffling and temporal sliding windows. This approach enhances the robustness and accuracy of video processing tasks, including denoising, object removal, and video classification, while reducing reliance on large, labeled datasets. The resulting improvements have significant implications for applications ranging from medical image analysis to video enhancement and autonomous systems.