Countless videos exist in the world with diverse content and styles. However, in many cases, the original videos captured or created are not perfect, and many video processing algorithms are proposed to process these diverse videos for various purposes. Video temporal consistency is a common goal for various video processing algorithms, while these algorithms are designed for different downstream applications. The video temporal consistency denotes the property that correspondences of consecutive frames in a video share the consistent features (e.g., color). While this property exists in most natural videos, it might be destroyed when videos are processed by algorithms. Video temporal consistency in video processing is challenging since it is related to several hard problems in compute...[ Read more ]

Countless videos exist in the world with diverse content and styles. However, in many cases, the original videos captured or created are not perfect, and many video processing algorithms are proposed to process these diverse videos for various purposes. Video temporal consistency is a common goal for various video processing algorithms, while these algorithms are designed for different downstream applications. The video temporal consistency denotes the property that correspondences of consecutive frames in a video share the consistent features (e.g., color). While this property exists in most natural videos, it might be destroyed when videos are processed by algorithms. Video temporal consistency in video processing is challenging since it is related to several hard problems in computer vision, including correspondence estimation, task-specific reconstruction, and other problems. This thesis focuses on studying the video temporal consistency problem with machine learning. Since temporal consistency is a common goal for video processing algorithms, can deep networks learn the temporal consistency from large-scale data or a few data? We propose several approaches that can obtain satisfying performance on various video processing tasks.