THESIS
2022
1 online resource (xv, 89 pages) : illustrations (chiefly color)
Abstract
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
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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.
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