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• W4386075733 abstract "Existing implicit neural representation (INR) methods do not fully exploit spatiotemporal redundancies in videos. Index-based INRs ignore the content-specific spatial features and hybrid INRs ignore the contextual dependency on adjacent frames, leading to poor modeling capability for scenes with large motion or dynamics. We analyze this limitation from the perspective of function fitting and reveal the importance of frame difference. To use explicit motion information, we propose Difference Neural Representation for Videos (DNeRV), which consists of two streams for content and frame difference. We also introduce a collaborative content unit for effective feature fusion. We test DNeRV for video compression, inpainting, and interpolation. D-NeRVachieves competitive results against the state-of-the-art neural compression approaches and outperforms existing implicit methods on downstream inpainting and interpolation for <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$960times 1920$</tex> videos." @default.
• W4386075733 created "2023-08-23" @default.
• W4386075733 creator A5000610870 @default.
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• W4386075733 date "2023-06-01" @default.
• W4386075733 modified "2023-09-27" @default.
• W4386075733 title "DNeRV: Modeling Inherent Dynamics via Difference Neural Representation for Videos" @default.
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• W4386075733 doi "https://doi.org/10.1109/cvpr52729.2023.00202" @default.
• W4386075733 hasPublicationYear "2023" @default.
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