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Rubiksnet

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Preview of RubiksNet: Learnable 3D Shift for Efficient Video Action Recognition
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RubiksNet: Learnable 3D Shift for Efficient Video Action Recognition (eccv20.pdf)

3.08 MBEN 17 pages
RubiksNet is an efficient architecture for video action recognition that replaces temporal convolutions with a learnable 3D spatiotemporal shift operation, reducing computational cost while maintaining accuracy
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Preview of PrivHAR: Supplementary Material - Privacy-preserving Human Action Recognition with Light Propagation Modeling
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PrivHAR: Supplementary Material - Privacy-preserving Human Action Recognition with Light Propagation Modeling (5080_supp.pdf)

2.05 MBEN 10 pages
This supplementary material provides detailed technical information, including modeling of light propagation, frequency analysis, face recognition metrics, deconvolutions attacks, hardware experiments, asset sources, potential negative impacts, and personal data considerations...
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To find more books about rubiksnet, you can use related keywords: RubiksNet: Learnable 3D Shift for Efficient Video Action Recognition, Rubiksnet

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