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title
3D CNNs with Adaptive Temporal Feature Resolutions
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2026-03-10 19:58:44
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3D CNNs with Adaptive Temporal Feature Resolutions 3D CNNs with Adaptive Temporal Feature Resolutions CVPR 2021 Mohsen Fayyaz * Emad Bahrami * Ali Diba Mehdi Noroozi Ehsan Adeli Luc Van Gool Juergen Gall [Paper] [GitHub] [Poster] [Talk] * indicates equal contribution Abstract While state-of-the-art 3D Convolutional Neural Networks (CNN) achieve very good results on action recognition datasets, they are computationally very expensive and require many GFLOPs. While the GFLOPs of a 3D CNN can be decreased by reducing the temporal feature resolution within the network, there is no setting that is optimal for all input clips. In this work, we therefore introduce a differentiable Similarity Guided Sampling (SGS) module, which can be plugged into any existing 3D CNN architecture. SGS empowers 3D CNNs by learning the similarity of temporal features and grouping similar features together. As a result, the temporal feature resolution is not ...
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