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Found 2 papers, 2 papers with code
PAD-Net: An Efficient Framework for Dynamic Networks
The main contributions of our work are challenging the basic commonsense in dynamic networks and proposing a partially dynamic network, namely PAD-Net, to transform the redundant dynamic parameters into static ones.
GaitMM: Multi-Granularity Motion Sequence Learning for Gait Recognition
In this study, we propose a multi-granularity motion representation network (GaitMM) for gait sequence learning.
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