Introduces the first heterogeneous multi-source mmWave point cloud HAR dataset and DAP-Net, which uses Doppler patterns for source-invariant action recognition and outperforms prior methods.
Neural Networks108, 533–543 (2018)
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MPL-MAE introduces recalibrated positional embedding and gated positional interface modules to reduce positional over-reliance in 3D masked autoencoders and improve semantic representation quality.
citing papers explorer
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DAP: Doppler-aware Point Network for Heterogeneous mmWave Action Recognition
Introduces the first heterogeneous multi-source mmWave point cloud HAR dataset and DAP-Net, which uses Doppler patterns for source-invariant action recognition and outperforms prior methods.
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Mitigating Positional Leakage in 3D Masked Autoencoders for Robust Representation Learning
MPL-MAE introduces recalibrated positional embedding and gated positional interface modules to reduce positional over-reliance in 3D masked autoencoders and improve semantic representation quality.