Introduces the first heterogeneous multi-source mmWave point cloud HAR dataset and DAP-Net architecture with Doppler reparameterization and text alignment for cross-source robustness.
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BEM is a training-free inference module that suppresses false positives in fixed-background object detection by maintaining background embedding prototypes and applying an inverse-similarity rank-weighted penalty to detection logits.
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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 architecture with Doppler reparameterization and text alignment for cross-source robustness.
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BEM: Training-Free Background Embedding Memory for False-Positive Suppression in Real-Time Fixed-Background Camera
BEM is a training-free inference module that suppresses false positives in fixed-background object detection by maintaining background embedding prototypes and applying an inverse-similarity rank-weighted penalty to detection logits.