SAMoE-C achieves near state-of-the-art accuracy on cross-scene CSI HAR by selectively activating scene-specific experts via an attention router while using only a tiny replay buffer for training.
Transfer Learning in Human Activity Recognition: A Survey
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Multilevel CNN-LSTM architectures using both late and intermediate feature fusion achieve higher accuracy in human activity recognition than late fusion alone on two benchmark datasets.
citing papers explorer
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Scene-Adaptive Continual Learning for CSI-based Human Activity Recognition with Mixture of Experts
SAMoE-C achieves near state-of-the-art accuracy on cross-scene CSI HAR by selectively activating scene-specific experts via an attention router while using only a tiny replay buffer for training.
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Multilevel neural networks with dual-stage feature fusion for human activity recognition
Multilevel CNN-LSTM architectures using both late and intermediate feature fusion achieve higher accuracy in human activity recognition than late fusion alone on two benchmark datasets.