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Multimodal Contrastive Learning with Hard Negative Sampling for Human Activity Recognition
However, a major component of successful contrastive learning is the selection of good positive and negative samples.
Multi-Stage Based Feature Fusion of Multi-Modal Data for Human Activity Recognition
In this work, we propose a multi-modal framework that learns to effectively combine features from RGB Video and IMU sensors, and show its robustness for MMAct and UTD-MHAD datasets.
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