Contrastive training with rich text prototypes closes the modality gap in zero-shot IMU HAR, raising unseen accuracy from 58.3% to 73.2% and macro F1 from 0.34 to 0.583 on PAMAP2 with 4 held-out classes.
Sez-harn: Self-explainable zero-shot human activity recognition net- work
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cs.LG 2years
2026 2verdicts
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The paper delivers a mechanism-centric taxonomy and unified perspective on explainable human activity recognition methods across sensing modalities.
citing papers explorer
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Closing the Modality Gap in Zero-Shot HAR: Contrastive Training and Separability-Optimized Prototypes on IMU Data
Contrastive training with rich text prototypes closes the modality gap in zero-shot IMU HAR, raising unseen accuracy from 58.3% to 73.2% and macro F1 from 0.34 to 0.583 on PAMAP2 with 4 held-out classes.
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Explainable Human Activity Recognition: A Unified Review of Concepts and Mechanisms
The paper delivers a mechanism-centric taxonomy and unified perspective on explainable human activity recognition methods across sensing modalities.