OrganicHAR discovers 4-8 activity categories per user from sensor signals, achieves 79% accuracy on coarse activities with ambient sensors alone and cuts VLM queries by 90% by triggering video analysis only at detected pattern moments.
Little data, big impact: Privacy-aware visual language models via minimal tuning
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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User studies reveal preferences for visual abstractions and distance-dependent low-resolution capture, leading to a configurable privacy policy for robot navigation.
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OrganicHAR: Towards Activity Discovery in Organic Settings for Privacy Preserving Sensors Using Efficient Video Analysis
OrganicHAR discovers 4-8 activity categories per user from sensor signals, achieves 79% accuracy on coarse activities with ambient sensors alone and cuts VLM queries by 90% by triggering video analysis only at detected pattern moments.
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Designing Privacy-Preserving Visual Perception for Robot Navigation Based on User Privacy Preferences
User studies reveal preferences for visual abstractions and distance-dependent low-resolution capture, leading to a configurable privacy policy for robot navigation.