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Jointly Learning Energy Expenditures and Activities Using Egocentric Multimodal Signals

CVPR 2017 · Video › Video-language understanding · approach: Egocentric video understanding · 73 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add acceleration features to visual features (ablation)EnergyRCN · Stanford-ECMmAP, all activities: 76.2 -> 78.1Helped
add acceleration features to visual features (ablation)EnergyRCN · Stanford-ECMabsolute error (kcal · m−1): 0.75 ± 0.29 -> 0.70 ± 0.27Helped
add visual features to acceleration features (ablation)EnergyRCN · Stanford-ECMmAP, all activities: 52.3 -> 78.1Helped
add visual features to acceleration features (ablation)EnergyRCN · Stanford-ECMabsolute error (kcal · m−1): 0.77 ± 0.36 -> 0.70 ± 0.27Helped
jointly train activity detection and energy regression instead of activity detection alone (ablation)EnergyRCN (V+A) · Stanford-ECMmAP, all activities: 76.7 -> 78.1Helped
jointly train activity detection and energy regression (ablation)EnergyRCN (V+A) · Stanford-ECMabsolute error (kcal · m−1): 0.699 -> 0.696No clear effect
fuse visual and acceleration features rather than use visual features alone (ablation)EnergyRCN · Stanford-ECM, vigorous activitiesmAP: 82.3 -> 82.2No clear effect
fuse visual and acceleration features rather than use visual features alone (ablation)EnergyRCN · Stanford-ECM, moderate activitiesmAP: 89.9 -> 92.1Helped
fuse visual and acceleration features rather than use visual features alone (ablation)EnergyRCN · Stanford-ECM, light activitiesmAP: 70.5 -> 78.3Helped
fuse visual and acceleration features rather than use visual features alone (ablation)EnergyRCN · Stanford-ECM, sedentary activitiesmAP: 71.0 -> 70.8No clear effect

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