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SPAct: Self-Supervised Privacy Preservation for Action Recognition

CVPR 2022 · Video › Action recognition · approach: Disentangled action recognition · 71 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace supervised privacy labels with self-supervised contrastive privacy removalSPAct · UCF101Top-1 accuracy: 62.10 -> 62.03No clear effect
Replace supervised privacy labels with self-supervised contrastive privacy removalSPAct · VISPR1cMAP: 55.32 -> 57.43Hurt
Use self-supervised rather than supervised anonymizationSPAct · VISPR1→VISPR2cMAP: 49.6 -> 47.1Helped
Use self-supervised rather than supervised anonymizationSPAct · P-HVU Scenes→ObjectscMAP: 6.43 -> 1.42Helped
Remove the privacy-removal branch (ablation)SPAct · VISPR1cMAP: 57.4 -> 64.1Hurt
Freeze the spatial SSL privacy model instead of updating it during minimax optimization (ablation)SPAct · VISPR1cMAP: 57.4 -> 62.2Hurt
Use spatio-temporal instead of spatial SSL for privacy removal (ablation)SPAct · UCF101Top-1 accuracy: 62.1 -> 56.4Hurt
Sample positive frames more than 64 frames apart (ablation)SPAct · VISPR1cMAP: 57.4 -> 58.7Hurt
Sample positive frames less than 8 frames apart (ablation)SPAct · VISPR1cMAP: 57.4 -> 57.1No clear effect
Replace NT-Xent with RotNet (ablation)SPAct · UCF101Top-1 accuracy: 62.1 -> 58.1Hurt
Replace NT-Xent with MoCo (ablation)SPAct · VISPR1cMAP: 57.4 -> 57.1No clear effect
Use a stronger auxiliary privacy model, combining ResNet-50 and MobileNetV1 (ablation)SPAct · VISPR1cMAP: 57.43 -> 56.20Helped

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