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Video Shadow Detection via Spatio-Temporal Interpolation Consistency Training

CVPR 2022 · Video › Video segmentation and tracking · approach: Weakly supervised video anomaly detection · 25 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add Feature Fusion Module to the encoder-decoder network (ablation)SANet · DSIoU: 0.433 -> 0.492Helped
add the refiner to ED+FFM (ablation)SANet · DSFβ: 0.616 -> 0.660Helped
add the Detail Attentive Module to ED+FFM+R (ablation)SANet · DSFβ: 0.660 -> 0.708Helped
add scale consistency to the basic SANet (ablation)STICT · DSFβ: 0.501 -> 0.518Helped
add temporal interpolation consistency to B+SC (ablation)STICT · DSMAE: 0.092 -> 0.079Helped
add spatial interpolation consistency to B+SC+TIC (ablation)STICT · DSIoU: 0.320 -> 0.370Helped
use random interpolation in RGB space instead of feature-space spatial interpolation (ablation)Spatial ICT · DSFβ: 0.646 -> 0.447Hurt
replace feature-space spatial interpolation with feature-space random interpolation (ablation)Spatial ICT · DSFβ: 0.646 -> 0.557Hurt
transfer image-trained SANet to video without consistency training (ablation)STICT · DSMAE: 0.028 -> 0.093Hurt
train with unlabeled video frames using STICT rather than direct image-to-video transferSANet · DSFβ: 0.501 -> 0.646Helped

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