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Progressive Unsupervised Learning for Visual Object Tracking

CVPR 2021 · Detection and segmentation › Tracking · approach: Foundation-model and correlation-filter tracking · 41 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add anchor-based hard negative mining (ablation)BD learning in AlexPUL · OTB-13AUC: 0.491 -> 0.515Helped
add anchor-based hard negative mining (ablation)BD learning in ResPUL · OTB-13AUC: 0.580 -> 0.598Helped
add temporal correspondence learning with 10-frame mining (ablation)AlexPUL with BD and AHM · OTB-13AUC: 0.515 -> 0.567Helped
increase temporal mining length to 20 frames (ablation)AlexPUL with temporal correspondence learning · OTB-13AUC: 0.567 -> 0.574Helped
add temporal correspondence learning with 20-frame mining (ablation)ResPUL with BD and AHM · OTB-13AUC: 0.598 -> 0.620Helped
use NR loss instead of BCE for noisy temporal patch pairs (ablation)AlexPUL with TCL-10 · OTB-13AUC: 0.530 -> 0.567Helped
use NR loss instead of BCE for noisy temporal patch pairs (ablation)AlexPUL with TCL-20 · OTB-13AUC: 0.526 -> 0.574Helped
train the unsupervised tracker without annotated videos or online adaptationResPUL · VOT16accuracy: 0.530 -> 0.554Mixed
train the unsupervised tracker without annotated videos or online adaptationResPUL · VOT16EAO: 0.303 -> 0.263Hurt
train the unsupervised tracker without annotated videos or online adaptationResPUL · TrackingNetSuccess: 0.543 -> 0.546Helped

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