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High-Performance Long-Term Tracking With Meta-Updater

CVPR 2020 · Architectures and efficiency › RNNs and normalization · approach: LSTM design and memory · 293 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use LTMU instead of SiamRPN++Long-term tracking · VOT2018LTF-score: 0.629 -> 0.690Helped
Use LTMU instead of CLGSLong-term tracking · VOT2019LTF-score: 0.674 -> 0.697Helped
Use LTMU instead of SPLTLong-term tracking · OxUvALT testMaxGM: 0.622 -> 0.751Helped
Set the meta-updater time step to 20 rather than 5 (ablation)LTMU · LaSOTSuccess: 0.553 -> 0.572Helped
Set the meta-updater time step to 20 rather than 10 (ablation)LTMU · LaSOTSuccess: 0.564 -> 0.572Helped
Set the meta-updater time step to 20 rather than 30 (ablation)LTMU · LaSOTSuccess: 0.570 -> 0.572Helped
Set the meta-updater time step to 20 rather than 50 (ablation)LTMU · LaSOTSuccess: 0.567 -> 0.572Helped
Add confidence score input (ablation)LTMU meta-updater · LaSOTSuccess: 0.561 -> 0.572Helped
Add response map input (ablation)LTMU meta-updater · LaSOTSuccess: 0.568 -> 0.572Helped
Add bounding box input (ablation)LTMU meta-updater · LaSOTSuccess: 0.563 -> 0.572Helped
Add appearance score input (ablation)LTMU meta-updater · LaSOTSuccess: 0.549 -> 0.572Helped
Train the meta-updater for 1 iteration rather than none (ablation)LTMU · LaSOTSuccess: 0.539 -> 0.562Helped

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