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Spatio-Temporal Video Re-Localization by Warp LSTM

CVPR 2019 · Video › Video-language understanding · approach: Temporal video grounding · 44 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use Warp LSTM instead of ConvLSTM (ablation)STVR model · AVAframe-mAP (IoU 0.5): 20.8 -> 21.8Helped
use Warp LSTM instead of ConvLSTM (ablation)STVR model · UCF-101-24frame-mAP (IoU 0.5): 53.5 -> 59.4Helped
add temporal information propagation with ConvLSTM rather than predicting independently per clip (ablation)Clip baseline · AVAframe-mAP (IoU 0.5): 18.8 -> 20.8Helped
add temporal information propagation with ConvLSTM rather than predicting independently per clip (ablation)Clip baseline · UCF-101-24frame-mAP (IoU 0.5): 52.9 -> 53.5Helped
replace ConvLSTM warping with accumulated optical flow (ablation)STVR model · AVAframe-mAP (IoU 0.5): 20.8 -> 20.2Hurt
replace ConvLSTM warping with accumulated optical flow (ablation)STVR model · UCF-101-24frame-mAP (IoU 0.5): 53.5 -> 52.0Hurt
replace polyharmonic interpolation with TrajGRU-style structure generation (ablation)STVR model · AVAframe-mAP (IoU 0.5): 20.8 -> 21.0No clear effect
replace polyharmonic interpolation with TrajGRU-style structure generation (ablation)STVR model · UCF-101-24frame-mAP (IoU 0.5): 53.5 -> 54.8Helped

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