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Revisiting Hierarchical Approach for Persistent Long-Term Video Prediction

ICLR 2021 · Video › Video segmentation and tracking · approach: Stochastic video prediction · 8 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
predict dense semantic structures stochastically and translate them into pixels hierarchicallyvideo prediction · Human Dancing, short-termCSIM: 0.7637 -> 0.8164Helped
predict dense semantic structures stochastically and translate them into pixels hierarchicallyvideo prediction · Human Dancing, short-termFVD: 1987.55 -> 1398.98Helped
predict dense semantic structures stochastically and translate them into pixels hierarchicallyvideo prediction · KITTI, short-termmIoU: 0.4225 -> 0.5137Helped
predict dense semantic structures stochastically and translate them into pixels hierarchicallyvideo prediction · KITTI, short-termFVD: 956.05 -> 762.73Helped
predict future structures stochastically (ablation)sequential dense structure generator · KITTI, long-termshot-wise FVDHelped
use recurrent rather than feedforward structure estimation (ablation)sequential dense structure generator · KITTI, short- to mid-term (t < 200)shot-wise FVDHelped
discretize predicted semantic label maps (ablation)sequential dense structure generator · KITTI, long-termshot-wise FVDHelped
keep structure prediction independent of generated RGB feedback (ablation)hierarchical video prediction · KITTI, long-termshot-wise FVDHelped
add instance-boundary map prediction (ablation)image generator on Cityscapes · Cityscapes, 6 future framesPSNR: 19.236 -> 20.427Helped
add instance-boundary map prediction (ablation)image generator on Cityscapes · Cityscapes, 6 future framesCSIM: 0.937 -> 0.938No clear effect

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