SEE-Net improves video prediction by using frame shuffling to enforce learning of natural temporal order, reporting state-of-the-art results on three synthetic and real-world datasets.
Robust cross-view gait identification with evidence: A discriminant gait gan (diggan) approach on 10000 people
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Order Matters: Shuffling Sequence Generation for Video Prediction
SEE-Net improves video prediction by using frame shuffling to enforce learning of natural temporal order, reporting state-of-the-art results on three synthetic and real-world datasets.