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.
Convolutional lstm network: A machine learning approach for precipitation nowcasting
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Both ConvLSTM and exponential moving average modifications to a static saliency model achieve state-of-the-art video saliency prediction on DHF1K after SALICON pre-training and yield similar maps.
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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.
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Simple vs complex temporal recurrences for video saliency prediction
Both ConvLSTM and exponential moving average modifications to a static saliency model achieve state-of-the-art video saliency prediction on DHF1K after SALICON pre-training and yield similar maps.