500 citations · 1.9k across the 32 of their papers we have counts for
5 papers · 1 filter
ModeRNN: Harnessing Spatiotemporal Mode Collapse in Unsupervised Predictive Learning
Zhiyu Yao, Yunbo Wang, Haixu Wu +2
Learning predictive models for unlabeled spatiotemporal data is challenging in part because visual dynamics can be highly entangled in real scenes, making existing approaches prone…
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
Jiehui Xu, Haixu Wu, Jianmin Wang +1
Unsupervised detection of anomaly points in time series is a challenging problem, which requires the model to derive a distinguishable criterion. Previous methods tackle the proble…
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
Haixu Wu, Jiehui Xu, Jianmin Wang +1
Extending the forecasting time is a critical demand for real applications, such as extreme weather early warning and long-term energy consumption planning. This paper studies the l…
PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning
Yunbo Wang, Haixu Wu, Jianjin Zhang +4
The predictive learning of spatiotemporal sequences aims to generate future images by learning from the historical context, where the visual dynamics are believed to have modular s…
MotionRNN: A Flexible Model for Video Prediction with Spacetime-Varying Motions
Haixu Wu, Zhiyu Yao, Jianmin Wang +1
This paper tackles video prediction from a new dimension of predicting spacetime-varying motions that are incessantly changing across both space and time. Prior methods mainly capt…