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Spatiotemporal predictive learning (ST-PL) is a hotspot with numerousapplications, such as object movement and meteorological prediction. It aims atpredicting the subsequent frames via observed sequences. However, inherentuncertainty among consecutive frames exacerbates the difficulty in long-termprediction. To tackle the increasing ambiguity during forecasting, we designCMS-LSTM to focus on context correlations and multi-scale spatiotemporal flowwith details on fine-grained locals, containing two elaborate designed blocks:Context Embedding (CE) and Spatiotemporal Expression (SE) blocks. CE isdesigned for abundant context interactions, while SE focuses on multi-scalespatiotemporal expression in hidden states. The newly introduced blocks alsofacilitate other spatiotemporal models (e.g., PredRNN, SA-ConvLSTM) to producerepresentative implicit features for ST-PL and improve prediction quality.Qualitative and quantitative experiments demonstrate the effectiveness andflexibility of our proposed method. With fewer params, CMS-LSTM outperformsstate-of-the-art methods in numbers of metrics on two representative benchmarksand scenarios. Code is available at https://github.com/czh-98/CMS-LSTM.

CMS-LSTM: Context Embedding and Multi-Scale Spatiotemporal Expression LSTM for Predictive Learning