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Spatio-temporal predictive learning plays a crucial role in self-supervisedlearning, with wide-ranging applications across a diverse range of fields.Previous approaches for temporal modeling fall into two categories:recurrent-based and recurrent-free methods. The former, while meticulouslyprocessing frames one by one, neglect short-term spatio-temporal informationredundancies, leading to inefficiencies. The latter naively stack framessequentially, overlooking the inherent temporal dependencies. In this paper, were-examine the two dominant temporal modeling approaches within the realm ofspatio-temporal predictive learning, offering a unified perspective. Buildingupon this analysis, we introduce USTEP (Unified Spatio-TEmporal Predictivelearning), an innovative framework that reconciles the recurrent-based andrecurrent-free methods by integrating both micro-temporal and macro-temporalscales. Extensive experiments on a wide range of spatio-temporal predictivelearning demonstrate that USTEP achieves significant improvements over existingtemporal modeling approaches, thereby establishing it as a robust solution fora wide range of spatio-temporal applications.

Revisiting the Temporal Modeling in Spatio-Temporal Predictive Learning under A Unified View