Revisiting the Temporal Modeling in Spatio-Temporal Predictive Learning
  under A Unified View
Cheng Tan Jue Wang Zhangyang Gao Siyuan Li Lirong Wu Jun Xia Stan Z. Li
Spatio-temporal predictive learning plays a crucial role in self-supervised
learning, 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 meticulously
processing frames one by one, neglect short-term spatio-temporal information
redundancies, leading to inefficiencies. The latter naively stack frames
sequentially, overlooking the inherent temporal dependencies. In this paper, we
re-examine the two dominant temporal modeling approaches within the realm of
spatio-temporal predictive learning, offering a unified perspective. Building
upon this analysis, we introduce USTEP (Unified Spatio-TEmporal Predictive
learning), an innovative framework that reconciles the recurrent-based and
recurrent-free methods by integrating both micro-temporal and macro-temporal
scales. Extensive experiments on a wide range of spatio-temporal predictive
learning demonstrate that USTEP achieves significant improvements over existing
temporal modeling approaches, thereby establishing it as a robust solution for
a wide range of spatio-temporal applications.