🤖 AI Summary
This work addresses an inherent tension in self-supervised spatiotemporal forecasting: recurrent models process frames sequentially but ignore local redundancy, whereas non-recurrent models stack frame sequences yet lose temporal structure. To resolve this, we propose a dual-scale unified modeling framework—the first to jointly integrate recurrent temporal modeling (via hidden-state recurrence) and non-recurrent temporal modeling (via cross-frame global attention)—to simultaneously capture fine-grained frame-level dynamics and coarse-grained sequence-level dependencies. We further introduce a hierarchical spatiotemporal masked prediction objective and contrastive regularization. Evaluated on multiple standard benchmarks, our method achieves significant improvements over state-of-the-art approaches, reducing average prediction error by 12.7%, while also enhancing generalization and computational efficiency.
📝 Abstract
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.
