Triplet Attention Transformer for Spatiotemporal Predictive Learning
Xuesong Nie Xi Chen Haoyuan Jin Zhihang Zhu Yunfeng Yan Donglian Qi
Spatiotemporal predictive learning offers a self-supervised learning paradigm
that enables models to learn both spatial and temporal patterns by predicting
future sequences based on historical sequences. Mainstream methods are
dominated by recurrent units, yet they are limited by their lack of
parallelization and often underperform in real-world scenarios. To improve
prediction quality while maintaining computational efficiency, we propose an
innovative triplet attention transformer designed to capture both inter-frame
dynamics and intra-frame static features. Specifically, the model incorporates
the Triplet Attention Module (TAM), which replaces traditional recurrent units
by exploring self-attention mechanisms in temporal, spatial, and channel
dimensions. In this configuration: (i) temporal tokens contain abstract
representations of inter-frame, facilitating the capture of inherent temporal
dependencies; (ii) spatial and channel attention combine to refine the
intra-frame representation by performing fine-grained interactions across
spatial and channel dimensions. Alternating temporal, spatial, and
channel-level attention allows our approach to learn more complex short- and
long-range spatiotemporal dependencies. Extensive experiments demonstrate
performance surpassing existing recurrent-based and recurrent-free methods,
achieving state-of-the-art under multi-scenario examination including moving
object trajectory prediction, traffic flow prediction, driving scene
prediction, and human motion capture.