Wavelet-Driven Spatiotemporal Predictive Learning: Bridging Frequency and Time Variations - Nie, Xuesong; Yan, Yunfeng; Li, Siyuan; Tan, Cheng; Chen, Xi; Jin, Haoyuan; Zhu, Zhihang; Li, Stan Z.; Qi, Donglian (Proceedings of the AAAI Conference on Artificial Intelligence, 2024)
Spatiotemporal predictive learning is a paradigm that empowers models to learn spatial and temporal patterns by predicting future frames from past frames in an unsupervised manner. This method typically uses recurrent units to capture long-term dependencies, but these units often come with high computational costs and limited performance in real-world scenes. This paper presents an innovative Wavelet-based SpatioTemporal (WaST) framework, which extracts and adaptively controls both low and high-frequency components at image and feature levels via 3D discrete wavelet transform for faster processing while maintaining high-quality predictions. We propose a Time-Frequency Aware Translator uniquely crafted to efficiently learn short- and long-range spatiotemporal information by individually modeling spatial frequency and temporal variations. Meanwhile, we design a wavelet-domain High-Frequency Focal Loss that effectively supervises high-frequency variations. Extensive experiments across various real-world scenarios, such as driving scene prediction, traffic flow prediction, human motion capture, and weather forecasting, demonstrate that our proposed WaST achieves state-of-the-art performance over various spatiotemporal prediction methods.