OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive
Learning
- URL: http://arxiv.org/abs/2306.11249v2
- Date: Wed, 18 Oct 2023 00:02:52 GMT
- Title: OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive
Learning
- Authors: Cheng Tan, Siyuan Li, Zhangyang Gao, Wenfei Guan, Zedong Wang, Zicheng
Liu, Lirong Wu, Stan Z. Li
- Abstract summary: We propose OpenSTL to categorize prevalent approaches into recurrent-based and recurrent-free models.
We conduct standard evaluations on datasets across various domains, including synthetic moving object trajectory, human motion, driving scenes, traffic flow and forecasting weather.
We find that recurrent-free models achieve a good balance between efficiency and performance than recurrent models.
- Score: 67.07363529640784
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Spatio-temporal predictive learning is a learning paradigm that enables
models to learn spatial and temporal patterns by predicting future frames from
given past frames in an unsupervised manner. Despite remarkable progress in
recent years, a lack of systematic understanding persists due to the diverse
settings, complex implementation, and difficult reproducibility. Without
standardization, comparisons can be unfair and insights inconclusive. To
address this dilemma, we propose OpenSTL, a comprehensive benchmark for
spatio-temporal predictive learning that categorizes prevalent approaches into
recurrent-based and recurrent-free models. OpenSTL provides a modular and
extensible framework implementing various state-of-the-art methods. We conduct
standard evaluations on datasets across various domains, including synthetic
moving object trajectory, human motion, driving scenes, traffic flow and
weather forecasting. Based on our observations, we provide a detailed analysis
of how model architecture and dataset properties affect spatio-temporal
predictive learning performance. Surprisingly, we find that recurrent-free
models achieve a good balance between efficiency and performance than recurrent
models. Thus, we further extend the common MetaFormers to boost recurrent-free
spatial-temporal predictive learning. We open-source the code and models at
https://github.com/chengtan9907/OpenSTL.
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