SIAM: A Simple Alternating Mixer for Video Prediction

• URL: http://arxiv.org/abs/2311.11683v2
• Date: Mon, 20 May 2024 16:46:02 GMT
• Title: SIAM: A Simple Alternating Mixer for Video Prediction
• Authors: Xin Zheng, Ziang Peng, Yuan Cao, Hongming Shan, Junping Zhang,
• Abstract summary: Video predicting future frames from the previous ones has broad applications as autonomous driving and forecasting weather. We explicitly model these features in a unified encoder-decoder framework and propose a novel simple simple (SIAM) SIAM lies in the design of alternating mixing (Da) blocks, which can model spatial, temporal, andtemporal features.
• Score: 42.03590872477933
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video prediction, predicting future frames from the previous ones, has broad applications such as autonomous driving and weather forecasting. Existing state-of-the-art methods typically focus on extracting either spatial, temporal, or spatiotemporal features from videos. Different feature focuses, resulting from different network architectures, may make the resultant models excel at some video prediction tasks but perform poorly on others. Towards a more generic video prediction solution, we explicitly model these features in a unified encoder-decoder framework and propose a novel simple alternating Mixer (SIAM). The novelty of SIAM lies in the design of dimension alternating mixing (DaMi) blocks, which can model spatial, temporal, and spatiotemporal features through alternating the dimensions of the feature maps. Extensive experimental results demonstrate the superior performance of the proposed SIAM on four benchmark video datasets covering both synthetic and real-world scenarios.

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