SimVP: Simpler yet Better Video Prediction

By Zhangyang Gao, Cheng Tan, Lirong Wu, Stan Z. Li. Computer Vision and Pattern Recognition, 2022

438 citations recorded by the source corpus.

From CNN, RNN, to ViT, we have witnessed remarkable advancements in video prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training strategies. We admire these progresses but are confused about the necessity: is there a simple method that can perform comparably well? This paper proposes SimVp, a simple video prediction model that is completely built upon CNN and trained by MSE loss in an end-to-end fashion. Without introducing any additional tricks and complicated strategies, we can achieve state-of-the-art performance on five benchmark datasets. Through extended experiments, we demonstrate that SimVP has strong generalization and extensibility on real-world datasets. The significant reduction of training cost makes it easier to scale to complex scenarios. We believe SimVP can serve as a solid baseline to stimulate the further development of video prediction.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/07d3837fff7bc872def43f34a62864e753c10a7f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Limin Wang
2. Yufei Zhang
3. Xia Zhao
4. Xuming Han
5. Muhammet Deveci
6. Milan Deepak Parmar

Citations in the corpus

• A review of convolutional neural networks in computer vision (2024, positive): “Thus, Gao et al. introduced SimVP, a simple yet effective CNN video prediction model (Gao et al. 2022).”