A Lightweight Model with Separable CNN and LSTM for Video Prediction
Mareeta Mathai, Ying Liu, Nam Ling · 2022
Future frame prediction is an emerging, yet challenging task in the deep learning field due to its inherent uncertainty and complex spatiotemporal dynamics. The state-of-the-art methods achieve significant accuracy at the expense of complex, computationally intensive deep neural networks, which makes it difficult to deploy in mobile devices. In the light of recent wide popularity of Green AI which aims for efficient environment friendly solutions alongside accuracy, we propose a lightweight model using 3D separable convolutions, which can predict future video frames with reduced model size and reasonable accuracy-complexity tradeoffs as compared to the state-of-the-art methods.