Exploiting Inductive Biases in Video Modeling through Neural CDEs

• DOI: 10.48550/arxiv.2311.04986
• Publication Date: 2023-01-01
• Authors: Chiu, Johnathan; Duffield, Samuel; Hunter-Gordon, Max; Donatella, Kaelan; Aifer, Max; Gu, Andi
• Keywords: FOS: Computer and information sciences; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition

We introduce a novel approach to video modeling that leverages controlled differential equations (CDEs) to address key challenges in video tasks, notably video interpolation and mask propagation. We apply CDEs at varying resolutions leading to a continuous-time U-Net architecture. Unlike traditional methods, our approach does not require explicit optical flow learning, and instead makes use of the inherent continuous-time features of CDEs to produce a highly expressive video model. We demonstrate competitive performance against state-of-the-art models for video interpolation and mask propagation tasks.