ColorMNet: A Memory-based Deep Spatial-Temporal Feature Propagation Network for Efficient Video Colorization

The proposed ColorMNet effectively explores spatial-temporal features for video colorization by: 1) using a large-pretrained visual model to guide the estimation of robust spatial features for each frame, 2) developing a memory-based feature propagation module to adaptively propagate useful features from far-apart frames, and 3) exploiting the similar contents of adjacent frames through a local attention module.