Spatial Lifting for Dense Prediction
Spatial Lifting (SL) is a method for dense prediction that lifts 2D inputs into a higher-dimensional space using replication, then processes them with higher-dimensional networks. This creates richer spatial representations while reducing model size and inference cost. Training involves supervising the network with ground-truth masks replicated in the lifted dimension, and quality estimation uses slice consistency.