introduces a weight prediction model to predict the model parameters from its few-shot parameters.
the meta-learning strategy is designed to disentangle the learning of class-agnostic and class-specific weight parameters.
this work focuses on weight parameter generation/prediction for the detector network (box head) by learning a meta-model that regresses the class-specific parameters trained from few-shot to class-specific parameters trained from large base classes.
Training
in the first stage, a detector is trained on large base classes, specifically to learn the class-agnostic parameters.
in the second stage, only the class-specific parameters are learned while the class-agnostic parameters are fixed. The meta model T is trained by receiving the class-specific weights in the last layer and class-specific weights from few-shot training as inputs, where T is parameterized by the class-agnostic weights.
Loss function for training T is: