Fig. 2
From: A spatially aware global and local perspective approach for few-shot incremental learning
The Framework of the proposed Spatial-aware Global and Local Perspectives approach. Data initialization is utilized to obtain the base sample set by base-task sampler and generate the diverse images by data augmentation as the input of the model. To enhance semantic representations of features, the spatial feature enhancer is constructed by the relationship information of the spatial feature in the global and local scopes, which encourages the model to pay attention to the dominant region in features. Based on the learned model above, the incremental learning module aims to incrementally update the parameters of the classifier to effectively recognize new category data.
