Title: Adaptive Parametric Prototype Learning for Cross-Domain Few-Shot Classification. Abstract: Cross-domain few-shot classification induces a much more challenging problem than its in-domain counterpart due to the existence of domain shifts between the training and test tasks. 
In this paper, we develop a novel Adaptive Parametric Prototype Learning (APPL) method under the meta-learning convention for cross-domain few-shot classification. 
Different from existing prototypical few-shot methods that use the averages of support instances to calculate the class prototypes, we propose to learn class prototypes from the concatenated features of the support set in a parametric fashion and meta-learn the model by enforcing prototype-based regularization on the query set. 
In addition, we fine-tune the model in the target domain in a transductive manner using a weighted-moving-average self-training approach on the query instances. 
We conduct experiments on multiple cross-domain few-shot benchmark datasets. 
The empirical results demonstrate that APPL yields superior performance than many state-of-the-art methods. 