  In this paper, we present a new method, Transductive Multi-Head Few-Shot
learning (TMHFS), to address the Cross-Domain Few-Shot Learning (CD-FSL)
challenge. The TMHFS method extends the Meta-Confidence Transduction (MCT) and
Dense Feature-Matching Networks (DFMN) method [2] by introducing a new
prediction head, i.e, an instance-wise global classification network based on
semantic information, after the common feature embedding network. We train the
embedding network with the multiple heads, i.e,, the MCT loss, the DFMN loss
and the semantic classifier loss, simultaneously in the source domain. For the
few-shot learning in the target domain, we first perform fine-tuning on the
embedding network with only the semantic global classifier and the support
instances, and then use the MCT part to predict labels of the query set with
the fine-tuned embedding network. Moreover, we further exploit data
augmentation techniques during the fine-tuning and test stages to improve the
prediction performance. The experimental results demonstrate that the proposed
methods greatly outperform the strong baseline, fine-tuning, on four different
target domains.
