  Machine learning models fail to perform well on real-world applications when
1) the category distribution P(Y) of the training dataset suffers from
long-tailed distribution and 2) the test data is drawn from different
conditional distributions P(X|Y). Existing approaches cannot handle the
scenario where both issues exist, which however is common for real-world
applications. In this study, we took a step forward and looked into the problem
of long-tailed classification under domain shifts. We designed three novel core
functional blocks including Distribution Calibrated Classification Loss,
Visual-Semantic Mapping and Semantic-Similarity Guided Augmentation.
Furthermore, we adopted a meta-learning framework which integrates these three
blocks to improve domain generalization on unseen target domains. Two new
datasets were proposed for this problem, named AWA2-LTS and ImageNet-LTS. We
evaluated our method on the two datasets and extensive experimental results
demonstrate that our proposed method can achieve superior performance over
state-of-the-art long-tailed/domain generalization approaches and the
combinations. Source codes and datasets can be found at our project page
https://xiaogu.site/LTDS.
