  Dealing with severe class imbalance poses a major challenge for real-world
applications, especially when the accurate classification and generalization of
minority classes is of primary interest. In computer vision, learning from long
tailed datasets is a recurring theme, especially for natural image datasets.
While existing solutions mostly appeal to sampling or weighting adjustments to
alleviate the pathological imbalance, or imposing inductive bias to prioritize
non-spurious associations, we take novel perspectives to promote sample
efficiency and model generalization based on the invariance principles of
causality. Our proposal posits a meta-distributional scenario, where the data
generating mechanism is invariant across the label-conditional feature
distributions. Such causal assumption enables efficient knowledge transfer from
the dominant classes to their under-represented counterparts, even if the
respective feature distributions show apparent disparities. This allows us to
leverage a causal data inflation procedure to enlarge the representation of
minority classes. Our development is orthogonal to the existing extreme
classification techniques thus can be seamlessly integrated. The utility of our
proposal is validated with an extensive set of synthetic and real-world
computer vision tasks against SOTA solutions.
