  Classifying the sub-categories of an object from the same super-category
(e.g., bird) in a fine-grained visual classification (FGVC) task highly relies
on mining multiple discriminative features. Existing approaches mainly tackle
this problem by introducing attention mechanisms to locate the discriminative
parts or feature encoding approaches to extract the highly parameterized
features in a weakly-supervised fashion. In this work, we propose a lightweight
yet effective regularization method named Channel DropBlock (CDB), in
combination with two alternative correlation metrics, to address this problem.
The key idea is to randomly mask out a group of correlated channels during
training to destruct features from co-adaptations and thus enhance feature
representations. Extensive experiments on three benchmark FGVC datasets show
that CDB effectively improves the performance.
