  Real-world data is predominantly unbalanced and long-tailed, but deep models
struggle to recognize rare classes in the presence of frequent classes. Often,
classes can be accompanied by side information like textual descriptions, but
it is not fully clear how to use them for learning with unbalanced long-tail
data. Such descriptions have been mostly used in (Generalized) Zero-shot
learning (ZSL), suggesting that ZSL with class descriptions may also be useful
for long-tail distributions. We describe DRAGON, a late-fusion architecture for
long-tail learning with class descriptors. It learns to (1) correct the bias
towards head classes on a sample-by-sample basis; and (2) fuse information from
class-descriptions to improve the tail-class accuracy. We also introduce new
benchmarks CUB-LT, SUN-LT, AWA-LT for long-tail learning with
class-descriptions, building on existing learning-with-attributes datasets and
a version of Imagenet-LT with class descriptors. DRAGON outperforms
state-of-the-art models on the new benchmark. It is also a new SoTA on existing
benchmarks for GFSL with class descriptors (GFSL-d) and standard (vision-only)
long-tailed learning ImageNet-LT, CIFAR-10, 100, and Places365.
