  In this work, we introduce a novel strategy for long-tail recognition that
addresses the tail classes' few-shot problem via training-free knowledge
transfer. Our objective is to transfer knowledge acquired from information-rich
common classes to semantically similar, and yet data-hungry, rare classes in
order to obtain stronger tail class representations. We leverage the fact that
class prototypes and learned cosine classifiers provide two different,
complementary representations of class cluster centres in feature space, and
use an attention mechanism to select and recompose learned classifier features
from common classes to obtain higher quality rare class representations. Our
knowledge transfer process is training free, reducing overfitting risks, and
can afford continual extension of classifiers to new classes. Experiments show
that our approach can achieve significant performance boosts on rare classes
while maintaining robust common class performance, outperforming directly
comparable state-of-the-art models.
