  Main challenges in long-tailed recognition come from the imbalanced data
distribution and sample scarcity in its tail classes. While techniques have
been proposed to achieve a more balanced training loss and to improve tail
classes data variations with synthesized samples, we resort to leverage readily
available unlabeled data to boost recognition accuracy. The idea leads to a new
recognition setting, namely semi-supervised long-tailed recognition. We argue
this setting better resembles the real-world data collection and annotation
process and hence can help close the gap to real-world scenarios. To address
the semi-supervised long-tailed recognition problem, we present an alternate
sampling framework combining the intuitions from successful methods in these
two research areas. The classifier and feature embedding are learned separately
and updated iteratively. The class-balanced sampling strategy has been
implemented to train the classifier in a way not affected by the pseudo labels'
quality on the unlabeled data. A consistency loss has been introduced to limit
the impact from unlabeled data while leveraging them to update the feature
embedding. We demonstrate significant accuracy improvements over other
competitive methods on two datasets.
