  Despite recent advancements in out-of-distribution (OOD) detection, most
current studies assume a class-balanced in-distribution training dataset, which
is rarely the case in real-world scenarios. This paper addresses the
challenging task of long-tailed OOD detection, where the in-distribution data
follows a long-tailed class distribution. The main difficulty lies in
distinguishing OOD data from samples belonging to the tail classes, as the
ability of a classifier to detect OOD instances is not strongly correlated with
its accuracy on the in-distribution classes. To overcome this issue, we propose
two simple ideas: (1) Expanding the in-distribution class space by introducing
multiple abstention classes. This approach allows us to build a detector with
clear decision boundaries by training on OOD data using virtual labels. (2)
Augmenting the context-limited tail classes by overlaying images onto the
context-rich OOD data. This technique encourages the model to pay more
attention to the discriminative features of the tail classes. We provide a clue
for separating in-distribution and OOD data by analyzing gradient noise.
Through extensive experiments, we demonstrate that our method outperforms the
current state-of-the-art on various benchmark datasets. Moreover, our method
can be used as an add-on for existing long-tail learning approaches,
significantly enhancing their OOD detection performance. Code is available at:
https://github.com/Stomach-ache/Long-Tailed-OOD-Detection .
