  A dramatic increase in real-world video volume with extremely diverse and
emerging topics naturally forms a long-tailed video distribution in terms of
their categories, and it spotlights the need for Video Long-Tailed Recognition
(VLTR). In this work, we summarize the challenges in VLTR and explore how to
overcome them. The challenges are: (1) it is impractical to re-train the whole
model for high-quality features, (2) acquiring frame-wise labels requires
extensive cost, and (3) long-tailed data triggers biased training. Yet, most
existing works for VLTR unavoidably utilize image-level features extracted from
pretrained models which are task-irrelevant, and learn by video-level labels.
Therefore, to deal with such (1) task-irrelevant features and (2) video-level
labels, we introduce two complementary learnable feature aggregators. Learnable
layers in each aggregator are to produce task-relevant representations, and
each aggregator is to assemble the snippet-wise knowledge into a video
representative. Then, we propose Minority-Oriented Vicinity Expansion (MOVE)
that explicitly leverages the class frequency into approximating the vicinity
distributions to alleviate (3) biased training. By combining these solutions,
our approach achieves state-of-the-art results on large-scale VideoLT and
synthetically induced Imbalanced-MiniKinetics200. With VideoLT features from
ResNet-50, it attains 18% and 58% relative improvements on head and tail
classes over the previous state-of-the-art method, respectively.
