  Label distributions in real-world are oftentimes long-tailed and imbalanced,
resulting in biased models towards dominant labels. While long-tailed
recognition has been extensively studied for image classification tasks,
limited effort has been made for video domain. In this paper, we introduce
VideoLT, a large-scale long-tailed video recognition dataset, as a step toward
real-world video recognition. Our VideoLT contains 256,218 untrimmed videos,
annotated into 1,004 classes with a long-tailed distribution. Through extensive
studies, we demonstrate that state-of-the-art methods used for long-tailed
image recognition do not perform well in the video domain due to the additional
temporal dimension in video data. This motivates us to propose FrameStack, a
simple yet effective method for long-tailed video recognition task. In
particular, FrameStack performs sampling at the frame-level in order to balance
class distributions, and the sampling ratio is dynamically determined using
knowledge derived from the network during training. Experimental results
demonstrate that FrameStack can improve classification performance without
sacrificing overall accuracy. Code and dataset are available at:
https://github.com/17Skye17/VideoLT.
