Current training pipelines in object recognition neglect Hue Jittering when
doing data augmentation as it not only brings appearance changes that are
detrimental to classification, but also the implementation is inefficient in
practice. In this study, we investigate the effect of hue variance in the
context of video recognition and find this variance to be beneficial since
static appearances are less important in videos that contain motion
information. Based on this observation, we propose a data augmentation method
for video recognition, named Motion Coherent Augmentation (MCA), that
introduces appearance variation in videos and implicitly encourages the model
to prioritize motion patterns, rather than static appearances. Concretely, we
propose an operation SwapMix to efficiently modify the appearance of video
samples, and introduce Variation Alignment (VA) to resolve the distribution
shift caused by SwapMix, enforcing the model to learn appearance invariant
representations. Comprehensive empirical evaluation across various
architectures and different datasets solidly validates the effectiveness and
generalization ability of MCA, and the application of VA in other augmentation
methods. Code is available at this https URL

本研究探讨了色调变化对视频识别的影响，并提出了一种名为 Motion Coherent Augmentation（MCA）的数据增强方法，通过引入视频中的外观变化，隐式地鼓励模型优先考虑动态模式而非静态外观。我们提出了一个名为 SwapMix 的操作来高效地修改视频样本的外观，并引入了 Variation Alignment（VA）来解决 SwapMix 引起的分布偏移，强制模型学习外观不变表示。全面的实证评估验证了 MCA 的有效性和泛化能力，以及 VA 在其他增强方法中的应用。