  Recently, Mix-style data augmentation methods (e.g., Mixup and CutMix) have
shown promising performance in various visual tasks. However, these methods are
primarily designed for single-label images, ignoring the considerable
discrepancies between single- and multi-label images, i.e., a multi-label image
involves multiple co-occurred categories and fickle object scales. On the other
hand, previous multi-label image classification (MLIC) methods tend to design
elaborate models, bringing expensive computation. In this paper, we introduce a
simple but effective augmentation strategy for multi-label image
classification, namely SpliceMix. The "splice" in our method is two-fold: 1)
Each mixed image is a splice of several downsampled images in the form of a
grid, where the semantics of images attending to mixing are blended without
object deficiencies for alleviating co-occurred bias; 2) We splice mixed images
and the original mini-batch to form a new SpliceMixed mini-batch, which allows
an image with different scales to contribute to training together. Furthermore,
such splice in our SpliceMixed mini-batch enables interactions between mixed
images and original regular images. We also offer a simple and non-parametric
extension based on consistency learning (SpliceMix-CL) to show the flexible
extensibility of our SpliceMix. Extensive experiments on various tasks
demonstrate that only using SpliceMix with a baseline model (e.g., ResNet)
achieves better performance than state-of-the-art methods. Moreover, the
generalizability of our SpliceMix is further validated by the improvements in
current MLIC methods when married with our SpliceMix. The code is available at
https://github.com/zuiran/SpliceMix.
