  We propose 'Hide-and-Seek' a general purpose data augmentation technique,
which is complementary to existing data augmentation techniques and is
beneficial for various visual recognition tasks. The key idea is to hide
patches in a training image randomly, in order to force the network to seek
other relevant content when the most discriminative content is hidden. Our
approach only needs to modify the input image and can work with any network to
improve its performance. During testing, it does not need to hide any patches.
The main advantage of Hide-and-Seek over existing data augmentation techniques
is its ability to improve object localization accuracy in the weakly-supervised
setting, and we therefore use this task to motivate the approach. However,
Hide-and-Seek is not tied only to the image localization task, and can
generalize to other forms of visual input like videos, as well as other
recognition tasks like image classification, temporal action localization,
semantic segmentation, emotion recognition, age/gender estimation, and person
re-identification. We perform extensive experiments to showcase the advantage
of Hide-and-Seek on these various visual recognition problems.
