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.

Hide-and-Seek是一种通用的数据增强技术，它可以在各种视觉识别任务中提高网络性能，尤其在弱监督定位方面具有优势。其核心思想是将训练图像中的一些信息随机隐藏，从而迫使网络寻找其他相关内容，而这项技术不仅仅适用于图像定位，也可用于视频分类、语义分割、情感识别、年龄/性别估计和人物再识别等任务。

隐藏和寻找：一种用于弱监督定位及其它的数据增强技术