Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond
This addresses the challenge of limited localization accuracy in weakly-supervised learning for computer vision, though it is incremental as it builds on existing data augmentation methods.
The paper tackles the problem of improving object localization accuracy in weakly-supervised settings by proposing 'Hide-and-Seek', a data augmentation technique that hides patches in training images to force networks to seek other relevant content, resulting in enhanced performance across various visual recognition tasks.
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.