Weakly- and Semi-Supervised Object Localization

Abstract

Weakly supervised object localization deals with the lack of location-level labels to train localization models. Recently a new evaluation protocol is proposed in which full supervision is available but limited to only a small validation set. It motives us to explore semi-supervised learning for addressing this problem. In particular, the localization model is developed via self-training: we use a small amount of data with full supervision to train a class-agnostic detector, and use it to generate pseudo bounding boxes for data with weak supervision. Furthermore, we propose a selection algorithm to discover high-quality pseudo labels, and deal with data imbalance caused by pseudo labeling. We demonstrate the superiority of the proposed method with performance on par with the state of the art on two benchmarks.