Rethinking the Route Towards Weakly Supervised Object Localization

Chen-Lin Zhang, Yun-Hao Cao, Jianxin Wu

Abstract:

Weakly supervised object localization (WSOL) aims to localize objects with only image-level labels. Previous methods often try to utilize feature maps and classification weights to localize objects using image level annotations indirectly. In this paper, we demonstrate that weakly supervised object localization should be divided into two parts: the class-agnostic object localization and the object classification. For class-agnostic object localization, we should use class-agnostic methods to generate noisy pseudo annotations and then perform bounding box regression on them without class labels. We propose the pseudo supervised
object localization (PSOL) method as a new way to solve WSOL. Our PSOL models have good transferability across
different datasets without fine-tuning. With the generated pseudo bounding boxes, we achieve 58.00% localization accuracy on ImageNet and 74.97% localization accuracy on CUB-200, which have a large edge over previous models.