  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: class-agnostic object
localization and 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 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.
