  We present a two-stage learning framework for weakly supervised object
localization (WSOL). While most previous efforts rely on high-level feature
based CAMs (Class Activation Maps), this paper proposes to localize objects
using the low-level feature based activation maps. In the first stage, an
activation map generator produces activation maps based on the low-level
feature maps in the classifier, such that rich contextual object information is
included in an online manner. In the second stage, we employ an evaluator to
evaluate the activation maps predicted by the activation map generator. Based
on this, we further propose a weighted entropy loss, an attentive erasing, and
an area loss to drive the activation map generator to substantially reduce the
uncertainty of activations between object and background, and explore less
discriminative regions. Based on the low-level object information preserved in
the first stage, the second stage model gradually generates a well-separated,
complete, and compact activation map of object in the image, which can be
easily thresholded for accurate localization. Extensive experiments on
CUB-200-2011 and ImageNet-1K datasets show that our framework surpasses
previous methods by a large margin, which sets a new state-of-the-art for WSOL.
