  Weakly supervised object localization is a challenging task in which the
object of interest should be localized while learning its appearance.
State-of-the-art methods recycle the architecture of a standard CNN by using
the activation maps of the last layer for localizing the object. While this
approach is simple and works relatively well, object localization relies on
different features than classification, thus, a specialized localization
mechanism is required during training to improve performance. In this paper, we
propose a convolutional, multi-scale spatial localization network that provides
accurate localization for the object of interest. Experimental results on
CUB-200-2011 and ImageNet datasets show that our proposed approach provides
competitive performance for weakly supervised localization.
