Abstract: 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 the improvements of our proposed approach w.r.t. state-of-the-art methods.
Authors: Akhil Meethal, Marco Pedersoli, Soufiane Belharbi, Eric Granger (Universite du Quebec)