Multi-Scale Low-Discriminative Feature Reactivation for Weakly Supervised Object Localization.

Abstract

For weakly supervised object localization (WSOL), how to avoid the network focusing only on some small discriminative parts is a main challenge needed to solve. The widely-used Class Activation Mapping (CAM) based paradigm usually employs Adversarial Learning (AL) strategy to search more object parts by constantly hiding discovered object features, but the adversarial process is difficult to control. In this paper, we propose a novel CAM-based framework with Multi-scale Low-Discriminative Feature Reactivation (mLDFR) for WSOL. The mLDFR framework reactivates the low-discriminative object parts via bottom-up continuous feature maps recalibration and multi-scale object category mapping. Compared with the AL-based methods, our method fully improves the localization power of the network without damaging the classification power and can perform multi-instance localization, which are hard to achieve under the AL-based framework. Moreover, the mLDFR framework is flexible, and can be built on the top of various classical CNN backbones. Experimental results demonstrate the superiority of our method. With VGG16 as backbone, we achieve 46.96% Cls-Loc top1 err and 66.12% CorLoc on ILSVRC2014, 38.07% Cls-Loc top1 err and 75.04% CorLoc on CUB200-2011, surpassing the state-of-the-arts by a large margin.