Positive-weighting feature enhancement for weakly supervised object localization

Abstract

Weakly Supervised Object Localization (WSOL) techniques identify the object location by only using image-level labels, without any bonding box annotations. A restriction of these techniques is that traditionally a well-trained model only focuses on the most discriminative parts of an image, instead of the whole object. To overcome this problem, most of the WSOL methods dropped some or all crucial discriminative parts to force the model to learn from the secondary discriminative region as well. However, only a small number of methods are dedicated to improving the utilization of the weights within the deep neural network model. In this paper, we propose a plug-in module, Positive-weighting Feature Enhancement (PFE), to correctly utilize existing information embedded in positive weights. The proposed method is made up of two major components: 1) a complementary Noisy Feature Elimination loss (NFE) to minimize noisy features, and 2) a Dynamic Concealment Mask (DCM) to prevent secondary discriminative features from being eliminated while minimizing noisy features. Based on the experiments, our solution outperforms previous method without modifying the architecture on two single-object benchmark datasets, CUB-200-2011 and ILSVRC 2012, and also on one multi-objects dataset, VOC 2007.