Where to Look?: Mining Complementary Image Regions for Weakly Supervised Object Localization

Humans possess an innate capability of recognizing objects and their corresponding parts and confine their attention to that location in a visual scene where the object is spatially present. Recently, efforts to train machines to mimic this ability of humans in the form of weakly supervised object localization, using training labels only at the image-level, have garnered a lot of attention. Nonetheless, one of the well-known problems that most of the existing methods suffer from is localizing only the most discriminative part of an object. Such methods provide very little or no focus on other pertinent parts of the object. In this paper, we propose a novel way of scrupulously localizing objects using training with labels as for the entire image by mining information from complementary regions in an image. Primarily, we adapt to regional dropout at complementary spatial locations to create two intermediate images. With the help of a novel Channel-wise Assisted Attention Module (CAAM) coupled with a Spatial Self-Attention Module (SSAM), we parallely train our model to leverage the information from complementary image regions for excellent localization. Finally, we fuse the attention maps generated by the two classifiers using our Attention-based Fusion Loss. Several experimental studies manifest the superior performance of our proposed approach. Our method demonstrates a significant increase in localization performance over the existing state-of-the-art methods on CUB-200-2011 and ILSVRC 2016 datasets.