Attention-based Dual-Branches Localization Network for Weakly Supervised Object Localization

Weakly supervised object localization exploits the last convolutional feature maps of classification model and the weights of Fully-Connected (FC) layer to achieves localization. However, high-level feature maps for localization lack edge features. Additionally, the weights are specific to classification task, causing only discriminative regions to be discovered. In order to fuse edge features and adjust the attention distribution for feature map channels, we propose an efficient method called Attention-based Dual-Branches Localization (ADBL) Network, in which dual-branches structure and attention mechanism are adopted to mine edge features and non-discriminative features for locating more target areas. Specifically, dual-branches structure cascades low-level feature maps to mine target object edge regions. Additionally, during inference stage, attention mechanism assigns appropriate attention for different features to preserve non-discriminative areas. Extensive experiments on both ILSVRC and CUB-200-2011 datasets show that the ADBL method achieves substantial performance improvements.