Weakly Supervised Object Localization Using Self-Paced Pyramid Adversarial Learning

Weakly Supervised Object Localization (WSOL) has increasingly attracted interests for only using image-level supervision instead of location annotations. Some common challenges for existing methods are that they cover only the most discriminative part of the object. And a substantial amount of noise in training causes ambiguities for learning in a robust manner. In this paper, we propose to address these drawbacks by Self-Paced Pyramid Adversarial Learning(SPAL). Specifically, for suppressing noise, we use self-paced learning (SPL) to training data from simple to complex and from coarse to fine. And our network divides two subnetworks: 1) coarse pyramid network(CPN), 2) fine pyramid network (FPN). In CPN, we aim to utilize pyramid adversarial erase mechanism to process the feature maps of different scale. Consequently, CPN can cover the entire object to generate initial object proposals. Then CPN builds the relevance score as pseudo labels of proposals. In FPN, object proposals and pseudo labels can be trained to locate precise object boundaries. Finally, We also propose adversarial loss function to fit our network. Detailed experimental results on the PASCAL VOC dataset demonstrate that SPAL performs promising against the state-of-the-art methods.