Uncertainty-Aware Graph-Guided Weakly Supervised Object Detection

Weakly supervised object detection is an important and challenging task in the computer vision community. In this paper, we treat weakly supervised object detection as a self-training learning task. Based on the framework of self-training, weakly supervised object detection has two uncertainties during training, i.e., the uncertainty of the pseudo labels and the uncertainty of bounding box regression. To this end, we propose an uncertainty-aware graph-guided self-training framework to eliminate these uncertainties. First, we adopt a precise positive and negative sampling strategy to generate pseudo labels to solve the problem of pseudo label uncertainty. Then, we design a weighted location refinement branch based on Bayesian uncertainty modeling to overcome the bounding box regression uncertainty. Moreover, the imbalance between classification and localization tasks prevents the model from generating the task-aware feature map, and redundant proposals, if not handled properly, also introduce uncertainty to the detector. To overcome this problem, we design a graph-guided module that not only balances the two tasks from the perspective of features but also makes full use of proposals. Furthermore, the relation graph of proposals is constructed by clustering proposals, and then, the graph convolution network (GCN) is applied to propagate information on the graph. Thus, accurate feature representations of the objects are obtained through the graph-guided module, and the classification and localization tasks can promote each other. Extensive experiments on the PASCAL VOC 2007 and 2012 datasets demonstrate the effectiveness of our framework, and we obtain 55.2% and 52.0% mAPs on VOC2007 and VOC2012, respectively, showing its superiority over the state-of-the-art approaches by a large margin.