A weakly supervised object detection method based on attention mechanism

Abstract

Object detection is one of the basic problems in computer vision. Currently, the detection models based on fully supervised learning which demand fine labeled data such as bounding box annotated images are the mainstream of this research field. However, obtaining high-precision tagged images usually costs huge time and human labor. To lighten the restriction for training data of detection models, we propose an attention-based weakly supervised object detection model which can be trained only using image-level annotated images. The weakly supervised object detection model consists of two stages. In the first stage, an attention-based convolutional neural network (CNN) is designed to enhance the localization ability of CNN and generate coarse detection results. In the second stage, a neural network for edge detection is utilized to get the fine results based on the coarse results in stage one. Tested on PascalVOC 2007 and 2012, the proposed weakly supervised learning detection model achieves 53.4mAP and 48.9mAP in these two datasets, respectively, which is competitive with the state-of-the-art weakly supervised learning detection models and reduces the gap with the fully supervised learning detection models.