Weakly supervised object detection based on deep metric learning

Abstract

Abstract Weakly supervised object detection is a hot issue in the computer vision field, which aims to train a high performance detection model with low cost annotation data. The existing methods of weakly supervised object detection only summarize the object category but don’t consider the similarity between the objects in the optimization process. For solving this problem to improve detection accuracy, this paper proposes a weakly supervised object detection model based on deep metric learning. In the initial training phase, an initial metric has been learned in advance to measure the similarity between these objects; in the correction phase, we propose an adjacent instance mining method based on proxy samples, this approach expands the model’s recognition view, and prevents premature locking the wrong object position. We design a series of experiments on the PASCAL VOC2007 dataset to prove the effectiveness of this method.