Progressive Contextual Instance Refinement for Weakly Supervised Object Detection in Remote Sensing Images

Abstract

Weakly supervised learning has been attracting much attention due to its broad applications, which only requires image-level annotations to indicate whether there exist objects in the images. Currently, most of the existing weakly supervised object detection (WSOD) methods are inclined to seek only one top-scoring object instance per image from noisy proposals to train the corresponding object detector. However, more than one same-class instances often exist in the large-scale, cluttered remote sensing images. Thus, selecting only one top-scoring proposal usually results in highlighting the most representative part of an object rather than the whole object, which may cause learning a suboptimal object detector by losing much important information. To address this problem, a novel end-to-end progressive contextual instance refinement (PCIR) method is proposed to perform WSOD. Specifically, a dual-contextual instance refinement (DCIR) strategy is designed to divert the focus of the detection network from the local distinct part to the whole object and further to other potential instances by leveraging both local and global context information. Benefiting from DCIR, a progressive proposal self-pruning (PPSP) strategy is further developed to mitigate the influence of the complex background by dynamically rejecting the negative training proposals. Comprehensive experiments on the challenging NWPU VHR-10.v2 and DIOR data sets clearly demonstrate that the proposed method can significantly boost the detection accuracy compared with the state of the arts.