Multiple Instance Graph Learning for Weakly Supervised Remote Sensing Object Detection
Binglu Wang, Yongqiang Zhao, Xuelong Li
Abstract
Weakly supervised object detection (WSOD) has recently attracted much attention in the field of remote sensing, where only image-level labels that distinguish the existence of an object in images are required. However, existing methods frequently treat the most discriminative area of an object as the optimal solution and, meanwhile, ignore the fact that more than one instance may exist in a certain class in remote sensing images (RSIs). To address the issue, we propose a unique multiple instance graph (MIG) learning framework for WSOD in RSIs. The motivation of this work is twofold: 1) a spatial graph-based vote (SGV) mechanism is proposed to find high-quality objects by collecting the top-ranking votes with highly spatial overlap and 2) an appearance graph-based instance mining (AGIM) model is further constructed to exploit all possible instances with the same class by propagating the label information according to the apparent similarity. It is noted that the formulated MIG framework that collaborates SGV and AGIM is independent of extra hyperparameters or annotations. Experimental results reported for two well-known benchmarks, i.e., NWPU VHR-10.v2 and DIOR, testify to the superiority of the proposed framework by 55.9% and 25.11% mAPs.