Based on multiple instance detection networks (MIDN), plenty of works have contributed tremendous efforts to weakly supervised object detection (WSOD). However, most methods neglect the fact that the overwhelming negative instances exist in each image during the training phase, which would mislead the training and make the network fall into local minima. To tackle this problem, an online progressive instance-balanced sampling (OPIS) algorithm based on hard sampling and soft sampling is proposed in this paper. The algorithm includes two modules: a progressive instance balance (PIB) module and a progressive instance reweighting (PIR) module. The PIB module combining random sampling and IoU-balanced sampling progressively mines hard negative instances while balancing positive instances and negative instances. The PIR module further utilizes classifier scores and IoUs of adjacent refinements to reweight the weights of positive instances for making the network focus on positive instances. Extensive experimental results on the PASCAL VOC 2007 and 2012 datasets demonstrate the proposed method can significantly improve the baseline, which is also comparable to many existing state-of-the-art results. In addition, compared to the baseline, the proposed method requires no extra network parameters and the supplementary training overheads are small, which could be easily integrated into other methods based on the instance classifier refinement paradigm.
翻译:在多个实例探测网络的基础上,大量工作为薄弱监督的物体探测(WSOD)做出了巨大努力。然而,大多数方法忽视了以下事实:在培训阶段,每个图像中都存在压倒性的负面实例,这会误导培训,使网络落到本地迷你地带。为解决这一问题,本文件提出了基于硬取样和软取样的在线渐进式实例平衡抽样算法。算法包括两个模块:渐进式实例平衡模块(PIB)和渐进式重排(PIR)模块。PIB模块将随机抽样和IOU平衡的取样逐步地排除硬性负性实例,同时平衡正面实例和负性实例。PIR模块还进一步利用分类分数和相邻的细微缩缩图,以重新权衡使网络聚焦正面实例的正面实例的权重。关于2007年和2012年PASCAL VOC数据集的广泛实验结果表明,拟议方法可以大大改进基线,这也与许多现有的状态重标值模型相近。此外,与基线相比,拟议方法不需要以其他模式为基础,因此,拟议方法可以很容易地升级为其他模式。