论文标题
在线渐进式实例平衡抽样，用于弱监督的对象检测
Online progressive instance-balanced sampling for weakly supervised object detection
论文作者
论文摘要
基于多个实例检测网络（MIDN），大量作品为弱监督的对象检测（WSOD）做出了巨大的努力。但是，大多数方法忽略了一个事实，即在训练阶段，每个图像中都存在压倒性的负面实例，这会误导培训并使网络落入本地最小值。为了解决这个问题，本文提出了基于硬采样和软采样的在线渐进实例平衡采样（OPIS）算法。该算法包括两个模块：渐进实例平衡（PIB）模块和渐进实例重新加权（PIR）模块。 PIB模块结合了随机抽样和iou均衡采样，逐渐地挖掘了硬性实例，同时平衡了积极实例和负面实例。 PIR模块进一步利用了分类器得分和相邻的改进，以重新重新获得积极实例的权重，以使网络专注于积极实例。 Pascal VOC 2007和2012数据集的广泛实验结果表明，所提出的方法可以显着改善基线，这也可以与许多现有的最新结果相媲美。此外，与基线相比，所提出的方法不需要额外的网络参数，并且补充训练开销很小，可以根据实例分类器细化范式轻松地集成到其他方法中。
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.
