论文标题
对象实例挖掘用于弱监督的对象检测
Object Instance Mining for Weakly Supervised Object Detection
论文作者
论文摘要
在过去的几年中，仅使用图像级注释仅使用图像级注释的弱监督对象检测（WSOD）引起了人们的关注。使用多个实例学习的现有方法很容易属于本地Optima，因为这种机制倾向于从每个类别的图像中最歧视对象学习。因此，这些方法遭受了丢失的对象实例，从而降低了WSOD的性能。为了解决此问题，本文介绍了一个端到端对象实例挖掘（OIM）框架，以进行弱监督的对象检测。 OIM尝试通过在没有任何其他注释的情况下引入有关空间和外观图的信息传播来检测每个图像中存在的所有可能的对象实例。在迭代学习过程中，可以逐步检测并利用与同一类的歧视对象实例进行培训。此外，我们设计了一个对象实例重新加权损失，以了解每个对象实例的较大部分以进一步提高性能。在两个公开可用数据库（VOC 2007和2012）上的实验结果证明了拟议方法的功效。
Weakly supervised object detection (WSOD) using only image-level annotations has attracted growing attention over the past few years. Existing approaches using multiple instance learning easily fall into local optima, because such mechanism tends to learn from the most discriminative object in an image for each category. Therefore, these methods suffer from missing object instances which degrade the performance of WSOD. To address this problem, this paper introduces an end-to-end object instance mining (OIM) framework for weakly supervised object detection. OIM attempts to detect all possible object instances existing in each image by introducing information propagation on the spatial and appearance graphs, without any additional annotations. During the iterative learning process, the less discriminative object instances from the same class can be gradually detected and utilized for training. In addition, we design an object instance reweighted loss to learn larger portion of each object instance to further improve the performance. The experimental results on two publicly available databases, VOC 2007 and 2012, demonstrate the efficacy of proposed approach.
