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.

本文提出一种基于 end-to-end 的物体实例挖掘框架，通过引入空间和外观图上的信息传递，尝试检测每张图像中可能存在的所有物体实例，以解决针对仅有图像级注释的弱监督物体检测（WSOD）的性能问题，并在 VOC 2007 和 2012 两个公共数据库上进行试验，结果验证了所提出方法的有效性。