  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.
