  Weakly supervised object detection (WSOD) is a challenging task that requires
simultaneously learn object classifiers and estimate object locations under the
supervision of image category labels. A major line of WSOD methods roots in
multiple instance learning which regards images as bags of instances and
selects positive instances from each bag to learn the detector. However, a
grand challenge emerges when the detector inclines to converge to
discriminative parts of objects rather than the whole objects. In this paper,
under the hypothesis that optimal solutions are included in local minima, we
propose a discovery-and-selection approach fused with multiple instance
learning (DS-MIL), which finds rich local minima and select optimal solution
from multiple local minima. To implement DS-MIL, an attention module is
proposed so that more context information can be captured by feature maps and
more valuable proposals can be collected during training. With proposal
candidates, a selection module is proposed to select informative instances for
object detector. Experimental results on commonly used benchmarks show that our
proposed DS-MIL approach can consistently improve the baselines, reporting
state-of-the-art performance.
