  Weakly supervised object detection (WSOD) aims to classify and locate objects
with only image-level supervision. Many WSOD approaches adopt multiple instance
learning as the initial model, which is prone to converge to the most
discriminative object regions while ignoring the whole object, and therefore
reduce the model detection performance. In this paper, a novel cascade
attentive dropout strategy is proposed to alleviate the part domination
problem, together with an improved global context module. We purposely discard
attentive elements in both channel and space dimensions, and capture the
inter-pixel and inter-channel dependencies to induce the model to better
understand the global context. Extensive experiments have been conducted on the
challenging PASCAL VOC 2007 benchmarks, which achieve 49.8% mAP and 66.0%
CorLoc, outperforming state-of-the-arts.
