  Weakly supervised object detection (WSOD) aims at learning precise object
detectors with only image-level tags. In spite of intensive research on deep
learning (DL) approaches over the past few years, there is still a significant
performance gap between WSOD and fully supervised object detection. In fact,
most existing WSOD methods only consider the visual appearance of each region
proposal but ignore employing the useful context information in the image. To
this end, this paper proposes an interactive end-to-end WSDO framework called
JLWSOD with two innovations: i) two types of WSOD-specific context information
(i.e., instance-wise correlation andsemantic-wise correlation) are proposed and
introduced into WSOD framework; ii) an interactive graph contrastive learning
(iGCL) mechanism is designed to jointly optimize the visual appearance and
context information for better WSOD performance. Specifically, the iGCL
mechanism takes full advantage of the complementary interpretations of the
WSOD, namely instance-wise detection and semantic-wise prediction tasks,
forming a more comprehensive solution. Extensive experiments on the widely used
PASCAL VOC and MS COCO benchmarks verify the superiority of JLWSOD over
alternative state-of-the-art approaches and baseline models (improvement of
3.6%~23.3% on mAP and 3.4%~19.7% on CorLoc, respectively).
