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Weakly supervised object detection (WSOD) aims to tackle the object detectionproblem using only labeled image categories as supervision. A common approachused in WSOD to deal with the lack of localization information is MultipleInstance Learning, and in recent years methods started adopting MultipleInstance Detection Networks (MIDN), which allows training in an end-to-endfashion. In general, these methods work by selecting the best instance from apool of candidates and then aggregating other instances based on similarity. Inthis work, we claim that carefully selecting the aggregation criteria canconsiderably improve the accuracy of the learned detector. We start byproposing an additional refinement step to an existing approach (OICR), whichwe call refinement knowledge distillation. Then, we present an adaptivesupervision aggregation function that dynamically changes the aggregationcriteria for selecting boxes related to one of the ground-truth classes,background, or even ignored during the generation of each refinement modulesupervision. Experiments in Pascal VOC 2007 demonstrate that our KnowledgeDistillation and smooth aggregation function significantly improves theperformance of OICR in the weakly supervised object detection and weaklysupervised object localization tasks. These improvements make the Boosted-OICRcompetitive again versus other state-of-the-art approaches.

Distilling Knowledge from Refinement in Multiple Instance Detection Networks