  Weakly supervised object detection (WSOD) aims to tackle the object detection
problem using only labeled image categories as supervision. A common approach
used in WSOD to deal with the lack of localization information is Multiple
Instance Learning, and in recent years methods started adopting Multiple
Instance Detection Networks (MIDN), which allows training in an end-to-end
fashion. In general, these methods work by selecting the best instance from a
pool of candidates and then aggregating other instances based on similarity. In
this work, we claim that carefully selecting the aggregation criteria can
considerably improve the accuracy of the learned detector. We start by
proposing an additional refinement step to an existing approach (OICR), which
we call refinement knowledge distillation. Then, we present an adaptive
supervision aggregation function that dynamically changes the aggregation
criteria for selecting boxes related to one of the ground-truth classes,
background, or even ignored during the generation of each refinement module
supervision. Experiments in Pascal VOC 2007 demonstrate that our Knowledge
Distillation and smooth aggregation function significantly improves the
performance of OICR in the weakly supervised object detection and weakly
supervised object localization tasks. These improvements make the Boosted-OICR
competitive again versus other state-of-the-art approaches.
