  Weakly Supervised Object Detection (WSOD), aiming to train detectors with
only image-level dataset, has arisen increasing attention for researchers. In
this project, we focus on two-phase WSOD architecture which integrates a
powerful detector with a pure WSOD model. We explore the effectiveness of some
representative detectors utilized as the second-phase detector in two-phase
WSOD and propose a two-phase WSOD architecture. In addition, we present a
strategy to establish the pseudo ground truth (PGT) used to train the
second-phase detector. Unlike previous works that regard top one bounding boxes
as PGT, we consider more bounding boxes to establish the PGT annotations. This
alleviates the insufficient learning problem caused by the low recall of PGT.
We also propose some strategies to refine the PGT during the training of the
second detector. Our strategies suspend the training in specific epoch, then
refine the PGT by the outputs of the second-phase detector. After that, the
algorithm continues the training with the same gradients and weights as those
before suspending. Elaborate experiments are conduceted on the PASCAL VOC 2007
dataset to verify the effectiveness of our methods. As results demonstrate, our
two-phase architecture improves the mAP from 49.17% to 53.21% compared with the
single PCL model. Additionally, the best PGT generation strategy obtains a 0.7%
mAP increment. Our best refinement strategy boosts the performance by 1.74%
mAP. The best results adopting all of our methods achieve 55.231% mAP which is
the state-of-the-art performance.
