  Current state-of-the-art weakly supervised object detection (WSOD) studies
mainly follow a two-stage training strategy which integrates a fully supervised
detector (FSD) with a pure WSOD model. There are two main problems hindering
the performance of the two-phase WSOD approaches, i.e., insufficient learning
problem and strict reliance between the FSD and the pseudo ground truth (PGT)
generated by the WSOD model. This paper proposes pseudo ground truth refinement
network (PGTRNet), a simple yet effective method without introducing any extra
learnable parameters, to cope with these problems. PGTRNet utilizes multiple
bounding boxes to establish the PGT, mitigating the insufficient learning
problem. Besides, we propose a novel online PGT refinement approach to steadily
improve the quality of PGT by fully taking advantage of the power of FSD during
the second-phase training, decoupling the first and second-phase models.
Elaborate experiments are conducted on the PASCAL VOC 2007 benchmark to verify
the effectiveness of our methods. Experimental results demonstrate that PGTRNet
boosts the backbone model by 2.1% mAP and achieves the state-of-the-art
performance.
