Recent progress in weakly supervised object detection is featured by a
combination of multiple instance detection networks (MIDN) and ordinal online
refinement. However, with only image-level annotation, MIDN inevitably assigns
high scores to some unexpected region proposals when generating pseudo labels.
These inaccurate high-scoring region proposals will mislead the training of
subsequent refinement modules and thus hamper the detection performance. In
this work, we explore how to ameliorate the quality of pseudo-labeling in MIDN.
Formally, we devise Cyclic-Bootstrap Labeling (CBL), a novel weakly supervised
object detection pipeline, which optimizes MIDN with rank information from a
reliable teacher network. Specifically, we obtain this teacher network by
introducing a weighted exponential moving average strategy to take advantage of
various refinement modules. A novel class-specific ranking distillation
algorithm is proposed to leverage the output of weighted ensembled teacher
network for distilling MIDN with rank information. As a result, MIDN is guided
to assign higher scores to accurate proposals among their neighboring ones,
thus benefiting the subsequent pseudo labeling. Extensive experiments on the
prevalent PASCAL VOC 2007 \& 2012 and COCO datasets demonstrate the superior
performance of our CBL framework. Code will be available at
this https URL

该研究探索了如何通过引入可靠的教师网络的排名信息来优化 MIDN 的伪标签质量，提出了循环引导标记（CBL）框架，通过新颖的类别特定排名蒸馏算法，指导 MIDN 在相邻提议中分配更高的分数，并在主流数据集上证明了其优越的性能。