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 https://github.com/Yinyf0804/WSOD-CBL/.

翻译：近年来，弱监督目标检测的进展主要体现为多实例检测网络（MIDN）与在线序贯优化相结合的方法。然而，在仅依赖图像级标注的情况下，MIDN在生成伪标签时不可避免地会对某些非预期的候选区域赋予高分。这些不准确的高分候选区域会误导后续优化模块的训练，进而降低检测性能。本研究旨在探索如何改善MIDN中伪标签的质量。为此，我们提出循环自举标注（CBL）——一种新型弱监督目标检测流程，通过引入可信教师网络的排序信息来优化MIDN。具体而言，我们采用加权指数移动平均策略，融合各优化模块的优势来构建该教师网络。同时，提出新颖的类别级排序蒸馏算法，利用加权集成教师网络的输出来对MIDN进行排序知识蒸馏。通过这种方式，MIDN被引导为相邻候选区域中更准确的提议分配更高分值，从而有利于后续伪标签生成。在主流PASCAL VOC 2007&2012以及COCO数据集上的大量实验表明，我们提出的CBL框架具有卓越性能。相关代码已开源至https://github.com/Yinyf0804/WSOD-CBL/。