Weakly supervised object detection (WSOD) aims at learning precise object detectors with only image-level tags. In spite of intensive research on deep learning (DL) approaches over the past few years, there is still a significant performance gap between WSOD and fully supervised object detection. In fact, most existing WSOD methods only consider the visual appearance of each region proposal but ignore employing the useful context information in the image. To this end, this paper proposes an interactive end-to-end WSDO framework called JLWSOD with two innovations: i) two types of WSOD-specific context information (i.e., instance-wise correlation andsemantic-wise correlation) are proposed and introduced into WSOD framework; ii) an interactive graph contrastive learning (iGCL) mechanism is designed to jointly optimize the visual appearance and context information for better WSOD performance. Specifically, the iGCL mechanism takes full advantage of the complementary interpretations of the WSOD, namely instance-wise detection and semantic-wise prediction tasks, forming a more comprehensive solution. Extensive experiments on the widely used PASCAL VOC and MS COCO benchmarks verify the superiority of JLWSOD over alternative state-of-the-art approaches and baseline models (improvement of 3.6%~23.3% on mAP and 3.4%~19.7% on CorLoc, respectively).

翻译：弱监督目标检测旨在仅使用图像级标签学习精准的目标检测器。尽管近年来深度学习方法取得大量研究，弱监督与全监督目标检测之间仍存在显著性能差距。实际上，多数现有弱监督方法仅考虑各候选区域的视觉外观，而忽视了图像中蕴含的上下文信息。为此，本文提出一种名为JLWSOD的交互式端到端弱监督目标检测框架，包含两项创新：一) 提出并引入两种弱监督专用上下文信息（即实例级关联与语义级关联）；二) 设计交互式图对比学习机制，联合优化视觉外观与上下文信息以提升弱监督性能。具体而言，该机制充分利用弱监督的互补性解释——实例级检测任务与语义级预测任务，形成更全面的解决方案。在广泛使用的PASCAL VOC和MS COCO基准测试上的大量实验表明，JLWSOD在mAP（提升3.6%~23.3%）和CorLoc（提升3.4%~19.7%）指标上均优于现有最优方法与基线模型。