论文标题
D2DF2WOD：通过渐进域适应的弱监督对象检测的学习对象提案
D2DF2WOD: Learning Object Proposals for Weakly-Supervised Object Detection via Progressive Domain Adaptation
论文作者
论文摘要
弱监督对象检测（WSOD）模型试图利用图像级注释来代替准确但昂贵的对象对象定位标签。通常，这种情况会导致在推理时对象检测和本地化不合格。为了解决此问题，我们提出了D2DF2WOD，这是一种双域完全对监督的对象检测框架，该框架利用具有精确对象定位注释的合成数据，以补充自然图像目标域，其中只有图像级标签可用。在其热身域的适应阶段，该模型学习了一个完全监督的对象检测器（FSOD），以提高目标域中对象建议的精度，同时也学习了目标域特异性和检测意识的建议特征。在其主要WSOD阶段，WSOD模型专门调整为目标域。 WSOD模型的特征提取器和对象提案生成器构建在微型FSOD模型上。我们在五个双域图像基准上测试D2DF2WOD。结果表明，与最先进的方法相比，我们的方法会持续改进对象检测和本地化。
Weakly-supervised object detection (WSOD) models attempt to leverage image-level annotations in lieu of accurate but costly-to-obtain object localization labels. This oftentimes leads to substandard object detection and localization at inference time. To tackle this issue, we propose D2DF2WOD, a Dual-Domain Fully-to-Weakly Supervised Object Detection framework that leverages synthetic data, annotated with precise object localization, to supplement a natural image target domain, where only image-level labels are available. In its warm-up domain adaptation stage, the model learns a fully-supervised object detector (FSOD) to improve the precision of the object proposals in the target domain, and at the same time learns target-domain-specific and detection-aware proposal features. In its main WSOD stage, a WSOD model is specifically tuned to the target domain. The feature extractor and the object proposal generator of the WSOD model are built upon the fine-tuned FSOD model. We test D2DF2WOD on five dual-domain image benchmarks. The results show that our method results in consistently improved object detection and localization compared with state-of-the-art methods.
