Expensive bounding-box annotations have limited the development of object detection task. Thus, it is necessary to focus on more challenging task of few-shot object detection. It requires the detector to recognize objects of novel classes with only a few training samples. Nowadays, many existing popular methods adopting training way similar to meta-learning have achieved promising performance, such as Meta R-CNN series. However, support data is only used as the class attention to guide the detecting of query images each time. Their relevance to each other remains unexploited. Moreover, a lot of recent works treat the support data and query images as independent branch without considering the relationship between them. To address this issue, we propose a dynamic relevance learning model, which utilizes the relationship between all support images and Region of Interest (RoI) on the query images to construct a dynamic graph convolutional network (GCN). By adjusting the prediction distribution of the base detector using the output of this GCN, the proposed model serves as a hard auxiliary classification task, which guides the detector to improve the class representation implicitly. Comprehensive experiments have been conducted on Pascal VOC and MS-COCO dataset. The proposed model achieves the best overall performance, which shows its effectiveness of learning more generalized features. Our code is available at https://github.com/liuweijie19980216/DRL-for-FSOD.

翻译：昂贵的边界框标注限制了目标检测任务的发展。因此，有必要关注更具挑战性的小样本目标检测任务。该任务要求检测器仅通过少量训练样本就能识别新类别的目标。目前，许多采用类似元学习训练方式的流行方法已取得令人瞩目的性能，例如Meta R-CNN系列。然而，支持数据每次仅作为类别注意力引导查询图像的检测，其与查询图像之间的相关性尚未被充分利用。此外，近期大量工作将支持数据和查询图像视为独立分支，未考虑它们之间的关系。为解决这一问题，我们提出一种动态相关性学习模型，该模型利用所有支持图像与查询图像上感兴趣区域（RoI）之间的关系来构建动态图卷积网络（GCN）。通过使用该GCN的输出调整基础检测器的预测分布，所提模型充当一个硬辅助分类任务，隐式引导检测器改进类别表示。在Pascal VOC和MS-COCO数据集上进行了全面实验。所提模型取得了最佳整体性能，证明其在学习更通用特征方面的有效性。我们的代码已开源至 https://github.com/liuweijie19980216/DRL-for-FSOD。