论文标题
通过元对抗性学习，用于几次射击对象检测的分层注意力网络
Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learning
论文作者
论文摘要
很少有射击对象检测（FSOD）旨在对新类别进行分类和检测。由于结构限制，现有的元学习方法不足以利用支持和查询图像之间的特征。我们提出了一个层次的注意网络，该网络具有依次大的接收场，以充分利用查询和支持图像。另外，元学习不能很好地区分类别，因为它决定了支持和查询图像是否匹配。换句话说，基于度量的分类学习是无效的，因为它不直接起作用。因此，我们提出了一种称为元对抗性学习的对比学习方法，该方法直接有助于实现元学习策略的目的。最后，我们通过实现明显的利润来建立一个新的最新网络。我们的方法带来了2.3、1.0、1.3、3.4和2.4％的AP改进，以改进可可数据集上的1-30张对象检测。我们的代码可在以下网址找到：https：//github.com/infinity7428/hanmcl
Few-shot object detection (FSOD) aims to classify and detect few images of novel categories. Existing meta-learning methods insufficiently exploit features between support and query images owing to structural limitations. We propose a hierarchical attention network with sequentially large receptive fields to fully exploit the query and support images. In addition, meta-learning does not distinguish the categories well because it determines whether the support and query images match. In other words, metric-based learning for classification is ineffective because it does not work directly. Thus, we propose a contrastive learning method called meta-contrastive learning, which directly helps achieve the purpose of the meta-learning strategy. Finally, we establish a new state-of-the-art network, by realizing significant margins. Our method brings 2.3, 1.0, 1.3, 3.4 and 2.4% AP improvements for 1-30 shots object detection on COCO dataset. Our code is available at: https://github.com/infinity7428/hANMCL
