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

翻译：微小的天体探测(FSOD)旨在分类和探测少数新类别图像。现有的元学习方法由于结构限制,对支持和查询图像之间特征的利用不足。我们建议建立一个分级关注网络,按顺序排列大、可接受字段的分级网,以充分利用查询和支持图像。此外,元学习没有很好地区分这些类别,因为它确定支持和查询图像是否匹配。换句话说,基于标准的分类学习是无效的,因为它不直接发挥作用。因此,我们提议一种反比学习方法,称为元通信学习,直接帮助实现元学习战略的目的。最后,我们通过实现显著的边距,建立了一个新的最先进的网络。我们的方法为COCOCO数据集的1至30个射线物体探测带来了2.3、1.0、1.3、3.4和2.4%的AP改进。我们的代码可在以下查阅:https://github.com/infity7428/hANMCL。我们的代码可在https://github. com/infinity7428/hANMCL上查阅。