Few-shot object detection (FSOD) identifies objects from extremely few annotated samples. Most existing FSOD methods, recently, apply the two-stage learning paradigm, which transfers the knowledge learned from abundant base classes to assist the few-shot detectors by learning the global features. However, such existing FSOD approaches seldom consider the localization of objects from local to global. Limited by the scarce training data in FSOD, the training samples of novel classes typically capture part of objects, resulting in such FSOD methods cannot detect the completely unseen object during testing. To tackle this problem, we propose an Extensible Co-Existing Attention (ECEA) module to enable the model to infer the global object according to the local parts. Essentially, the proposed module continuously learns the extensible ability on the base stage with abundant samples and transfers it to the novel stage, which can assist the few-shot model to quickly adapt in extending local regions to co-existing regions. Specifically, we first devise an extensible attention mechanism that starts with a local region and extends attention to co-existing regions that are similar and adjacent to the given local region. We then implement the extensible attention mechanism in different feature scales to progressively discover the full object in various receptive fields. Extensive experiments on the PASCAL VOC and COCO datasets show that our ECEA module can assist the few-shot detector to completely predict the object despite some regions failing to appear in the training samples and achieve the new state of the art compared with existing FSOD methods.

翻译：小样本目标检测（FSOD）旨在从极少标注样本中识别目标。近期多数FSOD方法采用两阶段学习范式，通过学习全局特征将基类知识迁移至辅助小样本检测器。然而，此类现有FSOD方法鲜少考虑从局部到全局的目标定位。受限于FSOD中训练数据匮乏，新类别的训练样本通常仅覆盖目标的部分区域，导致这类方法无法检测测试阶段中完全不可见的目标。为解决该问题，我们提出可扩展共现注意力（ECEA）模块，使模型能根据局部区域推断全局目标。该模块在基类阶段通过大量样本持续学习可扩展能力，并将其迁移至新类阶段，协助小样本模型快速适应将局部区域扩展至共现区域的过程。具体而言，我们首先设计可扩展注意力机制：以局部区域为起点，将注意扩展至与其相似且相邻的共现区域；随后在不同特征尺度上实现该机制，以逐步发现各感受野中的完整目标。在PASCAL VOC和COCO数据集上的大量实验表明，即使训练样本中部分区域缺失，我们的ECEA模块仍能辅助小样本检测器完整预测目标，并在现有FSOD方法中实现新的最优性能。