As few-shot object detectors are often trained with abundant base samples and fine-tuned on few-shot novel examples,the learned models are usually biased to base classes and sensitive to the variance of novel examples. To address this issue, we propose a meta-learning framework with two novel feature aggregation schemes. More precisely, we first present a Class-Agnostic Aggregation (CAA) method, where the query and support features can be aggregated regardless of their categories. The interactions between different classes encourage class-agnostic representations and reduce confusion between base and novel classes. Based on the CAA, we then propose a Variational Feature Aggregation (VFA) method, which encodes support examples into class-level support features for robust feature aggregation. We use a variational autoencoder to estimate class distributions and sample variational features from distributions that are more robust to the variance of support examples. Besides, we decouple classification and regression tasks so that VFA is performed on the classification branch without affecting object localization. Extensive experiments on PASCAL VOC and COCO demonstrate that our method significantly outperforms a strong baseline (up to 16\%) and previous state-of-the-art methods (4\% in average). Code will be available at: \url{https://github.com/csuhan/VFA}

翻译：由于小样本目标检测器通常使用大量基类样本进行训练，并在少量新类样本上进行微调，因此学习到的模型往往偏向于基类，且对新类样本的方差敏感。为解决这一问题，我们提出了一种包含两种新颖特征聚合方案的元学习框架。具体而言，我们首先提出了一种类别无关聚合（CAA）方法，其中查询特征和支持特征可以不受类别限制地进行聚合。不同类别之间的交互促进了类别无关表征，并减少了基类与新类之间的混淆。基于CAA，我们进一步提出了一种变分特征聚合（VFA）方法，该方法将支持样本编码为类别级支持特征，以实现稳健的特征聚合。我们利用变分自编码器来估计类别分布，并从分布中采样变分特征，这些特征对支持样本的方差更具鲁棒性。此外，我们将分类和回归任务解耦，使得VFA仅在分类分支上执行，而不会影响目标定位。在PASCAL VOC和COCO数据集上的大量实验表明，我们的方法显著优于强基线（最高提升16%）以及先前的最先进方法（平均提升4%）。代码将在以下地址公开：\url{https://github.com/csuhan/VFA}