Few-shot detection is a major task in pattern recognition which seeks to localize objects using models trained with few labeled data. One of the mainstream few-shot methods is transfer learning which consists in pretraining a detection model in a source domain prior to its fine-tuning in a target domain. However, it is challenging for fine-tuned models to effectively identify new classes in the target domain, particularly when the underlying labeled training data are scarce. In this paper, we devise a novel sparse context transformer (SCT) that effectively leverages object knowledge in the source domain, and automatically learns a sparse context from only few training images in the target domain. As a result, it combines different relevant clues in order to enhance the discrimination power of the learned detectors and reduce class confusion. We evaluate the proposed method on two challenging few-shot object detection benchmarks, and empirical results show that the proposed method obtains competitive performance compared to the related state-of-the-art.

翻译：小样本检测是模式识别领域的一项重要任务，旨在利用少量标注数据训练的模型对目标进行定位。主流的小样本方法之一为迁移学习，其流程包括先在源域预训练检测模型，再在目标域进行微调。然而，当目标域中用于训练的标注数据稀缺时，微调后的模型难以有效识别新类别。本文提出一种新颖的稀疏上下文Transformer（SCT），该方法能有效利用源域中的目标知识，并自动从目标域中仅有的少量训练图像中学习稀疏上下文。由此，该方法整合了不同相关线索，以增强所学检测器的判别能力并减少类别混淆。我们在两个具有挑战性的小样本目标检测基准上对所提方法进行了评估，实验结果表明，与相关最先进方法相比，所提方法获得了具有竞争力的性能。