Few-shot object detection (FSOD) aims to detect objects using only few examples. It's critically needed for many practical applications but so far remains challenging. We propose a meta-learning based few-shot object detection method by transferring meta-knowledge learned from data-abundant base classes to data-scarce novel classes. Our method incorporates a coarse-to-fine approach into the proposal based object detection framework and integrates prototype based classifiers into both the proposal generation and classification stages. To improve proposal generation for few-shot novel classes, we propose to learn a lightweight matching network to measure the similarity between each spatial position in the query image feature map and spatially-pooled class features, instead of the traditional object/nonobject classifier, thus generating category-specific proposals and improving proposal recall for novel classes. To address the spatial misalignment between generated proposals and few-shot class examples, we propose a novel attentive feature alignment method, thus improving the performance of few-shot object detection. Meanwhile we jointly learn a Faster R-CNN detection head for base classes. Extensive experiments conducted on multiple FSOD benchmarks show our proposed approach achieves state of the art results under (incremental) few-shot learning settings.
翻译:微小的物体探测( FSOD) 仅用几个例子来探测物体。 它对于许多实际应用来说是极为需要的,但迄今为止仍然具有挑战性。 我们提出一种基于元学习的微小物体探测方法,将从数据丰富的基础分类中学到的元知识转移到数据残缺的新分类类中。 我们的方法将粗略的对齐方法纳入基于目标探测框架的提案,并将原型的分类器纳入建议生成和分类阶段。 为了改进微小的物体探测的生成,我们提议学习一个轻量匹配网络,以测量查询图像图和空间集合的分类特征中每个空间位置的相似性,而不是传统的物体/非目标分类器,从而产生特定类别的建议,改进新分类的建议。 为了解决生成的建议和微小的分类示例之间的空间错配,我们提出了一种新的注意特性调整方法,从而改进微小物体探测的性能。 与此同时,我们共同学习了基础类中更快的R-CN检测头。 在多种FSOD基准下进行的广泛实验, 显示我们提议的艺术成果的状态。