Few-shot object detection (FSOD) aims to detect objects using only a few examples. How to adapt state-of-the-art object detectors to the few-shot domain remains challenging. Object proposal is a key ingredient in modern object detectors. However, the quality of proposals generated for few-shot classes using existing methods is far worse than that of many-shot classes, e.g., missing boxes for few-shot classes due to misclassification or inaccurate spatial locations with respect to true objects. To address the noisy proposal problem, we propose a novel meta-learning based FSOD model by jointly optimizing the few-shot proposal generation and fine-grained few-shot proposal classification. To improve proposal generation for few-shot classes, we propose to learn a lightweight metric-learning based prototype matching network, instead of the conventional simple linear object/nonobject classifier, e.g., used in RPN. Our non-linear classifier with the feature fusion network could improve the discriminative prototype matching and the proposal recall for few-shot classes. To improve the fine-grained few-shot proposal classification, we propose a novel attentive feature alignment method to address the spatial misalignment between the noisy proposals and few-shot classes, thus improving the performance of few-shot object detection. Meanwhile we learn a separate Faster R-CNN detection head for many-shot base classes and show strong performance of maintaining base-classes knowledge. Our model achieves state-of-the-art performance on multiple FSOD benchmarks over most of the shots and metrics.
翻译:少见的物体探测(FSOD)旨在仅用几个例子来探测物体。 如何将最新最先进的物体探测器改造成少见的域仍然具有挑战性。 对象建议是现代物体探测器中的一个关键成份。 然而,使用现有方法为少见的类别提出的提议的质量远远不如使用多发类的建议的质量,例如,由于对真实对象的分类错误或空间位置不准确,少发类缺少的方框。 为了解决吵闹的提案问题,我们提议了一个基于新颖的元学习模式,即FSOD模式,通过联合优化微小的投标书生成和精细微微微微的微微微微的微分分数点建议分类。为了改进少发类的投标书生成,我们提议学习一个轻量的、基于少发的标准化的原型匹配网络,而不是在RPN中使用的常规的简单线性对象/非显微项分类。 我们与特性融合网络的非线性分类可以改进有歧视的原型比对少发级的比重的模型和提议。 为了改进微微的微的微的微的微的微的微的微的微的光谱建议,我们提议, 我们提议用于最精微的快速的快速的检测的多分级的级的级,让我们的性能测量的性能的性能测试的性能的级,我们提议。