We propose Cos R-CNN, a simple exemplar-based R-CNN formulation that is designed for online few-shot object detection. That is, it is able to localise and classify novel object categories in images with few examples without fine-tuning. Cos R-CNN frames detection as a learning-to-compare task: unseen classes are represented as exemplar images, and objects are detected based on their similarity to these exemplars. The cosine-based classification head allows for dynamic adaptation of classification parameters to the exemplar embedding, and encourages the clustering of similar classes in embedding space without the need for manual tuning of distance-metric hyperparameters. This simple formulation achieves best results on the recently proposed 5-way ImageNet few-shot detection benchmark, beating the online 1/5/10-shot scenarios by more than 8/3/1%, as well as performing up to 20% better in online 20-way few-shot VOC across all shots on novel classes.

通过基于样本的R-CNN方法，我们提出了Cos R-CNN，它适用于在线少样本目标检测，能够在很少的样本示例未经微调的情况下定位和分类图像中的新对象类别。这种简单的公式在最近提出的5路ImageNet少样本检测基准上取得最好的结果，在在线1/5/10-shot情景中优于8%/3%/1%，在新颖类别的各个拍摄数下VOG上表现更好，最高可达20%。

在线少样本物体检测的 Cos R-CNN