In this paper, we study object detection using a large pool of unlabeled images and only a few labeled images per category, named "few-shot object detection". The key challenge consists in generating trustworthy training samples as many as possible from the pool. Using few training examples as seeds, our method iterates between model training and high-confidence sample selection. In training, easy samples are generated first and, then the poorly initialized model undergoes improvement. As the model becomes more discriminative, challenging but reliable samples are selected. After that, another round of model improvement takes place. To further improve the precision and recall of the generated training samples, we embed multiple detection models in our framework, which has proven to outperform the single model baseline and the model ensemble method. Experiments on PASCAL VOC'07 indicate that by using as few as three or four samples selected for each category, our method produces very competitive results when compared to the state-of-the-art weakly-supervised approaches using a large number of image-level labels.

该论文研究了一种名为“few-example object detection”的目标检测方法，使用了大量未标记图像和每类仅少量标记图像，并提出了基于模型训练和高置信度样本选择的策略来生成高信度的训练样本；通过嵌入多个检测模型来进一步提高样本的精度和召回率，并在实验中证明了其与使用大量图像级别标签的最先进弱监督方法相比，使用每类仅3-4个样本可获得非常有竞争力的结果。

模型通信的少样本目标检测