论文标题
协作RPN的改进几射门对象检测
Cooperating RPN's Improve Few-Shot Object Detection
论文作者
论文摘要
从很少的培训示例中学习图像中的对象（很少射击对象检测）是具有挑战性的，因为看到提案盒的分类器几乎没有培训数据。当有一个或两个培训例子时，就会发生一个特别具有挑战性的训练制度。在这种情况下，如果区域提案网络（RPN）甚至错过了一个高度的跨工会（IOU）训练框，则分类器的对象外观如何变化的模型会受到严重影响。我们使用多个不同但合作的RPN。我们的RPN受过训练的不同，但没有太大的不同。这样做可以在很少的射击环境中对可可和Pascal VOC的艺术状态进行显着改善。这种效果似乎独立于分类器或数据集的选择。
Learning to detect an object in an image from very few training examples - few-shot object detection - is challenging, because the classifier that sees proposal boxes has very little training data. A particularly challenging training regime occurs when there are one or two training examples. In this case, if the region proposal network (RPN) misses even one high intersection-over-union (IOU) training box, the classifier's model of how object appearance varies can be severely impacted. We use multiple distinct yet cooperating RPN's. Our RPN's are trained to be different, but not too different; doing so yields significant performance improvements over state of the art for COCO and PASCAL VOC in the very few-shot setting. This effect appears to be independent of the choice of classifier or dataset.
