论文标题
IFS-RCNN：增量少数弹药实例细分器
iFS-RCNN: An Incremental Few-shot Instance Segmenter
论文作者
论文摘要
本文解决了增量少数实例细分，当访问旧课程的培训示例时，新对象类的一些示例不再可用，目标是在新旧课程上都表现良好。我们通过在其第二阶段扩展了共同的mask-RCNN框架来做出两个贡献 - 即，我们根据概率函数指定了一个新的对象类分类器和新的不确定性指导的边界盒预测器。前者利用贝叶斯学习来解决新课程的培训示例的很少。后者不仅学到了预测对象边界框，还学会了估计预测的不确定性作为边界框改进的指导。我们还根据估计的对象类分布和边界盒不确定性指定了两个新的损失功能。我们的贡献在COCO数据集上产生了显着的性能增长，特别是，在AP实例分段度量中，新类中的增益+6在新类中的增益+6。此外，我们是第一个评估更具挑战性LVIS数据集上的增量射击设置的人。
This paper addresses incremental few-shot instance segmentation, where a few examples of new object classes arrive when access to training examples of old classes is not available anymore, and the goal is to perform well on both old and new classes. We make two contributions by extending the common Mask-RCNN framework in its second stage -- namely, we specify a new object class classifier based on the probit function and a new uncertainty-guided bounding-box predictor. The former leverages Bayesian learning to address a paucity of training examples of new classes. The latter learns not only to predict object bounding boxes but also to estimate the uncertainty of the prediction as guidance for bounding box refinement. We also specify two new loss functions in terms of the estimated object-class distribution and bounding-box uncertainty. Our contributions produce significant performance gains on the COCO dataset over the state of the art -- specifically, the gain of +6 on the new classes and +16 on the old classes in the AP instance segmentation metric. Furthermore, we are the first to evaluate the incremental few-shot setting on the more challenging LVIS dataset.
