We address a weakly-supervised low-shot instance segmentation, an annotation-efficient training method to deal with novel classes effectively. Since it is an under-explored problem, we first investigate the difficulty of the problem and identify the performance bottleneck by conducting systematic analyses of model components and individual sub-tasks with a simple baseline model. Based on the analyses, we propose ENInst with sub-task enhancement methods: instance-wise mask refinement for enhancing pixel localization quality and novel classifier composition for improving classification accuracy. Our proposed method lifts the overall performance by enhancing the performance of each sub-task. We demonstrate that our ENInst is 7.5 times more efficient in achieving comparable performance to the existing fully-supervised few-shot models and even outperforms them at times.

翻译：我们研究了一种弱监督低样本实例分割方法，这是一种高效利用标注的训练方式，用于有效处理新类别。由于该问题尚未得到充分探索，我们首先通过使用简单基线模型对模型组件和子任务进行系统分析，探究了问题的难点并识别了性能瓶颈。基于这些分析，我们提出了ENInst及其子任务增强方法：实例级掩码精细化以提升像素定位质量，以及新颖分类器组合以提高分类准确率。所提方法通过增强每个子任务的性能来整体提升模型表现。我们证明，在达到与现有全监督少样本模型相当的性能时，ENInst的效率提高了7.5倍，甚至有时还能超越它们。