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：摘要：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，旨在有效处理新类别。由于该问题尚未被充分探索，我们首先通过系统性分析简单基线模型中的组件与子任务，探究了问题的难点并识别出性能瓶颈。基于分析结果，我们提出了ENInst方法，其中包含子任务增强策略：用于提升像素定位质量的实例级掩码细化，以及用于改进分类精度的新型分类器组合。所提方法通过增强各子任务性能，整体提升了系统表现。实验表明，与现有全监督少样本模型相比，ENInst在达到相近性能时效率提升了7.5倍，有时甚至能超越全监督模型。