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：我们研究了弱监督低样本实例分割问题，这是一种高效利用标注、有效处理新类别的训练方法。由于该问题尚未被充分探索，我们首先通过系统分析模型组件及简单基线模型中的子任务，揭示了问题的难点并识别了性能瓶颈。基于这些分析，我们提出ENInst方法，包含子任务增强策略：实例级掩膜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精度。所提方法通过提升各子任务性能，全面增强了整体表现。实验证明，我们的ENInst在达到与现有全监督少样本模型相当性能时，效率提高了7.5倍，且有时甚至超越后者。