Few-shot instance segmentation 是将少样本学习范式扩展到实例分割任务,旨在利用极少数量的新类别样本来从查询图像中分割实例对象。传统方法通常通过原型学习、点估计来处理该任务。然而,这种机制容易受到噪声干扰,并且由于数据极度稀缺而存在偏差。为了克服点估计机制的缺点,我们提出了一种新的称为 MaskDiff 的方法,该方法建模了一个二值掩码的基础条件分布,该掩码在物体区域和 K 个样本信息的条件下。受到数据裁剪方法的启发,我们使用扩散概率模型建模掩码分布,从而扩展了数据密度低的区域。此外,我们也提出了无分类器引导掩码采样来将类别信息整合到二进制掩码生成过程中。我们的提出的方法相对于现有方法更为稳定,同时在 COCO 数据集的基类和新类上均始终表现出优异,且无需任何额外的操作。
Few-shot instance segmentation extends the few-shot learning paradigm to the
instance segmentation task, which tries to segment instance objects from a
query image with a few annotated examples of novel categories. Conventional
approaches have attempted to address the task via prototype learning, known as
point estimation. However, this mechanism is susceptible to noise and suffers
from bias due to a significant scarcity of data. To overcome the disadvantages
of the point estimation mechanism, we propose a novel approach, dubbed
MaskDiff, which models the underlying conditional distribution of a binary
mask, which is conditioned on an object region and $K$-shot information.
Inspired by augmentation approaches that perturb data with Gaussian noise for
populating low data density regions, we model the mask distribution with a
diffusion probabilistic model. In addition, we propose to utilize
classifier-free guided mask sampling to integrate category information into the
binary mask generation process. Without bells and whistles, our proposed method
consistently outperforms state-of-the-art methods on both base and novel
classes of the COCO dataset while simultaneously being more stable than
existing methods.