A significant bottleneck in training deep networks for part segmentation is
the cost of obtaining detailed annotations. We propose a framework to exploit
coarse labels such as figure-ground masks and keypoint locations that are
readily available for some categories to improve part segmentation models. A
key challenge is that these annotations were collected for different tasks and
with different labeling styles and cannot be readily mapped to the part labels.
To this end, we propose to jointly learn the dependencies between labeling
styles and the part segmentation model, allowing us to utilize supervision from
diverse labels. To evaluate our approach we develop a benchmark on the
Caltech-UCSD birds and OID Aircraft dataset. Our approach outperforms baselines
based on multi-task learning, semi-supervised learning, and competitive methods
relying on loss functions manually designed to exploit sparse-supervision.

该研究提出了一种使用粗略标注来改进部分分割模型的框架，并成功地在 Caltech-UCSD birds 等数据集上将其应用于深度网络训练中。

利用粗略监督改善少样本部件分割

Improving Few-Shot Part Segmentation using Coarse Supervision

Learning high quality class representations from few examples is a key
problem in metric-learning approaches to few-shot learning. To accomplish this,
we introduce a novel architecture where class representations are conditioned
for each few-shot trial based on a target image. We also deviate from
traditional metric-learning approaches by training a network to perform
comparisons between classes rather than relying on a static metric comparison.
This allows the network to decide what aspects of each class are important for
the comparison at hand. We find that this flexible architecture works well in
practice, achieving state-of-the-art performance on the Caltech-UCSD birds
fine-grained classification task.

提出了一种新的神经网络结构，利用基于目标图像的条件来学习高质量的类别表示，采用动态学习方式进行类别之间的对比，对 Caltech-UCSD 鸟类细粒度分类任务取得了最先进的性能。