  In applied image segmentation tasks, the ability to provide numerous and
precise labels for training is paramount to the accuracy of the model at
inference time. However, this overhead is often neglected, and recently
proposed segmentation architectures rely heavily on the availability and
fidelity of ground truth labels to achieve state-of-the-art accuracies. Failure
to acknowledge the difficulty in creating adequate ground truths can lead to an
over-reliance on pre-trained models or a lack of adoption in real-world
applications. We introduce Points2Polygons (P2P), a model which makes use of
contextual metric learning techniques that directly addresses this problem.
Points2Polygons performs well against existing fully-supervised segmentation
baselines with limited training data, despite using lightweight segmentation
models (U-Net with a ResNet18 backbone) and having access to only weak labels
in the form of object centroids and no pre-training. We demonstrate this on
several different small but non-trivial datasets. We show that metric learning
using contextual data provides key insights for self-supervised tasks in
general, and allow segmentation models to easily generalize across
traditionally label-intensive domains in computer vision.
