  Broad-scale marine surveys performed by underwater vehicles significantly
increase the availability of coral reef imagery, however it is costly and
time-consuming for domain experts to label images. Point label propagation is
an approach used to leverage existing image data labeled with sparse point
labels. The resulting augmented ground truth generated is then used to train a
semantic segmentation model. Here, we first demonstrate that recent advances in
foundation models enable generation of multi-species coral augmented ground
truth masks using denoised DINOv2 features and K-Nearest Neighbors (KNN),
without the need for any pre-training or custom-designed algorithms. For
extremely sparsely labeled images, we propose a labeling regime based on
human-in-the-loop principles, resulting in significant improvement in
annotation efficiency: If only 5 point labels per image are available, our
proposed human-in-the-loop approach improves on the state-of-the-art by 17.3%
for pixel accuracy and 22.6% for mIoU; and by 10.6% and 19.1% when 10 point
labels per image are available. Even if the human-in-the-loop labeling regime
is not used, the denoised DINOv2 features with a KNN outperforms the prior
state-of-the-art by 3.5% for pixel accuracy and 5.7% for mIoU (5 grid points).
We also provide a detailed analysis of how point labeling style and the
quantity of points per image affects the point label propagation quality and
provide general recommendations on maximizing point label efficiency.
