  In recent years, the rapid development of deep learning has brought great
advancements to image and video segmentation methods based on neural networks.
However, to unleash the full potential of such models, large numbers of
high-quality annotated images are necessary for model training. Currently, many
widely used open-source image segmentation software relies heavily on manual
annotation which is tedious and time-consuming. In this work, we introduce
EISeg, an Efficient Interactive SEGmentation annotation tool that can
drastically improve image segmentation annotation efficiency, generating highly
accurate segmentation masks with only a few clicks. We also provide various
domain-specific models for remote sensing, medical imaging, industrial quality
inspections, human segmentation, and temporal aware models for video
segmentation. The source code for our algorithm and user interface are
available at: https://github.com/PaddlePaddle/PaddleSeg.
