  Acquiring the most representative examples via active learning (AL) can
benefit many data-dependent computer vision tasks by minimizing efforts of
image-level or pixel-wise annotations. In this paper, we propose a novel
Collaborative Panoptic-Regional Active Learning framework (CPRAL) to address
the semantic segmentation task. For a small batch of images initially sampled
with pixel-wise annotations, we employ panoptic information to initially select
unlabeled samples. Considering the class imbalance in the segmentation dataset,
we import a Regional Gaussian Attention module (RGA) to achieve
semantics-biased selection. The subset is highlighted by vote entropy and then
attended by Gaussian kernels to maximize the biased regions. We also propose a
Contextual Labels Extension (CLE) to boost regional annotations with contextual
attention guidance. With the collaboration of semantics-agnostic panoptic
matching and regionbiased selection and extension, our CPRAL can strike a
balance between labeling efforts and performance and compromise the semantics
distribution. We perform extensive experiments on Cityscapes and BDD10K
datasets and show that CPRAL outperforms the cutting-edge methods with
impressive results and less labeling proportion.
