CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation
This work addresses the challenge of high annotation costs in semantic segmentation for computer vision applications, offering an incremental improvement over existing active learning methods.
The paper tackles the problem of reducing annotation effort in semantic segmentation by proposing CPRAL, a collaborative active learning framework that uses panoptic information and regional attention to select and extend labels, achieving state-of-the-art results with less labeling on Cityscapes and BDD10K datasets.
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.