CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation
Keywords:Computer Vision (CV)
AbstractAcquiring 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 region-biased 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.
How to Cite
Qiao, Y., Zhu, J., Long, C., Zhang, Z., Wang, Y., Du, Z., & Yang, X. (2022). CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 36(2), 2108-2116. https://doi.org/10.1609/aaai.v36i2.20107
AAAI Technical Track on Computer Vision II