CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation

• URL: http://arxiv.org/abs/2112.05975v1
• Date: Sat, 11 Dec 2021 13:13:13 GMT
• Title: CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation
• Authors: Yu Qiao, Jincheng Zhu, Chengjiang Long, Zeyao Zhang, Yuxin Wang, Zhenjun Du, Xin Yang
• Abstract summary: We propose a Collaborative Panoptic-Regional Active Learning framework (CPRAL) to address the semantic segmentation task. Considering the class imbalance in the segmentation dataset, we import a Regional Gaussian Attention module (RGA) to achieve semantics-biased selection. We show that CPRAL outperforms the cutting-edge methods with impressive results and less labeling proportion.
• Score: 35.11139361684248
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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