CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation
| Autor: | Qiao, Yu, Zhu, Jincheng, Long, Chengjiang, Zhang, Zeyao, Wang, Yuxin, Du, Zhenjun, Yang, Xin |
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| Rok vydání: | 2021 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: This is not the final version of our paper, and we will upload a final version later |
| Databáze: | arXiv |
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