Abstract
We consider the problem of cost effective active learning for semantic segmentation, which aims at reducing the efforts of semantically annotating images. Current studies have ignored the inclusion of cost of labeling into their active learning frameworks. To this end, we first present a novel cost prediction module based on what we call the M-Net. M-Net combines the power of unsupervised W-Net and supervised U-Net to compute a refined segmentation map. The refined segmentation map is used to estimate the cost of annotations. The cost of annotation is estimated by the number of clicks required to annotate an image. To solve this task, we make use of the harris corner detector algorithm to estimate the location of the clicks required to annotate an image. Finally, we employ a multi armed bandit setting to minimize the cost of annotations while maximizing the performance of the semantic segmentation task. The M-Net outperforms fully supervised U-Net with +4.37 Acc and +3.75 mIoU. The proposed active learning framework also outperforms the existing baselines to prove the relevance of the approach in the current paradigm.
Original language | English |
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Title of host publication | IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings |
Publisher | IEEE |
Pages | 4990-4993 |
Number of pages | 4 |
ISBN (Electronic) | 9798350320107 |
DOIs | |
Publication status | Published - 2023 |
Event | 43rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 - Pasadena Convention Center, Pasadena, United States Duration: 16 Jul 2023 → 21 Jul 2023 Conference number: 43 https://2023.ieeeigarss.org/index.php |
Publication series
Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
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Volume | 2023-July |
Conference
Conference | 43rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 |
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Abbreviated title | IGARSS 2023 |
Country/Territory | United States |
City | Pasadena |
Period | 16/07/23 → 21/07/23 |
Internet address |
Keywords
- Active Learning
- Semantic Segmentation
- Uncertainty
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