Class Balanced Dynamic Acquisition for Domain Adaptive Semantic Segmentation using Active Learning

• URL: http://arxiv.org/abs/2311.14146v1
• Date: Thu, 23 Nov 2023 18:35:26 GMT
• Title: Class Balanced Dynamic Acquisition for Domain Adaptive Semantic Segmentation using Active Learning
• Abstract summary: Domain adaptive active learning is leading the charge in label-efficient training of neural networks. For semantic segmentation, state-of-the-art models jointly use two criteria of uncertainty and diversity to select training labels. We show that such methods currently suffer from a class imbalance issue which degrades their performance for larger active learning budgets.
• Score: 3.175227858236288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Domain adaptive active learning is leading the charge in label-efficient training of neural networks. For semantic segmentation, state-of-the-art models jointly use two criteria of uncertainty and diversity to select training labels, combined with a pixel-wise acquisition strategy. However, we show that such methods currently suffer from a class imbalance issue which degrades their performance for larger active learning budgets. We then introduce Class Balanced Dynamic Acquisition (CBDA), a novel active learning method that mitigates this issue, especially in high-budget regimes. The more balanced labels increase minority class performance, which in turn allows the model to outperform the previous baseline by 0.6, 1.7, and 2.4 mIoU for budgets of 5%, 10%, and 20%, respectively. Additionally, the focus on minority classes leads to improvements of the minimum class performance of 0.5, 2.9, and 4.6 IoU respectively. The top-performing model even exceeds the fully supervised baseline, showing that a more balanced label than the entire ground truth can be beneficial.

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