ViewAL: Active Learning With Viewpoint Entropy for Semantic Segmentation

Method overview: in each round of active selec- tion, we first train a semantic segmentation network on the existing labeled data. Second, we use the trained network to compute a view entropy and a view divergence score for each unlabeled superpixel. We then select a batch of su- perpixels based on these scores, and finally request their re- spective labels from the oracle. This is repeated until the labeling budget is exhausted or all training data is labeled.

We propose ViewAL , a novel active learning strategy for semantic segmentation that exploits viewpoint consis- tency in multi-view datasets. Our core idea is that incon- sistencies in model predictions across viewpoints provide a very reliable measure of uncertainty and encourage the model to perform well irrespective of the viewpoint under which objects are observed. To incorporate this uncertainty measure, we introduce a new viewpoint entropy formula- tion, which is the basis of our active learning strategy. In addition, we propose uncertainty computations on a super- pixel level, which exploits inherently localized signal in the segmentation task, directly lowering the annotation costs. This combination of viewpoint entropy and the use of su- perpixels allows to efficiently select samples that are highly informative for improving the network. We demonstrate that our proposed active learning strategy not only yields the best-performing models for the same amount of required labeled data, but also significantly reduces labeling effort. Our method achieves 95% ofmaximum achievable network performance using only 7%, 17%, and 24% labeled data on SceneNet-RGBD, ScanNet, and Matterport3D, respec- tively. On these datasets, the best state-of-the-art method achieves the same performance with 14%, 27% and 33% la- beled data. Finally, we demonstrate that labeling using su- perpixels yields the same quality ofground-truth compared to labeling whole images, but requires 25% less time.

我们提出了一种新的语义分割主动学习策略viewAL,它利用了多视图数据集中的视点一致性。我们的核心思想是,不同视角的模型预测的不一致性提供了一个非常可靠的不确定性度量,并鼓励模型能够很好地执行,而不考虑观察对象的视角。为了引入这种不确定性度量,我们引入了一个新的观点熵公式,这是我们主动学习策略的基础。此外,我们提出了在超像素水平上的不确定性计算,在分割任务中利用固有的局部化信号,直接降低注释成本。视点熵和像素的使用相结合,可以有效地选择信息量高的样本来改善网络。我们证明,我们所提出的主动学习策略不仅能为相同数量的所需标记数据生成性能最好的模型,而且显著地减少了标记工作。我们的方法仅使用SceneNet RGBD、ScanNet和Matterport3D上的7%、17%和24%的标记数据,就可以实现95%的最大网络性能。在这些数据集上,最先进的方法可以获得相同的性能,分别为14%、27%和33%。最后,我们证明了使用su-perpixels标记与标记整个图像产生的地面真实质量相同,但所需的时间减少了25%

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