BaSAL：用于LiDAR语义分割的尺寸平衡热启动主动学习
BaSAL: Size-Balanced Warm Start Active Learning for LiDAR Semantic Segmentation
AI总结：
针对LiDAR语义分割中类别不平衡与冷启动问题，提出尺寸平衡热启动主动学习BaSAL，利用类别尺寸采样构建平衡数据集，无需预训练模型，仅用5%标注即达全量训练性能。
摘要：
主动学习旨在通过反复查询标注者以从无标注数据池中标记最具信息量的样本，并利用这些样本训练模型，从而减少昂贵的数据标注需求。我们识别出现有LiDAR语义分割主动学习方法中的两个问题。首先，它们忽视了LiDAR语义分割数据集固有的严重类别不平衡。其次，为了在没有标注数据可用时启动主动学习循环，它们从随机选择的数据样本中训练初始模型，导致性能低下。这种情况被称为冷启动问题。为了解决这些问题，我们提出BaSAL，一种基于每个对象类别具有特征尺寸这一观察的尺寸平衡热启动主动学习模型。通过根据尺寸对对象簇进行采样，我们可以创建尺寸平衡的数据集，该数据集也更具类别平衡性。此外，与熵或CoreSet等现有信息度量相比，基于尺寸的采样不需要预训练模型，从而有效解决了冷启动问题。结果表明，我们能够大幅提升初始模型的性能。将热启动和尺寸平衡采样与既定信息度量相结合，我们的方法在仅使用5%标注的情况下，达到了与在完整SemanticKITTI数据集上训练相当的性能，优于现有主动学习方法。我们还在nuScenes上匹配了现有的主动学习最先进水平。我们的代码可在https://github.com/Tony-WJR/BaSAL获取。
英文摘要：
Active learning strives to reduce the need for costly data annotation, by repeatedly querying an annotator to label the most informative samples from a pool of unlabeled data, and then training a model from these samples. We identify two problems with existing active learning methods for LiDAR semantic segmentation. First, they overlook the severe class imbalance inherent in LiDAR semantic segmentation datasets. Second, to bootstrap the active learning loop when there is no labeled data available, they train their initial model from randomly selected data samples, leading to low performance. This situation is referred to as the cold start problem. To address these problems we propose BaSAL, a size-balanced warm start active learning model, based on the observation that each object class has a characteristic size. By sampling object clusters according to their size, we can thus create a size-balanced dataset that is also more class-balanced. Furthermore, in contrast to existing information measures like entropy or CoreSet, size-based sampling does not require a pretrained model, thus addressing the cold start problem effectively. Results show that we are able to improve the performance of the initial model by a large margin. Combining warm start and size-balanced sampling with established information measures, our approach achieves comparable performance to training on the entire SemanticKITTI dataset, despite using only 5% of the annotations, outperforming existing active learning methods. We also match the existing state-of-the-art in active learning on nuScenes. Our code is available at: https://github.com/Tony-WJR/BaSAL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
