AdaSemSeg：面向地震岩相的自适应小样本语义分割
AdaSemSeg: An Adaptive Few-shot Semantic Segmentation of Seismic Facies
AI总结：
针对现有小样本语义分割方法无法适配类别数量变化的缺陷，提出 AdaSemSeg 方法，采用自监督算法初始化骨干网络，在三个地震岩相数据集上训练后，其在未见过的数据集上性能优于基线方法。
摘要：
利用深度学习方法自动解释地震图像颇具挑战，原因在于训练数据有限。小样本学习因具备在有限监督（少量训练预算）下适配新任务的能力，成为此类场景下合适的学习范式。现有小样本语义分割（FSSS）方法固定目标类别的数量，因此无法支持对类别数量不同的多个数据集进行联合训练。在地震岩相解释场景中，固定目标类别数量会抑制在某一岩相数据集上训练的模型向另一可能具有不同岩相数量的数据集的泛化能力。为解决这一缺陷，我们提出一种用于解释地震岩相的小样本语义分割方法，该方法可适配不同数据集间变化的岩相数量，命名为 AdaSemSeg。通常，FSSS 方法的骨干网络会使用从 ImageNet 数据集学习到的统计信息进行初始化以提升性能。由于地震图像缺乏如此庞大的带标注数据集，我们采用自监督算法在地震数据集上对骨干网络进行初始化。我们在三个具有不同岩相数量的公开地震岩相数据集上训练了 AdaSemSeg，并使用多种指标对所提方法进行评估。AdaSemSeg 在未参与训练的 unseen 数据集上的性能优于基于原型的小样本方法及基线方法。代码：this https URL
英文摘要：
Automated interpretation of seismic images using deep learning methods is challenging because of the limited availability of training data. Few-shot learning is a suitable learning paradigm in such scenarios due to its ability to adapt to a new task with limited supervision (small training budget). Existing few-shot semantic segmentation (FSSS) methods fix the number of target classes. Therefore, they do not support joint training on multiple datasets varying in the number of classes. In the context of the interpretation of seismic facies, fixing the number of target classes inhibits the generalization capability of a model trained on one facies dataset to another, which is likely to have a different number of facies. To address this shortcoming, we propose a few-shot semantic segmentation method for interpreting seismic facies that can adapt to the varying number of facies across the dataset, dubbed the AdaSemSeg. In general, the backbone network of FSSS methods is initialized with the statistics learned from the ImageNet dataset for better performance. The lack of such a huge annotated dataset for seismic images motivates using a self-supervised algorithm on seismic datasets to initialize the backbone network. We have trained the AdaSemSeg on three public seismic facies datasets with different numbers of facies and evaluated the proposed method on multiple metrics. The performance of the AdaSemSeg on unseen datasets (not used in training) is better than the prototype-based few-shot method and baselines. Code: https://github.com/Surojit-Utah/AdaSemSeg
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。
