首个遥感少样本语义分割基准，支持新类与旧类兼顾学习。
Generalized Few-Shot Semantic Segmentation in Remote Sensing: Challenge and Benchmark
- 提出遥感场景下通用少样本分割新框架，兼顾新旧类别表现。
- 在OpenEarthMap基础上扩展标注数据，支持跨类别泛化评估。
- 面向遥感土地覆盖变化监测，适合关注小样本学习的研究者。
在各类应用中，尤其是遥感领域，利用有限标注数据进行学习是一个挑战性问题。少样本语义分割通过少量标注样例来学习未见类别，是一种有效方法。而通用少样本分割设置进一步要求模型不仅适应新类别，还需保持对训练基类的良好性能。尽管已有研究在遥感少样本分割方面提出过数据集和基准，但本工作首次为遥感领域构建了通用少样本分割基准。该设定更贴近实际应用且更具挑战性，推动了在遥感背景下对该问题的探索。我们基于OpenEarthMap（OEM）数据集扩充了额外类别标注，用于通用少样本评估。该数据集随同2024年CVPR会议期间举办的L3D-IVU研讨会中的OpenEarthMap土地覆盖映射通用少样本挑战赛一同发布。本文还总结了数据集与挑战细节，并提供了挑战两阶段（验证集与测试集）的基准结果。
原文摘要 · Abstract (English)
Learning with limited labelled data is a challenging problem in various applications, including remote sensing. Few-shot semantic segmentation is one approach that can encourage deep learning models to learn from few labelled examples for novel classes not seen during the training. The generalized few-shot segmentation setting has an additional challenge which encourages models not only to adapt to the novel classes but also to maintain strong performance on the training base classes. While previous datasets and benchmarks discussed the few-shot segmentation setting in remote sensing, we are the first to propose a generalized few-shot segmentation benchmark for remote sensing. The generalized setting is more realistic and challenging, which necessitates exploring it within the remote sensing context. We release the dataset augmenting OpenEarthMap with additional classes labelled for the generalized few-shot evaluation setting. The dataset is released during the OpenEarthMap land cover mapping generalized few-shot challenge in the L3D-IVU workshop in conjunction with CVPR 2024. In this work, we summarize the dataset and challenge details in addition to providing the benchmark results on the two phases of the challenge for the validation and test sets.
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