Generalized Few-Shot Semantic Segmentation in Remote Sensing: Challenge and Benchmark

Clifford Broni-Bediako, Junshi Xia, Jian Song, Hongruixuan Chen, Mennatullah Siam, Naoto Yokoya
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Abstract

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.
遥感中的广义少镜头语义分割:挑战与基准
在包括遥感在内的各种应用中,利用有限的标记数据进行学习是一个具有挑战性的问题。少数镜头语义分割是一种方法,它可以鼓励深度学习模型从少数标记示例中学习训练期间未见的新类别。广义少镜头分割设置还有一个额外的挑战,即鼓励模型不仅要适应新类别,还要在训练基础类别上保持较强的性能。虽然之前的数据集和基准讨论了遥感中的少数镜头分割设置,但我们是第一个为遥感提出广义少数镜头分割基准的人。广义的设置更加现实和具有挑战性,因此有必要在遥感背景下对其进行探索。我们发布的数据集增强了 OpenEarthMap 的功能,增加了为广义少量照片评估设置而标注的类别。该数据集是在与 CVPR 2024 同时举行的 L3D-IVU 工作坊的 OpenEarthMap 土地覆被测绘通用少量照片挑战赛期间发布的。在这项工作中,我们总结了数据集和挑战赛的细节,并提供了两个阶段的挑战赛验证集和测试集的基准结果。
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