PBVS 2024 解决方案：极端长尾分布下 SAR 分类的自监督学习与采样策略
PBVS 2024 Solution: Self-Supervised Learning and Sampling Strategies for SAR Classification in Extreme Long-Tail Distribution
AI总结：
针对 PBVS 2024 极端长尾 SAR-EO 分类任务，提出两阶段自监督学习与采样策略，结合多模态及 SAR 到 EO 转换，最终准确率 21.45%，排名第 9。
摘要：
多模态学习研讨会（PBVS 2024）旨在通过利用难以解释但不受天气和可见光影响的合成孔径雷达（SAR）数据以及光电（EO）数据进行同时学习，提升自动目标识别（ATR）系统的性能。其子任务，即多模态空中图像挑战赛——分类，专注于根据一组 SAR-EO 图像对及其各自的类别标签，预测低分辨率空中图像的类别标签。提供的数据集由 SAR-EO 对组成，具有严重的长期尾分布特征，最大和最小类别之间的差异超过 1000 倍，这使得典型的长期尾方法难以应用。此外，SAR 和 EO 数据集之间的领域差异也使得标准多模态方法的有效性复杂化。为了应对这些重大挑战，我们提出了一种两阶段学习方法，利用自监督技术，结合多模态学习以及通过 SAR 到 EO 转换进行推理，以有效利用 EO 数据。在 PBVS 2024 多模态空中图像挑战赛——分类（SAR 分类）任务的最终测试阶段，我们的模型达到了 21.45% 的准确率、0.56 的 AUC 和 0.30 的总分，在竞赛中排名第 9。
英文摘要：
The Multimodal Learning Workshop (PBVS 2024) aims to improve the performance of automatic target recognition (ATR) systems by leveraging both Synthetic Aperture Radar (SAR) data, which is difficult to interpret but remains unaffected by weather conditions and visible light, and Electro-Optical (EO) data for simultaneous learning. The subtask, known as the Multi-modal Aerial View Imagery Challenge - Classification, focuses on predicting the class label of a low-resolution aerial image based on a set of SAR-EO image pairs and their respective class labels. The provided dataset consists of SAR-EO pairs, characterized by a severe long-tail distribution with over a 1000-fold difference between the largest and smallest classes, making typical long-tail methods difficult to apply. Additionally, the domain disparity between the SAR and EO datasets complicates the effectiveness of standard multimodal methods. To address these significant challenges, we propose a two-stage learning approach that utilizes self-supervised techniques, combined with multimodal learning and inference through SAR-to-EO translation for effective EO utilization. In the final testing phase of the PBVS 2024 Multi-modal Aerial View Image Challenge - Classification (SAR Classification) task, our model achieved an accuracy of 21.45%, an AUC of 0.56, and a total score of 0.30, placing us 9th in the competition.
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