用生成天气图像提升海上目标分类泛化能力
Domain Adaptation from Generated Multi-Weather Images for Unsupervised Maritime Object Classification
- 用生成数据构建平衡的源域，缓解真实数据长尾问题
- 结合CLIP增强特征，使模型在罕见类别上准确率显著提升
- 通过难度分数实现课程学习，优化训练过程
海上目标分类与识别对提升航海安全、环境监控和智能海况预测至关重要。然而，现有无监督方法在对象类别和天气条件上常面临长尾分布问题。本文利用大规模生成模型构建名为AIMO的数据集，涵盖多样化天气且类别均衡；同时收集包含长尾问题的真实图像数据集RMO。提出一种新型领域自适应方法，利用AIMO（源域）缓解RMO（目标域）中标签数据少、分布不均和领域偏移问题，借助如CLIP等视觉-语言模型增强源域特征泛化能力，并引入难度分数实现课程学习以优化训练流程。实验表明，该方法显著提升了分类准确率，尤其在稀有对象类别和天气条件下表现突出。数据集与代码将公开于https://github.com/honoria0204/AIMO。
原文摘要 · Abstract (English)
The classification and recognition of maritime objects are crucial for enhancing maritime safety, monitoring, and intelligent sea environment prediction. However, existing unsupervised methods for maritime object classification often struggle with the long-tail data distributions in both object categories and weather conditions. In this paper, we construct a dataset named AIMO produced by large-scale generative models with diverse weather conditions and balanced object categories, and collect a dataset named RMO with real-world images where long-tail issue exists. We propose a novel domain adaptation approach that leverages AIMO (source domain) to address the problem of limited labeled data, unbalanced distribution and domain shift in RMO (target domain), enhance the generalization of source features with the Vision-Language Models such as CLIP, and propose a difficulty score for curriculum learning to optimize training process. Experimental results shows that the proposed method significantly improves the classification accuracy, particularly for samples within rare object categories and weather conditions. Datasets and codes will be publicly available at https://github.com/honoria0204/AIMO.
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