Abstract
Multi-label classification (MLC) offers a more comprehensive semantic understanding of Remote Sensing (RS) imagery compared to traditional single-label classification (SLC). However, obtaining complete annotations for MLC is particularly challenging due to the complexity and high cost of the labeling process. As a practical alternative, single-positive multi-label learning (SPML) has emerged, where each image is annotated with only one relevant label, and the model is expected to recover the full set of labels. While scalable, SPML introduces significant supervision ambiguity, demanding specialized solutions for model training. Although various SPML methods have been proposed in the computer vision domain, research in the RS context remains limited. To bridge this gap, we propose Adaptive Gradient Calibration (AdaGC), a novel and generalizable SPML framework tailored to RS imagery. AdaGC adopts a gradient calibration (GC) mechanism combined with Mixup and a dual exponential moving average (EMA) module for robust pseudo-label generation. To maximize AdaGC's effectiveness, we introduce a simple yet theoretically grounded indicator to adaptively trigger GC after an initial warm-up stage based on training dynamics, thereby guaranteeing the effectiveness of GC in mitigating overfitting to label noise. Extensive experiments on two benchmark RS datasets under two distinct label noise types demonstrate that AdaGC achieves state-of-the-art (SOTA) performance while maintaining strong robustness across diverse settings.
Abstract (translated)
多标签分类（MLC）相比传统的单标签分类（SLC），能够为遥感（RS）影像提供更为全面的语义理解。然而，由于标注过程复杂且成本高昂，获取完整的MLC注释尤其具有挑战性。作为一种实用替代方案，单一正例多标签学习（SPML）应运而生，在这种模式下，每张图像仅被标记为一个相关的标签，模型则需要恢复出全部的标签集。尽管这种方法可扩展性强，但其引入了显著的学习监督模糊性，并且要求专门的方法来解决模型训练中的问题。虽然计算机视觉领域已经提出了各种SPML方法，但在遥感领域的研究仍然有限。 为了填补这一空白，我们提出了一种新的、具有通用性的SPML框架——自适应梯度校准（AdaGC），该框架特别针对遥感影像进行了优化。AdaGC采用了结合了Mixup和双指数移动平均（EMA）模块的梯度校准机制，以生成鲁棒伪标签。为了最大化AdaGC的效果，我们引入了一个简单但理论基础坚实的指标，在初始预热阶段后根据训练动态自适应地触发梯度校准，从而确保梯度校准在缓解过度拟合到标签噪声方面发挥有效作用。 在两个基准遥感数据集上进行的广泛实验验证了AdaGC在两种不同类型的标注噪音下的表现：实验结果显示，AdaGC实现了最先进的性能，并且在各种设置下保持了强大的鲁棒性。
URL
https://arxiv.org/abs/2510.08269
