Multi-label classification (MLC) faces challenges from label noise in training data due to annotating diverse semantic labels for each image. Current methods mainly target identifying and correcting label mistakes using trained MLC models, but still struggle with persistent noisy labels during training, resulting in imprecise recognition and reduced performance. Our paper addresses label noise in MLC by introducing a positive and unlabeled multi-label classification (PU-MLC) method. To counteract noisy labels, we directly discard negative labels, focusing on the abundance of negative labels and the origin of most noisy labels. PU-MLC employs positive-unlabeled learning, training the model with only positive labels and unlabeled data. The method incorporates adaptive re-balance factors and temperature coefficients in the loss function to address label distribution imbalance and prevent over-smoothing of probabilities during training. Additionally, we introduce a local-global convolution module to capture both local and global dependencies in the image without requiring backbone retraining. PU-MLC proves effective on MLC and MLC with partial labels (MLC-PL) tasks, demonstrating significant improvements on MS-COCO and PASCAL VOC datasets with fewer annotations. Code is available at: https://github.com/TAKELAMAG/PU-MLC.

翻译：多标签分类（MLC）面临训练数据中标签噪声的挑战，原因在于需为每幅图像标注多样化的语义标签。现有方法主要利用训练好的MLC模型识别和纠正标签错误，但在训练过程中仍难以处理持续存在的噪声标签，导致识别精度降低和性能衰减。本文针对MLC中的标签噪声问题，提出一种正标签与未标签数据多标签分类（PU-MLC）方法。为抵御噪声标签，我们直接舍弃负标签，聚焦于负标签的丰富性及噪声标签的主要来源。PU-MLC采用正-无标签学习策略，仅使用正标签与未标签数据训练模型。该方法在损失函数中引入自适应重平衡因子和温度系数，以应对标签分布不均衡问题，并防止训练过程中概率过度平滑。此外，我们提出局部-全局卷积模块，在不需重新训练骨干网络的情况下，同时捕获图像的局部与全局依赖关系。PU-MLC在MLC及部分标签多标签分类（MLC-PL）任务中均表现有效，在MS-COCO和PASCAL VOC数据集上以更少标注量实现了显著性能提升。代码开源地址：https://github.com/TAKELAMAG/PU-MLC。