Multi-label classification (MLC) suffers from the inevitable label noise in training data due to the difficulty in annotating various semantic labels in each image. To mitigate the influence of noisy labels, existing methods mainly devote to identifying and correcting the label mistakes via a trained MLC model. However, these methods still involve annoying noisy labels in training, which can result in imprecise recognition of noisy labels and weaken the performance. In this paper, considering that the negative labels are substantially more than positive labels, and most noisy labels are from the negative labels, we directly discard all the negative labels in the dataset, and propose a new method dubbed positive and unlabeled multi-label classification (PU-MLC). By extending positive-unlabeled learning into MLC task, our method trains model with only positive labels and unlabeled data, and introduces adaptive re-balance factor and adaptive temperature coefficient in the loss function to alleviate the catastrophic imbalance in label distribution and over-smoothing of probabilities in training. Furthermore, to capture both local and global dependencies in the image, we also introduce a local-global convolution module, which supplements global information into existing convolution layers with no retraining of backbone required. Our PU-MLC is simple and effective, and it is applicable to both MLC and MLC with partial labels (MLC-PL) tasks. Extensive experiments on MS-COCO and PASCAL VOC datasets demonstrate that our PU-MLC achieves significantly improvements on both MLC and MLC-PL settings with even fewer annotations. Code will be released.

翻译：多标签分类（MLC）因每张图像需标注多种语义标签的困难性，训练数据中不可避免存在标签噪声。为降低噪声标签的影响，现有方法主要致力于通过训练MLC模型识别并纠正标签错误。然而，这些方法在训练过程中仍包含令人困扰的噪声标签，导致噪声标签识别不精确并削弱模型性能。针对负标签数量远多于正标签且多数噪声标签来源于负标签这一现象，本文直接舍弃数据集中所有负标签，提出一种名为正标签与无标签多标签分类（PU-MLC）的新方法。通过将正-无标签学习扩展到MLC任务，该方法仅使用正标签和无标签数据训练模型，并在损失函数中引入自适应重平衡因子与自适应温度系数，以缓解标签分布的灾难性不平衡及训练概率过度平滑问题。此外，为捕获图像的局部与全局依赖关系，我们引入局部-全局卷积模块，该模块无需重训练主干网络即可为现有卷积层补充全局信息。PU-MLC方法简洁高效，适用于MLC及部分标签多标签分类（MLC-PL）任务。在MS-COCO和PASCAL VOC数据集上的大量实验表明，我们的PU-MLC在MLC与MLC-PL设定下，均能以更少的标注量实现显著性能提升。代码将公开发布。