Multi-label learning (MLL) requires comprehensive multi-semantic annotations that is hard to fully obtain, thus often resulting in missing labels scenarios. In this paper, we investigate Single Positive Multi-label Learning (SPML), where each image is associated with merely one positive label. Existing SPML methods only focus on designing losses using mechanisms such as hard pseudo-labeling and robust losses, mostly leading to unacceptable false negatives. To address this issue, we first propose a generalized loss framework based on expected risk minimization to provide soft pseudo labels, and point out that the former losses can be seamlessly converted into our framework. In particular, we design a novel robust loss based on our framework, which enjoys flexible coordination between false positives and false negatives, and can additionally deal with the imbalance between positive and negative samples. Extensive experiments show that our approach can significantly improve SPML performance and outperform the vast majority of state-of-the-art methods on all the four benchmarks.

翻译：多标签学习（MLL）需要全面的多语义标注，但这类标注难以完全获取，常导致标签缺失问题。本文研究单正例多标签学习（SPML），即每张图像仅关联一个正标签。现有SPML方法仅关注通过硬伪标签和鲁棒损失等机制设计损失函数，但大多会导致难以接受的假阴性结果。为解决此问题，我们首先提出基于期望风险最小化的广义损失框架以生成软伪标签，并指出现有损失可无缝转换至该框架。在此基础上，我们设计了一种新型鲁棒损失函数，该函数能灵活协调假阳性与假阴性，并额外处理正负样本不平衡问题。大量实验表明，我们的方法可显著提升SPML性能，并在全部四个基准测试中超越绝大多数现有最优方法。