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.

本文研究了单正多标签学习（Single Positive Multi-label Learning - SPML）的问题，提出了基于期望风险最小化的广义损失框架，用于生成软伪标签，并设计了一种能够处理正负样本不平衡同时灵活协调假阳性和假阴性的新型鲁棒损失函数。实验证明，该方法能够显著提高 SPML 性能，在四个基准测试上胜过绝大多数最先进的方法。