Boosting Single Positive Multi-label Classification with Generalized Robust Loss
This work addresses a practical challenge in multi-label classification for scenarios with incomplete annotations, offering an incremental improvement over existing methods.
The paper tackles the problem of single positive multi-label learning, where each image has only one known positive label, by proposing a generalized loss framework that provides soft pseudo labels and a novel robust loss to better balance false positives and false negatives. The approach significantly improves performance, outperforming most state-of-the-art methods on four benchmarks.
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.