Interactive Fusion Label Enhancement for Multi-Label Learning

Xingyu Zhao, Yuexuan An, Ning Xu, Lei Qi, Xin Geng

Abstract: Multi-Label Learning (MLL) involves the task of assigning a set of relevant labels to a given instance. Recently, Label Enhancement (LE) has gained significant attention in various MLL tasks, as it allows for effective mining the implicit relative importance information of different labels. However, in existing LE-based MLL methods, the LE process is decoupled from the MLL process. Consequently, the label distribution recovered by the LE process may not be suitable for training the predictive model, thus affecting the overall learning system. In this study, we propose a novel approach named interactive Fusion Label Enhancement for Multi-Label Learning (Flem) that seamlessly integrates the LE process with the MLL process. Specifically, we introduce a matching and interaction mechanism comprising a novel interaction label enhancement loss and a contrastive alignment approach to prevent object mismatch. Furthermore, we present a unified label distribution loss that establishes the relationship between the recovered label distribution and the training of the predictive model. By leveraging these losses, the label distributions obtained from the LE process can be efficiently utilized for training the predictive model. Experimental results on multiple benchmark datasets demonstrate the effectiveness of the proposed method.

External IDs:dblp:journals/tkdd/ZhaoAXQG25