Exploring Partial Multi-Label Learning via Integrating Semantic Co-occurrence Knowledge

JOURNAL OF L A T EX CLASS FILES, VOL. 18, NO. 9, SEPTEMBER 2024 1 Exploring Partial Multi-Label Learning via Integrating Semantic Co-occurrence Knowledge Xin Wu, Fei Teng, Y ue Feng, Kaibo Shi, Zhuosheng Lin, Ji Zhang and James Wang Abstract --Partial multi-label learning aims to extract knowledge from incompletely annotated data, which includes known correct labels, known incorrect labels, and unknown labels. The core challenge lies in accurately identifying the ambiguous relationships between labels and instances. In this paper, we emphasize that matching co-occurrence patterns between labels and instances is key to addressing this challenge. T o this end, we propose Semantic Co-occurrence Insight Network (SCINet), a novel and effective framework for partial multi-label learning. Specifically, SCINet introduces a bi-dominant prompter module, which leverages an off-the-shelf multimodal model to capture text-image correlations and enhance semantic alignment. T o reinforce instance-label interdependencies, we develop a cross-modality fusion module that jointly models inter-label correlations, inter-instance relationships, and co-occurrence patterns across instance-label assignments. Moreover, we propose an intrinsic semantic augmentation strategy that enhances the model's understanding of intrinsic data semantics by applying diverse image transformations, thereby fostering a synergistic relationship between label confidence and sample difficulty. Extensive experiments on four widely-used benchmark datasets demonstrate that SCINet surpasses state-of-the-art methods. I NTRODUCTION M UL TI-LABEL learning has demonstrated tremendous potential in fields. However, due to the high cost of labeling and the subjectivity of annotators, real-world datasets often suffer from incomplete and noisy labels. This challenge has spurred the exploration of partial multi-label learning methods aimed at addressing these issues more effectively. Consequently, driven by this research need, partial multi-label learning has garnered vibrant attention in machine learning [1], [2]. It represents a new paradigm for multi-label recognition This work was supported by the National Natural Science Foundation of China (No.62272398), Sichuan Science and Technology Program (No.2024NSFJQ0019).