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

Xin Wu

doi:10.1109/tmm.2026.3705189

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

2026-06-18

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DOI：10.1109/tmm.2026.3705189

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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. To this end, we propose the Semantic Co-occurrence Insight Network (SCINet), a novel and practical 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. To 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.

Keywords:

Multi-modal representation

partial multi-label learning

semantic co-occurrence

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

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