Correlative and Discriminative Label Grouping for Multi-Label Visual Prompt Tuning

• URL: http://arxiv.org/abs/2504.09990v1
• Date: Mon, 14 Apr 2025 08:52:50 GMT
• Title: Correlative and Discriminative Label Grouping for Multi-Label Visual Prompt Tuning
• Authors: LeiLei Ma, Shuo Xu, MingKun Xie, Lei Wang, Dengdi Sun, Haifeng Zhao,
• Abstract summary: Recent studies have overemphasized co-occurrence relationships among labels, leading to suboptimal models.<n>We propose the Multi-Label Visual Prompt Tuning framework to balance correlative and discriminative relationships among labels.<n>Our proposed approach achieves competitive results and outperforms SOTA methods on multiple pre-trained models.
• Score: 12.052388861361937
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modeling label correlations has always played a pivotal role in multi-label image classification (MLC), attracting significant attention from researchers. However, recent studies have overemphasized co-occurrence relationships among labels, which can lead to overfitting risk on this overemphasis, resulting in suboptimal models. To tackle this problem, we advocate for balancing correlative and discriminative relationships among labels to mitigate the risk of overfitting and enhance model performance. To this end, we propose the Multi-Label Visual Prompt Tuning framework, a novel and parameter-efficient method that groups classes into multiple class subsets according to label co-occurrence and mutual exclusivity relationships, and then models them respectively to balance the two relationships. In this work, since each group contains multiple classes, multiple prompt tokens are adopted within Vision Transformer (ViT) to capture the correlation or discriminative label relationship within each group, and effectively learn correlation or discriminative representations for class subsets. On the other hand, each group contains multiple group-aware visual representations that may correspond to multiple classes, and the mixture of experts (MoE) model can cleverly assign them from the group-aware to the label-aware, adaptively obtaining label-aware representation, which is more conducive to classification. Experiments on multiple benchmark datasets show that our proposed approach achieves competitive results and outperforms SOTA methods on multiple pre-trained models.

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