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arXiv · 2507.23263

Learning Semantic-Aware Threshold for Multi-Label Image Recognition with Partial Labels

Haoxian Ruan·Zhihua Xu·Zhijing Yang·Guang Ma·Jieming Xie·Changxiang Fan·Tianshui Chen
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Abstract

Multi-label image recognition with partial labels (MLR-PL) is designed to train models using a mix of known and unknown labels. Traditional methods rely on semantic or feature correlations to create pseudo-labels for unidentified labels using pre-set thresholds. This approach often overlooks the varying score distributions across categories, resulting in inaccurate and incomplete pseudo-labels, thereby affecting performance. In our study, we introduce the Semantic-Aware Threshold Learning (SATL) algorithm. This innovative approach calculates the score distribution for both positive and negative samples within each category and determines category-specific thresholds based on these distributions. These distributions and thresholds are dynamically updated throughout the learning process. Additionally, we implement a differential ranking loss to establish a significant gap between the score distributions of positive and negative samples, enhancing the discrimination of the thresholds. Comprehensive experiments and analysis on large-scale multi-label datasets, such as Microsoft COCO and VG-200, demonstrate that our method significantly improves performance in scenarios with limited labels.

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Haoxian Ruan, Zhihua Xu, Zhijing Yang, Guang Ma, Jieming Xie, Changxiang Fan, Tianshui Chen. 2025-07-31. Learning Semantic-Aware Threshold for Multi-Label Image Recognition with Partial Labels. https://arxiv.org/abs/2507.23263

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