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

• URL: http://arxiv.org/abs/2507.23263v1
• Date: Thu, 31 Jul 2025 05:54:10 GMT
• Title: Learning Semantic-Aware Threshold for Multi-Label Image Recognition with Partial Labels
• Authors: Haoxian Ruan, Zhihua Xu, Zhijing Yang, Guang Ma, Jieming Xie, Changxiang Fan, Tianshui Chen,
• Abstract summary: Multi-label image recognition with partial labels (MLR-PL) is designed to train models using a mix of known and unknown labels.<n>Traditional methods rely on semantic or feature correlations to create pseudo-labels for unidentified labels.<n>In our study, we introduce the Semantic-Aware Threshold Learning (SATL) algorithm.
• Score: 12.477433449244543
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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