Context-Based Semantic-Aware Alignment for Semi-Supervised Multi-Label Learning

• URL: http://arxiv.org/abs/2412.18842v1
• Date: Wed, 25 Dec 2024 09:06:54 GMT
• Title: Context-Based Semantic-Aware Alignment for Semi-Supervised Multi-Label Learning
• Authors: Heng-Bo Fan, Ming-Kun Xie, Jia-Hao Xiao, Sheng-Jun Huang,
• Abstract summary: Vision-language models pre-trained on large-scale image-text pairs could alleviate the challenge of limited labeled data under SSMLL setting.<n>We propose a context-based semantic-aware alignment method to solve the SSMLL problem.
• Score: 37.13424985128905
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the lack of extensive precisely-annotated multi-label data in real word, semi-supervised multi-label learning (SSMLL) has gradually gained attention. Abundant knowledge embedded in vision-language models (VLMs) pre-trained on large-scale image-text pairs could alleviate the challenge of limited labeled data under SSMLL setting.Despite existing methods based on fine-tuning VLMs have achieved advances in weakly-supervised multi-label learning, they failed to fully leverage the information from labeled data to enhance the learning of unlabeled data. In this paper, we propose a context-based semantic-aware alignment method to solve the SSMLL problem by leveraging the knowledge of VLMs. To address the challenge of handling multiple semantics within an image, we introduce a novel framework design to extract label-specific image features. This design allows us to achieve a more compact alignment between text features and label-specific image features, leading the model to generate high-quality pseudo-labels. To incorporate the model with comprehensive understanding of image, we design a semi-supervised context identification auxiliary task to enhance the feature representation by capturing co-occurrence information. Extensive experiments on multiple benchmark datasets demonstrate the effectiveness of our proposed method.

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