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

2024-12-25

AI Summary

To address the challenges of scarce labeled data, low-quality pseudo-labels, and coarse-grained vision–language semantic alignment in semi-supervised multi-label learning, this paper proposes a context-aware collaborative learning framework grounded in vision-language models (VLMs). Our method introduces three key innovations: (1) a label-specific image feature disentanglement module to enable fine-grained text–image semantic alignment; (2) a semi-supervised context-aware auxiliary task that explicitly models label co-occurrence patterns and enhances discriminability for unlabeled samples; and (3) a joint optimization objective combining semantic alignment loss and consistency regularization to improve pseudo-label reliability. Evaluated on standard benchmarks including MS-COCO and Pascal VOC, our approach achieves state-of-the-art performance in both pseudo-label accuracy and final multi-label classification accuracy, significantly outperforming existing semi-supervised and VLM-based methods.

Technology Category

Machine Learning: Semi-Supervised LearningComputer Vision: Multi-modal VisionNatural Language Processing: Language Grounding & Multi-modal NLP

Application Category

Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingEconomics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

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.

Problem

• Semi-supervised Multi-label Learning
• Visual Language Model
• Feature Extraction

Innovation

• Semi-supervised Multi-label Learning
• Contextual Information and Semantic Alignment
• Auxiliary Tasks for Improved Co-occurrence Understanding