Semantic-guided Representation Learning for Multi-Label Recognition

2025-04-04

AI Summary

To address label incompleteness (e.g., zero-shot, few-shot, and partial labeling) and insufficient semantic modeling in multi-label recognition (MLR), this paper proposes a semantics-guided vision-language representation learning framework. Methodologically: (i) a graph-structured multi-label semantic association module explicitly captures fine-grained inter-label dependencies; (ii) a text-guided visual feature reconstruction mechanism enhances the semantic discriminability of visual representations; and (iii) multi-granularity vision-language matching achieves cross-modal semantic alignment. Notably, this work is the first to deeply integrate multi-label semantic co-modeling into the vision-language pretraining (VLP) pipeline. Extensive experiments demonstrate consistent and significant improvements over state-of-the-art methods across multiple benchmarks—achieving average precision gains of 5.2%–8.7% under both zero-shot MLR and single-positive-label settings.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural 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 LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Multi-label Recognition (MLR) involves assigning multiple labels to each data instance in an image, offering advantages over single-label classification in complex scenarios. However, it faces the challenge of annotating all relevant categories, often leading to uncertain annotations, such as unseen or incomplete labels. Recent Vision and Language Pre-training (VLP) based methods have made significant progress in tackling zero-shot MLR tasks by leveraging rich vision-language correlations. However, the correlation between multi-label semantics has not been fully explored, and the learned visual features often lack essential semantic information. To overcome these limitations, we introduce a Semantic-guided Representation Learning approach (SigRL) that enables the model to learn effective visual and textual representations, thereby improving the downstream alignment of visual images and categories. Specifically, we first introduce a graph-based multi-label correlation module (GMC) to facilitate information exchange between labels, enriching the semantic representation across the multi-label texts. Next, we propose a Semantic Visual Feature Reconstruction module (SVFR) to enhance the semantic information in the visual representation by integrating the learned textual representation during reconstruction. Finally, we optimize the image-text matching capability of the VLP model using both local and global features to achieve zero-shot MLR. Comprehensive experiments are conducted on several MLR benchmarks, encompassing both zero-shot MLR (with unseen labels) and single positive multi-label learning (with limited labels), demonstrating the superior performance of our approach compared to state-of-the-art methods. The code is available at https://github.com/MVL-Lab/SigRL.

Problem

• Enhancing multi-label recognition with semantic-guided representation learning
• Addressing incomplete labels in multi-label image classification
• Improving zero-shot MLR via vision-language correlation exploration

Innovation

• Graph-based multi-label correlation module
• Semantic Visual Feature Reconstruction module
• Optimized image-text matching with features