Aligning the True Semantics: Constrained Decoupling and Distribution Sampling for Cross-Modal Alignment

2026-03-05

AI Summary

This work addresses the limitation of existing cross-modal alignment methods, which often conflate semantic and non-semantic information, leading to insufficient semantic consistency and alignment bias caused by modality gaps. To overcome this, we propose a semantic alignment framework based on constrained disentanglement and distribution sampling. Specifically, a dual-path UNet architecture adaptively disentangles visual and linguistic representations into semantic and modality-specific components, aligning only the extracted semantic factors. Furthermore, a multi-constraint optimization strategy combined with distribution-aware sampling is introduced to effectively bridge inter-modality discrepancies, thereby enhancing the reasonableness and robustness of alignment. Extensive experiments demonstrate that our method consistently outperforms state-of-the-art approaches across multiple benchmarks and backbone architectures, achieving performance gains of 6.6% to 14.2%.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Cross-modal alignment is a crucial task in multimodal learning aimed at achieving semantic consistency between vision and language. This requires that image-text pairs exhibit similar semantics. Traditional algorithms pursue embedding consistency to achieve semantic consistency, ignoring the non-semantic information present in the embedding. An intuitive approach is to decouple the embeddings into semantic and modality components, aligning only the semantic component. However, this introduces two main challenges: (1) There is no established standard for distinguishing semantic and modal information. (2) The modality gap can cause semantic alignment deviation or information loss. To align the true semantics, we propose a novel cross-modal alignment algorithm via \textbf{C}onstrained \textbf{D}ecoupling and \textbf{D}istribution \textbf{S}ampling (CDDS). Specifically, (1) A dual-path UNet is introduced to adaptively decouple the embeddings, applying multiple constraints to ensure effective separation. (2) A distribution sampling method is proposed to bridge the modality gap, ensuring the rationality of the alignment process. Extensive experiments on various benchmarks and model backbones demonstrate the superiority of CDDS, outperforming state-of-the-art methods by 6.6\% to 14.2\%.

Problem

• cross-modal alignment
• semantic consistency
• embedding decoupling
• modality gap
• non-semantic information

Innovation

• Constrained Decoupling
• Distribution Sampling
• Cross-Modal Alignment
• Semantic Disentanglement
• Modality Gap