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

Xiang Ma, Lexin Fang, Litian Xu, Caiming Zhang

TL;DR

A dual-path UNet is introduced to adaptively decouple the embeddings, applying multiple constraints to ensure effective separation and a distribution sampling method is proposed to bridge the modality gap, ensuring the rationality of the alignment process.

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\%.

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

TL;DR

A dual-path UNet is introduced to adaptively decouple the embeddings, applying multiple constraints to ensure effective separation and a distribution sampling method is proposed to bridge the modality gap, ensuring the rationality of the alignment process.

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\%.
Paper Structure (20 sections, 15 equations, 5 figures, 5 tables)

This paper contains 20 sections, 15 equations, 5 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Methodology
  4. Constrained decoupling
  5. Embedding extraction
  6. Dual-path UNet architecture
  7. The constraint on semantic component
  8. Related semantics identification
  9. Distribution sampling
  10. The constraint on modal component
  11. The constraint on information integrity
  12. Objective function
  13. Experiments
  14. Datasets and evalution metrics
  15. Implementation details
  16. ...and 5 more sections

Figures (5)

  • Figure 1: Comparison of cross-modal alignment and decoupling-based cross-modal alignment.
  • Figure 2: Overview of CDDS.
  • Figure 3: Related semantics identification.
  • Figure 4: Distribution sampling method.
  • Figure 5: Visualization of textual embeddings and semantic components, showing decoupling can bring textual embeddings with similar semantics (corresponding to the same image) closer. '#0' is the text's identifier.