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

arXiv:2603.05566v1 Announce Type: new 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

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Xiang Ma, Lexin Fang, Litian Xu, Caiming Zhang · · 1 min read · 18 views
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arXiv:2603.05566v1 Announce Type: new 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\%.

Executive Summary

This article presents a novel cross-modal alignment algorithm, CDDS, that addresses the challenges of traditional algorithms by introducing constrained decoupling and distribution sampling. The proposed method, which employs a dual-path UNet and distribution sampling, demonstrates superior performance on various benchmarks and model backbones, outperforming state-of-the-art methods by 6.6% to 14.2%. CDDS effectively separates semantic and modality information, bridging the modality gap, and ensuring rational alignment. The algorithm's adaptability and robustness make it a promising solution for achieving semantic consistency in multimodal learning. While the article contributes significantly to the field, its applicability and generalizability remain to be explored in more diverse scenarios.

Key Points

  • ▸ CDDS proposes a novel cross-modal alignment algorithm via constrained decoupling and distribution sampling.
  • ▸ The dual-path UNet and distribution sampling method bridge the modality gap and ensure rational alignment.
  • ▸ CDDS outperforms state-of-the-art methods by 6.6% to 14.2% on various benchmarks and model backbones.

Merits

Innovative approach

The constrained decoupling and distribution sampling method offers a unique solution to the challenges of traditional cross-modal alignment algorithms.

Improved performance

CDDS demonstrates superior performance on various benchmarks and model backbones, outperforming state-of-the-art methods.

Robustness and adaptability

The dual-path UNet and distribution sampling method ensure the adaptability and robustness of the algorithm.

Demerits

Limited exploration of applicability

The article's applicability and generalizability remain to be explored in more diverse scenarios.

Potential computational complexity

The dual-path UNet and distribution sampling method may introduce computational complexity, which could be a limitation in certain applications.

Expert Commentary

The article presents a significant contribution to the field of cross-modal alignment, addressing the challenges of traditional algorithms with a novel approach. However, further exploration of the algorithm's applicability and generalizability is necessary to fully understand its potential. Additionally, the potential computational complexity of the dual-path UNet and distribution sampling method should be carefully considered. Overall, CDDS demonstrates promising performance and has significant implications for applications that require semantic consistency in multimodal learning.

Recommendations

  • ✓ Further exploration of the algorithm's applicability and generalizability in diverse scenarios is recommended.
  • ✓ The potential computational complexity of the dual-path UNet and distribution sampling method should be carefully considered and optimized.

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