Distributional Vision-Language Alignment by Cauchy-Schwarz Divergence
#Vision-Language Alignment #Cauchy-Schwarz Divergence #CS-Aligner #Multimodal Learning #CLIP #InfoNCE #Cross-modal Generation #Distributional Alignment
📌 Key Takeaways
- Researchers developed CS-Aligner, a novel framework for vision-language alignment using Cauchy-Schwarz divergence
- The method overcomes limitations of previous approaches like CLIP that overlook distributional differences
- CS-Aligner captures both global distribution information and pairwise semantic relationships
- Experiments on text-to-image generation and cross-modality retrieval tasks demonstrate the method's effectiveness
📖 Full Retelling
A team of researchers led by Wenzhe Yin and including Zehao Xiao, Pan Zhou, Shujian Yu, Jiayi Shen, Jan-Jakob Sonke, and Efstratios Gavves has developed CS-Aligner, a novel framework for vision-language alignment using Cauchy-Schwarz divergence, in a paper accepted by ICLR 2026 after being submitted to arXiv's Computer Science > Machine Learning section on February 24, 2025, and revised on February 24, 2026, to overcome limitations in previous approaches like CLIP that overlook distributional differences and have inherent alignment-uniformity conflicts. The research addresses a fundamental challenge in multimodal machine learning where vision-language alignment is crucial for various downstream tasks such as cross-modal generation and retrieval. Previous approaches, particularly CLIP (Contrastive Language-Image Pre-training), have relied on InfoNCE (Noise-Contrastive Estimation) to maximize mutual information between text and image representations. However, these methods primarily focus on aligning pairwise samples across modalities while overlooking broader distributional differences, leading to suboptimal alignment when significant gaps exist between modalities. The CS-Aligner framework represents a significant advancement by integrating Cauchy-Schwarz divergence with mutual information to capture both the global distribution information of each modality and the pairwise semantic relationships. The researchers discovered that the CS divergence seamlessly addresses the InfoNCE's alignment-uniformity conflict and serves complementary roles with existing methods, resulting in tighter and more precise alignment. Additionally, by introducing distributional alignment, CS-Aligner enables incorporating additional information from unpaired data and token-level representations, enhancing flexible and fine-grained alignment in practical applications.
🏷️ Themes
Machine Learning, Multimodal AI, Vision-Language Alignment
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Original Source
--> Computer Science > Machine Learning arXiv:2502.17028 [Submitted on 24 Feb 2025 ( v1 ), last revised 24 Feb 2026 (this version, v3)] Title: Distributional Vision-Language Alignment by Cauchy-Schwarz Divergence Authors: Wenzhe Yin , Zehao Xiao , Pan Zhou , Shujian Yu , Jiayi Shen , Jan-Jakob Sonke , Efstratios Gavves View a PDF of the paper titled Distributional Vision-Language Alignment by Cauchy-Schwarz Divergence, by Wenzhe Yin and 6 other authors View PDF HTML Abstract: Vision-language alignment is crucial for various downstream tasks such as cross-modal generation and retrieval. Previous multimodal approaches like CLIP utilize InfoNCE to maximize mutual information, primarily aligning pairwise samples across modalities while overlooking distributional differences. In addition, InfoNCE has inherent conflict in terms of alignment and uniformity in multimodality, leading to suboptimal alignment with modality gaps. To overcome the limitations, we propose CS-Aligner, a novel framework that performs distributional vision-language alignment by integrating Cauchy-Schwarz divergence with mutual information. CS-Aligner captures both the global distribution information of each modality and the pairwise semantic relationships. We find that the CS divergence seamlessly addresses the InfoNCE's alignment-uniformity conflict and serves complementary roles with InfoNCE, yielding tighter and more precise alignment. Moreover, by introducing distributional alignment, CS-Aligner enables incorporating additional information from unpaired data and token-level representations, enhancing flexible and fine-grained alignment in practice. Experiments on text-to-image generation and cross-modality retrieval tasks demonstrate the effectiveness of our method on vision-language alignment. Comments: Accepted by ICLR2026 Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2502.17028 [cs.LG] (or arXiv:2502.17028v3 [cs.LG] for this version) https://doi.org/10.485...
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