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Theoretical Refinement of CLIP by Utilizing Linear Structure of Optimal Similarity

large-language-models › multimodal-llms

📄 Abstract

Abstract: In this study, we propose an enhancement to the similarity computation mechanism in multi-modal contrastive pretraining frameworks such as CLIP. Prior theoretical research has demonstrated that the optimal similarity metrics between paired modalities should correspond to the pointwise mutual information (PMI) between the two modalities. However, the current implementations of CLIP and its variants fail to fully utilize the underlying linear structure of PMI. We therefore propose KME-CLIP, which leverages this structure through the inner product in a reproducing kernel Hilbert space. We theoretically prove that our method can approximate PMI with arbitrary accuracy and empirically demonstrate that our approach overall outperforms the standard CLIP formulation across several retrieval and classification tasks.

Authors (6)

Naoki Yoshida

Satoshi Hayakawa

Yuhta Takida

Toshimitsu Uesaka

Hiromi Wakaki

Yuki Mitsufuji

Submitted

October 17, 2025

arXiv Category

cs.LG

arXiv PDF

Key Contributions

This paper proposes KME-CLIP, a novel enhancement to multi-modal contrastive pretraining frameworks like CLIP. It leverages the linear structure of Pointwise Mutual Information (PMI) in a reproducing kernel Hilbert space to approximate PMI with arbitrary accuracy, addressing the limitation of current CLIP implementations that fail to fully utilize this structure. This theoretical refinement leads to empirical improvements in retrieval and classification tasks.

Business Value

Enhancing the performance of multi-modal AI systems can lead to more accurate search engines, better content recommendation systems, and improved image captioning or visual question answering applications.

Paper Metadata

Innovation Type

Algorithmic Improvement

Deployment Feasibility

Likely feasible, as it builds upon existing frameworks like CLIP and uses established mathematical concepts (RKHS, KME).

Limitations Addressed

Current CLIP implementations do not fully utilize the underlying linear structure of Pointwise Mutual Information (PMI) for optimal similarity computation between modalities.

Performance Gains

Outperforms standard CLIP formulation across several retrieval and classification tasks.

Technical Tags

contrastive learningmulti-modal learningkernel methodsreproducing kernel Hilbert spacepointwise mutual informationsimilarity learningrepresentation learningdeep learningnatural language processingcomputer vision

Research Topics

Multi-modal LearningRepresentation LearningInformation TheoryKernel MethodsDeep Learning Theory

Methods & Architectures

Kernel Mean Embedding (KME)Inner product in RKHSContrastive Pretraining CLIP variantsKernel-based models

Applications & Tasks

Information Retrieval Image Classification Natural Language Understanding Improving similarity computationEnhancing multi-modal contrastive learning Image-text retrievalImage classificationZero-shot learning

Related Fields

Machine LearningInformation TheoryComputer VisionNatural Language ProcessingKernel Methods

Keywords

CLIPmulti-modal learningcontrastive learningsimilaritykernel methodsRKHSPMIrepresentation learningdeep learninginformation theoryretrievalclassificationKME-CLIP

Academic Context

#Multi-modal Learning#Representation Learning#Information Theory#Kernel Methods#Deep Learning Theory

Technology Stack

Frameworks & Libraries

CLIP

Commercial Potential

Potential Products

Enhanced search enginesImproved recommendation systemsAdvanced image captioning tools

Target Industries

TechnologyE-commerceMediaHealthcare

Use Case Examples

Finding images based on text descriptionsClassifying images using text labelsZero-shot image recognition

Competitive Edge

Offers a theoretically grounded improvement over standard CLIP by better exploiting the relationship between modalities, potentially leading to higher accuracy and better generalization.

Resource Requirements

Compute Needs

Likely moderate to high, depending on the scale of pretraining and fine-tuning.

Data Requirements

Requires large-scale paired image-text datasets for pretraining.

Deployment Constraints

May require significant computational resources for training/fine-tuning.

Scalability

Scalability depends on the underlying CLIP architecture and the efficiency of kernel methods.

Production Readiness

Maturity Level

Research

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