ARXIV:2603.01696 · VISION-LANGUAGE · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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Cross-modal Identity Mapping: Minimizing Information Loss in Modality Conversion via Reinforcement Learning

arXiv

Enhance image captioning accuracy in LVLMs by minimizing information loss through a reinforcement learning framework.

Blocked on Code›Score6.0Evidence unverified

Opportunity summary

Pain Enhance image captioning accuracy in LVLMs by minimizing information loss through a reinforcement learning framework.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

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PROBLEM

Enhance image captioning accuracy in LVLMs by minimizing information loss through a reinforcement learning framework. Minimizing such information loss will force LVLMs to focus on image details to generate precise descriptions.

METHOD

Full abstract

Large Vision-Language Models (LVLMs) often omit or misrepresent critical visual content in generated image captions. Minimizing such information loss will force LVLMs to focus on image details to generate precise descriptions. However, measuring information loss during modality conversion is inherently challenging due to the modal gap between visual content and text output. In this paper, we argue that the quality of an image caption is positively correlated with the similarity between images retrieved via text search using that caption. Based on this insight, we further propose Cross-modal Identity Mapping (CIM), a reinforcement learning framework that enhances image captioning without requiring additional annotations. Specifically, the method quantitatively evaluates the information loss from two perspectives: Gallery Representation Consistency and Query-gallery Image Relevance. Supervised under these metrics, LVLM minimizes information loss and aims to achieve identity mapping from images to captions. The experimental results demonstrate the superior performance of our method in image captioning, even when compared with Supervised Fine-Tuning. Particularly, on the COCO-LN500 benchmark, CIM achieves a 20% improvement in relation reasoning on Qwen2.5-VL-7B.The code will be released when the paper is accepted.

RESULT

ScienceToStartup currently rates this 6.0/10 on the public viability pass. Supervised under these metrics, LVLM minimizes information loss and aims to achieve identity mapping from images to captions.

WHY NOW

Vision-Language moved forward this cycle; last verified April 2026. Public score 6.0/10.

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Opportunity summary

Score6.0

PainEnhance image captioning accuracy in LVLMs by minimizing information loss through a reinforcement learning framework.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Enhance image captioning accuracy in LVLMs by minimizing information loss through a reinforcement learning framework.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Competitive landscape

Enhance image captioning accuracy in LVLMs by minimizing information loss through a reinforcement learning framework.

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Vision-Language

Adoption evidence

No public code link in the paper record yet

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6.0/10 public viability

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Enhance image captioning accuracy in LVLMs by minimizing information loss through a reinforcement learning framework.

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Cross-modal Identity Mapping: Minimizing Information Loss in Modality Conversion via Reinforcement Learning

Cross-modal Identity Mapping: Minimizing Information Loss in Modality Conversion via Reinforcement Learning

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