Cross-modal Identity Mapping: Minimizing Information Loss in Modality Conversion via Reinforcement Learning
arXiv:2603.01696v2 Announce Type: replace-cross 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
This research addresses a fundamental limitation in current Large Vision-Language Models, reflecting ongoing efforts to improve their accuracy and reliability in information extraction and generation.
Improving the fidelity of information transfer between modalities directly impacts the utility and trustworthiness of AI systems in critical applications, particularly where precise understanding of visual data is paramount.
This advancement changes how information loss in modality conversion is measured and potentially minimized, leading to more accurate and reliable LLM outputs derived from visual content.
- · AI developers
- · Industries relying on visual data analysis
- · Users of multimodal AI systems
- · Computer vision researchers
- · Generative AI models with poor cross-modal coherence
- · Companies relying on less accurate CV/LLM integrations
More accurate image captioning and visual content understanding by AI models will become possible.
Enhanced capabilities could lead to new applications in fields like medical imaging, autonomous navigation, and intelligent surveillance.
Increased trust in AI's ability to interpret complex visual information might accelerate the adoption of autonomous decision-making systems in sensitive domains.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI