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Cross-modal Identity Mapping: Minimizing Information Loss in Modality Conversion via Reinforcement Learning
arXiv – CS AI|Haonan Jia, Shichao Dong, Xin Dong, Zenghui Sun, Jin Wang, Jinsong Lan, Xiaoyong Zhu, Bo Zheng, Kaifu Zhang||5 views
🤖AI Summary
Researchers developed Cross-modal Identity Mapping (CIM), a reinforcement learning framework that improves image captioning in Large Vision-Language Models by minimizing information loss during visual-to-text conversion. The method achieved 20% improvement in relation reasoning on the COCO-LN500 benchmark using Qwen2.5-VL-7B without requiring additional annotations.
Key Takeaways
- →CIM uses reinforcement learning to enhance image captioning quality in LVLMs without needing additional training annotations.
- →The method evaluates information loss through Gallery Representation Consistency and Query-gallery Image Relevance metrics.
- →CIM outperformed Supervised Fine-Tuning approaches in experimental comparisons.
- →The framework achieved 20% improvement in relation reasoning on COCO-LN500 benchmark with Qwen2.5-VL-7B model.
- →The approach addresses critical issues of visual content omission and misrepresentation in AI-generated image captions.
#reinforcement-learning#computer-vision#multimodal-ai#image-captioning#lvlm#vision-language-models#information-loss#modal-conversion
Read Original →via arXiv – CS AI
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