ReflectCAP: Detailed Image Captioning with Reflective Memory
Summary: arXiv:2604.12357v1 Announce Type: new
Abstract: Detailed image captioning demands both factual grounding and fine-grained coverage, yet existing methods have struggled to achieve them simultaneously. We address this tension with Reflective Note-Guided Captioning (ReflectCAP), where a multi-agent pipeline analyzes what the target large vision-language model (LVLM) consistently hallucinates and what it systematically overlooks, distilling these patterns into reusable guidelines called Structured Reflection Notes.
At inference time, these notes steer the captioning model along both axes — what to avoid and what to attend to — yielding detailed captions that jointly improve factuality and coverage. Applying this method to 8 LVLMs spanning the GPT-4.1 family, Qwen series, and InternVL variants, ReflectCAP reaches the Pareto frontier of the trade-off between factuality and coverage and delivers substantial gains on CapArena-Auto, where generated captions are judged head-to-head against strong reference models.
Key Features of ReflectCAP
- Multi-Agent Pipeline: ReflectCAP utilizes a sophisticated multi-agent framework that evaluates and identifies consistent hallucinations and omissions in existing LVLMs.
- Structured Reflection Notes: The insights gained from the evaluation process are transformed into Structured Reflection Notes, which serve as guidance for improving caption quality.
- Improved Factuality and Coverage: By addressing both the avoidance of common errors and the focus on relevant details, ReflectCAP enhances caption quality significantly.
- Broad Application: The method has been tested across various LVLMs, including the GPT-4.1 family and Qwen series, ensuring versatility in application.
- Cost Efficiency: ReflectCAP offers a more favorable balance between caption quality and computational cost compared to existing methods, which often incur higher overhead.
Performance and Advantages
ReflectCAP has demonstrated its capability to reach the Pareto frontier in the relationship between factuality and coverage. This means that it not only enhances the accuracy of generated captions but also ensures that they are detailed and informative. In practical terms, this results in captions that better reflect the content of images while avoiding common pitfalls associated with automated captioning.
Moreover, the performance of ReflectCAP was rigorously evaluated using the CapArena-Auto benchmark, where it was found that the captions produced through this method outperformed those generated by strong reference models. This is a significant achievement, as it showcases ReflectCAP’s ability to generate high-quality captions that are both accurate and comprehensive.
Conclusion
In conclusion, ReflectCAP represents a significant advancement in the field of image captioning. By employing a unique multi-agent pipeline and Structured Reflection Notes, it effectively navigates the challenges of factual grounding and fine-grained coverage. As a result, ReflectCAP not only enhances the quality of image captions but also does so in a manner that is cost-effective and efficient, making it a compelling choice for real-world applications.