AIC CTU@AVerImaTeC：双检索器RAG用于图像-文本事实核查
AIC CTU@AVerImaTeC: dual-retriever RAG for image-text fact checking
AI总结：
AIC CTU@AVerImaTeC提出双检索器RAG方法，通过结合文本和图像检索模块，实现高效的图像-文本事实核查，具有低运行成本和易复现性。
摘要：
在本文中，我们展示了在AVerImaTeC共享任务中我们的第三名系统，该系统结合了我们去年的检索增强生成（RAG）流程和一个反向图像搜索（RIS）模块。尽管其简单性，我们的系统在使用OpenAI Batch API通过GPT5.1进行单次多模态LLM调用时，平均仅需$0.013即可实现具有竞争力的性能。我们的系统也易于复现和调整，仅由三个解耦模块组成——基于相似性搜索的文本检索模块、基于API访问RIS的图像检索模块以及使用GPT5.1的生成模块——因此我们建议它作为进一步实验的可访问起点。我们发布了其代码和提示，以及我们的向量存储和对方案运行成本及进一步改进方向的见解。
英文摘要：
In this paper, we present our 3rd place system in the AVerImaTeC shared task, which combines our last year's retrieval-augmented generation (RAG) pipeline with a reverse image search (RIS) module. Despite its simplicity, our system delivers competitive performance with a single multimodal LLM call per fact-check at just $0.013 on average using GPT5.1 via OpenAI Batch API. Our system is also easy to reproduce and tweak, consisting of only three decoupled modules - a textual retrieval module based on similarity search, an image retrieval module based on API-accessed RIS, and a generation module using GPT5.1 - which is why we suggest it as an accesible starting point for further experimentation. We publish its code and prompts, as well as our vector stores and insights into the scheme's running costs and directions for further improvement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
