LLandMark：一种用于地标感知多模态交互视频检索的多智能体框架
LLandMark: A Multi-Agent Framework for Landmark-Aware Multimodal Interactive Video Retrieval
AI总结：
LLandMark通过多智能体框架实现地标感知的多模态视频检索，结合LLM和OCR技术提升文化场景下的检索能力与可解释性。
摘要：
随着视频数据的多样性和规模不断增加，亟需具备多模态理解、自适应推理和领域特定知识整合能力的检索系统。本文提出了LLandMark，一种模块化的多智能体框架，用于处理现实世界中的复杂查询。该框架包含专门的智能体，跨四个阶段协作：查询解析与规划、地标推理、多模态检索和重排序答案合成。关键组件Landmark Knowledge Agent能够检测文化或空间地标，并将其转化为描述性视觉提示，增强基于CLIP的语义匹配能力以适应越南场景。为扩展能力，我们引入了LLM辅助的图像到图像流程，其中大型语言模型（Gemini 2.5 Flash）自主检测地标、生成图像搜索查询、检索代表性图像并执行基于CLIP的视觉相似性匹配，无需手动输入图像。此外，利用Gemini和LlamaIndex的OCR精修模块改进越南文本识别。实验结果表明，LLandMark实现了自适应、文化相关且可解释的检索性能。
英文摘要：
The increasing diversity and scale of video data demand retrieval systems capable of multimodal understanding, adaptive reasoning, and domain-specific knowledge integration. This paper presents LLandMark, a modular multi-agent framework for landmark-aware multimodal video retrieval to handle real-world complex queries. The framework features specialized agents that collaborate across four stages: query parsing and planning, landmark reasoning, multimodal retrieval, and reranked answer synthesis. A key component, the Landmark Knowledge Agent, detects cultural or spatial landmarks and reformulates them into descriptive visual prompts, enhancing CLIP-based semantic matching for Vietnamese scenes. To expand capabilities, we introduce an LLM-assisted image-to-image pipeline, where a large language model (Gemini 2.5 Flash) autonomously detects landmarks, generates image search queries, retrieves representative images, and performs CLIP-based visual similarity matching, removing the need for manual image input. In addition, an OCR refinement module leveraging Gemini and LlamaIndex improves Vietnamese text recognition. Experimental results show that LLandMark achieves adaptive, culturally grounded, and explainable retrieval performance.
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。
