自适应多智能体推理用于文本到视频检索
Adaptive Multi-Agent Reasoning for Text-to-Video Retrieval
AI总结：
本文提出自适应多智能体框架，通过动态协调检索、推理和查询重述智能体，提升文本到视频检索的零样本跨模态对齐与复杂查询处理能力。
摘要：
短视频平台的兴起和多模态大语言模型（MLLMs）的出现，加剧了对可扩展、有效、零样本文本到视频检索系统的需求。尽管大规模预训练的最新进展提高了零样本跨模态对齐，但现有方法在处理依赖查询的时序推理方面仍存在困难，限制了其在涉及时序、逻辑或因果关系的复杂查询上的效果。为了解决这些限制，我们提出了一种自适应多智能体检索框架，该框架根据每个查询的需求动态协调多个推理迭代中的专用智能体。该框架包括：（1）一个用于大规模视频语料库的检索智能体，（2）一个用于零样本上下文时序推理的推理智能体，以及（3）一个用于细化模糊查询并恢复性能的查询重述智能体。这些智能体由一个协调智能体动态协调，该智能体利用中间反馈和推理结果来指导执行。我们还引入了一种新的通信机制，该机制结合了检索性能记忆和历史推理轨迹以提高协调和决策能力。在三个跨越八年的TRECVid基准测试中，我们的框架在CLIP4Clip上实现了两倍的改进，并显著超越了最先进的方法。
英文摘要：
The rise of short-form video platforms and the emergence of multimodal large language models (MLLMs) have amplified the need for scalable, effective, zero-shot text-to-video retrieval systems. While recent advances in large-scale pretraining have improved zero-shot cross-modal alignment, existing methods still struggle with query-dependent temporal reasoning, limiting their effectiveness on complex queries involving temporal, logical, or causal relationships. To address these limitations, we propose an adaptive multi-agent retrieval framework that dynamically orchestrates specialized agents over multiple reasoning iterations based on the demands of each query. The framework includes: (1) a retrieval agent for scalable retrieval over large video corpora, (2) a reasoning agent for zero-shot contextual temporal reasoning, and (3) a query reformulation agent for refining ambiguous queries and recovering performance for those that degrade over iterations. These agents are dynamically coordinated by an orchestration agent, which leverages intermediate feedback and reasoning outcomes to guide execution. We also introduce a novel communication mechanism that incorporates retrieval-performance memory and historical reasoning traces to improve coordination and decision-making. Experiments on three TRECVid benchmarks spanning eight years show that our framework achieves a twofold improvement over CLIP4Clip and significantly outperforms state-of-the-art methods by a large margin.
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。
