综述2022-2024年生活日志检索挑战赛进展，聚焦多模态与大模型融合应用。
The State-of-the-Art in Lifelog Retrieval: A Review of Progress at the ACM Lifelog Search Challenge Workshop 2022-24
- 采用嵌入式检索（如CLIP、BLIP）与大语言模型结合提升交互效率。
- 基于嵌入的方法与大模型协同使系统在已知项搜索等任务中表现更优。
- 适合关注多模态检索、人机交互设计的研究者与开发者参考。
ACM生活日志搜索挑战赛（LSC）通过互动竞赛形式，推动对生活日志数据的探索与特定信息检索系统的比较。本文综述了2022至2024年期间在该赛事中展示的生活日志交互式检索技术进展。通过详尽的对比分析，我们揭示了在已知项搜索、问答和随意搜索三大任务中的关键改进。研究发现，嵌入式检索方法（如CLIP、BLIP）被广泛采用，大语言模型（LLMs）在对话式检索中集成度提高，多模态与协作式搜索界面持续创新。我们进一步探讨了具体检索技术与用户界面设计对系统性能的影响，强调需平衡检索复杂性与可用性。结果表明，以嵌入为基础的方法结合大语言模型具有显著前景；优化用户界面可提升易用性与效率。同时建议重新评估专家赛道中的多实例系统评估方式，以更好应对用户熟悉度与配置有效性带来的变异性。
原文摘要 · Abstract (English)
The ACM Lifelog Search Challenge (LSC) is a venue that welcomes and compares systems that support the exploration of lifelog data, and in particular the retrieval of specific information, through an interactive competition format. This paper reviews the recent advances in interactive lifelog retrieval as demonstrated at the ACM LSC from 2022 to 2024. Through a detailed comparative analysis, we highlight key improvements across three main retrieval tasks: known-item search, question answering, and ad-hoc search. Our analysis identifies trends such as the widespread adoption of embedding-based retrieval methods (e.g., CLIP, BLIP), increased integration of large language models (LLMs) for conversational retrieval, and continued innovation in multimodal and collaborative search interfaces. We further discuss how specific retrieval techniques and user interface (UI) designs have impacted system performance, emphasizing the importance of balancing retrieval complexity with usability. Our findings indicate that embedding-driven approaches combined with LLMs show promise for lifelog retrieval systems. Likewise, improving UI design can enhance usability and efficiency. Additionally, we recommend reconsidering multi-instance system evaluations within the expert track to better manage variability in user familiarity and configuration effectiveness.
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