Abstract
In recent years, significant developments have been made in both video retrieval and video moment retrieval tasks, which respectively retrieve complete videos or moments for a given text query. These advancements have greatly improved user satisfaction during the search process. However, previous work has failed to establish meaningful "interaction" between the retrieval system and the user, and its one-way retrieval paradigm can no longer fully meet the personalization and dynamic needs of at least 80.8\% of users. In this paper, we introduce the Interactive Video Corpus Retrieval (IVCR) task, a more realistic setting that enables multi-turn, conversational, and realistic interactions between the user and the retrieval system. To facilitate research on this challenging task, we introduce IVCR-200K, a high-quality, bilingual, multi-turn, conversational, and abstract semantic dataset that supports video retrieval and even moment retrieval. Furthermore, we propose a comprehensive framework based on multi-modal large language models (MLLMs) to help users interact in several modes with more explainable solutions. The extensive experiments demonstrate the effectiveness of our dataset and framework.
Abstract (translated)
近年来，在视频检索和视频时刻检索任务上取得了显著进展，这些任务分别根据给定的文本查询来搜索完整的视频或特定时刻。这些进步极大地提高了用户在搜索过程中的满意度。然而，之前的工作未能建立有意义的“互动”机制，其单向检索模式已无法充分满足至少80.8%用户的个性化和动态需求。 在这篇论文中，我们介绍了交互式视频语料库检索（IVCR）任务，这是一个更加现实的情景，它允许用户与检索系统之间进行多轮、对话式的、真实的互动。为了支持这一具有挑战性的研究任务，我们引入了IVCR-200K，这是一项高质量的双语、多回合、对话式和抽象语义数据集，支持视频检索甚至时刻检索。此外，我们提出了一种基于多模态大语言模型（MLLMs）的全面框架，帮助用户以多种方式与系统互动，并提供更具解释性的解决方案。 广泛的实验结果验证了我们的数据集和框架的有效性。
URL
https://arxiv.org/abs/2512.01312
