2DP-2MRC：面向多模态时刻检索的基于二维指针的机器阅读理解方法
2DP-2MRC: 2-Dimensional Pointer-based Machine Reading Comprehension Method for Multimodal Moment Retrieval
AI总结：
本文提出2DP-2MRC模型，通过AV-Encoder捕获粗粒度信息并结合二维指针编码器增强边界检测，在保持较低计算复杂度的同时显著提升多模态时刻检索的定位精度。
摘要：
时刻检索旨在根据给定的自然语言查询，在未剪辑视频中定位最相关的时刻。现有解决方案大致可分为基于时刻的方法和基于片段的方法。前者通常涉及大量计算，而后者由于忽略粗粒度信息，性能通常不如基于时刻的模型。因此，本文提出了一种新颖的面向时刻检索选择的基于二维指针的机器阅读理解模型（2DP-2MRC），以解决基于片段的方法中定位不精确的问题，同时保持比基于时刻的方法更低的计算复杂度。具体而言，我们引入了一个AV-Encoder来捕获时刻级和视频级的粗粒度信息。此外，还引入了一个二维指针编码器模块，以进一步增强目标时刻的边界检测。在HiREST数据集上的大量实验表明，2DP-2MRC显著优于现有基线模型。
英文摘要：
Moment retrieval aims to locate the most relevant moment in an untrimmed video based on a given natural language query. Existing solutions can be roughly categorized into moment-based and clip-based methods. The former often involves heavy computations, while the latter, due to overlooking coarse-grained information, typically underperforms compared to moment-based models. Hence, this paper proposes a novel 2-Dimensional Pointer-based Machine Reading Comprehension for Moment Retrieval Choice (2DP-2MRC) model to address the issue of imprecise localization in clip-based methods while maintaining lower computational complexity than moment-based methods. Specifically, we introduce an AV-Encoder to capture coarse-grained information at moment and video levels. Additionally, a 2D pointer encoder module is introduced to further enhance boundary detection for target moment. Extensive experiments on the HiREST dataset demonstrate that 2DP-2MRC significantly outperforms existing baseline models.
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