  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.
