The query-based moment retrieval is a problem of localising a specific clip
from an untrimmed video according a query sentence. This is a challenging task
that requires interpretation of both the natural language query and the video
content. Like in many other areas in computer vision and machine learning, the
progress in query-based moment retrieval is heavily driven by the benchmark
datasets and, therefore, their quality has significant impact on the field. In
this paper, we present a series of experiments assessing how well the benchmark
results reflect the true progress in solving the moment retrieval task. Our
results indicate substantial biases in the popular datasets and unexpected
behaviour of the state-of-the-art models. Moreover, we present new sanity check
experiments and approaches for visualising the results. Finally, we suggest
possible directions to improve the temporal sentence grounding in the future.
Our code for this paper is available at
this https URL .

本文旨在评估当前 benchmark 数据集反映真实查询基于片段检索的进展的能力，指出了目前数据集存在的偏差。作者同时提出了新的实验方法，以便更好地可视化结果，并在最后展望了未来的研究方向与改进。

基于查询的视频时刻检索中的隐含挑战揭示

Uncovering Hidden Challenges in Query-Based Video Moment Retrieval

Query-based moment retrieval aims to localize the most relevant moment in an
untrimmed video according to the given natural language query. Existing works
often only focus on one aspect of this emerging task, such as the query
representation learning, video context modeling or multi-modal fusion, thus
fail to develop a comprehensive system for further performance improvement. In
this paper, we introduce a novel Cross-Modal Interaction Network (CMIN) to
consider multiple crucial factors for this challenging task, including (1) the
syntactic structure of natural language queries; (2) long-range semantic
dependencies in video context and (3) the sufficient cross-modal interaction.
Specifically, we devise a syntactic GCN to leverage the syntactic structure of
queries for fine-grained representation learning, propose a multi-head
self-attention to capture long-range semantic dependencies from video context,
and next employ a multi-stage cross-modal interaction to explore the potential
relations of video and query contents. The extensive experiments demonstrate
the effectiveness of our proposed method.

本文提出一种新颖的交叉模态交互网络 (CMIN)，通过语法图卷积网络，多头自注意力和多阶段跨模态交互，综合考虑语言查询的句法结构、视频上下文语义依赖关系和跨模态交互，提高了视频检索准确性。