Temporal grounding aims to localize a video moment which is semantically
aligned with a given natural language query. Existing methods typically apply a
detection or regression pipeline on the fused representation with the research
focus on designing complicated prediction heads or fusion strategies. Instead,
from a perspective on temporal grounding as a metric-learning problem, we
present a Mutual Matching Network (MMN), to directly model the similarity
between language queries and video moments in a joint embedding space. This new
metric-learning framework enables fully exploiting negative samples from two
new aspects: constructing negative cross-modal pairs in a mutual matching
scheme and mining negative pairs across different videos. These new negative
samples could enhance the joint representation learning of two modalities via
cross-modal mutual matching to maximize their mutual information. Experiments
show that our MMN achieves highly competitive performance compared with the
state-of-the-art methods on four video grounding benchmarks. Based on MMN, we
present a winner solution for the HC-STVG challenge of the 3rd PIC workshop.
This suggests that metric learning is still a promising method for temporal
grounding via capturing the essential cross-modal correlation in a joint
embedding space. Code is available at this https URL

该文提出了一种度量学习框架下使用互相匹配网络来解决视频时序定位问题，通过构造负交叉模态对和跨不同视频的负对样本来增强联合表示学习的性能，实验表明该方法在四个视频时序定位基准测试上取得了与最先进方法相比高度竞争的表现，并为第三个 PIC 研讨会的 HC-STVG 挑战赛提供了获胜解决方案。