Video moment retrieval is a challenging task requiring fine-grained
interactions between video and text modalities. Recent work in image-text
pretraining has demonstrated that most existing pretrained models suffer from
information asymmetry due to the difference in length between visual and
textual sequences. We question whether the same problem also exists in the
video-text domain with an auxiliary need to preserve both spatial and temporal
information. Thus, we evaluate a recently proposed solution involving the
addition of an asymmetric co-attention network for video grounding tasks.
Additionally, we incorporate momentum contrastive loss for robust,
discriminative representation learning in both modalities. We note that the
integration of these supplementary modules yields better performance compared
to state-of-the-art models on the TACoS dataset and comparable results on
ActivityNet Captions, all while utilizing significantly fewer parameters with
respect to baseline.

视频时刻检索是一项具有挑战性的任务，需要视频和文本模态之间的精细交互。我们评估了一个最近提出的解决方案，在视频 grounding 任务中引入了不对称协同注意力网络，并在两个模态中加入了动量对比损失，其整合效果在 TACoS 数据集上表现更好，并在 ActivityNet Captions 上表现可比的结果，而且相对于基线模型，参数数量显著减少。