Moment retrieval in videos is a challenging task that aims to retrieve the most relevant video moment in an untrimmed video given a sentence description. Previous methods tend to perform self-modal learning and cross-modal interaction in a coarse manner, which neglect fine-grained clues contained in video content, query context, and their alignment. To this end, we propose a novel Multi-Granularity Perception Network (MGPN) that perceives intra-modality and inter-modality information at a multi-granularity level. Specifically, we formulate moment retrieval as a multi-choice reading comprehension task and integrate human reading strategies into our framework. A coarse-grained feature encoder and a co-attention mechanism are utilized to obtain a preliminary perception of intra-modality and inter-modality information. Then a fine-grained feature encoder and a conditioned interaction module are introduced to enhance the initial perception inspired by how humans address reading comprehension problems. Moreover, to alleviate the huge computation burden of some existing methods, we further design an efficient choice comparison module and reduce the hidden size with imperceptible quality loss. Extensive experiments on Charades-STA, TACoS, and ActivityNet Captions datasets demonstrate that our solution outperforms existing state-of-the-art methods. Codes are available at github.com/Huntersxsx/MGPN.

翻译：视频中的时刻检索是一项具有挑战性的任务，旨在根据给定的句子描述，从无修剪视频中检索出最相关的视频片段。以往的方法倾向于以粗粒度方式进行自我模态学习和跨模态交互，忽略了视频内容、查询上下文及其对齐中包含的细粒度线索。为此，我们提出了一种新颖的多粒度感知网络（MGPN），该网络能够在多粒度层面上感知模态内和模态间的信息。具体而言，我们将时刻检索建模为多项选择阅读理解任务，并将人类阅读策略融入我们的框架中。利用粗粒度特征编码器和共注意力机制，初步获取模态内和模态间的信息。随后，受人类处理阅读理解问题方式的启发，我们引入细粒度特征编码器和条件交互模块，以增强初始感知。此外，为缓解现有方法存在的巨大计算负担，我们进一步设计了一种高效的选择比较模块，并在隐蔽大小上做出调整，同时保持质量损失可忽略不计。在Charades-STA、TACoS和ActivityNet Captions数据集上进行的大量实验表明，我们的解决方案优于现有最先进的方法。代码可在github.com/Huntersxsx/MGPN获取。