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 this http URL.

该论文提出了一种新的多粒度感知网络 (MGPN)，旨在改善视频中的时刻检索，将时刻检索建模为一种多选阅读理解任务，结合人类阅读策略，并通过优化算法提高了检索精度。