  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.
