You Need to Read Again: Multi-granularity Perception Network for Moment Retrieval in Videos

• URL: http://arxiv.org/abs/2205.12886v1
• Date: Wed, 25 May 2022 16:15:46 GMT
• Title: You Need to Read Again: Multi-granularity Perception Network for Moment Retrieval in Videos
• Authors: Xin Sun, Xuan Wang, Jialin Gao, Qiong Liu, Xi Zhou
• Abstract summary: 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.
• Score: 19.711703590063976
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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