Joint Modality Synergy and Spatio-temporal Cue Purification for Moment Localization

Abstract

Currently, many approaches to the sentence query based moment location (SQML) task emphasize (inter-)modality interaction between video and language query via transformer-based cross-attention or contrastive learning. However, they could still face two issues: 1) modality interaction could be unexpectedly friendly to modality specific learning that merely learns modality specific patterns, and 2) modality interaction easily confuses spatio-temporal cues and ultimately makes time cues in the original video ambiguous. In this paper, we propose a modality synergy with spatio-temporal cue purification method (MS2P) for SQML to address the above two issues. Particularly, a conceptually simple modality synergy strategy is explored to keep features modality specific while absorbing the other modality complementary information with both carefully designed cross-attention unit and non-contrastive learning. As a result, modality specific semantics can be calibrated progressively in a safer way. To preserve time cues in original video, we further purify video representation into spatial and temporal parts to enhance localization resolution by the proposed two light-weight sentence-aware filtering operations. Experiments on Charades-STA, TACoS, and ActivityNet Caption datasets show our model outperforms the state-of-the-art approaches by a large margin.