  Video moment retrieval (VMR) identifies a specific moment in an untrimmed
video for a given natural language query. This task is prone to suffer the weak
visual-textual alignment problem innate in video datasets. Due to the
ambiguity, a query does not fully cover the relevant details of the
corresponding moment, or the moment may contain misaligned and irrelevant
frames, potentially limiting further performance gains. To tackle this problem,
we propose a background-aware moment detection transformer (BM-DETR). Our model
adopts a contrastive approach, carefully utilizing the negative queries matched
to other moments in the video. Specifically, our model learns to predict the
target moment from the joint probability of each frame given the positive query
and the complement of negative queries. This leads to effective use of the
surrounding background, improving moment sensitivity and enhancing overall
alignments in videos. Extensive experiments on four benchmarks demonstrate the
effectiveness of our approach.
