Background-aware Moment Detection for Video Moment Retrieval
This work addresses the ambiguity and misalignment issues in video moment retrieval, which is crucial for applications like video search and analysis, though it is incremental in improving existing methods.
The paper tackles the weak alignment problem in video moment retrieval by proposing a background-aware moment detection transformer (BM-DETR) that uses contrastive learning with negative queries to improve moment sensitivity and alignment, achieving state-of-the-art results on four benchmarks.
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 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. Our code is available at: \url{https://github.com/minjoong507/BM-DETR}