Video Moment Retrieval (MR) aims to localize moments within a video based on a given natural language query. Given the prevalent use of platforms like YouTube for information retrieval, the demand for MR techniques is significantly growing. Recent DETR-based models have made notable advances in performance but still struggle with accurately localizing short moments. Through data analysis, we identified limited feature diversity in short moments, which motivated the development of MomentMix. MomentMix employs two augmentation strategies: ForegroundMix and BackgroundMix, each enhancing the feature representations of the foreground and background, respectively. Additionally, our analysis of prediction bias revealed that short moments particularly struggle with accurately predicting their center positions of moments. To address this, we propose a Length-Aware Decoder, which conditions length through a novel bipartite matching process. Our extensive studies demonstrate the efficacy of our length-aware approach, especially in localizing short moments, leading to improved overall performance. Our method surpasses state-of-the-art DETR-based methods on benchmark datasets, achieving the highest R1 and mAP on QVHighlights and the highest R1@0.7 on TACoS and Charades-STA (such as a 2.46% gain in R1@0.7 and a 2.57% gain in mAP average for QVHighlights). The code is available at https://github.com/sjpark5800/LA-DETR.

本研究针对视频时刻检索中的短时刻定位问题，提出了MomentMix和长度感知解码器，显著增强了短时刻的特征表示及其中心位置预测。实验证明，该方法在多个基准数据集上超越了现有的DETR模型，特别是在短时刻的定位上表现突出，具有重要的实际应用潜力。

基于长度感知的DETR模型用于鲁棒时刻检索