  In this report, we present the ReLER@ZJU-Alibaba submission to the Ego4D
Natural Language Queries (NLQ) Challenge in CVPR 2022. Given a video clip and a
text query, the goal of this challenge is to locate a temporal moment of the
video clip where the answer to the query can be obtained. To tackle this task,
we propose a multi-scale cross-modal transformer and a video frame-level
contrastive loss to fully uncover the correlation between language queries and
video clips. Besides, we propose two data augmentation strategies to increase
the diversity of training samples. The experimental results demonstrate the
effectiveness of our method. The final submission ranked first on the
leaderboard.
