Papers › ReLER@ZJU-Alibaba Submission to the Ego4D Natural Language Queries Challenge 2022
ReLER@ZJU-Alibaba Submission to the Ego4D Natural Language Queries Challenge 2022
Naiyuan Liu, Xiaohan Wang, Xiaobo Li, Yi Yang, Yueting Zhuang
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.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Natural Language Queries | Ego4D | ReLER@ZJU-Alibaba | R@1 IoU=0.3 | 12.89 | #8 of 10 | Archive leaderboard | report |
| Natural Language Queries | Ego4D | ReLER@ZJU-Alibaba | R@1 IoU=0.5 | 8.14 | #8 of 10 | Archive leaderboard | report |
| Natural Language Queries | Ego4D | ReLER@ZJU-Alibaba | R@1 Mean(0.3 and 0.5) | 10.52 | #8 of 10 | Archive leaderboard | report |
| Natural Language Queries | Ego4D | ReLER@ZJU-Alibaba | R@5 IoU=0.3 | 15.41 | #8 of 10 | Archive leaderboard | report |
| Natural Language Queries | Ego4D | ReLER@ZJU-Alibaba | R@5 IoU=0.5 | 9.94 | #8 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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