This report describes our submission to the Ego4D Moment Queries Challenge 2023. Our submission extends ActionFormer, a latest method for temporal action localization. Our extension combines an improved ground-truth assignment strategy during training and a refined version of SoftNMS at inference time. Our solution is ranked 2nd on the public leaderboard with 26.62% average mAP and 45.69% Recall@1x at tIoU=0.5 on the test set, significantly outperforming the strong baseline from 2023 challenge. Our code is available at https://github.com/happyharrycn/actionformer_release.

翻译：本报告介绍了我们提交至2023年Ego4D时刻查询挑战赛的解决方案。该方案基于时序动作定位的最新方法ActionFormer进行扩展，训练阶段结合了改进的真实标注分配策略，推理阶段采用优化版的SoftNMS算法。我们的解决方案在公开排行榜上以26.62%的平均mAP及测试集上tIoU=0.5时45.69%的Recall@1x×指标位列第二，显著优于2023年挑战赛的强基线模型。相关代码已开源至https://github.com/happyharrycn/actionformer_release。