论文标题
CVPR 2022的技术报告Loveu AQTC挑战
Technical Report for CVPR 2022 LOVEU AQTC Challenge
论文作者
论文摘要
该技术报告介绍了AQTC的第二次获胜模型，AQTC是CVPR 2022年长期视频理解（LOVEU）挑战中新介绍的任务。这项挑战在视频中的多模式答案，多模式以及多样化和不断变化的按钮表示面临困难。我们通过提出一种新的上下文地面模块注意机制来解决此问题，以进行更有效的功能映射。此外，我们还对不同步骤网络和视频功能的按钮数量和消融研究进行了分析。结果，我们获得了Loveu竞赛曲目3中的总排名，特别是四分之二评估指标中的两名。我们的代码可从https://github.com/jaykim9870/ cvpr-22_loveu_unipyler获得。
This technical report presents the 2nd winning model for AQTC, a task newly introduced in CVPR 2022 LOng-form VidEo Understanding (LOVEU) challenges. This challenge faces difficulties with multi-step answers, multi-modal, and diverse and changing button representations in video. We address this problem by proposing a new context ground module attention mechanism for more effective feature mapping. In addition, we also perform the analysis over the number of buttons and ablation study of different step networks and video features. As a result, we achieved the overall 2nd place in LOVEU competition track 3, specifically the 1st place in two out of four evaluation metrics. Our code is available at https://github.com/jaykim9870/ CVPR-22_LOVEU_unipyler.
