Abstract
The Generic Event Boundary Detection (GEBD) task aims to build a model for segmenting videos into segments by detecting general event boundaries applicable to various classes. In this paper, based on last year's MAE-GEBD method, we have improved our model performance on the GEBD task by adjusting the data processing strategy and loss function. Based on last year's approach, we extended the application of pseudo-label to a larger dataset and made many experimental attempts. In addition, we applied focal loss to concentrate more on difficult samples and improved our model performance. Finally, we improved the segmentation alignment strategy used last year, and dynamically adjusted the segmentation alignment method according to the boundary density and duration of the video, so that our model can be more flexible and fully applicable in different situations. With our method, we achieve an F1 score of 86.03% on the Kinetics-GEBD test set, which is a 0.09% improvement in the F1 score compared to our 2022 Kinetics-GEBD method.
Abstract (translated)
通用事件边界检测任务旨在建立一个模型,通过检测适用于不同类别的通用事件边界,将视频分割成片段。在本文中,基于去年MAE-GEBD方法,我们通过调整数据处理策略和损失函数,提高了GEBD任务模型的性能。基于去年的方法,我们扩大应用范围,将伪标签扩展到更大的数据集,并做了很多实验尝试。此外,我们应用聚焦损失,更关注困难样本,提高了模型性能。最后,我们改进了去年使用的分割对齐策略,并动态调整分割对齐方法,根据视频的边界密度和持续时间,以便我们的模型更灵活,在不同情况下 fully applicable。使用我们的方法,我们在Kinetics-GEBD测试集上取得了F1得分86.03%,相比我们的2022年Kinetics-GEBD方法,F1得分有0.09%的提高。
URL
https://arxiv.org/abs/2306.15704
