MAE-GEBD:Winning the CVPR'2023 LOVEU-GEBD Challenge
Yuanxi Sun Rui He Youzeng Li Zuwei Huang Feng Hu Xu Cheng Jie Tang
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.