论文标题
SC-Transformer ++：通用事件边界检测的结构化上下文变压器
SC-Transformer++: Structured Context Transformer for Generic Event Boundary Detection
论文作者
论文摘要
本报告介绍了在CVPR 2022的提交通用事件边界检测（GEBD）挑战中使用的算法。在这项工作中，我们改善了GEBD现有的结构化上下文变压器（SC-TransFormer）方法。具体而言，在变压器编码器后，添加了变压器解码器模块以提取高质量的框架功能。最终分类是根据原始二进制分类器和新引入的多类分类器分支共同执行的。为了丰富运动信息，将光流作为新模式引入。最后，模型合奏用于进一步提高性能。所提出的方法在动力学-GEBD测试集上获得了86.49％的F1得分。与先前的SOTA方法相比，它提高了2.86％的F1分数。
This report presents the algorithm used in the submission of Generic Event Boundary Detection (GEBD) Challenge at CVPR 2022. In this work, we improve the existing Structured Context Transformer (SC-Transformer) method for GEBD. Specifically, a transformer decoder module is added after transformer encoders to extract high quality frame features. The final classification is performed jointly on the results of the original binary classifier and a newly introduced multi-class classifier branch. To enrich motion information, optical flow is introduced as a new modality. Finally, model ensemble is used to further boost performance. The proposed method achieves 86.49% F1 score on Kinetics-GEBD test set. which improves 2.86% F1 score compared to the previous SOTA method.
