Papers › SC-Transformer++: Structured Context Transformer for Generic Event Boundary Detection
SC-Transformer++: Structured Context Transformer for Generic Event Boundary Detection
Dexiang Hong, Xiaoqi Ma, Xinyao Wang, CongCong Li, YuFei Wang, Longyin Wen
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.
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