  This report presents the approach used in the submission of Generic Event
Boundary Detection (GEBD) Challenge at CVPR21. In this work, we design a
Cascaded Temporal Attention Network (CASTANET) for GEBD, which is formed by
three parts, the backbone network, the temporal attention module, and the
classification module. Specifically, the Channel-Separated Convolutional
Network (CSN) is used as the backbone network to extract features, and the
temporal attention module is designed to enforce the network to focus on the
discriminative features. After that, the cascaded architecture is used in the
classification module to generate more accurate boundaries. In addition, the
ensemble strategy is used to further improve the performance of the proposed
method. The proposed method achieves 83.30% F1 score on Kinetics-GEBD test set,
which improves 20.5% F1 score compared to the baseline method. Code is
available at https://github.com/DexiangHong/Cascade-PC.
