This paper describes our champion solution for the CVPR2022 Generic Event
Boundary Captioning (GEBC) competition. GEBC requires the captioning model to
have a comprehension of instantaneous status changes around the given video
boundary, which makes it much more challenging than conventional video
captioning task. In this paper, a Dual-Stream Transformer with improvements on
both video content encoding and captions generation is proposed: (1) We utilize
three pre-trained models to extract the video features from different
granularities. Moreover, we exploit the types of boundary as hints to help the
model generate captions. (2) We particularly design an model, termed as
Dual-Stream Transformer, to learn discriminative representations for boundary
captioning. (3) Towards generating content-relevant and human-like captions, we
improve the description quality by designing a word-level ensemble strategy.
The promising results on the GEBC test split demonstrate the efficacy of our
proposed model.

本文介绍了我们在 CVPR2022 Generic Event Boundary Captioning 竞赛中的冠军方案，提出了一种名为 Dual-Stream Transformer 的模型，利用三个预训练模型从不同颗粒度提取视频特征以辅助生成说说内容，设计了词级集成策略以提升生成质量，在 GEBC 测试集上取得了良好的效果。