  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.
