Our winning entry for the CVPR 2023 Generic Event Boundary Captioning (GEBC) competition is detailed in this paper. Unlike conventional video captioning tasks, GEBC demands that the captioning model possess an understanding of immediate changes in status around the designated video boundary, making it a difficult task. This paper proposes an effective model LLMVA-GEBC (Large Language Model with Video Adapter for Generic Event Boundary Captioning): (1) We utilize a pretrained LLM for generating human-like captions with high quality. (2) To adapt the model to the GEBC task, we take the video Q-former as an adapter and train it with the frozen visual feature extractors and LLM. Our proposed method achieved a 76.14 score on the test set and won the first place in the challenge. Our code is available at https://github.com/zjr2000/LLMVA-GEBC .

翻译：本文详细介绍了我们在CVPR 2023通用事件边界描述（GEBC）竞赛中的获奖方案。与传统的视频描述任务不同，GEBC要求描述模型能够理解指定视频边界周围状态的即时变化，这使其成为一项困难的任务。本文提出了一种有效的模型LLMVA-GEBC（用于通用事件边界描述的大语言模型与视频适配器）：（1）我们利用预训练的大语言模型来生成高质量、类人的描述。（2）为了使模型适应GEBC任务，我们采用视频Q-former作为适配器，并与冻结的视觉特征提取器及大语言模型一同进行训练。我们提出的方法在测试集上取得了76.14分，并在挑战赛中获得了第一名。我们的代码可在 https://github.com/zjr2000/LLMVA-GEBC 获取。