论文标题
通过预训练统一事件检测和字幕作为序列生成
Unifying Event Detection and Captioning as Sequence Generation via Pre-Training
论文作者
论文摘要
密集的视频字幕旨在为未修剪视频中的一系列事件生成相应的文本说明，这些描述可以分为两个子任务，即事件检测和事件字幕。与以前分别解决这两个子任务的作品不同，最近的作品着重于增强任务之间的任务之间的关联。但是，由于其特定于任务的解决方案的较大差异，设计用于事件检测和字幕的任务间相互作用并不是微不足道的。此外，以前的事件检测方法通常会忽略事件之间的时间依赖性，从而导致事件冗余或不一致问题。在本文中，我们将事件检测定义为序列生成任务，并提出一个统一的预训练和微调框架，以自然增强事件检测和字幕之间的任务间关联。由于该模型将每个事件预测为以前的事件为上下文，因此事件之间的相互依赖性被充分利用，因此我们的模型可以检测到视频中更多样化和一致的事件。 ActivityNet数据集上的实验表明，我们的模型优于最新方法，并且在超级大规模视频文本数据进行预训练时，可以进一步提高。代码可在\ url {https://github.com/qiqang/uedvc}上找到。
Dense video captioning aims to generate corresponding text descriptions for a series of events in the untrimmed video, which can be divided into two sub-tasks, event detection and event captioning. Unlike previous works that tackle the two sub-tasks separately, recent works have focused on enhancing the inter-task association between the two sub-tasks. However, designing inter-task interactions for event detection and captioning is not trivial due to the large differences in their task specific solutions. Besides, previous event detection methods normally ignore temporal dependencies between events, leading to event redundancy or inconsistency problems. To tackle above the two defects, in this paper, we define event detection as a sequence generation task and propose a unified pre-training and fine-tuning framework to naturally enhance the inter-task association between event detection and captioning. Since the model predicts each event with previous events as context, the inter-dependency between events is fully exploited and thus our model can detect more diverse and consistent events in the video. Experiments on the ActivityNet dataset show that our model outperforms the state-of-the-art methods, and can be further boosted when pre-trained on extra large-scale video-text data. Code is available at \url{https://github.com/QiQAng/UEDVC}.
