论文标题
使用文本和视觉特征通过Twitter流进行洪水检测
Flood Detection via Twitter Streams using Textual and Visual Features
论文作者
论文摘要
本文介绍了我们针对中世纪2020年洪水相关的多媒体任务的建议解决方案，该任务旨在分析和检测通过Twitter共享的多媒体内容中的洪水事件。总的来说，我们提出了四种不同的解决方案，包括一个多模式解决方案，该解决方案结合了强制运行的文本和视觉信息，以及三个单个模态图像和基于文本的解决方案作为可选运行。在多模式方法中，我们依赖于有监督的多模式比特式图模型，该模型将文本和视觉特征结合在早期融合中，在开发数据集中达到了.859的微型F1分数。对于基于文本的洪水事件检测，我们使用变压器网络（即意识到的意大利BERT模型）达到了.853的F1分数。对于基于图像的解决方案，我们采用了多个深层模型，在图像集和放置数据集上进行了预训练，分别在早期的融合中，分别在开发集中达到了.816和.805的F1分数。
The paper presents our proposed solutions for the MediaEval 2020 Flood-Related Multimedia Task, which aims to analyze and detect flooding events in multimedia content shared over Twitter. In total, we proposed four different solutions including a multi-modal solution combining textual and visual information for the mandatory run, and three single modal image and text-based solutions as optional runs. In the multimodal method, we rely on a supervised multimodal bitransformer model that combines textual and visual features in an early fusion, achieving a micro F1-score of .859 on the development data set. For the text-based flood events detection, we use a transformer network (i.e., pretrained Italian BERT model) achieving an F1-score of .853. For image-based solutions, we employed multiple deep models, pre-trained on both, the ImageNet and places data sets, individually and combined in an early fusion achieving F1-scores of .816 and .805 on the development set, respectively.
