论文标题
Twitter文本和图像中的洪水检测
Floods Detection in Twitter Text and Images
论文作者
论文摘要
在本文中，我们介绍了中世纪2020洪水与多媒体任务的方法，该任务旨在分析和结合社交媒体中的文本和视觉内容，以发现现实世界中的洪水事件。该任务主要集中于确定与特定区域相关的洪水相关的推文。我们提出了几项方案来应对挑战。对于基于文本的洪水事件检测，我们使用三种不同的方法，依靠单词和组合的意大利语版本和意大利语版本，分别在开发集中达到0.77％，0.68％和0.70％的F1评分。为了进行视觉分析，我们依赖于通过在Imagenet上预先训练的多个最新的深层模型提取的功能。然后，提取的特征用于训练多个单独的分类器，然后将其得分以较晚的融合方式组合在一起，以达到0.75％的F1分数。对于我们的强制性多模式运行，我们以最佳的融合方式将获得的分类分数与最佳的文本和视觉方案相结合。总体而言，通过多模式方案获得更好的结果，在开发集中达到0.80％的F1评分。
In this paper, we present our methods for the MediaEval 2020 Flood Related Multimedia task, which aims to analyze and combine textual and visual content from social media for the detection of real-world flooding events. The task mainly focuses on identifying floods related tweets relevant to a specific area. We propose several schemes to address the challenge. For text-based flood events detection, we use three different methods, relying on Bog of Words (BOW) and an Italian Version of Bert individually and in combination, achieving an F1-score of 0.77%, 0.68%, and 0.70% on the development set, respectively. For the visual analysis, we rely on features extracted via multiple state-of-the-art deep models pre-trained on ImageNet. The extracted features are then used to train multiple individual classifiers whose scores are then combined in a late fusion manner achieving an F1-score of 0.75%. For our mandatory multi-modal run, we combine the classification scores obtained with the best textual and visual schemes in a late fusion manner. Overall, better results are obtained with the multimodal scheme achieving an F1-score of 0.80% on the development set.
