Images shared on social media help crisis managers in terms of gaining situational awareness and assessing incurred damages, among other response tasks. As the volume and velocity of such content are really high, therefore, real-time image classification became an urgent need in order to take a faster response. Recent advances in computer vision and deep neural networks have enabled the development of models for real-time image classification for a number of tasks, including detecting crisis incidents, filtering irrelevant images, classifying images into specific humanitarian categories, and assessing the severity of the damage. For developing real-time robust models, it is necessary to understand the capability of the publicly available pretrained models for these tasks. In the current state-of-art of crisis informatics, it is under-explored. In this study, we address such limitations. We investigate ten different architectures for four different tasks using the largest publicly available datasets for these tasks. We also explore the data augmentation, semi-supervised techniques, and a multitask setup. In our extensive experiments, we achieve promising results.
翻译:在社交媒体上分享的图像有助于危机管理者了解情况并评估所造成的损害,除其他应对任务外,这些内容的数量和速度都非常高,因此,为了更快地作出反应,迫切需要实时图像分类。计算机视觉和深神经网络方面的最新进展使得能够为一些任务开发实时图像分类模型,包括发现危机事件、过滤无关图像、将图像划入特定人道主义类别并评估损害的严重程度。为了开发实时强健模型,有必要了解公开提供的这些任务预先培训模型的能力。在目前危机信息学的状态下,我们探索了这些局限性。我们在这项研究中利用为这些任务提供的最大公开数据集,为四项不同的任务调查了10个不同的结构。我们还探索了数据增强、半超强技术和多任务设置。我们在广泛的实验中取得了有希望的结果。