Focusing Attention across Multiple Images for Multimodal Event Detection

Multimodal social event detection has been attracting tremendous research attention in recent years, due to that it provides comprehensive and complementary understanding of social events and is important to public security and administration. Most existing works have been focusing on the fusion of multimodal information, especially for single image and text fusion. Such single image-text pair processing breaks the correlations between images of the same post and may affect the accuracy of event detection. In this work, we propose to focus attention across multiple images for multimodal event detection, which is also more reasonable for tweets with short text and multiple images. Towards this end, we elaborate a novel Multi-Image Focusing Network (MIFN) to connect text content with visual aspects in multiple images. Our MIFN consists of a feature extractor, a multi-focal network and an event classifier. The multi-focal network implements a focal attention across all the images, and fuses the most related regions with texts as multimodal representation. The event classifier finally predict the social event class based on the multimodal representations. To evaluate the effectiveness of our proposed approach, we conduct extensive experiments on a commonly-used disaster dataset. The experimental results demonstrate that, in both humanitarian event detection task and its variant of hurricane disaster, the proposed MIFN outperforms all the baselines. The ablation studies also exhibit the ability to filter the irrelevant regions across images which results in improving the accuracy of multimodal event detection.