Real-time collection of tweets about the COVID-19 pandemic in highly affected Italy• Automatic geotagging of tweets and detection of showing faces in their visual content• Further analysis of tweets to detect trending topics, user communities, and events• An online platform that visualises the analysed tweets in multi-level aspects• An interactive map and a visual analytics dashboard to monitor the pandemic crisis
Nowadays, multimedia retrieval has become a task of high importance, due to the need for efficient and fast access to very large and heterogeneous multimedia collections. An interesting challenge within the aforementioned task is the efficient combination of different modalities in a multimedia object and especially the fusion between textual and visual information. The fusion of multiple modalities for retrieval in an unsupervised way has been mostly based on early, weighted linear, graph-based and diffusion-based techniques. In contrast, we present a strategy for fusing textual and visual modalities, through the combination of a non-linear fusion model and a graph-based late fusion approach. The fusion strategy is based on the construction of a uniform multimodal contextual similarity matrix and the non-linear combination of relevance scores from query-based similarity vectors. The proposed late fusion approach is evaluated in the multimedia retrieval task, by applying it to two multimedia collections, namely the WIKI11 and IAPR-TC12. The experimental results indicate its superiority over the baseline method in terms of Mean Average Precision for both considered datasets.
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