… In this paper we provide a high-level and multi-perspective roadmap to mine, model, and
study LRLEs. Among the various aspects, we develop a multi-modal approach to solve the …

… Also, [10], [11] employ topic models such as LDA to extract high-level features from the
text contents. There are also exist deep learning-based approaches to capture the complex …

… of our proposed deep multi-modal multilabel learning approach to illicit drug trafficking activity
detection. Our … Using topic models for twitter hashtag recommendation. In WWW. 593–596. …

… In the future, we aim to analyze the task with more advanced early and late fusion techniques
to better utilize the multi-modal information. Furthermore, we plan to use complex GF. For …

… describes the general process of creating multi-modal datasets as attributed graphs from
unstructured volunteered information content harvested from social media. These graphs would …

… comprehensive survey of these models and hope this paper … firstly introduce the background
of multi-modal pre-training by … , and advantages of multi-modal pre-training models (MM-…

… • We propose a novel multi-modal multi-task learning model for contextual … grate multi-modal
representations and control features transfer for downstream tasks adaptively. • Our model …

… Intuitively, if we are able to spot the uncommon cooccurring entities in the multi-modal
post contents, such as the entity pair “shark” and “subway” in Fig.1 (b) 1, it would facilitate …

… features delineated from the topic modeling process as the classification input outperforms
approaches using explicit statistical features; (2) combining multi-modal data visibly improves …

… multi-modal and redundant properties of CQA systems. Our model converts each question/answer
into a multi-modal … the relational information within multi-modal content. Specifically, to …