Employ Multimodal Machine Learning for Content quality analysis
This addresses the need for better content quality analysis in mainstream media sites, where information is often presented graphically, but it appears incremental as it builds on existing multimodal techniques.
The paper tackled the problem of identifying high-quality content by proposing a multimodal approach that combines image and text features using a Siamese network with rank loss, achieving more accurate results compared to other methods.
The task of identifying high-quality content becomes increasingly important, and it can improve overall reading time and CTR(click-through rate estimates). Generalizes quality analysis only focused on single Modal,such as image or text,but in today's mainstream media sites a lot of information is presented in graphic form.In this paper we propose a MultiModal quality recognition approach for the quality score. First we use two feature extractors,one for image and another for the text. After that we use an Siamese Network with the rank loss as the optimization objective.Compare with other approach,our approach get a more accuracy result.