TT-BLIP: Enhancing Fake News Detection Using BLIP and Tri-Transformer

arXiv:2403.12481v1 Announce Type: new
Abstract: Detecting fake news has received a lot of attention. Many previous methods concatenate independently encoded unimodal data, ignoring the benefits of integrated multimodal information. Also, the absence of specialized feature extraction for text and images further limits these methods. This paper introduces an end-to-end model called TT-BLIP that applies the bootstrapping language-image pretraining for unified vision-language understanding and generation (BLIP) for three types of information: BERT and BLIP\textsubscript{Txt} for text, ResNet and BLIP\textsubscript{Img} for images, …

abstract arxiv attention benefits cs.cv cs.lg data detection extraction fake fake news feature feature extraction images information multimodal paper text transformer type