In this study, we propose feature extraction for multimodal meme classification using Deep Learning approaches. A meme is usually a photo or video with text shared by the young generation on social media platforms that expresses a culturally relevant idea. Since they are an efficient way to express emotions and feelings, a good classifier that can classify the sentiment behind the meme is important. To make the learning process more efficient, reduce the likelihood of overfitting, and improve the generalizability of the model, one needs a good approach for joint feature extraction from all modalities. In this work, we proposed to use different multimodal neural network approaches for multimodal feature extraction and use the extracted features to train a classifier to identify the sentiment in a meme.

翻译：在这项研究中,我们建议采用深层学习方法对多式联运网膜分类进行特征提取。 网膜通常是由年轻一代在表达文化相关理念的社交媒体平台上分享文字的照片或视频。 由于这是表达情感和感情的有效方式,因此,一个能够对网膜背后的情绪进行分类的良好分类器非常重要。 要提高学习过程的效率,降低超配的可能性,并提高模型的通用性,就需要一种从各种模式中联合提取特征的好方法。 在这项工作中,我们建议使用不同的多式联运神经网络方法进行多式联运地物提取,并使用提取的特征来培训分类器识别迷宫中的情绪。