Memes have gained popularity as a means to share visual ideas through the Internet and social media by mixing text, images and videos, often for humorous purposes. Research enabling automated analysis of memes has gained attention in recent years, including among others the task of classifying the emotion expressed in memes. In this paper, we propose a novel model, cluster-based deep ensemble learning (CDEL), for emotion classification in memes. CDEL is a hybrid model that leverages the benefits of a deep learning model in combination with a clustering algorithm, which enhances the model with additional information after clustering memes with similar facial features. We evaluate the performance of CDEL on a benchmark dataset for emotion classification, proving its effectiveness by outperforming a wide range of baseline models and achieving state-of-the-art performance. Further evaluation through ablated models demonstrates the effectiveness of the different components of CDEL.

翻译：模因通过混合文本、图像和视频在互联网及社交媒体上分享视觉创意，常以幽默为目的，因而广受欢迎。近年来，能够实现模因自动分析的研究引起了广泛关注，其中情感分类任务尤为突出。本文提出了一种新颖的模型——基于聚类的深度集成学习（Cluster-based Deep Ensemble Learning, CDEL），用于模因情感分类。CDEL是一种混合模型，融合了深度学习模型与聚类算法的优势，通过将具有相似面部特征的模因进行聚类，为模型补充额外信息。我们在情感分类基准数据集上评估了CDEL的性能，结果表明其优于多种基线模型，实现了当前最优水平。通过消融实验的进一步评估，验证了CDEL各组成部分的有效性。