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arXiv 2302.08343cs.CVcs.CL
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基于神经网络的深度包围学习网络模因情感分类

Cluster-based Deep Ensemble Learning for Emotion Classification in Internet Memes

Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Queen Mary University of London（伦敦玛丽女王大学）

Xiaoyu Guo, Jing Ma, Arkaitz Zubiaga

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摘要：

模因作为一种通过互联网和社交媒体分享视觉想法的手段，通过混合文本，图像和视频，通常是为了幽默的目的而流行。近年来，使模因的自动分析成为可能的研究得到了关注，其中包括对模因中表达的情感进行分类的任务。在本文中，我们提出了一种新的模型，基于聚类的深度集成学习（CDEL），在模因的情感分类。CDEL是一种混合模型，它利用了深度学习模型与聚类算法相结合的优势，在对具有相似面部特征的模因进行聚类后，通过额外信息增强了模型。我们评估了CDEL在情感分类基准数据集上的性能，通过超越各种基线模型并实现最先进的性能来证明其有效性。通过消融模型进行的进一步评价证明了CDEL不同组件的有效性。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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