Internet memes have become widely used by people for online communication and interaction, particularly through social media. Interest in meme-generation research has been increasing rapidly. In this study, we address the problem of meme generation as an image captioning task, which uses an encoder–decoder architecture to generate Chinese meme texts that match image content. First, to train the model on the characteristics of Chinese memes, we collected a dataset of 3,000 meme images with 30,000 corresponding humorous Chinese meme texts. Second, we introduced a Chinese meme generation system that can generate humorous and relevant texts from any given image. Our system used a pre-trained ResNet-50 for image feature extraction and a state-of-the-art transformer-based GPT-2 model to generate Chinese meme texts. Finally, we combined the generated text and images to form common image memes. We performed qualitative evaluations of the generated Chinese meme texts through different user studies. The evaluation results revealed that the Chinese memes generated by our model were indistinguishable from real ones.

中文翻译：

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使用深度神经网络自动生成中文模因


互联网模因已被人们广泛用于在线交流和互动，特别是通过社交媒体。对模因生成研究的兴趣一直在迅速增加。在这项研究中，我们将模因生成问题作为图像字幕任务来解决，它使用编码器-解码器架构来生成与图像内容匹配的中文模因文本。首先，为了训练模型的中文模因特征，我们收集了 3,000 张模因图像和 30,000 个相应的幽默中文模因文本的数据集。其次，我们引入了一个中文模因生成系统，可以从任何给定的图像生成幽默且相关的文本。我们的系统使用预先训练的 ResNet-50 进行图像特征提取，并使用最先进的基于 Transformer 的 GPT-2 模型来生成中文 meme 文本。最后，我们将生成的文本和图像结合起来形成常见的图像模因。我们通过不同的用户研究对生成的中文模因文本进行了定性评估。评估结果显示，我们的模型生成的中文模因与真实模因无法区分。