BLUE在Memotion 2.0 2022：你有我的图像，我的文字和我的Transformer
BLUE at Memotion 2.0 2022: You have my Image, my Text and my Transformer
摘要：
模因在互联网上很流行，并随着我们的文化不断发展和演变。对网络传播模因的自动理解可以揭示人们的普遍情感和文化态度。在这项工作中，我们提出了团队蓝色的解决方案的第二版的记忆共享任务。我们展示了两种方法的模因分类（即情绪，幽默，进攻，讽刺和动机水平）使用纯文本的方法，使用BERT，和一个多模态多任务Transformer网络上的模因图像和它的标题输出的最终分数。在这两种方法中，我们利用最先进的预训练模型进行文本（BERT，Sentence Transformer）和图像处理（EfficientNetV4，CLIP）。通过我们的努力，我们在任务A中获得第一名，在任务B中获得第二名，在任务C中获得第三名。此外，我们的团队在所有三项任务中获得了最高的平均分。
英文摘要：
Memes are prevalent on the internet and continue to grow and evolve alongside our culture. An automatic understanding of memes propagating on the internet can shed light on the general sentiment and cultural attitudes of people. In this work, we present team BLUE's solution for the second edition of the MEMOTION shared task. We showcase two approaches for meme classification (i.e. sentiment, humour, offensive, sarcasm and motivation levels) using a text-only method using BERT, and a Multi-Modal-Multi-Task transformer network that operates on both the meme image and its caption to output the final scores. In both approaches, we leverage state-of-the-art pretrained models for text (BERT, Sentence Transformer) and image processing (EfficientNetV4, CLIP). Through our efforts, we obtain first place in task A, second place in task B and third place in task C. In addition, our team obtained the highest average score for all three tasks.
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。
