论文标题
Memotion 2.0 2022：您有我的图像，我的文字和我的变压器
BLUE at Memotion 2.0 2022: You have my Image, my Text and my Transformer
论文作者
论文摘要
模因在互联网上普遍存在，并继续与我们的文化一起发展和发展。自动理解在互联网上传播的模因可以阐明人们的普遍情绪和文化态度。在这项工作中，我们介绍了Blue团队的解决方案，以解决第二版Memotion共享任务。我们使用仅使用BERT的文本方法以及一个多模式 - 穆尔特 - 任务变压器网络，以模因图像及其标题来输出最终得分。在这两种方法中，我们都利用了文本（BERT，句子变压器）和图像处理（EfficityNetv4，剪辑）的最新预验证模型。通过我们的努力，我们获得了任务A任务B的第一名，任务C中的第三名。此外，我们的团队获得了所有三个任务的最高平均得分。
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.
