  This paper presents a robust solution to the Memotion 3.0 Shared Task. The
goal of this task is to classify the emotion and the corresponding intensity
expressed by memes, which are usually in the form of images with short captions
on social media. Understanding the multi-modal features of the given memes will
be the key to solving the task. In this work, we use CLIP to extract aligned
image-text features and propose a novel meme sentiment analysis framework,
consisting of a Cooperative Teaching Model (CTM) for Task A and a Cascaded
Emotion Classifier (CEC) for Tasks B&C. CTM is based on the idea of knowledge
distillation, and can better predict the sentiment of a given meme in Task A;
CEC can leverage the emotion intensity suggestion from the prediction of Task C
to classify the emotion more precisely in Task B. Experiments show that we
achieved the 2nd place ranking for both Task A and Task B and the 4th place
ranking for Task C, with weighted F1-scores of 0.342, 0.784, and 0.535
respectively. The results show the robustness and effectiveness of our
framework. Our code is released at github.
