# NYCU-TWO at Memotion 3: Good Foundation, Good Teacher, then you have Good Meme Analysis

arXiv: https://arxiv.org/abs/2302.06078 · HTML: https://mlsanity.com/paper/2302.06078/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 13 Feb 2023.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [CoCa: Contrastive Captioners are Image-Text Foundation Models](https://mlsanity.com/paper/2205.01917/) (2022)
- [SimVLM: Simple Visual Language Model Pretraining with Weak Supervision](https://mlsanity.com/paper/2108.10904/) (2021)
- [Multimodal Transformer for Unaligned Multimodal Language Sequences](https://mlsanity.com/paper/1906.00295/) (2019)
- [Contrastive Learning of Medical Visual Representations from Paired Images and Text](https://mlsanity.com/paper/2010.00747/) (2020)
- [Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation](https://mlsanity.com/paper/1905.08094/) (2019)
- [Tensor Fusion Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/1707.07250/) (2017)
- [Extending Multilingual BERT to Low-Resource Languages](https://mlsanity.com/paper/2004.13640/) (2020)
- [Multimodal Sentiment Analysis To Explore the Structure of Emotions](https://mlsanity.com/paper/1805.10205/) (2018)
- [BLUE at Memotion 2.0 2022: You have my Image, my Text and my Transformer](https://mlsanity.com/paper/2202.07543/) (2022)

## Built on by (most cited first, 1 of 1)

- [Overview of Memotion 3: Sentiment and Emotion Analysis of Codemixed Hinglish Memes](https://mlsanity.com/paper/2309.06517/) (2023)

## Abstract (arXiv)

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.