# Cross-Modal Augmentation for Few-Shot Multimodal Fake News Detection

arXiv: https://arxiv.org/abs/2407.12880 · HTML: https://mlsanity.com/paper/2407.12880/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 63 references that are in the map.
- First posted on arXiv 16 Jul 2024.

## Builds on (most cited first, 30 of 36)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [CLIP-Adapter: Better Vision-Language Models with Feature Adapters](https://mlsanity.com/paper/2110.04544/) (2021)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [Robust fine-tuning of zero-shot models](https://mlsanity.com/paper/2109.01903/) (2021)
- [Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling](https://mlsanity.com/paper/2111.03930/) (2021)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [GLIPv2: Unifying Localization and Vision-Language Understanding](https://mlsanity.com/paper/2206.05836/) (2022)
- [Cross-Modal Contrastive Learning for Text-to-Image Generation](https://mlsanity.com/paper/2101.04702/) (2021)
- [OpenPrompt: An Open-source Framework for Prompt-learning](https://mlsanity.com/paper/2111.01998/) (2021)
- [Few-Shot Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.02536/) (2017)
- [Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese](https://mlsanity.com/paper/2211.01335/) (2022)
- [PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining](https://mlsanity.com/paper/2204.14095/) (2022)
- [Attentional Pooling for Action Recognition](https://mlsanity.com/paper/1711.01467/) (2017)
- [Multimodal Fake News Detection via CLIP-Guided Learning](https://mlsanity.com/paper/2205.14304/) (2022)
- [Domain-Adaptive Few-Shot Learning](https://mlsanity.com/paper/2003.08626/) (2020)
- [Baby steps towards few-shot learning with multiple semantics](https://mlsanity.com/paper/1906.01905/) (2019)
- [Cross-modal Contrastive Learning for Multimodal Fake News Detection](https://mlsanity.com/paper/2302.14057/) (2023)
- [Real-time Event Detection on Social Data Streams](https://mlsanity.com/paper/1907.11229/) (2019)
- [Meta-Learning for Few-Shot NMT Adaptation](https://mlsanity.com/paper/2004.02745/) (2020)
- [Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification](https://mlsanity.com/paper/2107.12262/) (2021)
- [An Emotional Analysis of False Information in Social Media and News Articles](https://mlsanity.com/paper/1908.09951/) (2019)
- [Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection](https://mlsanity.com/paper/2309.16424/) (2023)
- [Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of COVID-19 Infodemic](https://mlsanity.com/paper/2106.11702/) (2021)

## Built on by (most cited first, 3 of 3)

- [AMPLE: Emotion-Aware Multimodal Fusion Prompt Learning for Fake News Detection](https://mlsanity.com/paper/2410.15591/) (2024)
- [LLM-MRD: LLM-Guided Multi-View Reasoning Distillation for Fake News Detection](https://mlsanity.com/paper/2603.19293/) (2026)
- [KITE: A Tri-Modal Transformer Integrating Text, Images, and Knowledge Graphs for Fake News Detection](https://mlsanity.com/paper/2606.07651/) (2026)

## Abstract (arXiv)

The nascent topic of fake news requires automatic detection methods to quickly learn from limited annotated samples. Therefore, the capacity to rapidly acquire proficiency in a new task with limited guidance, also known as few-shot learning, is critical for detecting fake news in its early stages. Existing approaches either involve fine-tuning pre-trained language models which come with a large number of parameters, or training a complex neural network from scratch with large-scale annotated datasets. This paper presents a multimodal fake news detection model which augments multimodal features using unimodal features. For this purpose, we introduce Cross-Modal Augmentation (CMA), a simple approach for enhancing few-shot multimodal fake news detection by transforming n-shot classification into a more robust (n × z)-shot problem, where z represents the number of supplementary features. The proposed CMA achieves SOTA results over three benchmark datasets, utilizing a surprisingly simple linear probing method to classify multimodal fake news with only a few training samples. Furthermore, our method is significantly more lightweight than prior approaches, particularly in terms of the number of trainable parameters and epoch times. The code is available here: urlhttps://github.com/zgjiangtoby/FNDfewshot