Summary of Text or Image? What Is More Important in Cross-domain Generalization Capabilities Of Hate Meme Detection Models?, by Piush Aggarwal et al.
Text or Image? What is More Important in Cross-Domain Generalization Capabilities of Hate Meme Detection Models?
by Piush Aggarwal, Jawar Mehrabanian, Weigang Huang, Özge Alacam, Torsten Zesch
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research investigates cross-domain generalization in detecting hateful memes across different domains, presenting intriguing findings. The study demonstrates that the textual component is crucial for multimodal hate meme detection, while the image component is sensitive to specific training datasets. The results show that hate-text classifiers perform similarly to hate-meme classifiers in a zero-shot setting. Additionally, introducing captions generated from images of memes to the hate-meme classifier worsens performance by an average F1 of 0.02. The study uses blackbox explanations to identify the substantial contribution of the text modality (average of 83%), which diminishes with the introduction of meme’s image captions (52%). Furthermore, the evaluation on a newly created confounder dataset reveals higher performance on text confounders as compared to image confounders with an average ΔF1 of 0.18. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks into how computers can tell if memes are mean or not, and it’s really important for keeping people safe online. The study found that most of the information needed is in the words used in the meme, rather than the pictures. This means that machines can be trained to spot mean memes without needing all the context. The results show that when computers try to understand text-only mean messages, they do just as well as when trying to understand whole memes. However, adding captions from images of memes makes it harder for computers to recognize mean ones. |
Keywords
» Artificial intelligence » Domain generalization » Zero shot
