Papers with multimodal deep neural network
MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets (2021.findings-emnlp)
Copied to clipboard
Shraman Pramanick, Shivam Sharma, Dimitar Dimitrov, Md. Shad Akhtar, Preslav Nakov, Tanmoy Chakraborty
| Challenge: | a growing number of harmful memes are being used for trolling, cyberbullying and abuse . a new approach to detect harmful meme images and texts is emerging . |
| Approach: | They propose a multimodal deep neural network that detects harmful memes . they extend the recently released HarMeme dataset with additional memes and a new topic . |
| Outcome: | The proposed framework outperforms rival methods in detecting harmful memes and their target social entities. |
Deciphering Hate: Identifying Hateful Memes and Their Targets (2024.acl-long)
Copied to clipboard
| Challenge: | a growing body of research has focused on the negative aspects of memes in high-resource languages like Bengali . a new dataset for Bengali hateful memes is designed to detect their targeted entities . |
| Approach: | They propose a multimodal dataset that analyzes the modality of memes and compares them with other datasets. |
| Outcome: | The proposed dataset outperforms state-of-the-art datasets on Bengali hateful memes . the proposed dataset is generalizable on other low-resource hateful memes datasets compared with baselines based on the proposed model . |