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Hateful Meme Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
- 21
Hateful meme classification aims to detect harmful content within the text or images of memes.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Improving Multimodal Hateful Meme Detection Exploiting LMM-Generated Knowledge
Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection
Demystifying Hateful Content: Leveraging Large Multimodal Models for Hateful Meme Detection with Explainable Decisions
Prompt-enhanced Network for Hateful Meme Classification
MemeCLIP: Leveraging CLIP Representations for Multimodal Meme Classification
Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language Models
Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning
Mapping Memes to Words for Multimodal Hateful Meme Classification
Decoding the Underlying Meaning of Multimodal Hateful Memes
Prompting for Multimodal Hateful Meme Classification
Hate-CLIPper: Multimodal Hateful Meme Classification based on Cross-modal Interaction of CLIP Features
0/1 Deep Neural Networks via Block Coordinate Descent
On Explaining Multimodal Hateful Meme Detection Models
Detecting Harmful Memes and Their Targets
Disentangling Hate in Online Memes
Learning Transferable Visual Models From Natural Language Supervision
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.