https://github.com/IDT-ITI/LMM-CLIP-meme
공식 코드Improving Multimodal Hateful Meme Detection Exploiting LMM-Generated Knowledge
Papers with Code 아카이브
Meme Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Meme classification refers to the task of classifying internet memes.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/JingbiaoMei/RGCL
공식 코드Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning
https://github.com/Muennighoff/vilio
공식 코드Vilio: State-of-the-art Visio-Linguistic Models applied to Hateful Memes
https://github.com/SiddhanthHegde/You-Need-to-Pay-More-Attention
공식 코드https://github.com/adeeph/memeclassification
공식 코드https://github.com/ahmed-mahran/mami2022
공식 코드Codec at SemEval-2022 Task 5: Multi-Modal Multi-Transformer Misogynous Meme Classification Framework
https://github.com/ckoutlis/memefier
공식 코드MemeFier: Dual-stage Modality Fusion for Image Meme Classification
https://github.com/exploration-lab/iitk-semeval-2024-task-4-pursuasion-techniques
공식 코드IITK at SemEval-2024 Task 4: Hierarchical Embeddings for Detection of Persuasion Techniques in Memes
https://github.com/facebookresearch/mmf
공식 코드The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes
https://github.com/facebookresearch/vissl
공식 코드Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
https://github.com/flamenlp/M3H
공식 코드Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification
https://github.com/gokulkarthik/hateclipper
공식 코드https://github.com/hate-alert/banglaabusememe
공식 코드BanglaAbuseMeme: A Dataset for Bengali Abusive Meme Classification
https://github.com/hkbunlp/explainhm-www2024
공식 코드Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language Models
https://github.com/juszzi/pen
공식 코드https://github.com/miccunifi/issues
공식 코드Mapping Memes to Words for Multimodal Hateful Meme Classification
https://github.com/openai/CLIP
공식 코드Learning Transferable Visual Models From Natural Language Supervision
https://github.com/rizavelioglu/hateful_memes-hate_detectron
공식 코드https://github.com/siddhantbikram/memeclip
공식 코드MemeCLIP: Leveraging CLIP Representations for Multimodal Meme Classification
https://github.com/social-ai-studio/hatred
공식 코드https://github.com/social-ai-studio/matk
공식 코드https://github.com/social-ai-studio/pro-cap
공식 코드Pro-Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme Detection
https://github.com/vasilikikou/memegraphs
공식 코드https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/AndresPMD/Clip_CMR
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/FreddeFrallan/Multilingual-CLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/Gahyeonkim09/AAPL
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/IMvision12/keras-vision-models
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/Kaushalya/medclip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/NYU-DICE-Lab/open_clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/OML-Team/open-metric-learning
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/PaddlePaddle/PASSL/blob/main/docs/Train_CLIP_model.md
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/SebKleiner/Hateful_Memes
The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes
https://github.com/SforAiDl/CountCLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/YvanG/VQGAN-CLIP
Learning Transferable Visual Models From Natural Language Supervision
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.