@inproceedings{behzadi-etal-2022-mitra,
title = "Mitra Behzadi at {S}em{E}val-2022 Task 5 : Multimedia Automatic Misogyny Identification method based on {CLIP}",
author = "Behzadi, Mitra and
Derakhshan, Ali and
Harris, Ian",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.99",
doi = "10.18653/v1/2022.semeval-1.99",
pages = "724--727",
abstract = "Everyday more users are using memes on social media platforms to convey a message with text and image combined. Although there are many fun and harmless memes being created and posted, there are also ones that are hateful and offensive to particular groups of people. In this article present a novel approach based on the CLIP network to detect misogynous memes and find out the types of misogyny in that meme. We participated in Task A and Task B of the Multimedia Automatic Misogyny Identification (MaMi) challenge and our best scores are 0.694 and 0.681 respectively.",
}
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%0 Conference Proceedings
%T Mitra Behzadi at SemEval-2022 Task 5 : Multimedia Automatic Misogyny Identification method based on CLIP
%A Behzadi, Mitra
%A Derakhshan, Ali
%A Harris, Ian
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F behzadi-etal-2022-mitra
%X Everyday more users are using memes on social media platforms to convey a message with text and image combined. Although there are many fun and harmless memes being created and posted, there are also ones that are hateful and offensive to particular groups of people. In this article present a novel approach based on the CLIP network to detect misogynous memes and find out the types of misogyny in that meme. We participated in Task A and Task B of the Multimedia Automatic Misogyny Identification (MaMi) challenge and our best scores are 0.694 and 0.681 respectively.
%R 10.18653/v1/2022.semeval-1.99
%U https://aclanthology.org/2022.semeval-1.99
%U https://doi.org/10.18653/v1/2022.semeval-1.99
%P 724-727
Markdown (Informal)
[Mitra Behzadi at SemEval-2022 Task 5 : Multimedia Automatic Misogyny Identification method based on CLIP](https://aclanthology.org/2022.semeval-1.99) (Behzadi et al., SemEval 2022)
ACL