@inproceedings{huertas-garcia-etal-2022-aida,
title = "{AIDA}-{UPM} at {S}em{E}val-2022 Task 5: Exploring Multimodal Late Information Fusion for Multimedia Automatic Misogyny Identification",
author = "Huertas-Garc{\'\i}a, {\'A}lvaro and
Liz, Helena and
Villar-Rodr{\'\i}guez, Guillermo and
Mart{\'\i}n, Alejandro and
Huertas-Tato, Javier and
Camacho, David",
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-1.107",
doi = "10.18653/v1/2022.semeval-1.107",
pages = "771--779",
abstract = "This paper describes the multimodal late fusion model proposed in the SemEval-2022 Multimedia Automatic Misogyny Identification (MAMI) task. The main contribution of this paper is the exploration of different late fusion methods to boost the performance of the combination based on the Transformer-based model and Convolutional Neural Networks (CNN) for text and image, respectively. Additionally, our findings contribute to a better understanding of the effects of different image preprocessing methods for meme classification. We achieve 0.636 F1-macro average score for the binary subtask A, and 0.632 F1-macro average score for the multi-label subtask B. The present findings might help solve the inequality and discrimination women suffer on social media platforms.",
}
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%0 Conference Proceedings
%T AIDA-UPM at SemEval-2022 Task 5: Exploring Multimodal Late Information Fusion for Multimedia Automatic Misogyny Identification
%A Huertas-García, Álvaro
%A Liz, Helena
%A Villar-Rodríguez, Guillermo
%A Martín, Alejandro
%A Huertas-Tato, Javier
%A Camacho, David
%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 huertas-garcia-etal-2022-aida
%X This paper describes the multimodal late fusion model proposed in the SemEval-2022 Multimedia Automatic Misogyny Identification (MAMI) task. The main contribution of this paper is the exploration of different late fusion methods to boost the performance of the combination based on the Transformer-based model and Convolutional Neural Networks (CNN) for text and image, respectively. Additionally, our findings contribute to a better understanding of the effects of different image preprocessing methods for meme classification. We achieve 0.636 F1-macro average score for the binary subtask A, and 0.632 F1-macro average score for the multi-label subtask B. The present findings might help solve the inequality and discrimination women suffer on social media platforms.
%R 10.18653/v1/2022.semeval-1.107
%U https://aclanthology.org/2022.semeval-1.107
%U https://doi.org/10.18653/v1/2022.semeval-1.107
%P 771-779
Markdown (Informal)
[AIDA-UPM at SemEval-2022 Task 5: Exploring Multimodal Late Information Fusion for Multimedia Automatic Misogyny Identification](https://aclanthology.org/2022.semeval-1.107) (Huertas-García et al., SemEval 2022)
ACL