@inproceedings{srivastava-etal-2026-cpslab2,
title = "{CPSL}ab2@{MML}ow 2026: A Hierarchical Multimodal Framework with Stratified K-Fold Training for Political Meme Classification",
author = "Srivastava, Anika and
Kumar, Vishal and
Sharma, Harshita and
Aggarwal, Nitisha and
Jain Saxena, Geetika and
Pundir, Amit and
Singh, Sanjeev",
editor = "Ponnusamy, Rahul and
Kumaresan, Prasanna Kumar and
Rajiakodi, Saranya and
B, Premjith and
MC, Shunmuga Priya and
Murugappan, Abirami and
G, Jyothish Lal and
V, Sowmya and
Chakravarthi, Bharathi Raja",
booktitle = "Proceedings of the Fifth Workshop on Multimodal Machine Learning in Low-resource Languages",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.mmlow-main.10/",
pages = "91--96",
ISBN = "979-8-89176-445-3",
abstract = "Political memes communicate opinions using the interplay between visual and textual information, posing a challenging multimodal classification task. We present a hierarchical multimodal framework for political meme classification, which decomposes the task into stance and target entity prediction. Our framework was evaluated on the data from the Multi-Level Political Meme Classification Shared Task at MMLow @ AACL-IJCNLP 2026. The task comprised two levels of classification: (i) predicting the political stance of a meme as either Support/Praise or Troll/Oppose, and (ii) identifying the target of the meme combined with the stance. We approached the task by first predicting the Level-1 labels followed by predicting the target entity (Person/Party) and then combining them in a deterministic manner to predict the final Level-2 labels. Our method used a SigLIP vision transformer for stance classification and a hybrid SigLIP-XLM-RoBERTa architecture for target classification with late fusion. We trained our models using 5-fold stratified cross-validation with checkpoint ensembling. We also performed ablation experiments to analyze the contribution of individual modalities, observing that visual features are more informative for stance prediction while textual features contain more information for target identification, which supports the multimodal task at hand. Our system achieved a macro F1 score of 0.9027 and 0.6694 for Level-1 and Level-2 classification, respectively, achieving a final score of 0.7860, ranking first in the shared task."
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<abstract>Political memes communicate opinions using the interplay between visual and textual information, posing a challenging multimodal classification task. We present a hierarchical multimodal framework for political meme classification, which decomposes the task into stance and target entity prediction. Our framework was evaluated on the data from the Multi-Level Political Meme Classification Shared Task at MMLow @ AACL-IJCNLP 2026. The task comprised two levels of classification: (i) predicting the political stance of a meme as either Support/Praise or Troll/Oppose, and (ii) identifying the target of the meme combined with the stance. We approached the task by first predicting the Level-1 labels followed by predicting the target entity (Person/Party) and then combining them in a deterministic manner to predict the final Level-2 labels. Our method used a SigLIP vision transformer for stance classification and a hybrid SigLIP-XLM-RoBERTa architecture for target classification with late fusion. We trained our models using 5-fold stratified cross-validation with checkpoint ensembling. We also performed ablation experiments to analyze the contribution of individual modalities, observing that visual features are more informative for stance prediction while textual features contain more information for target identification, which supports the multimodal task at hand. Our system achieved a macro F1 score of 0.9027 and 0.6694 for Level-1 and Level-2 classification, respectively, achieving a final score of 0.7860, ranking first in the shared task.</abstract>
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%0 Conference Proceedings
%T CPSLab2@MMLow 2026: A Hierarchical Multimodal Framework with Stratified K-Fold Training for Political Meme Classification
%A Srivastava, Anika
%A Kumar, Vishal
%A Sharma, Harshita
%A Aggarwal, Nitisha
%A Jain Saxena, Geetika
%A Pundir, Amit
%A Singh, Sanjeev
%Y Ponnusamy, Rahul
%Y Kumaresan, Prasanna Kumar
%Y Rajiakodi, Saranya
%Y B, Premjith
%Y MC, Shunmuga Priya
%Y Murugappan, Abirami
%Y G, Jyothish Lal
%Y V, Sowmya
%Y Chakravarthi, Bharathi Raja
%S Proceedings of the Fifth Workshop on Multimodal Machine Learning in Low-resource Languages
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-445-3
%F srivastava-etal-2026-cpslab2
%X Political memes communicate opinions using the interplay between visual and textual information, posing a challenging multimodal classification task. We present a hierarchical multimodal framework for political meme classification, which decomposes the task into stance and target entity prediction. Our framework was evaluated on the data from the Multi-Level Political Meme Classification Shared Task at MMLow @ AACL-IJCNLP 2026. The task comprised two levels of classification: (i) predicting the political stance of a meme as either Support/Praise or Troll/Oppose, and (ii) identifying the target of the meme combined with the stance. We approached the task by first predicting the Level-1 labels followed by predicting the target entity (Person/Party) and then combining them in a deterministic manner to predict the final Level-2 labels. Our method used a SigLIP vision transformer for stance classification and a hybrid SigLIP-XLM-RoBERTa architecture for target classification with late fusion. We trained our models using 5-fold stratified cross-validation with checkpoint ensembling. We also performed ablation experiments to analyze the contribution of individual modalities, observing that visual features are more informative for stance prediction while textual features contain more information for target identification, which supports the multimodal task at hand. Our system achieved a macro F1 score of 0.9027 and 0.6694 for Level-1 and Level-2 classification, respectively, achieving a final score of 0.7860, ranking first in the shared task.
%U https://aclanthology.org/2026.mmlow-main.10/
%P 91-96
Markdown (Informal)
[CPSLab2@MMLow 2026: A Hierarchical Multimodal Framework with Stratified K-Fold Training for Political Meme Classification](https://aclanthology.org/2026.mmlow-main.10/) (Srivastava et al., MMLow 2026)
ACL