Papers by Gitanjali Kumari
CM-Off-Meme: Code-Mixed Hindi-English Offensive Meme Detection with Multi-Task Learning by Leveraging Contextual Knowledge (2024.lrec-main)
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| Challenge: | Existing studies on detecting offensive memes have focused on identifying them as implicit and explicit . detecting memes requires contextual knowledge, but there is no such dataset for the code-mixed Hindi-English domain. |
| Approach: | They propose an end-to-end multitask model that integrates contextual knowledge and psycho-linguistic knowledge to detect offensive memes. |
| Outcome: | The proposed model is able to detect offensive memes and explicit memes in a large-scale dataset. |
Unintended Bias Detection and Mitigation in Misogynous Memes (2024.eacl-long)
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| Challenge: | Existing models that detect misogyny are not able to detect unintended biases in memes, perpetuating harmful stereotypes and reinforcing negative attitudes. |
| Approach: | They propose to measure and mitigate unintentional bias in misogynous memes detection models by using a contextualized scene graph-based multimodal network (CTXSGMNet) they also evaluate their generalizability by evaluating their performance on a few benchmark meme datasets. |
| Outcome: | The proposed model achieves state-of-the-art performance on the SemEval-2022 Task 5 (MAMI task) dataset, showcasing its promising performance in terms of Equity of Odds and F1 score. |
M3Hop-CoT: Misogynous Meme Identification with Multimodal Multi-hop Chain-of-Thought (2024.emnlp-main)
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| Challenge: | Recent studies have shown that Large Language Models (LLMs) neglect cultural diversity and key aspects like emotion and contextual knowledge hidden in the visual modalities. |
| Approach: | They propose a framework for misogynous meme identification using a multimodal multimodal prompting principle and a CLIP-based classifier. |
| Outcome: | The proposed framework performs well on the SemEval-2022 task 5 dataset, and is generalizable across different datasets. |
MemeDetoxNet: Balancing Toxicity Reduction and Context Preservation (2025.findings-acl)
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| Challenge: | Toxic memes spread harmful and offensive content and pose a significant challenge in online environments. |
| Approach: | They propose a framework to mitigate toxicity in toxic memes by leveraging a set of pre-trained models that can interpret the visual and textual components of memes. |
| Outcome: | The proposed framework reduces toxicity on publicly available meme datasets by 10-20% compared to the previous methods. |