MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection (2025.acl-long)
Copied to clipboard
| Challenge: | a rapid expansion of memes on social media highlights the need for effective methods to detect harmful content. |
| Approach: | They propose a multi-agent framework for zero-shot harmful meme detection that does not rely on annotated data. |
| Outcome: | The proposed framework outperforms existing zero-shot approaches on three meme datasets. |
Similar Papers
Towards Low-Resource Harmful Meme Detection with LMM Agents (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for harmful meme detection are limited due to the dynamic nature of memes . eliciting knowledge-revising behavior within the LMM agent is a key factor in achieving this goal . |
| Approach: | They propose an agency-driven framework for low-resource harmful meme detection . they use annotated memes to leverage label information as auxiliary signals for model . |
| Outcome: | The proposed framework achieves superior performance than state-of-the-art methods on the low-resource harmful meme detection task. |
MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets (2021.findings-emnlp)
Copied to clipboard
Shraman Pramanick, Shivam Sharma, Dimitar Dimitrov, Md. Shad Akhtar, Preslav Nakov, Tanmoy Chakraborty
| Challenge: | a growing number of harmful memes are being used for trolling, cyberbullying and abuse . a new approach to detect harmful meme images and texts is emerging . |
| Approach: | They propose a multimodal deep neural network that detects harmful memes . they extend the recently released HarMeme dataset with additional memes and a new topic . |
| Outcome: | The proposed framework outperforms rival methods in detecting harmful memes and their target social entities. |
Beneath the Surface: Unveiling Harmful Memes with Multimodal Reasoning Distilled from Large Language Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for harmful meme detection ignore in-depth cognition of meme text and image . authors propose a framework for learning reasonable thoughts from LLMs for better multimodal fusion . |
| Approach: | They propose to use large language models to learn reasonable thoughts from LLMs for better multimodal fusion and lightweight fine-tuning. |
| Outcome: | The proposed approach achieves superior performance than state-of-the-art methods on the harmful meme detection task. |
DISARM: Detecting the Victims Targeted by Harmful Memes (2022.findings-naacl)
Copied to clipboard
| Challenge: | DISARM is a framework that uses named-entity recognition and person identification to detect all entities a meme is referring to and then incorporates a novel contextualized deep neural network to classify whether the meme intends to harm these entities. |
| Approach: | They propose a framework that uses named-entity recognition and person identification to detect all entities a meme is referring to and incorporates a novel contextualized deep neural network to classify whether the meme intends to harm them. |
| Outcome: | The proposed framework outperforms 10 unimodal and multimodal systems and reduces error rate of harmful target identification by 9 % absolute over baseline systems. |
Detecting Harmful Memes and Their Targets (2021.findings-acl)
Copied to clipboard
Shraman Pramanick, Dimitar Dimitrov, Rituparna Mukherjee, Shivam Sharma, Md. Shad Akhtar, Preslav Nakov, Tanmoy Chakraborty
| Challenge: | a growing body of research on meme analysis has focused on detecting harmful memes and their social entities . a meme is a form of content that is often harmless and designed to look funny . but its multimodal nature and camouflaged semantics make its analysis challenging . |
| Approach: | They propose to use multimodal models to detect harmful memes and identify social entities that harmful meme targets. |
| Outcome: | The proposed model can detect harmful memes and the social entities they target . the proposed model lacks the appropriate contexts and is poorly validated . |
AdamMeme: Adaptively Probe the Reasoning Capacity of Multimodal Large Language Models on Harmfulness (2025.acl-long)
Copied to clipboard
| Challenge: | Existing models that assess mLLMs on harmful meme understanding are inaccurate and lack accuracy. |
| Approach: | They propose a framework that adaptively probes the reasoning capabilities of mLLMs . their framework systematically reveals the varying performance of different target mllms a . |
| Outcome: | The proposed framework systematically reveals the performance of different target mLLMs. |
Beyond Single-View Detection: A Dual-Space Reasoning Framework for Interpretable Harmful Meme Understanding (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for identifying harmful memes rely on modal alignment or black-box classifiers . BPDMoE-Hate provides visual explanations for viewpoint selection and hierarchical structuring . |
| Approach: | They propose a framework that conceptualizes harmful meme detection as a process of "viewpoint decoupling and hierarchical fusion" they propose BPDMoE-Hate, which generates adversarial binary perspectives via VLMs and incorporates an adaptive viewpoint gating to facilitate viewpoint selection. |
| Outcome: | The proposed framework surpasses existing methods in performance and provides visual explanations for viewpoint selection and hierarchical structuring. |
MemeDetoxNet: Balancing Toxicity Reduction and Context Preservation (2025.findings-acl)
Copied to clipboard
| 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. |
A Context-Aware Contrastive Learning Framework for Hateful Meme Detection and Segmentation (2025.findings-naacl)
Copied to clipboard
| Challenge: | Empirical experiments show HateSieve surpasses existing LMMs in performance with fewer trainable parameters . |
| Approach: | They propose a framework to enhance detection and segmentation of hateful elements in memes by creating a triplet dataset and an Image-Text Alignment module. |
| Outcome: | HateSieve features a new framework that creates semantically correlated memes and generates contextual embeddings for accurate meme segmentation. |
MEMEX: Detecting Explanatory Evidence for Memes via Knowledge-Enriched Contextualization (2023.acl-long)
Copied to clipboard
| Challenge: | Besides digital archiving of memes and their metadata, there is no efficient way to deduce a meme’s context dynamically. |
| Approach: | They propose a task to mine the context that succinctly explains the background of a meme and a related document to capture cross-modal semantic dependencies between the meme and the context. |
| Outcome: | The proposed dataset outperforms existing systems and shows that it can capture cross-modal semantic dependencies between the meme and the context. |