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Detecting Hateful Memes Using a Multimodal Deep Ensemble

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arxiv 2012.13235 v1 pith:DOHXVRA3 submitted 2020-12-24 cs.LG cs.CLcs.CV

Vlad Sandulescu This is my paper
classification cs.LGcs.CLcs.CV
keywords performanceaccuracyalgorithmsarchitecturesbaselinesbeenbringchallenges
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

While significant progress has been made using machine learning algorithms to detect hate speech, important technical challenges still remain to be solved in order to bring their performance closer to human accuracy. We investigate several of the most recent visual-linguistic Transformer architectures and propose improvements to increase their performance for this task. The proposed model outperforms the baselines by a large margin and ranks 5$^{th}$ on the leaderboard out of 3,100+ participants.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection

    cs.CL 2025-07 conditional novelty 6.0 of 10

    MIND uses unlabeled similar memes, bidirectional AI insight derivation, and multi-agent debate to improve zero-shot harmful meme detection on HarM, FHM, and MAMI.

  2. Representation Decomposition for Learning Similarity and Contrastness Across Modalities for Affective Computing

    cs.CL 2025-06 conditional novelty 4.0 of 10

    A method that decomposes CLIP image and text features into a shared low-rank component and modality-specific sparse components, then uses an attention-weighted soft prompt to guide an LLM for sentiment, emotion, and h...

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