Papers › Hate-CLIPper: Multimodal Hateful Meme Classification based on Cross-modal Interaction...

Hate-CLIPper: Multimodal Hateful Meme Classification based on Cross-modal Interaction of CLIP Features

12 Oct 2022arXiv:2210.05916archive 2025-07-28

Gokul Karthik Kumar, Karthik Nandakumar

Hateful memes are a growing menace on social media. While the image and its corresponding text in a meme are related, they do not necessarily convey the same meaning when viewed individually. Hence, detecting hateful memes requires careful consideration of both visual and textual information. Multimodal pre-training can be beneficial for this task because it effectively captures the relationship between the image and the text by representing them in a similar feature space. Furthermore, it is essential to model the interactions between the image and text features through intermediate fusion. Most existing methods either employ multimodal pre-training or intermediate fusion, but not both. In this work, we propose the Hate-CLIPper architecture, which explicitly models the cross-modal interactions between the image and text representations obtained using Contrastive Language-Image Pre-training (CLIP) encoders via a feature interaction matrix (FIM). A simple classifier based on the FIM representation is able to achieve state-of-the-art performance on the Hateful Memes Challenge (HMC) dataset with an AUROC of 85.8, which even surpasses the human performance of 82.65. Experiments on other meme datasets such as Propaganda Memes and TamilMemes also demonstrate the generalizability of the proposed approach. Finally, we analyze the interpretability of the FIM representation and show that cross-modal interactions can indeed facilitate the learning of meaningful concepts. The code for this work is available at https://github.com/gokulkarthik/hateclipper.

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create_model gokulkarthik/hateclipper/engine.py official repository unverified MIT (permissive) · c38a0b5fb9cb3cff · report
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Tasks

Hateful Meme ClassificationMeme Classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Hateful Meme Classification HarMeme Hate-CLIPper AUROC 91.87 #4 of 8 Archive leaderboard report
Hateful Meme Classification HarMeme Hate-CLIPper Accuracy 83.90 #4 of 8 Archive leaderboard report
Hateful Meme Classification Harm-P hateclipper Accuracy 87.6 #4 of 5 Archive leaderboard report
Hateful Meme Classification Harm-P hateclipper F1 86.9 #4 of 5 Archive leaderboard report
Hateful Meme Classification PrideMM HateCLIPper Accuracy 75.5 #6 of 7 Archive leaderboard report
Hateful Meme Classification PrideMM HateCLIPper F1 74.1 #6 of 7 Archive leaderboard report
Meme Classification Hateful Memes Hate-CLIPper - Align ROC-AUC 0.858 #7 of 17 Archive leaderboard report
Meme Classification MultiOFF HateCLIPper Accuracy 62.4 #3 of 5 Archive leaderboard report
Meme Classification MultiOFF HateCLIPper F1 54.8 #3 of 5 Archive leaderboard report
Meme Classification Tamil Memes Hate-CLIPper Micro-F1 0.59 #1 of 2 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position EncodingsAdamAttentionBPECLIPDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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