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Hateful Meme Detection: Leveraging PVLMs for Zero-shot VQA Probingby@memeology
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Hateful Meme Detection: Leveraging PVLMs for Zero-shot VQA Probing

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This study explores using pre-trained vision-language models (PVLMs) for hateful meme detection without fine-tuning, showcasing BERT's superiority and introducing PromptHate with probe-captioning. Limitations include heuristic probing question usage, suggesting future directions for optimization and deeper interpretation using gradient-based approaches.
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Authors:

(1) Rui Cao, Singapore Management University;

(2) Ming Shan Hee, Singapore University of Design and Technology;

(3) Adriel Kuek, DSO National Laboratories;

(4) Wen-Haw Chong, Singapore Management University;

(5) Roy Ka-Wei Lee, Singapore University of Design and Technology

(6) Jing Jiang, Singapore Management University.

Abstract and Introduction

Related Work

Preliminary

Proposed Method

Experiment

Conclusion and References

Appendix

6 CONCLUSION

In this study, we attempt to leverage pre-trained vision-language models (PVLMs) in a low-computation-cost manner to aid the task of hateful meme detection. Specifically, without any fine-tuning of PVLMs, we probe them in a zero-shot VQA manner to generate hateful content-centric image captions. With the distilled knowledge from large PVLMs, we observe that a simple language model, BERT, can surpass all multimodal pre-trained BERT models of a similar scale. PromptHate with probe-captioning outperforms previous results significantly and achieves the new state-of-the art on three benchmarks.


Limitations: We would like to point out a few limitations of the proposed method, suggesting potential future directions. Firstly, we heuristically use answers to all probing questions as Pro-Cap, even though some questions may be irrelevant to the meme target. We report the performance of PromptHate with the answer from one probing question in Appendix D, highlighting that using all questions may not be the optimal solution. A future direction could involve training a model to dynamically select probing questions that are most relevant for meme detection. Secondly, although we demonstrate the effectiveness of Pro-Cap through performance and a case study in this paper, more thorough analysis is needed. For instance, in the future, we could use a gradient-based interpretation approach [31] to examine how different probing questions influence the final results, thereby enhancing the interpretation of the models.

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Table 10: Comparison between Pro-CapPromptHate and basic PromptHate on MAMI dataset.


Table 11: Number of parameters in VQA models.


This paper is available on arxiv under CC 4.0 license.