Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Caption Enriched Samples for Improving Hateful Memes Detection   The recently introduced hateful meme challenge demonstrates the difficulty of
determining whether a meme is hateful or not. Specifically, both unimodal
language models and multimodal vision-language models cannot reach the human
level of performance. Motivated by the need to model the contrast between the
image content and the overlayed text, we suggest applying an off-the-shelf
image captioning tool in order to capture the first. We demonstrate that the
incorporation of such automatic captions during fine-tuning improves the
results for various unimodal and multimodal models. Moreover, in the unimodal
case, continuing the pre-training of language models on augmented and original
caption pairs, is highly beneficial to the classification accuracy.

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The categories of this paper are: physics
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The categories of this paper are: chem-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: eess
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The categories of this paper are: nucl-th
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The categories of this paper are: math-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: cond-mat
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The categories of this paper are: bayes-an
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The categories of this paper are: supr-con
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The categories of this paper are: dg-ga
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-ex
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The categories of this paper are: math
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The categories of this paper are: solv-int
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The categories of this paper are: gr-qc