Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: I-Tuning: Tuning Language Models with Image for Caption Generation   Recently, tuning the pre-trained language model (PLM) in a
parameter-efficient manner becomes a popular topic in the natural language
processing area. However, most of them focus on tuning the PLM with the
text-only information. In this work, we propose a new perspective to tune the
frozen PLM with images for caption generation. We denote our method as
I-Tuning, which can automatically filter the vision information from images to
adjust the output hidden states of PLM. Evaluating on the image captioning
tasks (MSCOCO and Flickr30k Captioning), our method achieves comparable or even
better performance than the previous models which have 2-4 times more trainable
parameters and/or consume a large amount of cross-modal pre-training data.

18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph
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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: patt-sol
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The categories of this paper are: acc-phys
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The categories of this paper are: bayes-an
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The categories of this paper are: stat
10
The categories of this paper are: q-bio
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The categories of this paper are: hep-ex