Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: UniT: Multimodal Multitask Learning with a Unified Transformer   We propose UniT, a Unified Transformer model to simultaneously learn the most
prominent tasks across different domains, ranging from object detection to
natural language understanding and multimodal reasoning. Based on the
transformer encoder-decoder architecture, our UniT model encodes each input
modality with an encoder and makes predictions on each task with a shared
decoder over the encoded input representations, followed by task-specific
output heads. The entire model is jointly trained end-to-end with losses from
each task. Compared to previous efforts on multi-task learning with
transformers, we share the same model parameters across all tasks instead of
separately fine-tuning task-specific models and handle a much higher variety of
tasks across different domains. In our experiments, we learn 7 tasks jointly
over 8 datasets, achieving strong performance on each task with significantly
fewer parameters. Our code is available in MMF at https://mmf.sh.

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The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
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The categories of this paper are: funct-an
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The categories of this paper are: acc-phys
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The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: eess