Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DU-VLG: Unifying Vision-and-Language Generation via Dual
  Sequence-to-Sequence Pre-training   Due to the limitations of the model structure and pre-training objectives,
existing vision-and-language generation models cannot utilize pair-wise images
and text through bi-directional generation. In this paper, we propose DU-VLG, a
framework which unifies vision-and-language generation as sequence generation
problems. DU-VLG is trained with novel dual pre-training tasks: multi-modal
denoising autoencoder tasks and modality translation tasks. To bridge the gap
between image understanding and generation, we further design a novel
commitment loss. We compare pre-training objectives on image captioning and
text-to-image generation datasets. Results show that DU-VLG yields better
performance than variants trained with uni-directional generation objectives or
the variant without the commitment loss. We also obtain higher scores compared
to previous state-of-the-art systems on three vision-and-language generation
tasks. In addition, human judges further confirm that our model generates real
and relevant images as well as faithful and informative captions.

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The categories of this paper are: cond-mat
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The categories of this paper are: cmp-lg
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The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
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The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
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The categories of this paper are: q-fin
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The categories of this paper are: econ
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The categories of this paper are: mtrl-th
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The categories of this paper are: eess
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The categories of this paper are: comp-gas
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: acc-phys