Corpus ID: 235458223

Semi-Autoregressive Transformer for Image Captioning

@article{Zhou2021SemiAutoregressiveTF,
  title={Semi-Autoregressive Transformer for Image Captioning},
  author={Yuanen Zhou and Yong Zhang and Zhenzhen Hu and Meng Wang},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.09436}
}
  • Yuanen Zhou, Yong Zhang, +1 author Meng Wang
  • Published 2021
  • Computer Science
  • ArXiv
Current state-of-the-art image captioning models adopt autoregressive decoders, i.e. they generate each word by conditioning on previously generated words, which leads to heavy latency during inference. To tackle this issue, non-autoregressive image captioning models have recently been proposed to significantly accelerate the speed of inference by generating all words in parallel. However, these non-autoregressive models inevitably suffer from large generation quality degradation since they… Expand

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References

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TLDR
A partially non-autoregressive model, named PNAIC, is introduced, which considers a caption as a series of concatenated word groups, and is capable of generating accurate captions as well as preventing common incoherent errors. Expand
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