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Award ID contains: 1718262

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  1. ; ; (, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV))
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  2. ; ; ; ; ; ; ; (, IEEE Transactions on Pattern Analysis and Machine Intelligence)
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  3. ; ; (, Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV))
    null (Ed.)
    Accepted Manuscript Full Text Available
  4. ; (, International Journal of Computer Vision)
    null (Ed.)
    Full Text Available 
  5. ; ; ; ; (, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR))
    null (Ed.)
    Accepted Manuscript Full Text Available
  6. ; (, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR))
    null (Ed.)
    Accepted Manuscript Full Text Available
  7. ; (, Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI))
    null (Ed.)
    Accepted Manuscript Full Text Available
  8. ; ; (, Proceedings of the Winter Conference on Applications of Computer Vision (WACV))
    null (Ed.)
    Full Text Available 
  9. ; (, Proceedings of the European Conference on Computer Vision (ECCV))
    null (Ed.)
    Full Text Available 
  10. ; ; ; (, The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020)
    Growing up with bedtime tales, even children could easily tell how a story should develop; but selecting a coherent and reasonable ending for a story is still not easy for machines. To successfully choose an ending requires not only detailed analysis of the context, but also applying commonsense reasoning and basic knowledge. Previous work has shown that language models trained on very large corpora could capture common sense in an implicit and hard-to-interpret way. We explore another direction and present a novel method that explicitly incorporates commonsense knowledge from a structured dataset, and demonstrate the potential for improving story completion. 
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