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✨ Official PyTorch Implementation for EMNLP'19 Paper, "Dual Attention Networks for Visual Reference Resolution in Visual Dialog"

Home Page: https://www.aclweb.org/anthology/D19-1209

License: MIT License

Python 100.00%
computer-vision natural-language-processing visual-dialog

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dan-visdial's Issues

Making custom inferences

Thank you for sharing your code. I am trying to reproduce and understand it.
It will be of great help if you could kindly provide some information on how to get custom inferences, i.e., providing an image, query, and dialog; and generating the answer. Thank you.

question about code

Hello author, although your article has been published for many years, I still think it is very important, so I read your code recently.
I have one doubts when I read the code, and I want to ask you for advice. At the end of the decoder, you used the logsoftmax function to calculate the probability of the candidate answer, and then input it into the nn.CrossEntropy function, but as far as I know, nn.CrossEntropy function comes with a logsoftmax calculation. What is the purpose of doing this?

How to get v0.9 data

Hi, @Gi-Cheon Kang ! I have a question about the dataset.
How can I get v0.9 data for reimplementing your model?

Thank you very much!

Reproduce your result

Thank you for publishing your code. it's such a great work appearing in EMNLP2019.

Could you provide us the pretrained model so that we can reproduce your result as reported in your paper?

Model NDCG MRR R@1 R@5 R@10 Mean
DAN 0.5759 0.6320 49.63 79.75 89.35 4.30

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