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Generalized cross-modal NNs; new audiovisual benchmark (IEEE TNNLS 2019)

Python 100.00%
deep-neural-networks audiovisual-classification multimodal cross-modal keras multimedia audiovisual classification

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xflow's Issues

about Y

Hi Catalina, it is glad to see your codes. Wonderful. But I have a few questions.
1 digits_data.py What is Y nad P? and why you set Y = np.empty(shape=(total,), dtype=np.int) and P = np.empty(shape=(total,), dtype=np.dtype('a16')) as an array.
2 Currently, I want to use your code for audio and image classification. How could I update code in digits_data.py? Because you use sliding window for short video? many thanks.

How were min_frames and max-frames determined?

Hi,

I would like to know how were min_frames and max-frames for determined as if I count the number of frames for the Digits dataset you supply in your GitHub, I found a min of 7 and a max of 63 when in the code you present in the git, you have them respectively at 6 and 58.
Thanks in advance for your answer

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