yunbo426 / mim Goto Github PK
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Code release for "Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics" (CVPR 2019)
Interesting work! Could you please provide the full hyper-parameters setting for TaxiBJ and other datasets in your experiment? I want to reproduce your results but I am not very clear when I read the papers.
self.params.update(self.configs.__dict__['__flags'])
KeyError: '__flags'
Hi,
Thank you for the work. I have a question about TaxiBJ dataset. How do you visualise results? When I save the prediction, it's grayscale.
Please advise.
Thank you!
Hi, nice paper. For the github, could you please provide the training commands that you used?
大佬些,我看到论文中预测的帧存在结果比较模糊的问题,请问是否考虑过类似RNN+GAN的模型来预测生成更清晰的未来帧?
关于基线数据集,除了移动MNIST,以及低像素的Human3.6M,是否尝试过更高分辨率的图像?
另,README.md中的图片挂了.
The program can only run when the ’model_name‘(in run.py line44) is ’mim‘.
The program cannot run when the name is other, such as [convlstm, predcnn, predrnn, predrnn_pp, convlstm_net](in run.py line 43).
So I think we should change 'convlstm_net' to 'mim'(in run.py line44).
Could you share the code of other models[convlstm, predcnn, predrnn, predrnn_pp, convlstm_net]?
Hi, I saw Radar Echo Dataset in the paper.
Where can I download the Radar Echo Dataset? Thanks.
Hello, I want to know that the channel of taxibj dataset is 2. How to calculate the ssim between the predict frames and input frames?
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