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License: MIT License
Heterogeneous Memory Enhanced Multimodal Attention Model for VideoQA
License: MIT License
Hi,
Thanks for your great work first!
Recently, I ran your code and found that the cost time of an iteration is very long during training.
Your implementation of the attention module contains many for
loops and I wonder that if there exists a more efficient implementation of your proposed method?
Thanks.
Since the baseline uses VGG, the packed features provided by yours are ResNet?
And can you share the features of MSRVTT dataset?
Hi, I found that the model provided at 3800 steps has acc 0.317, while I trained by myself and got 0.28.
Are there other tricks?
Hey,
This repo currently has no license. Will you add a license so that it can be used in publishable research?
Hi! Thanks a lot for sharing the codes. I wonder how to get the experiment results of different question types on MSVD-QA dataset?
You said 35 for TGIF-QA. the motion features are obtained via a sequence of frames. However, the length is still Nv. Does there any misunderstandings? Thanks.
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