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Unofficial tensorflow implementation of "Bottom-up and Top-down attention for VQA" (TF v. 1.13)

License: MIT License

Python 97.50% Shell 2.50%

bottom-up-attention-tf's Introduction

Hello 😄

I am Doyup Lee. Now, I am an AI researcher at Runway, New York.
I definetly believe that machine learning can make human life much happier.
Of course, I love to learn new technologies.

Research Interests

Large-Scale Generative Models
Multi-modal and unified representation learning
Training DNNs with limited labels
Robustness of deep learning and Anomaly Detection
Technology Innovation

Contact

e-mail: [email protected]
google scholar: https://scholar.google.co.kr/citations?user=doyuplee&user=5rAj44kAAAAJ
linked-in: https://www.linkedin.com/in/doyuplee/
facebook: https://www.facebook.com/doyup2

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bottom-up-attention-tf's Issues

where do the weights of the attention model update?

Hi
in the attention model where the weights w are applied to the input vector w* concat[imFeatures,QuestionEmbddding]
I can't figure out where the values of w come from or how they are updated?!!! can you help with this problem?

Disk or RAM

Hi.. i am trying to run your code... i have 32 GB of RAM and more than 100GB of disk space. 3 GB of my RAM gets occupied for loading the dictionary and i get stuck there.... do i need a bigger RAM? Like 64GB of RAM space?

Feature normalization

could you help on this matter?
Did you do any normalization on the visual features?

could you publish a best weights you have trained?

hello, i'm learning this code. could you publish a best weights you have trained? thank you very much

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