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vision-language's Introduction

GQA: Visual Reasoning in the Real World

Data structure

├── Question Number
    ├── Annotations
    |   ├── answer
    |   ├── full Answer
    |   └── question
    │   
    ├── answer
    ├── entailed
    ├── equivalent
    ├── fullAnswer
    ├── groups
    ├── imageId
    ├── isBalanced
    ├── question
    ├── semantic
    ├── semanticStr
    └── types
        ├── detailed
        ├── semantic
        └── structural
  • answer
  • imageId
  • question

Network Architecture

캡처

Image-Question Aggregator

캡처2

Requirements

  • tensorflow-gpu==1.13.1
  • numpy==1.16.2
  • tensorflow-hub==0.4.0
  • python==3.7.3
  • cv2==4.0.0
  • tqdm==4.31.1

vision-language's People

Contributors

leaderj1001 avatar plemeri avatar

Stargazers

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Watchers

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Forkers

hxdaze

vision-language's Issues

[PPT Contents]

  • GQA 설명 (2 장)
  • 네트워크 그림, 원래 하려고 했던 것에서 어떻게 바뀌어서 현재 사용되고 있는지? (3 장)
  • 진행상황 (1-2장)
    • 현재 학습 진행중이고,
    • 향후에 어떻게 할 예정이다 + CVPR Workshop 제출 예정

[Todo]

  • ",", "?"는 어떻게 처리하는게 좋은지? -> 바꾼다면 pkl도 다시 만들어야함.
  • ELMo, training 할 것인지, freezing할 것인지?
  • Epochs
  • max_len 조절 -> max_len: 29
  • batch iterator 시간 줄이기
  • reporting(loss, acc, tensorboard)
  • model save, load
  • augmentation

[Experiments]

태훈: token_weights 사용한 것 / 안한 것
명준: ELMo fine tuning 함.

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