Awesome_VQA

==主要介绍使用VQA2.0数据集作为评价指标的论文，使用CLEVR数据集的因为指标已经很高，所以不是关注的重点
主流数据集VQA2.0介绍
Clevr数据集
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数据集链接

CLevr 处理代码n2nmn VQA: VQA2 coco tdiuc

许多网络的基础
ottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
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使用视觉关系/场景图来做

Relation-aware Graph Attention Network for Visual Question Answering 所看结果最高，代码公开，还是图神经网络结果好 emmm..
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Generating Natural Language Explanations for Visual Question Answering Using Scene Graphs and Visual Attention 没有评价指标，但是是在场景图基础上做的VQA
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使用self-attenton/图卷积

LEARNING TO COUNT OBJECTS IN NATURAL IMAGES FOR VISUAL QUESTION ANSWERING
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Learning Conditioned Graph Structures for Interpretable Visual Question Answering
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Multi-modal Learning with Prior Visual Relation Reasoning
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[详解](./detail/Multi-modal Learning with Prior Visual Relation Reasoning.md)

Bilinear Attention Networks
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Visual Entailment: A Novel Task for Fine-Grained Image Understanding 使用self-attention
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MUREL: Multimodal Relational Reasoning for Visual Question Answering
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用一个语句处理方法，提取出句子中的关键字，然后进行预测

Deep Compositional Question Answering with Neural Module Networks 这个方向的鼻祖，利用问题产生不同的网络，还没找见代码
[Bibtex](https://dblp.uni-trier.de/rec/bibtex/journals/corr/AndreasRDK15）

Learning to Reason: End-to-End Module Networksfor Visual Question Answering
[Bibtex](https://dblp.uni-trier.de/rec/bibtex/journals/corr/HuARDS17）

Inferring and Executing Programs for Visual Reasoning
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FiLM: Visual Reasoning with a General Conditioning Layer
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Learning to Reason: End-to-End Module Networks for Visual Question Answering
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其他

Question Guided Modular Routing Networks for Visual Question Answering创建了一个路由网络
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Answer Them All! Toward Universal Visual Question Answering Models
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Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering
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BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection
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Compositional Attention Networks for Machine Reasoning
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