【自然语言处理】--视觉问答（Visual Question Answering，VQA）从初始到应用 - 代码天地

【自然语言处理】--视觉问答（Visual Question Answering，VQA）从初始到应用

其他 2018-07-19 08:09:45 阅读次数: 0

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转载自www.cnblogs.com/LHWorldBlog/p/9333596.html

【自然语言处理】--视觉问答（Visual Question Answering，VQA）从初始到应用

VQA（Visual Question Answering）技术

[VQA论文阅读]RUBi Reducing Unimodal Biases for Visual Question Answering

Visual Question Answering with Memory

斯坦福大学-自然语言处理入门笔记第二十课问答系统（question answering）

NLP——Question Answering 问答模型

Visual Question Answering in Tensorflow实战

深度学习神经网络学习笔记-多模态方向-09-VQA: Visual Question Answering

【CVPR2018】Learning Visual Knowledge Memory Networks For Visual Question Answering【VQA视觉知识记忆网络】

【NIPS2018】Reasoning with Graph Convolution Nets for Factual Visual Question Answering【事实视觉问答推理】

Retrieval Augmented Visual Question Answering with Outside Knowledge

Co-VQA : Answering by Interactive Sub Question Sequence

Learning Visual Knowledge Memory Networks for Visual Question Answering论文解读

视频问答与推理(Video Question Answering and Reasoning)——论文调研

基于Python实现的新技术在Visual Question Answering中的应用课程论文+项目源码

论文阅读 Question Answering

Question Answering with Subgraph Embedding

Improving the Robustness of Question Answering Systems to Question Paraphrasing

论文阅读：Learning Visual Question Answering by Bootstrapping Hard Attention

《iVQA: Inverse Visual Question Answering》读书笔记

论文解读：Where To Look: Focus Regions for Visual Question Answering

论文解读：A Focused Dynamic Attention Model for Visual Question Answering

Learning Conditioned Graph Structures for Interpretable Visual Question Answering论文解读

深度学习神经网络学习笔记-多模态方向-06-VQA-Med: Overview of the Medical Visual Question Answering Task at ImageCLEF 2

论文学习8-How Question Generation Can Help Question Answering over Knowledge Base（KBQA-知识问答）

Lecture 10: (Textual) Question Answering

Unsupervised Question Answering by Cloze Translation

论文笔记 Learning Visual Knowledge Memory Networks for Visual Question Answering （CVPR2018)

Transformers预训练模型使用：抽取式问答 Extractive Question Answering

开放域问答论文-Generator-Retriever-Generator: A Novel Approach to Open-domain Question Answering

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