Survey of Visual Question Answering: Datasets and Techniques
arXiv:1705.03865
Abstract
Visual question answering (or VQA) is a new and exciting problem that combines natural language processing and computer vision techniques. We present a survey of the various datasets and models that have been used to tackle this task. The first part of the survey details the various datasets for VQA and compares them along some common factors. The second part of this survey details the different approaches for VQA, classified into four types: non-deep learning models, deep learning models without attention, deep learning models with attention, and other models which do not fit into the first three. Finally, we compare the performances of these approaches and provide some directions for future work.
arXiv comment10 pages, 3 figures, 3 tables Added references, corrected typos, made references less wordy
References in corpus (7)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- Hierarchical Question-Image Co-Attention for Visual Question Answering
- VQA: Visual Question Answering
- Dynamic Coattention Networks For Question Answering
- Simple Baseline for Visual Question Answering
- Visual Question Answering: A Survey of Methods and Datasets
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- Biomedical Question Answering: A Survey of Approaches and Challenges
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- Learning by Abstraction: The Neural State Machine
- Zero-Shot Transfer VQA Dataset
- Learning to Caption Images through a Lifetime by Asking Questions
- Trying Bilinear Pooling in Video-QA
- Cross-Dataset Adaptation for Visual Question Answering
- On the Flip Side: Identifying Counterexamples in Visual Question Answering