摘要:视觉问答是计算机视觉领域和自然语言处理领域的交叉方向，近年来受到了广泛关注.在视觉问答任务中，算法需要回答基于特定图片（或视频）的问题.自2014年第一个视觉问答数据集发布以来，若干大规模数据集在近5年内被陆续发布，并有大量算法在此基础上被提出.已有的综述性研究重点针对视觉问答任务的发展进行了总结，但近年来，有研究发现，视觉问答模型强烈依赖语言偏见和数据集的分布，特别是自VQA-CP数据集发布以来，许多模型的效果大幅度下降.主要详细介绍近年来提出的算法以及发布的数据集，特别是讨论了算法在加强鲁棒性方面的研究.对视觉问答任务的算法进行分类总结，介绍了其动机、细节以及局限性.最后讨论了视觉问答任务的挑战及展望.



Abstract:Visual question answering (VQA) is an interdisciplinary direction in the field of computer vision and natural language processing. It has received extensive attention in recent years. In the visual question answering, the algorithm is required to answer questions based on specific pictures (or videos). Since the first visual question answering dataset was released in 2014, several large-scale datasets have been released in the past five years, and a large number of algorithms have been proposed based on them. Existing research has focused on the development of visual question answering, but in recent years, visual question answering has been found to rely heavily on language bias and the distribution of datasets, especially since the release of the VQA-CP dataset, the accuracy of many models has been greatly reduced. This paper mainly introduces the proposed algorithms and the released datasets in recent years, especially discusses the research of algorithms on strengthening the robustness. The algorithms of visual question answering are summarized and their motivation, details, and limitations are also introduced. Finally, the challenge and prospect of visual question answering are discussed.



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