论文标题
评估现实世界中的图像质量问题
Assessing Image Quality Issues for Real-World Problems
论文作者
论文摘要
我们介绍了一个新的大规模数据集，该数据集将图像质量问题评估与两个实用的视觉任务联系起来：图像字幕和视觉问题答案。首先，我们为盲人拍摄的39,181张图像确定是否足以识别内容以及从六个选项中观察到哪些质量缺陷。这些标签是我们做出以下贡献的关键基础：（1）一个新问题和算法，用于确定图像是否不足以识别内容，因此无法进行字幕，（2）确定六个质量缺陷中的一个新问题和算法包含图像中的哪个问题中的哪个问题，（3）视觉上是否有一个不合时宜的内容，即在视觉上是否有一个不合格的兴趣，并且不合时宜地启用了不合格的兴趣，是否不合时宜地不存在任何不合格的内容。从视野中，以及（4）通过自动确定图像是否不足的质量，因此不应加上字幕来创建更有效地创建大型图像字幕数据集的新颖应用。我们公开展示我们的数据集和代码，以促进这项工作的未来扩展：https：//vizwiz.org。
We introduce a new large-scale dataset that links the assessment of image quality issues to two practical vision tasks: image captioning and visual question answering. First, we identify for 39,181 images taken by people who are blind whether each is sufficient quality to recognize the content as well as what quality flaws are observed from six options. These labels serve as a critical foundation for us to make the following contributions: (1) a new problem and algorithms for deciding whether an image is insufficient quality to recognize the content and so not captionable, (2) a new problem and algorithms for deciding which of six quality flaws an image contains, (3) a new problem and algorithms for deciding whether a visual question is unanswerable due to unrecognizable content versus the content of interest being missing from the field of view, and (4) a novel application of more efficiently creating a large-scale image captioning dataset by automatically deciding whether an image is insufficient quality and so should not be captioned. We publicly-share our datasets and code to facilitate future extensions of this work: https://vizwiz.org.
