Blind Users Accessing Their Training Images in Teachable Object Recognizers
Blind Users Accessing Their Training Images in Teachable Object Recognizers
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DOI：
10.1145/3517428.3544824
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Jonggi Hong;Jaina Gandhi;Ernest Essuah Mensah;Ebrima Jarjue;Kyungjun Lee;Hernisa Kacorri
中科院分区：
文献类型：
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作者：
Jonggi Hong;Jaina Gandhi;Ernest Essuah Mensah;Ebrima Jarjue;Kyungjun Lee;Hernisa Kacorri
可教对象识别器为盲人的实际需求提供了一种解决方案-实例级对象识别。他们认为人们可以通过视觉检查他们提供的照片进行培训，这对盲人来说是一个关键和无法实现的步骤。在这项工作中，我们设计了解决这一挑战的数据描述符。它们能在真实的时间内表明照片中的物体是否被裁剪或过小，是否包括一只手，照片是否模糊，以及照片之间有多少差异。我们的描述符内置于开源测试平台iOS应用程序中，称为MYCam。在（N = 12）盲人参与者家中的远程用户研究中，我们展示了描述符，即使是容易出错的描述符，如何支持实验，并对训练集的质量产生积极影响，这些训练集可以转化为模型性能，尽管这种增益并不均匀。参与者发现该应用程序简单易用，表明他们可以有效地训练它，并且描述符很有用。然而，许多人发现培训是乏味的，围绕信息，时间和认知负荷之间的平衡需要展开讨论。
Teachable object recognizers provide a solution for a very practical need for blind people – instance level object recognition. They assume one can visually inspect the photos they provide for training, a critical and inaccessible step for those who are blind. In this work, we engineer data descriptors that address this challenge. They indicate in real time whether the object in the photo is cropped or too small, a hand is included, the photos is blurred, and how much photos vary from each other. Our descriptors are built into open source testbed iOS app, called MYCam. In a remote user study in (N = 12) blind participants’ homes, we show how descriptors, even when error-prone, support experimentation and have a positive impact in the quality of training set that can translate to model performance though this gain is not uniform. Participants found the app simple to use indicating that they could effectively train it and that the descriptors were useful. However, many found the training being tedious, opening discussions around the need for balance between information, time, and cognitive load.