Numerous applications have been developed to assist visually impaired
individuals that employ a machine learning unit to process visual input.
However, a critical challenge with these applications is the sub-optimal
quality of images captured by the users. Given the complexity of operating a
camera for visually impaired individuals, we advocate for the use of Apple Live
Photos and Android Motion Photos technologies. In this study, we introduce a
straightforward methodology to evaluate and contrast the efficacy of
Live/Motion Photos against traditional image-based approaches. Our findings
reveal that both Live Photos and Motion Photos outperform single-frame images
in common visual assisting tasks, specifically in object classification and
VideoQA. We validate our results through extensive experiments on the ORBIT
dataset, which consists of videos collected by visually impaired individuals.
Furthermore, we conduct a series of ablation studies to delve deeper into the
impact of deblurring and longer temporal crops.

我们介绍了一种评估和对比 Live/Motion 照片与传统基于图像的方法在视觉辅助任务中的有效性的简单方法，并发现 Live 照片和运动照片在常见视觉辅助任务中，特别是物体分类和 VideoQA 中，优于单帧图像。通过在视障个体采集的视频组成的 ORBIT 数据集上进行了大量实验验证了我们的结果。此外，我们进行了一系列的消融研究，以深入研究去模糊和较长时间剪辑的影响。

赋能视障人士：苹果实时照片和安卓动态照片的新应用

Empowering Visually Impaired Individuals: A Novel Use of Apple Live  Photos and Android Motion Photos

Object recognition has made great advances in the last decade, but
predominately still relies on many high-quality training examples per object
category. In contrast, learning new objects from only a few examples could
enable many impactful applications from robotics to user personalization. Most
few-shot learning research, however, has been driven by benchmark datasets that
lack the high variation that these applications will face when deployed in the
real-world. To close this gap, we present the ORBIT dataset and benchmark,
grounded in the real-world application of teachable object recognizers for
people who are blind/low-vision. The dataset contains 3,822 videos of 486
objects recorded by people who are blind/low-vision on their mobile phones. The
benchmark reflects a realistic, highly challenging recognition problem,
providing a rich playground to drive research in robustness to few-shot,
high-variation conditions. We set the benchmark's first state-of-the-art and
show there is massive scope for further innovation, holding the potential to
impact a broad range of real-world vision applications including tools for the
blind/low-vision community. We release the dataset at
this https URL and benchmark code at
this https URL

通过发布 ORBIT 数据集和基准测试结果，本研究展示了通过少量见例子的学习，实现物品识别这一具备良好应用前景的任务在现实场景中的挑战和机会，并在此基础上提出了具有挑战性的测试基准，为解决此问题提供研究奠定了基础。