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。