A Survey of Mobile Computing for the Visually Impaired
arXiv:1811.10120
Abstract
The number of visually impaired or blind (VIB) people in the world is estimated at several hundred million. Based on a series of interviews with the VIB and developers of assistive technology, this paper provides a survey of machine-learning based mobile applications and identifies the most relevant applications. We discuss the functionality of these apps, how they align with the needs and requirements of the VIB users, and how they can be improved with techniques such as federated learning and model compression. As a result of this study we identify promising future directions of research in mobile perception, micro-navigation, and content-summarization.
References in corpus (10)
- Distilling the Knowledge in a Neural Network
- MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
- SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
- Communication-Efficient Learning of Deep Networks from Decentralized Data
- MobileNetV2: Inverted Residuals and Linear Bottlenecks
- Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1
- FitNets: Hints for Thin Deep Nets
- Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation
- Trained Ternary Quantization
- SEP-Nets: Small and Effective Pattern Networks