Mobile User
Mobile user research focuses on understanding and optimizing the user experience and performance of applications and services on mobile devices. Current research emphasizes efficient deep learning model deployment on resource-constrained mobile hardware, often employing techniques like model compression, quantization, and distributed training with architectures such as transformers and convolutional neural networks. This field is crucial for advancing mobile AI applications, improving user privacy through on-device processing, and enabling new capabilities in areas like personalized healthcare, augmented reality, and robotics.
Papers
ORLA*: Mobile Manipulator-Based Object Rearrangement with Lazy A Star
Kai Gao, Zhaxizhuoma, Yan Ding, Shiqi Zhang, Jingjin Yu
Robust 6DoF Pose Estimation Against Depth Noise and a Comprehensive Evaluation on a Mobile Dataset
Zixun Huang, Keling Yao, Seth Z. Zhao, Chuanyu Pan, Chenfeng Xu, Kathy Zhuang, Tianjian Xu, Weiyu Feng, Allen Y. Yang