Tools: Python; Pytorch
Introduction: Construct a perceptual quality evaluation network for 3D point clouds using deep learning
Highlight: Achieved the PLCC accuracy of 91.22% on SJTU-PCQA (current: 77%) and 88.49% on M-PCCD(current: 86%); Submitted the academic paper
Tools: Python; Pytorch
Introduction: Recognize the macro (creating a salad or sandwich) and micro actions (chopping vegetables or peeling fruit) occurring during cooking based on the motion data collected by accelerometers
Highlight: Achieved the recognition accuracy of 53.53% for macro actions, 59.73% for micro-actions, and 56.63% for overall performance, which ranked in the top 5% among competitors; Invited to publish paper in Springer
Tools: Python; Pytorch
Introduction: Face recognition using residual neural networks
Highlight: Achieved a final recognition accuracy of 97% with class average correct rate of 85%
Tools: Python; Machine Learning
Introduction: Face detection and recognition with tradition machine learning algorithms
Highlight: all algorithms were realized by manually without using any integrated package
