206 citations · 365 across the 7 of their papers we have counts for
7 papers
LID 2020: The Learning from Imperfect Data Challenge Results
Yunchao Wei, Shuai Zheng, Ming-Ming Cheng +32
Learning from imperfect data becomes an issue in many industrial applications after the research community has made profound progress in supervised learning from perfectly annotate…
Real-time Human Activity Recognition Using Conditionally Parametrized Convolutions on Mobile and Wearable Devices
Xin Cheng, Lei Zhang, Yin Tang +3
Recently, deep learning has represented an important research trend in human activity recognition (HAR). In particular, deep convolutional neural networks (CNNs) have achieved stat…
DanHAR: Dual Attention Network For Multimodal Human Activity Recognition Using Wearable Sensors
Wenbin Gao, Lei Zhang, Qi Teng +2
Human activity recognition (HAR) in ubiquitous computing has been beginning to incorporate attention into the context of deep neural networks (DNNs), in which the rich sensing data…
Layer-wise training convolutional neural networks with smaller filters for human activity recognition using wearable sensors
Yin Tang, Qi Teng, Lei Zhang +2
Recently, convolutional neural networks (CNNs) have set latest state-of-the-art on various human activity recognition (HAR) datasets. However, deep CNNs often require more computin…
Conditionally Learn to Pay Attention for Sequential Visual Task
Jun He, Quan-Jie Cao, Lei Zhang
Sequential visual task usually requires to pay attention to its current interested object conditional on its previous observations. Different from popular soft attention mechanism,…
Attention-based Convolutional Neural Network for Weakly Labeled Human Activities Recognition with Wearable Sensors
Kun Wang, Jun He, Lei Zhang
Unlike images or videos data which can be easily labeled by human being, sensor data annotation is a time-consuming process. However, traditional methods of human activity recognit…