no code implementations • 16 Jul 2022 • Daqian Shi, Xiaolei Diao, Hao Tang, Xiaomin Li, Hao Xing, Hao Xu
SENet aims to preserve the structural consistency of the character and normalize complex noise.
no code implementations • 13 Jul 2022 • Hao Xing, Yifan Cao, Maximilian Biber, Mingchuan Zhou, Darius Burschka
Supervised learning depth estimation methods can achieve good performance when trained on high-quality ground-truth, like LiDAR data.
no code implementations • 12 Jul 2022 • Hao Xing, Darius Burschka
However, in most existing Graph Convolutional Networks, the local attention mask is defined based on natural connections of human skeleton joints and ignores the dynamic relations for example between head, hands and feet joints.
1 code implementation • 8 Oct 2021 • Huance Xu, Chao Huang, Yong Xu, Lianghao Xia, Hao Xing, Dawei Yin
Social recommendation which aims to leverage social connections among users to enhance the recommendation performance.
1 code implementation • 8 Oct 2021 • Chao Huang, Huance Xu, Yong Xu, Peng Dai, Lianghao Xia, Mengyin Lu, Liefeng Bo, Hao Xing, Xiaoping Lai, Yanfang Ye
While many recent efforts show the effectiveness of neural network-based social recommender systems, several important challenges have not been well addressed yet: (i) The majority of models only consider users' social connections, while ignoring the inter-dependent knowledge across items; (ii) Most of existing solutions are designed for singular type of user-item interactions, making them infeasible to capture the interaction heterogeneity; (iii) The dynamic nature of user-item interactions has been less explored in many social-aware recommendation techniques.
no code implementations • 6 Sep 2021 • Hao Xing, Yuxuan Xue, Mingchuan Zhou, Darius Burschka
Our approach achieves the bestperformance on precision and accuracy of human fall event detection, compared with other existing dictionary learning methods.
no code implementations • 26 Jul 2019 • Qing Li, Xiaojiang Peng, Liangliang Cao, Wenbin Du, Hao Xing, Yu Qiao
Instead of collecting product images by labor-and time-intensive image capturing, we take advantage of the web and download images from the reviews of several e-commerce websites where the images are casually captured by consumers.