1.1k citations · 2.2k across the 32 of their papers we have counts for
10 papers · 1 filter
Temporal Graph Modeling for Skeleton-based Action Recognition
Jianan Li, Xuemei Xie, Zhifu Zhao +3
Graph Convolutional Networks (GCNs), which model skeleton data as graphs, have obtained remarkable performance for skeleton-based action recognition. Particularly, the temporal dyn…
From Semantic Communication to Semantic-aware Networking: Model, Architecture, and Open Problems
Guangming Shi, Yong Xiao, Yingyu Li +1
Existing communication systems are mainly built based on Shannon's information theory which deliberately ignores the semantic aspects of communication. The recent iteration of wire…
Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT Networks
Yong Xiao, Yingyu Li, Guangming Shi +1
This paper studies an edge intelligence-based IoT network in which a set of edge servers learn a shared model using federated learning (FL) based on the datasets uploaded from a mu…
2D+3D Facial Expression Recognition via Discriminative Dynamic Range Enhancement and Multi-Scale Learning
Yang Jiao, Yi Niu, Trac D. Tran +1
In 2D+3D facial expression recognition (FER), existing methods generate multi-view geometry maps to enhance the depth feature representation. However, this may introduce false esti…
EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation
Yang Jiao, Trac D. Tran, Guangming Shi
This paper addresses the challenging unsupervised scene flow estimation problem by jointly learning four low-level vision sub-tasks: optical flow , stereo-depth $\textb…
Towards Self-learning Edge Intelligence in 6G
Yong Xiao, Guangming Shi, Yingyu Li +2
Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and…
A Novel Transferability Attention Neural Network Model for EEG Emotion Recognition
Yang Li, Boxun Fu, Fu Li +2
The existed methods for electroencephalograph (EEG) emotion recognition always train the models based on all the EEG samples indistinguishably. However, some of the source (trainin…
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network
Qian Ning, Weisheng Dong, Guangming Shi +2
Deep neural networks (DNNs) based methods have achieved great success in single image super-resolution (SISR). However, existing state-of-the-art SISR techniques are designed like…
Towards Ubiquitous AI in 6G with Federated Learning
Yong Xiao, Guangming Shi, Marwan Krunz
With 5G cellular systems being actively deployed worldwide, the research community has started to explore novel technological advances for the subsequent generation, i.e., 6G. It i…
MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment
Hancheng Zhu, Leida Li, Jinjian Wu +2
Recently, increasing interest has been drawn in exploiting deep convolutional neural networks (DCNNs) for no-reference image quality assessment (NR-IQA). Despite of the notable suc…