11 citations · 25 across the 16 of their papers we have counts for
5 papers · 1 filter
Anonymization for Skeleton Action Recognition
Saemi Moon, Myeonghyeon Kim, Zhenyue Qin +2
Skeleton-based action recognition attracts practitioners and researchers due to the lightweight, compact nature of datasets. Compared with RGB-video-based action recognition, skele…
Disentangling Noise from Images: A Flow-Based Image Denoising Neural Network
Yang Liu, Saeed Anwar, Zhenyue Qin +3
The prevalent convolutional neural network (CNN) based image denoising methods extract features of images to restore the clean ground truth, achieving high denoising accuracy. Howe…
Position-Sensing Graph Neural Networks: Proactively Learning Nodes Relative Positions
Zhenyue Qin, Yiqun Zhang Saeed Anwar, Dongwoo Kim +3
Most existing graph neural networks (GNNs) learn node embeddings using the framework of message passing and aggregation. Such GNNs are incapable of learning relative positions betw…
Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action Recognition
Zhenyue Qin, Yang Liu, Pan Ji +5
Skeleton sequences are lightweight and compact, and thus are ideal candidates for action recognition on edge devices. Recent skeleton-based action recognition methods extract featu…
Invertible Denoising Network: A Light Solution for Real Noise Removal
Yang Liu, Zhenyue Qin, Saeed Anwar +4
Invertible networks have various benefits for image denoising since they are lightweight, information-lossless, and memory-saving during back-propagation. However, applying inverti…