18 citations · 81 across the 17 of their papers we have counts for
12 papers
SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition
Cong Wu, Xiao-Jun Wu, Josef Kittler +4
Contrastive learning has achieved great success in skeleton-based action recognition. However, most existing approaches encode the skeleton sequences as entangled spatiotemporal re…
Mixture-of-Domain-Adapters: Decoupling and Injecting Domain Knowledge to Pre-trained Language Models Memories
Shizhe Diao, Tianyang Xu, Ruijia Xu +2
Pre-trained language models (PLMs) demonstrate excellent abilities to understand texts in the generic domain while struggling in a specific domain. Although continued pre-training…
LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Images
Hui Li, Tianyang Xu, Xiao-Jun Wu +2
Deep learning based fusion methods have been achieving promising performance in image fusion tasks. This is attributed to the network architecture that plays a very important role…
SDA-Net: Selective Depth Attention Networks for Adaptive Multi-scale Feature Representation
Qingbei Guo, Xiao-Jun Wu, Zhiquan Feng +2
Existing multi-scale solutions lead to a risk of just increasing the receptive field sizes while neglecting small receptive fields. Thus, it is a challenging problem to effectively…
TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Network
Dongyu Rao, Xiao-Jun Wu, Tianyang Xu
The end-to-end image fusion framework has achieved promising performance, with dedicated convolutional networks aggregating the multi-modal local appearance. However, long-range de…
Unsupervised Image Fusion Method based on Feature Mutual Mapping
Dongyu Rao, Xiao-Jun Wu, Tianyang Xu +1
Deep learning-based image fusion approaches have obtained wide attention in recent years, achieving promising performance in terms of visual perception. However, the fusion module…