115 citations · 153 across the 9 of their papers we have counts for
9 papers
Attention Hybrid Variational Net for Accelerated MRI Reconstruction
Guoyao Shen, Boran Hao, Mengyu Li +4
The application of compressed sensing (CS)-enabled data reconstruction for accelerating magnetic resonance imaging (MRI) remains a challenging problem. This is due to the fact that…
Paste, Inpaint and Harmonize via Denoising: Subject-Driven Image Editing with Pre-Trained Diffusion Model
Xin Zhang, Jiaxian Guo, Paul Yoo +2
Text-to-image generative models have attracted rising attention for flexible image editing via user-specified descriptions. However, text descriptions alone are not enough to elabo…
RFAConv: Receptive-Field Attention Convolution for Improving Convolutional Neural Networks
Xin Zhang, Chen Liu, Degang Yang +4
In the realm of deep learning, spatial attention mechanisms have emerged as a vital method for enhancing the performance of convolutional neural networks. However, these mechanisms…
Machine Learning Automated Approach for Enormous Synchrotron X-Ray Diffraction Data Interpretation
Xiaodong Zhao, YiXuan Luo, Juejing Liu +6
Manual analysis of XRD data is usually laborious and time consuming. The deep neural network (DNN) based models trained by synthetic XRD patterns are proved to be an automatic, acc…
Co-Occurrence Matters: Learning Action Relation for Temporal Action Localization
Congqi Cao, Yizhe Wang, Yue Lu +2
Temporal action localization (TAL) is a prevailing task due to its great application potential. Existing works in this field mainly suffer from two weaknesses: (1) They often negle…
Graph Contrastive Learning with Personalized Augmentation
Xin Zhang, Qiaoyu Tan, Xiao Huang +1
Graph contrastive learning (GCL) has emerged as an effective tool for learning unsupervised representations of graphs. The key idea is to maximize the agreement between two augment…
Adaptive Domain Generalization via Online Disagreement Minimization
Xin Zhang, Ying-Cong Chen
Deep neural networks suffer from significant performance deterioration when there exists distribution shift between deployment and training. Domain Generalization (DG) aims to safe…
PTGCF: Printing Texture Guided Color Fusion for Impressionism Oil Painting Style Rendering
Jing Geng, Li'e Ma, Xiaoquan Li +1
As a major branch of Non-Photorealistic Rendering (NPR), image stylization mainly uses the computer algorithms to render a photo into an artistic painting. Recent work has shown th…
Domain Prompt Learning for Efficiently Adapting CLIP to Unseen Domains
Xin Zhang, Shixiang Shane Gu, Yutaka Matsuo +1
Domain generalization (DG) is a difficult transfer learning problem aiming to learn a generalizable model for unseen domains. Recent foundation models (FMs) are robust to many dist…