23 citations · 25 across the 7 of their papers we have counts for
7 papers
BaRA: Bayesian Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning
Zhibin Duan, Yuhong Wang, Jiahong Fu +3
While Low-rank adaptation (LoRA) enables highly efficient fine-tuning by constraining task-specific updates to fixed low-rank subspaces, this rigid design limits representational f…
Vanilla Group Equivariant Vision Transformer: Simple and Effective
Jiahong Fu, Qi Xie, Deyu Meng +1
Incorporating symmetry priors as inductive biases to design equivariant Vision Transformers (ViTs) has emerged as a promising avenue for enhancing their performance. However, exist…
Rotation Equivariant Arbitrary-scale Image Super-Resolution
Qi Xie, Jiahong Fu, Zongben Xu +1
The arbitrary-scale image super-resolution (ASISR), a recent popular topic in computer vision, aims to achieve arbitrary-scale high-resolution recoveries from a low-resolution inpu…
A Regularization-Guided Equivariant Approach for Image Restoration
Yulu Bai, Jiahong Fu, Qi Xie +1
Equivariant and invariant deep learning models have been developed to exploit intrinsic symmetries in data, demonstrating significant effectiveness in certain scenarios. However, t…
Rotation-Equivariant Self-Supervised Method in Image Denoising
Hanze Liu, Jiahong Fu, Qi Xie +1
Self-supervised image denoising methods have garnered significant research attention in recent years, for this kind of method reduces the requirement of large training datasets. Co…
Rotation Equivariant Proximal Operator for Deep Unfolding Methods in Image Restoration
Jiahong Fu, Qi Xie, Deyu Meng +1
The deep unfolding approach has attracted significant attention in computer vision tasks, which well connects conventional image processing modeling manners with more recent deep l…
KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution
Jiahong Fu, Hong Wang, Qi Xie +3
Although current deep learning-based methods have gained promising performance in the blind single image super-resolution (SISR) task, most of them mainly focus on heuristically co…