32 citations · 77 across the 9 of their papers we have counts for
6 papers · 1 filter
S3IM: Stochastic Structural SIMilarity and Its Unreasonable Effectiveness for Neural Fields
Zeke Xie, Xindi Yang, Yujie Yang +5
Recently, Neural Radiance Field (NeRF) has shown great success in rendering novel-view images of a given scene by learning an implicit representation with only posed RGB images. Ne…
S-MLPv2: Improved Spatial-Shift MLP Architecture for Vision
Tan Yu, Xu Li, Yunfeng Cai +2
Recently, MLP-based vision backbones emerge. MLP-based vision architectures with less inductive bias achieve competitive performance in image recognition compared with CNNs and vis…
Rethinking Token-Mixing MLP for MLP-based Vision Backbone
Tan Yu, Xu Li, Yunfeng Cai +2
In the past decade, we have witnessed rapid progress in the machine vision backbone. By introducing the inductive bias from the image processing, convolution neural network (CNN) h…
S-MLP: Spatial-Shift MLP Architecture for Vision
Tan Yu, Xu Li, Yunfeng Cai +2
Recently, visual Transformer (ViT) and its following works abandon the convolution and exploit the self-attention operation, attaining a comparable or even higher accuracy than CNN…
Advanced Variations of Two-Dimensional Principal Component Analysis for Face Recognition
Meixiang Zhao, Zhigang Jia, Yunfeng Cai +2
The two-dimensional principal component analysis (2DPCA) has become one of the most powerful tools of artificial intelligent algorithms. In this paper, we review 2DPCA and its vari…
Relaxed 2-D Principal Component Analysis by Norm for Face Recognition
Xiao Chen, Zhi-Gang Jia, Yunfeng Cai +1
A relaxed two dimensional principal component analysis (R2DPCA) approach is proposed for face recognition. Different to the 2DPCA, 2DPCA- and G2DPCA, the R2DPCA utilizes the l…