most citedRefConv: Re-parameterized Refocusing Convolution for Powerful ConvNets

16 citations · 17 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2024

Towards the Spectral bias Alleviation by Normalizations in Coordinate Networks

Zhicheng Cai, Hao Zhu, Qiu Shen +2

Representing signals using coordinate networks dominates the area of inverse problems recently, and is widely applied in various scientific computing tasks. Still, there exists an…

cs.CV20241 cited

Conv-INR: Convolutional Implicit Neural Representation for Multimodal Visual Signals

Zhicheng Cai

Implicit neural representation (INR) has recently emerged as a promising paradigm for signal representations. Typically, INR is parameterized by a multiplayer perceptron (MLP) whic…

cs.CV2024

Encoding Semantic Priors into the Weights of Implicit Neural Representation

Zhicheng Cai, Qiu Shen

Implicit neural representation (INR) has recently emerged as a promising paradigm for signal representations, which takes coordinates as inputs and generates corresponding signal v…

cs.CV202316 cited

RefConv: Re-parameterized Refocusing Convolution for Powerful ConvNets

Zhicheng Cai, Xiaohan Ding, Qiu Shen +1

We propose Re-parameterized Refocusing Convolution (RefConv) as a replacement for regular convolutional layers, which is a plug-and-play module to improve the performance without a…

cs.CV2023

FalconNet: Factorization for the Light-weight ConvNets

Zhicheng Cai, Qiu Shen

Designing light-weight CNN models with little parameters and Flops is a prominent research concern. However, three significant issues persist in the current light-weight CNNs: i) t…

cs.LG2023

Evolution: A Unified Formula for Feature Operators from a High-level Perspective

Zhicheng Cai

Traditionally, different types of feature operators (e.g., convolution, self-attention and involution) utilize different approaches to extract and aggregate the features. Resemblan…