most citedReborn Mechanism: Rethinking the Negative Phase Information Flow in Convolutional Neural Network

2 citations · 5 across the 8 of their papers we have counts for

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cs.CV2023

X-MLP: A Patch Embedding-Free MLP Architecture for Vision

Xinyue Wang, Zhicheng Cai, Chenglei Peng

Convolutional neural networks (CNNs) and vision transformers (ViT) have obtained great achievements in computer vision. Recently, the research of multi-layer perceptron (MLP) archi…

cs.CV2023

Learn to Enhance the Negative Information in Convolutional Neural Network

Zhicheng Cai, Chenglei Peng, Qiu Shen

This paper proposes a learnable nonlinear activation mechanism specifically for convolutional neural network (CNN) termed as LENI, which learns to enhance the negative information…

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.CV2021★ 1 cited

Interflow: Aggregating Multi-layer Feature Mappings with Attention Mechanism

Zhicheng Cai

Traditionally, CNN models possess hierarchical structures and utilize the feature mapping of the last layer to obtain the prediction output. However, it can be difficulty to settle…

cs.CV2021★ 2 cited

Reborn Mechanism: Rethinking the Negative Phase Information Flow in Convolutional Neural Network

Zhicheng Cai, Kaizhu Huang, Chenglei Peng

This paper proposes a novel nonlinear activation mechanism typically for convolutional neural network (CNN), named as reborn mechanism. In sharp contrast to ReLU which cuts off the…