RaftMLP: How Much Can Be Done Without Attention and with Less Spatial Locality?

9 August 2021

Papers citing "RaftMLP: How Much Can Be Done Without Attention and with Less Spatial Locality?"

9 / 9 papers shown

Title
Incorporating Exponential Smoothing into MLP: A Simple but Effective Sequence Model Jiqun Chu Zuoquan Lin AI4TS 25 2 0 26 Mar 2024
CS-Mixer: A Cross-Scale Vision MLP Model with Spatial-Channel Mixing Jianwei Cui David A. Araujo Suman Saha Md Faisal Kabir BDL 36 0 0 25 Aug 2023
Efficient Deep Spiking Multi-Layer Perceptrons with Multiplication-Free Inference Boyan Li Luziwei Leng Shuaijie Shen Kaixuan Zhang Jianguo Zhang Jianxing Liao Ran Cheng 28 7 0 21 Jun 2023
Sequencer: Deep LSTM for Image Classification Yuki Tatsunami Masato Taki VLM ViT 16 78 0 04 May 2022
HyperMixer: An MLP-based Low Cost Alternative to Transformers Florian Mai Arnaud Pannatier Fabio Fehr Haolin Chen François Marelli F. Fleuret James Henderson 20 11 0 07 Mar 2022
ConvMLP: Hierarchical Convolutional MLPs for Vision Jiachen Li Ali Hassani Steven Walton Humphrey Shi 41 55 0 09 Sep 2021
MLP-Mixer: An all-MLP Architecture for Vision Ilya O. Tolstikhin N. Houlsby Alexander Kolesnikov Lucas Beyer Xiaohua Zhai ... Andreas Steiner Daniel Keysers Jakob Uszkoreit Mario Lucic Alexey Dosovitskiy 271 2,603 0 04 May 2021
Transformer in Transformer Kai Han An Xiao Enhua Wu Jianyuan Guo Chunjing Xu Yunhe Wang ViT 284 1,524 0 27 Feb 2021
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions Wenhai Wang Enze Xie Xiang Li Deng-Ping Fan Kaitao Song Ding Liang Tong Lu Ping Luo Ling Shao ViT 274 3,622 0 24 Feb 2021