pytorch-attention 1.0.0

Pytorch implementation of popular Attention Mechanisms, Vision Transformers, MLP-Like models and CNNs.

This codebase is a PyTorch implementation of various attention mechanisms, CNNs, Vision Transformers and MLP-Like models.

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Attention mechanisms

• Squeeze-and-Excitation Networks (CVPR 2018) pdf
• CBAM: convolutional block attention module (ECCV 2018) pdf
• Bam: Bottleneck attention module(BMVC 2018) pdf
• A2-nets: Double attention networks (NeurIPS 2018) pdf
• Srm : A style-based recalibration module for convolutional neural networks (ICCV 2019) pdf
• Gcnet: Non-local networks meet squeeze-excitation networks and beyond (ICCVW 2019) pdf
• Selective Kernel Networks (CVPR 2019) pdf
• Linear Context Transform Block (AAAI 2020) pdf
• Gated Channel Transformation for Visual Recognition (CVPR 2020) pdf
• Ecanet: Efficient channel attention for deep convolutional neural networks (CVPR 2020) pdf
• Rotate to Attend: Convolutional Triplet Attention Module (WACV 2021) pdf
• Gaussian Context Transformer (CVPR 2021) pdf
• Coordinate Attention for Efficient Mobile Network Design (CVPR 2021) pdf
• SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks (ICML 2021) pdf

Vision Transformers

• An image is worth 16x16 words: Transformers for image recognition at scale (ICLR 2021) pdf
• XCiT: Cross-Covariance Image Transformer (NeurIPS 2021) pdf
• Rethinking Spatial Dimensions of Vision Transformers (ICCV 2021) pdf
• CvT: Introducing Convolutions to Vision Transformers (ICCV 2021) pdf
• CMT: Convolutional Neural Networks Meet Vision Transformers (CVPR 2022) pdf
• MetaFormer is Actually What You Need for Vision (CVPR 2022) pdf
• MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer (ICLR 2022) pdf
• DilateFormer: Multi-Scale Dilated Transformer for Visual Recognition (TMM 2023) pdf
• BViT: Broad Attention based Vision Transformer (TNNLS 2023) pdf

Convolutional Neural Networks(CNNs)

• Network In Network (ICLR 2014) pdf
• Deep Residual Learning for Image Recognition (CVPR 2016) pdf
• Wide Residual Networks (BMVC 2016) pdf
• Densely Connected Convolutional Networks (CVPR 2017) pdf
• Deep Pyramidal Residual Networks (CVPR 2017) pdf
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (CVPR 2017) pdf
• MobileNetV2: Inverted Residuals and Linear Bottlenecks (CVPR 2018) pdf
• Searching for MobileNetV3 (ICCV 2019) pdf
• Res2Net: A New Multi-scale Backbone Architecture (TPAMI 2019) pdf
• GhostNet: More Features from Cheap Operations (CVPR 2020) pdf
• A ConvNet for the 2020s (CVPR 2022) pdf

MLP-Like Models

• MLP-Mixer: An all-MLP Architecture for Vision (NeurIPS 2021) pdf
• Pay Attention to MLPs (NeurIPS 2021) pdf
• Global Filter Networks for Image Classification (NeurIPS 2021) pdf
• Sparse MLP for Image Recognition: Is Self-Attention Really Necessary? (AAAI 2022) pdf
• DynaMixer: A Vision MLP Architecture with Dynamic Mixing (ICML 2022) pdf
• Patches Are All You Need? (TMLR 2022) pdf
• Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition (TPAMI 2022) pdf
• CycleMLP: A MLP-like Architecture for Dense Prediction (ICLR 2022) pdf