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2021-2023顶会190+篇ViT高分论文总结（通用ViT、高效ViT、训练transformer、卷积transformer等）

news 文章来源：https://blog.csdn.net/weixin_42645636/article/details/132813873 2024/11/6 5:19:47

今天分享近三年（2021-2023）各大顶会中的视觉Transformer论文，有190+篇，涵盖通用ViT、高效ViT、训练transformer、卷积transformer等细分领域。

全部论文原文及开源代码文末直接领取

General Vision Transformer（通用ViT）

1、GPViT: "GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation", ICLR, 2023

标题：GPViT: 一种具有组传播的高分辨率非层次结构视觉Transformer

内容：本文提出了一种高效的替代组传播块(GP块)来交换全局信息。在每个GP块中，特征首先由一定数量的可学习组标记分组，然后在组特征间进行组传播以交换全局信息，最后通过一个transformer解码器将更新后的组特征中的全局信息返回到图像特征。作者在各种视觉识别任务上评估了GPViT，包括图像分类、语义分割、目标检测和实例分割。与之前的工作相比，该方法在所有任务上都取得了显著的性能提升，特别是在需要高分辨率输出的任务上，例如在语义分割任务ADE20K上，GPViT-L3的性能比Swin Transformer-B高出2.0 mIoU，而参数数量只有其一半。

2、CPVT: "Conditional Positional Encodings for Vision Transformers", ICLR, 2023

标题：条件位置编码在视觉transformer中的应用

内容：本文提出了一种针对视觉Transformer的条件位置编码(CPE)方案。与以前预定义且与输入标记无关的固定或可学习位置编码不同，CPE是动态生成的，并取决于输入标记的局部邻域。因此，CPE可以轻松概括到比模型在训练期间见过的更长的输入序列。此外，CPE可以在视觉任务中保持所需的平移等价性，从而提高性能。作者使用一个简单的位置编码生成器(PEG)来实现CPE，并无缝集成到当前的Transformer框架中。基于PEG，作者提出了条件位置编码视觉Transformer(CPVT)。实验证明，CPVT的注意力图与学习到的位置编码非常相似，并取得了优于状态的结果。

3、LipsFormer: "LipsFormer: Introducing Lipschitz Continuity to Vision Transformers", ICLR, 2023

标题：LipsFormer: 在视觉Transformer中引入Lipschitz连续性

内容：本文提出了一种称为LipsFormer的Lipschitz连续Transformer，在理论和实验上探索了提高基于Transformer的模型训练稳定性的方法。与之前通过学习率预热、层规范化、注意力机制和权重初始化来解决训练不稳定的经验技巧不同，本文认为Lipschitz连续性是确保训练稳定性的更本质的特性。在LipsFormer中，不稳定的Transformer组件模块被Lipschitz连续的对应物替换：LayerNorm被CenterNorm替换，Xavier初始化被谱初始化替换，点积注意力被缩放余弦相似度注意力替换，并引入加权残差连接。作者证明引入的这些模块满足Lipschitz连续性，并导出了LipsFormer的Lipschitz常数上确界。

其他51篇

BiFormer: "BiFormer: Vision Transformer with Bi-Level Routing Attention", CVPR, 2023
AbSViT: "Top-Down Visual Attention from Analysis by Synthesis", CVPR, 2023
DependencyViT: "Visual Dependency Transformers: Dependency Tree Emerges From Reversed Attention", CVPR, 2023
ResFormer: "ResFormer: Scaling ViTs with Multi-Resolution Training", CVPR, 2023
SViT: "Vision Transformer with Super Token Sampling", CVPR, 2023
PaCa-ViT: "PaCa-ViT: Learning Patch-to-Cluster Attention in Vision Transformers", CVPR, 2023
GC-ViT: "Global Context Vision Transformers", ICML, 2023
MAGNETO: "MAGNETO: A Foundation Transformer", ICML, 2023
SMT: "Scale-Aware Modulation Meet Transformer", ICCV, 2023
CrossFormer++: "CrossFormer++: A Versatile Vision Transformer Hinging on Cross-scale Attention", arXiv, 2023
QFormer: "Vision Transformer with Quadrangle Attention" arXiv, 2023
LIT: "Less is More: Pay Less Attention in Vision Transformers", AAAI, 2022
DTN: "Dynamic Token Normalization Improves Vision Transformer", ICLR, 2022
RegionViT: "RegionViT: Regional-to-Local Attention for Vision Transformers", ICLR, 2022
CrossFormer: "CrossFormer: A Versatile Vision Transformer Based on Cross-scale Attention", ICLR, 2022
CSWin: "CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows", CVPR, 2022
MPViT: "MPViT: Multi-Path Vision Transformer for Dense Prediction", CVPR, 2022
Diverse-ViT: "The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy", CVPR, 2022
DW-ViT: "Beyond Fixation: Dynamic Window Visual Transformer", CVPR, 2022
MixFormer: "MixFormer: Mixing Features across Windows and Dimensions", CVPR, 2022
DAT: "Vision Transformer with Deformable Attention", CVPR, 2022
Swin-Transformer-V2: "Swin Transformer V2: Scaling Up Capacity and Resolution", CVPR, 2022
MSG-Transformer: "MSG-Transformer: Exchanging Local Spatial Information by Manipulating Messenger Tokens", CVPR, 2022
NomMer: "NomMer: Nominate Synergistic Context in Vision Transformer for Visual Recognition", CVPR, 2022
Shunted: "Shunted Self-Attention via Multi-Scale Token Aggregation", CVPR, 2022
PyramidTNT: "PyramidTNT: Improved Transformer-in-Transformer Baselines with Pyramid Architecture", CVPRW, 2022
ReMixer: "ReMixer: Object-aware Mixing Layer for Vision Transformers", CVPRW, 2022
UN: "Unified Normalization for Accelerating and Stabilizing Transformers", ACMMM, 2022
Wave-ViT: "Wave-ViT: Unifying Wavelet and Transformers for Visual Representation Learning", ECCV, 2022
DaViT: "DaViT: Dual Attention Vision Transformers", ECCV, 2022
MaxViT: "MaxViT: Multi-Axis Vision Transformer", ECCV, 2022
VSA: "VSA: Learning Varied-Size Window Attention in Vision Transformers", ECCV, 2022
LITv2: "Fast Vision Transformers with HiLo Attention", NeurIPS, 2022
ViT：An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale （ICLR 2021
Perceiver：Perceiver: General Perception with Iterative Attention（ICML 2021）
PiT：Rethinking Spatial Dimensions of Vision Transformers（ICCV 2021）
VT：Visual Transformers: Where Do Transformers Really Belong in Vision Models?（ICCV 2021）
PVT：Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions（ICCV 2021）
iRPE：Rethinking and Improving Relative Position Encoding for Vision Transformer（ICCV 2021）
CaiT：Going deeper with Image Transformers（ICCV 2021）
Swin-Transformer：Swin Transformer: Hierarchical Vision Transformer using Shifted Windows（ICCV 2021）
T2T-ViT：Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet（ICCV 2021）
DPT：DPT: Deformable Patch-based Transformer for Visual Recognition（ACMMM 2021）
Focal: "Focal Attention for Long-Range Interactions in Vision Transformers", NeurIPS, 2021
Twins: "Twins: Revisiting Spatial Attention Design in Vision Transformers", NeurIPS, 2021
ARM: "Blending Anti-Aliasing into Vision Transformer", NeurIPS, 2021
DVT: "Not All Images are Worth 16x16 Words: Dynamic Vision Transformers with Adaptive Sequence Length", NeurIPS, 2021
TNT: "Transformer in Transformer", NeurIPS, 2021
ViTAE: "ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias", NeurIPS, 2021
DeepViT: "DeepViT: Towards Deeper Vision Transformer", arXiv, 2021
LV-ViT: "All Tokens Matter: Token Labeling for Training Better Vision Transformers", NeurIPS, 2021

Efficient Vision Transformer（高效VIT）

1、Tri-Level: "Peeling the Onion: Hierarchical Reduction of Data Redundancy for Efficient Vision Transformer Training", AAAI, 2023

标题：一层一层剥开洋葱:用于高效视觉Transformer训练的数据冗余分层降低

内容：本文从三个稀疏角度提出了一种端到端高效训练框架，称为Tri-Level E-ViT。具体来说，作者利用分层数据冗余降低方案，通过在三个级别探索稀疏性：数据集中的训练示例数，每个示例中的patch(token)数，以及位于注意力权重中的token间的连接数。通过大量实验，证明了所提出的技术可以显著加速各种ViT架构的训练，同时保持准确率。

2、ToMe: "Token Merging: Your ViT But Faster", ICLR, 2023

标题：Token融合:你的ViT变得更快

内容：作者提出了Token Merging (ToMe)，这是一种简单的方法，可以在不需要训练的情况下增加现有ViT模型的吞吐量。ToMe使用一个通用且轻量级的匹配算法逐步合并transformer中相似的token，其速度与剪枝相当，但更准确。开箱即用，ToMe可以使最先进的ViT-L @ 512和ViT-H @ 518模型在图像上的吞吐量提高2倍，在视频上的ViT-L吞吐量提高2.2倍，其准确率仅下降0.2-0.3%。ToMe也可以轻松地在训练期间应用，在实践中将MAE在视频上的微调速度提高近2倍。 ToMe训练可以进一步最小化准确率下降，在音频上使ViT-B的吞吐量提高2倍，准确率仅下降0.4% mAP。从定性上看，作者发现ToMe可以将对象部分合并为一个token，甚至可以跨多个视频帧。总体而言，ToMe的准确率和速度在图像、视频和音频方面与最先进的技术相当。

3、HiViT: "HiViT: A Simpler and More Efficient Design of Hierarchical Vision Transformer", ICLR, 2023

标题：HiViT:一种更简单、更高效的分层视觉Transformer设计

内容：在本文中，作者提出了一种新的分层视觉Transformer设计，称为HiViT(Hierarchical ViT的缩写)，它在MIM中同时具有高效率和良好性能。关键是删除不必要的“局部单元间操作”，导出结构简单的分层视觉Transformer，其中掩蔽单元可以像普通视觉Transformer一样串行化。为此，作者从Swin Transformer开始，(i)将掩蔽单元大小设置为Swin Transformer主阶段的标记大小，(ii)在主阶段之前关闭单元间自注意力，(iii)消除主阶段之后的所有操作。

其他39篇

STViT: "Making Vision Transformers Efficient from A Token Sparsification View", CVPR, 2023
SparseViT: "SparseViT: Revisiting Activation Sparsity for Efficient High-Resolution Vision Transformer", CVPR, 2023
Slide-Transformer: "Slide-Transformer: Hierarchical Vision Transformer with Local Self-Attention", CVPR, 2023
RIFormer: "RIFormer: Keep Your Vision Backbone Effective While Removing Token Mixer", CVPR, 2023
EfficientViT: "EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention", CVPR, 2023
Castling-ViT: "Castling-ViT: Compressing Self-Attention via Switching Towards Linear-Angular Attention During Vision Transformer Inference", CVPR, 2023
ViT-Ti: "RGB no more: Minimally-decoded JPEG Vision Transformers", CVPR, 2023
LTMP: "Learned Thresholds Token Merging and Pruning for Vision Transformers", ICMLW, 2023
Evo-ViT: "Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer", AAAI, 2022
PS-Attention: "Pale Transformer: A General Vision Transformer Backbone with Pale-Shaped Attention", AAAI, 2022
ShiftViT: "When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism", AAAI, 2022
EViT: "Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations", ICLR, 2022
QuadTree: "QuadTree Attention for Vision Transformers", ICLR, 2022
Anti-Oversmoothing: "Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice", ICLR, 2022
QnA: "Learned Queries for Efficient Local Attention", CVPR, 2022
LVT: "Lite Vision Transformer with Enhanced Self-Attention", CVPR, 2022
A-ViT: "A-ViT: Adaptive Tokens for Efficient Vision Transformer", CVPR, 2022
Rev-MViT: "Reversible Vision Transformers", CVPR, 2022
ATS: "Adaptive Token Sampling For Efficient Vision Transformers", ECCV, 2022
EdgeViT: "EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformers", ECCV,2022
SReT: "Sliced Recursive Transformer", ECCV, 2022
SiT: "Self-slimmed Vision Transformer", ECCV, 2022
M(3)ViT: "M(3)ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design", NeurIPS, 2022
ResT-V2: "ResT V2: Simpler, Faster and Stronger", NeurIPS, 2022
EfficientFormer: "EfficientFormer: Vision Transformers at MobileNet Speed", NeurIPS, 2022
GhostNetV2: "GhostNetV2: Enhance Cheap Operation with Long-Range Attention", NeurIPS, 2022
DeiT: "Training data-efficient image transformers & distillation through attention", ICML, 2021
ConViT: "ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases", ICML, 2021
HVT: "Scalable Visual Transformers with Hierarchical Pooling", ICCV, 2021
CrossViT: "CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification", ICCV, 2021
ViL: "Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding", ICCV, 2021
Visformer: "Visformer: The Vision-friendly Transformer", ICCV, 2021
MultiExitViT: "Multi-Exit Vision Transformer for Dynamic Inference", BMVC, 2021
SViTE: "Chasing Sparsity in Vision Transformers: An End-to-End Exploration", NeurIPS, 2021
DGE: "Dynamic Grained Encoder for Vision Transformers", NeurIPS, 2021
GG-Transformer: "Glance-and-Gaze Vision Transformer", NeurIPS, 2021
DynamicViT: "DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification", NeurIPS, 2021
ResT: "ResT: An Efficient Transformer for Visual Recognition", NeurIPS, 2021
SOFT: "SOFT: Softmax-free Transformer with Linear Complexity", NeurIPS, 2021

Conv + Transformer（卷积+Transformer）

1、SATA: "Accumulated Trivial Attention Matters in Vision Transformers on Small Datasets", WACV, 2023

标题：小数据集上视觉Transformer中的累积微不足道的注意力非常重要

内容：作者提出通过阈值将注意力权重划分为微不足道和非微不足道，然后通过所提出的Trivial WeIghts Suppression Transformation (TWIST)抑制累积的微不足道注意力权重，以减少注意力噪音。在CIFAR-100和Tiny-ImageNet数据集上的大量实验表明，作者的抑制方法将Vision Transformer的准确率提高了高达2.3%。

2、SparK: "Sparse and Hierarchical Masked Modeling for Convolutional Representation Learning", ICLR, 2023

标题：卷积表示学习的稀疏分层遮挡建模

内容：作者识别并克服了将BERT风格的预训练或遮蔽图像建模扩展到卷积网络(convnets)的两个关键障碍：(i) 卷积操作无法处理不规则的、随机遮蔽的输入图像，(ii) BERT预训练的单尺度性质与convnet的层次结构不一致。对于(i)，作者将未遮蔽的像素视为3D点云的稀疏voxel，并使用稀疏卷积进行编码。这是2D遮蔽建模中首次使用稀疏卷积。对于(ii)，作者开发了一个分层解码器，用于从多尺度编码特征重构图像。该方法称为稀疏遮蔽建模(SparK)，它是通用的：可以直接用于任何卷积模型，无需backbone修改。

3、MOAT: "MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models", ICLR, 2023

标题：MOAT: 交替移动卷积和注意力产生强大的视觉模型

内容：本文提出了MOAT，这是一类建立在移动卷积(即逆残差块)和注意力机制之上的神经网络。与当前将移动卷积块和transformer块分开堆叠的工作不同，作者有效地将它们合并成一个MOAT块。从一个标准的Transformer块开始，用移动卷积块替换其多层感知机，并进一步在自注意力操作之前对其进行重排序。移动卷积块不仅增强了网络的表示能力，还产生了更好的下采样特征。概念简单的MOAT网络出人意料地有效，在ImageNet-1K上取得了89.1%的top-1准确率，在ImageNet-1K-V2上取得了81.5%的top-1准确率，均使用了ImageNet22K预训练。

其他14篇

InternImage: "InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions", CVPR, 2023
PSLT: "PSLT: A Light-weight Vision Transformer with Ladder Self-Attention and Progressive Shift", TPAMI, 2023
MobileViT: "MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer", ICLR, 2022
Mobile-Former: "Mobile-Former: Bridging MobileNet and Transformer", CVPR, 2022
TinyViT: "TinyViT: Fast Pretraining Distillation for Small Vision Transformers", ECCV, 2022
ParC-Net: "ParC-Net: Position Aware Circular Convolution with Merits from ConvNets and Transformer", ECCV, 2022
?: "How to Train Vision Transformer on Small-scale Datasets?", BMVC, 2022
DHVT: "Bridging the Gap Between Vision Transformers and Convolutional Neural Networks on Small Datasets", NeurIPS, 2022
iFormer: "Inception Transformer", NeurIPS, 2022
LeViT: "LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference", ICCV, 2021
CeiT: "Incorporating Convolution Designs into Visual Transformers", ICCV, 2021
Conformer: "Conformer: Local Features Coupling Global Representations for Visual Recognition", ICCV, 2021
CoaT: "Co-Scale Conv-Attentional Image Transformers", ICCV, 2021
CvT: "CvT: Introducing Convolutions to Vision Transformers", ICCV, 2021

Training + Transformer（训练+Transformer）

1、MixPro: "MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer", ICLR, 2023

标题：MixPro: 使用MaskMix和渐进式注意力标记的数据增强，用于视觉Transformer

内容：作者分别在图像空间和标签空间中提出了MaskMix和渐进式注意力标记(PAL)。具体来说，从图像空间的角度来看，作者设计了MaskMix，它根据网格状遮罩混合两张图像。每个遮罩补丁的大小是可调的，并且是图像补丁大小的整数倍，这确保每个图像补丁只来自一张图像并包含更多的全局内容。从标签空间的角度来看，作者设计了PAL，它利用渐进因子动态重新加权混合注意力标签的注意力权重。最后，作者将MaskMix和渐进式注意力标记组合起来，作为新的数据增强方法，命名为MixPro。

2、ConMIM: "Masked Image Modeling with Denoising Contrast", ICLR, 2023

标题：Masked Image Modeling with Denoising Contrast

内容：MIM最近在视觉Transformers(ViTs)上取得了state-of-the-art的表现，其核心是通过去噪自动编码机制增强网络对图像块级上下文的建模能力。与之前的工作不同，作者没有额外增加图像标记器的训练阶段，而是发掘了对比学习在去噪自动编码上的巨大潜力，并提出了一种纯MIM方法ConMIM，它产生简单的图像内部块间对比约束作为遮挡补丁预测的唯一学习目标。作者进一步通过非对称设计增强了去噪机制，包括图像扰动和模型进度率，以改进网络预训练。

3、MFM: "Masked Frequency Modeling for Self-Supervised Visual Pre-Training", ICLR, 2023

标题：基于遮挡的频域建模用于自监督视觉预训练

内容：作者提出了遮挡频率建模(MFM)，这是一种基于频域的统一方法，用于视觉模型的自监督预训练。它与在空间域中随机插入遮挡令牌到输入嵌入不同，MFM从频域的角度出发。具体来说，MFM首先遮挡输入图像的一部分频率分量，然后在频谱上预测缺失的频率。作者的关键洞见是，在频域中预测遮挡的组件比在空间域中预测遮挡的补丁更适合揭示潜在的图像模式，因为存在大量的空间冗余。该发现表明，在遮挡预测策略的正确配置下，高频分量中的结构信息和低频分量中的低级统计信息对于学习良好的表示都很有用。

其他46篇

VisualAtom: "Visual Atoms: Pre-training Vision Transformers with Sinusoidal Waves", CVPR, 2023
LGSimCLR: "Learning Visual Representations via Language-Guided Sampling", CVPR, 2023
DisCo-CLIP: "DisCo-CLIP: A Distributed Contrastive Loss for Memory Efficient CLIP Training", CVPR, 2023
MaskCLIP: "MaskCLIP: Masked Self-Distillation Advances Contrastive Language-Image Pretraining", CVPR, 2023
MAGE: "MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesis", CVPR, 2023 (Google).
MixMIM: "MixMIM: Mixed and Masked Image Modeling for Efficient Visual Representation Learning", CVPR, 2023
iTPN: "Integrally Pre-Trained Transformer Pyramid Networks", CVPR, 2023
DropKey: "DropKey for Vision Transformer", CVPR, 2023
FlexiViT: "FlexiViT: One Model for All Patch Sizes", CVPR, 2023
CLIPPO: "CLIPPO: Image-and-Language Understanding from Pixels Only", CVPR, 2023
DMAE: "Masked Autoencoders Enable Efficient Knowledge Distillers", CVPR, 2023
HPM: "Hard Patches Mining for Masked Image Modeling", CVPR, 2023
MaskAlign: "Stare at What You See: Masked Image Modeling without Reconstruction", CVPR, 2023
RILS: "RILS: Masked Visual Reconstruction in Language Semantic Space", CVPR, 2023
FDT: "Revisiting Multimodal Representation in Contrastive Learning: From Patch and Token Embeddings to Finite Discrete Tokens", CVPR, 2023
OpenCLIP: "Reproducible scaling laws for contrastive language-image learning", CVPR, 2023
DiHT: "Filtering, Distillation, and Hard Negatives for Vision-Language Pre-Training", CVPR, 2023
M3I-Pretraining: "Towards All-in-one Pre-training via Maximizing Multi-modal Mutual Information", CVPR, 2023
SN-Net: "Stitchable Neural Networks", CVPR, 2023
MAE-Lite: "A Closer Look at Self-supervised Lightweight Vision Transformers", ICML, 2023
GHN-3: "Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?", ICML, 2023
A(2)MIM: "Architecture-Agnostic Masked Image Modeling - From ViT back to CNN", ICML, 2023
PQCL: "Patch-level Contrastive Learning via Positional Query for Visual Pre-training", ICML, 2023
DreamTeacher: "DreamTeacher: Pretraining Image Backbones with Deep Generative Models", ICCV, 2023
BEiT: "BEiT: BERT Pre-Training of Image Transformers", ICLR, 2022
iBOT: "Image BERT Pre-training with Online Tokenizer", ICLR, 2022
AutoProg: "Automated Progressive Learning for Efficient Training of Vision Transformers", CVPR, 2022
MAE: "Masked Autoencoders Are Scalable Vision Learners", CVPR, 2022
SimMIM: "SimMIM: A Simple Framework for Masked Image Modeling", CVPR, 2022
SelfPatch: "Patch-Level Representation Learning for Self-Supervised Vision Transformers", CVPR, 2022
Bootstrapping-ViTs: "Bootstrapping ViTs: Towards Liberating Vision Transformers from Pre-training", CVPR, 2022
TransMix: "TransMix: Attend to Mix for Vision Transformers", CVPR, 2022
data2vec: "data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language", ICML, 2022
SSTA: "Self-supervised Models are Good Teaching Assistants for Vision Transformers", ICML, 2022
MP3: "Position Prediction as an Effective Pretraining Strategy", ICML, 2022
CutMixSL: "Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning", IJCAI, 2022
BootMAE: "Bootstrapped Masked Autoencoders for Vision BERT Pretraining", ECCV, 2022
TokenMix: "TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformers", ECCV, 2022
?: "Locality Guidance for Improving Vision Transformers on Tiny Datasets", ECCV, 2022
HAT: "Improving Vision Transformers by Revisiting High-frequency Components", ECCV, 2022
AttMask: "What to Hide from Your Students: Attention-Guided Masked Image Modeling", ECCV, 2022
SLIP: "SLIP: Self-supervision meets Language-Image Pre-training", ECCV, 2022
mc-BEiT: "mc-BEiT: Multi-Choice Discretization for Image BERT Pre-training", ECCV, 2022
SL2O: "Scalable Learning to Optimize: A Learned Optimizer Can Train Big Models", ECCV, 2022
TokenMixup: "TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers", NeurIPS, 2022
GreenMIM: "Green Hierarchical Vision Transformer for Masked Image Modeling", NeurIPS, 2022

Robustness + Transformer（鲁棒性+Transformer）16篇

RobustCNN: "Can CNNs Be More Robust Than Transformers?", ICLR, 2023
DMAE: "Denoising Masked AutoEncoders are Certifiable Robust Vision Learners", ICLR, 2023
TGR: "Transferable Adversarial Attacks on Vision Transformers with Token Gradient Regularization", CVPR, 2023
?: "Vision Transformers are Robust Learners", AAAI, 2022
PNA: "Towards Transferable Adversarial Attacks on Vision Transformers", AAAI, 2022
MIA-Former: "MIA-Former: Efficient and Robust Vision Transformers via Multi-grained Input-Adaptation", AAAI, 2022
Patch-Fool: "Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?", ICLR, 2022
Smooth-ViT: "Certified Patch Robustness via Smoothed Vision Transformers", CVPR, 2022
RVT: "Towards Robust Vision Transformer", CVPR, 2022
VARS: "Visual Attention Emerges from Recurrent Sparse Reconstruction", ICML, 2022
FAN: "Understanding The Robustness in Vision Transformers", ICML, 2022
CFA: "Robustifying Vision Transformer without Retraining from Scratch by Test-Time Class-Conditional Feature Alignment", IJCAI, 2022
?: "Understanding Adversarial Robustness of Vision Transformers via Cauchy Problem", ECML-PKDD, 2022
ViP: "ViP: Unified Certified Detection and Recovery for Patch Attack with Vision Transformers", ECCV, 2022
?: "When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture", NeurIPS, 2022
RobustViT: "Optimizing Relevance Maps of Vision Transformers Improves Robustness", NeurIPS, 2022

Model Compression + Transformer（模型压缩 + Transformer）12篇

TPS: "Joint Token Pruning and Squeezing Towards More Aggressive Compression of Vision Transformers", CVPR, 2023
BinaryViT: "BinaryViT: Pushing Binary Vision Transformers Towards Convolutional Models", CVPRW, 2023
OFQ: "Oscillation-free Quantization for Low-bit Vision Transformers", ICML, 2023
UPop: "UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers", ICML, 2023
COMCAT: "COMCAT: Towards Efficient Compression and Customization of Attention-Based Vision Models", ICML, 2023
UVC: "Unified Visual Transformer Compression", ICLR, 2022
MiniViT: "MiniViT: Compressing Vision Transformers with Weight Multiplexing", CVPR, 2022
SPViT: "SPViT: Enabling Faster Vision Transformers via Soft Token Pruning", ECCV, 2022
PSAQ-ViT: "Patch Similarity Aware Data-Free Quantization for Vision Transformers", ECCV, 2022
Q-ViT: "Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer", NeurIPS, 2022
VTC-LFC: "VTC-LFC: Vision Transformer Compression with Low-Frequency Components", NeurIPS, 2022
PSAQ-ViT-V2: "PSAQ-ViT V2: Towards Accurate and General Data-Free Quantization for Vision Transformers", arXiv, 2022

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