| RWKV: Reinventing RNNs for the Transformer Era | arxiv [May'23] | EleutherAI |
| Patches are All You Need ? | TMLR'23 | CMU |
| Seperable Self Attention for Mobile Vision Transformers | TMLR'23 | Apple |
| Parameter-efficient Fine-tuning for Vision Transformers | AAAI'23 | MSR & UCSC |
| EfficientFormer: Vision Transformers at MobileNet Speed | NeurIPS'22 | Snap Inc |
| Neighborhood Attention Transformer | CVPR'23 | Meta AI |
| Training Compute Optimal Large Language Models | NeurIPS'22 | DeepMind |
| CMT: Convolutional Neural Networks meet Vision Transformers | CVPR'22 | Huawei Noah’s Ark Lab |
| Patch Slimming for Efficient Vision Transformers | CVPR'22 | Huawei Noah’s Ark Lab |
| Lite Vision Transformer with Enhanced Self-Attention | CVPR'22 | Johns Hopkins University, Adobe |
| TubeDETR: Spatio-Temporal Video Grounding with Transformers | CVPR'22 (Oral) | CNRS & Inria |
| Beyond Fixation: Dynamic Window Visual Transformer | CVPR'22 | UT Sydney & RMIT University |
| BEiT: BERT Pre-Training of Image Transformers | ICLR'22 (Oral) | MSR |
| How Do Vision Transformers Work? | ICLR'22 (Spotlight) | NAVER AI |
| Scale Efficiently: Insights from Pretraining and FineTuning Transformers | ICLR'22 | Google Research |
| Tuformer: Data-Driven Design of Expressive Transformer by Tucker Tensor Representation | ICLR'22 | UoMaryland |
| DictFormer: Tiny Transformer with Shared Dictionary | ICLR'22 | Samsung Research |
| QuadTree Attention for Vision Transformers | ICLR'22 | Alibaba AI Lab |
| Expediting Vision Transformers via Token Reorganization | ICLR'22 (Spotlight) | UC San Diego & Tencent AI Lab |
| UniFormer: Unified Transformer for Efficient Spatial-Temporal Representation Learning | ICLR'22 | SIAT-SenseTime |
| Hierarchical Transformers Are More Efficient Language Models | NAACL'22 | Google Research, UoWarsaw |
| Transformer in Transformer | NeurIPS'21 | Huawei Noah's Ark |
| Long-Short Transformer: Efficient Transformers for Language and Vision | NeurIPS'21 | NVIDIA |
| Memory-efficient Transformers via Top-k Attention | EMNLP Workshop '21 | Allen AI |
| Swin Transformer: Hierarchical Vision Transformer using Shifted Windows | ICCV'21 best paper | MSR |
| Rethinking Spatial Dimensions of Vision Transformers | ICCV'21 | NAVER AI |
| What makes for hierarchical vision transformers | arxiv [Sept'21] | HUST |
| AutoAttend: Automated Attention Representation Search | ICML'21 | Tsinghua University |
| Rethinking Attention with Performers | ICLR'21 Oral | Google |
| LambdaNetworks: Modeling long-range Interactions without Attention | ICLR'21 | Google Research |
| HyperGrid Transformers | ICLR'21 | Google Research |
| LocalViT: Bringing Locality to Vision Transformers | arxiv [April'21] | ETH Zurich |
| Compressive Transformers for Long Range Sequence Modelling | ICLR'20 | DeepMind |
| Improving Transformer Models by Reordering their Sublayers | ACL'20 | FAIR, Allen AI |
| Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned | ACL'19 | Yandex |