Vision Transformers are Circulant Attention Learners
Fuente:
arXiv
Saved in:
| Main Authors: | Han, Dongchen, Li, Tianyu, Wang, Ziyi, Huang, Gao |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Demystify Mamba in Vision: A Linear Attention Perspective
by: Han, Dongchen, et al.
Published: (2024)
by: Han, Dongchen, et al.
Published: (2024)
Linear-Time Global Visual Modeling without Explicit Attention
by: He, Ruize, et al.
Published: (2026)
by: He, Ruize, et al.
Published: (2026)
Linear Differential Vision Transformer: Learning Visual Contrasts via Pairwise Differentials
by: Pu, Yifan, et al.
Published: (2025)
by: Pu, Yifan, et al.
Published: (2025)
ViT$^3$: Unlocking Test-Time Training in Vision
by: Han, Dongchen, et al.
Published: (2025)
by: Han, Dongchen, et al.
Published: (2025)
Efficient Diffusion Transformer with Step-wise Dynamic Attention Mediators
by: Pu, Yifan, et al.
Published: (2024)
by: Pu, Yifan, et al.
Published: (2024)
Agent Attention: On the Integration of Softmax and Linear Attention
by: Han, Dongchen, et al.
Published: (2023)
by: Han, Dongchen, et al.
Published: (2023)
Bridging the Divide: Reconsidering Softmax and Linear Attention
by: Han, Dongchen, et al.
Published: (2024)
by: Han, Dongchen, et al.
Published: (2024)
Representative Attention For Vision Transformers
by: Li, Yuntong, et al.
Published: (2026)
by: Li, Yuntong, et al.
Published: (2026)
S2AFormer: Strip Self-Attention for Efficient Vision Transformer
by: Xu, Guoan, et al.
Published: (2025)
by: Xu, Guoan, et al.
Published: (2025)
Multi-Attribute Vision Transformers are Efficient and Robust Learners
by: Gani, Hanan, et al.
Published: (2024)
by: Gani, Hanan, et al.
Published: (2024)
GSVA: Generalized Segmentation via Multimodal Large Language Models
by: Xia, Zhuofan, et al.
Published: (2023)
by: Xia, Zhuofan, et al.
Published: (2023)
Structured Initialization for Attention in Vision Transformers
by: Zheng, Jianqiao, et al.
Published: (2024)
by: Zheng, Jianqiao, et al.
Published: (2024)
Vision Transformers with Hierarchical Attention
by: Liu, Yun, et al.
Published: (2021)
by: Liu, Yun, et al.
Published: (2021)
Lightweight Vision Transformer with Window and Spatial Attention for Food Image Classification
by: Gao, Xinle, et al.
Published: (2025)
by: Gao, Xinle, et al.
Published: (2025)
Self-Supervised Vision Transformers Are Efficient Segmentation Learners for Imperfect Labels
by: Lee, Seungho, et al.
Published: (2024)
by: Lee, Seungho, et al.
Published: (2024)
Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners
by: Park, Keon-Hee, et al.
Published: (2024)
by: Park, Keon-Hee, et al.
Published: (2024)
Prototypical Transformer as Unified Motion Learners
by: Han, Cheng, et al.
Published: (2024)
by: Han, Cheng, et al.
Published: (2024)
Step by Step Network
by: Han, Dongchen, et al.
Published: (2025)
by: Han, Dongchen, et al.
Published: (2025)
Learning with Unmasked Tokens Drives Stronger Vision Learners
by: Kim, Taekyung, et al.
Published: (2023)
by: Kim, Taekyung, et al.
Published: (2023)
Multi-manifold Attention for Vision Transformers
by: Konstantinidis, Dimitrios, et al.
Published: (2022)
by: Konstantinidis, Dimitrios, et al.
Published: (2022)
HAViT: Historical Attention Vision Transformer
by: Banik, Swarnendu, et al.
Published: (2026)
by: Banik, Swarnendu, et al.
Published: (2026)
Group Diffusion Transformers are Unsupervised Multitask Learners
by: Huang, Lianghua, et al.
Published: (2024)
by: Huang, Lianghua, et al.
Published: (2024)
Contrastive Masked Autoencoders are Stronger Vision Learners
by: Huang, Zhicheng, et al.
Published: (2022)
by: Huang, Zhicheng, et al.
Published: (2022)
Video Diffusion Transformers are In-Context Learners
by: Fei, Zhengcong, et al.
Published: (2024)
by: Fei, Zhengcong, et al.
Published: (2024)
Polyline Path Masked Attention for Vision Transformer
by: Zhao, Zhongchen, et al.
Published: (2025)
by: Zhao, Zhongchen, et al.
Published: (2025)
Artifacts and Attention Sinks: Structured Approximations for Efficient Vision Transformers
by: Lu, Andrew, et al.
Published: (2025)
by: Lu, Andrew, et al.
Published: (2025)
EViT: An Eagle Vision Transformer with Bi-Fovea Self-Attention
by: Shi, Yulong, et al.
Published: (2023)
by: Shi, Yulong, et al.
Published: (2023)
BinaryAttention: One-Bit QK-Attention for Vision and Diffusion Transformers
by: Xiao, Chaodong, et al.
Published: (2026)
by: Xiao, Chaodong, et al.
Published: (2026)
ViT-AdaLA: Adapting Vision Transformers with Linear Attention
by: Li, Yifan, et al.
Published: (2026)
by: Li, Yifan, et al.
Published: (2026)
Attn-Adapter: Attention Is All You Need for Online Few-shot Learner of Vision-Language Model
by: Bui, Phuoc-Nguyen, et al.
Published: (2025)
by: Bui, Phuoc-Nguyen, et al.
Published: (2025)
Improving Vision Transformers by Overlapping Heads in Multi-Head Self-Attention
by: Zhang, Tianxiao, et al.
Published: (2024)
by: Zhang, Tianxiao, et al.
Published: (2024)
Multiple-Exit Tuning: Towards Inference-Efficient Adaptation for Vision Transformer
by: Liu, Zheng, et al.
Published: (2024)
by: Liu, Zheng, et al.
Published: (2024)
Learning Visual Prompts for Guiding the Attention of Vision Transformers
by: Rezaei, Razieh, et al.
Published: (2024)
by: Rezaei, Razieh, et al.
Published: (2024)
Decision-Aware Attention Propagation for Vision Transformer Explainability
by: Jo, Sehyeong, et al.
Published: (2026)
by: Jo, Sehyeong, et al.
Published: (2026)
PADRe: A Unifying Polynomial Attention Drop-in Replacement for Efficient Vision Transformer
by: Letourneau, Pierre-David, et al.
Published: (2024)
by: Letourneau, Pierre-David, et al.
Published: (2024)
Collaborative Low-Rank Adaptation for Pre-Trained Vision Transformers
by: Liu, Zheng, et al.
Published: (2025)
by: Liu, Zheng, et al.
Published: (2025)
Spiking Vision Transformer with Saccadic Attention
by: Wang, Shuai, et al.
Published: (2025)
by: Wang, Shuai, et al.
Published: (2025)
Simulate, Refocus and Ensemble: An Attention-Refocusing Scheme for Domain Generalization
by: Wang, Ziyi, et al.
Published: (2025)
by: Wang, Ziyi, et al.
Published: (2025)
There is More to Attention: Statistical Filtering Enhances Explanations in Vision Transformers
by: Ayyar, Meghna P, et al.
Published: (2025)
by: Ayyar, Meghna P, et al.
Published: (2025)
Calibration Attention: Learning Reliability-Aware Representations for Vision Transformers
by: Liang, Wenhao, et al.
Published: (2025)
by: Liang, Wenhao, et al.
Published: (2025)
Similar Items
-
Demystify Mamba in Vision: A Linear Attention Perspective
by: Han, Dongchen, et al.
Published: (2024) -
Linear-Time Global Visual Modeling without Explicit Attention
by: He, Ruize, et al.
Published: (2026) -
Linear Differential Vision Transformer: Learning Visual Contrasts via Pairwise Differentials
by: Pu, Yifan, et al.
Published: (2025) -
ViT$^3$: Unlocking Test-Time Training in Vision
by: Han, Dongchen, et al.
Published: (2025) -
Efficient Diffusion Transformer with Step-wise Dynamic Attention Mediators
by: Pu, Yifan, et al.
Published: (2024)