Vision Transformer

Follow this topic Ask about this topic

Recent momentum

-47%

16 papers in the last 28 days · 0.3% of indexed attention

Twelve weeks of publication activity for this topic as it is defined today.

Weekly history

Recent digests

What was published in this topic, kept on the site without email delivery.

Period ending 2026-09-21

5 new papers

A weekly snapshot of new work published in Vision Transformer.

Period ending 2026-09-14

4 new papers

A weekly snapshot of new work published in Vision Transformer.

Period ending 2026-09-07

7 new papers

A weekly snapshot of new work published in Vision Transformer.

231 papers

Latest in Vision Transformer

Browse all topics →

Recursive Vision Transformer with Dynamic Depth and Width Adjustment for Resource-Efficient Image Semantic Communication
May 27, 2026Zhilong Zhang, Xinhui Zhang, Gongyu Jin +3Self-Supervised Vision Transformers Vision Transformer
Is an Image Also Worth 16x16=256 Superpixels? A Framework for Attentional Image Classification
May 26, 2026Pedro Henrique da Costa Avelar, Anderson R. Tavares, Luís C. LambVision Transformer Progressive Attention Fusion
AdaMerge: Salience-Aware Adaptive Token Merging for Training-Free Acceleration of Vision Transformers
May 26, 2026Semi Lee, Hyejin Go, Hyesong ChoiVision Transformer Ad
CSV-ViT: A Vision Transformer with the Variable-sized Cortical Supervertices for Detection of Alzheimer's Disease Pathologies
May 26, 2026Geonwoo Baek, Ikbeom JangAlzheimer Self-Supervised Vision Transformers
X-Edit: Exact, Explicit, and Explainable Null-Space Editing for Medical Vision Transformers
May 24, 2026Yuanye Liu, Siyuan Zhou, Ke Zhang +3Self-Supervised Vision Transformers Medical Vision-Language Models
Self-Supervised Contrastive Learning for Cardiac MR Sequence Classification
May 24, 2026Yuli Wang, Hyewon Jung, Dongshen Peng +10Cardiac Magnetic Resonance Imaging Vision Transformer
Muon in Vision Transformers: Optimizer-Recipe Interactions and Gradient Spectra
May 23, 2026Ben S. Southworth, Shuai Jiang, Daniel McBride +2Vision Transformer Muon
Vision Transformers Need Better Token Interaction
May 22, 2026Linxiang SuVision Transformer Dense Prediction
Revitalizing Dense Material Segmentation: Stabilized Vision Transformers and the Generalization Paradox
May 22, 2026Allan Kazakov, Duygu Cakir, Hilal Kurt İrfanoğlu +1Vision Transformer Paradox
Slimmable ConvNeXt: Width-Adaptive Inference for Efficient Multi-Device Deployment
May 21, 2026Janek Haberer, Jon Eike Wilhelm, Olaf LandsiedelMobilenetv2 Resnet-18
ASAP: Attention Sink Anchored Pruning
May 21, 2026Jaehyuk Lee, Hanyoung Kim, Yanggee Kim +1Visual Token Pruning Vision Transformer
Balancing Uncertainty and Diversity of Samples: Leveraging Diversity of Least, High Confidence Samples for Effective Active Learning
May 21, 2026Vipul Arya, S. H. Shabbeer Basha, Srikrishna U N +2Active Learning Unlabeled Data
Accelerating Vision Foundation Models with Drop-in Depthwise Convolution
May 21, 2026Carmelo Scribano, Mohammad Mahdi, Nedyalko Prisadnikov +5Vision Transformer Vision Foundation Models
Dropout Universality: Scaling Laws and Optimal Scheduling at the Edge-of-Chaos
May 20, 2026Lucas Fernandez SarmientoDropout Scaling Laws
Vision Transformers and Convolutional Neural Networks for Land Use Scene Classification
May 20, 2026Arun D. KulkarniNormalized Difference Vegetation Index Convolutional Neural Networks
FTerViT: Fully Ternary Vision Transformer
May 20, 2026Szymon Ruciński, Pietro Bonazzi, Engin Türetken +3Self-Supervised Vision Transformers Vision Transformer
Weierstrass Positional Encoding for Vision Transformers
May 20, 2026Zhihang Xin, Rui Wang, Xitong Hu +1Positional Encoding Vision Transformer
Mechanistic Interpretability for Learning Assurance of a Vision-Based Landing System
May 20, 2026Romeo Valentin, Olivia Beyer Bruvik, Marc R. Schlichting +1Aerotrack Mechanistic Interpretability
Faster or Stronger: Towards Flexible Visual Place Recognition via Weighted Aggregation and Token Pruning
May 19, 2026Zichao Zeng, June Moh Goo, Junwei Zheng +4Visual Place Recognition Visual Token Pruning
UniRefiner: Teaching Pre-trained ViTs to Self-Dispose Dross via Contrastive Register
May 19, 2026Congpei Qiu, Zhaoyu Hu, Wei Ke +3Self-Supervised Vision Transformers Vision Transformer
LESSViT: Robust Hyperspectral Representation Learning under Spectral Configuration Shift
May 18, 2026Haozhe Si, Yuxuan Wan, Yuqing Wang +2Hyperspectral Imaging Self-Supervised Vision Transformers
Systematic Evaluation of Vision Transformers for Automated Cervical Cancer Classification: Optimization, Statistical Validation, and Clinical Interpretability
May 17, 2026Nisreen Albzour, Sarah S. LamNon-Small Cell Lung Cancer Vision Transformer
Registers Matter for Pixel-Space Diffusion Transformers
May 15, 2026Nikita Starodubcev, Ilia Sudakov, Ilya Drobyshevskiy +2Multimodal Diffusion Transformers Self-Supervised Vision Transformers
Deep Pre-Alignment for VLMs
May 14, 2026Tianyu Yu, Kechen Fang, Zihao Wan +5Vision-Language Alignment Vision Transformer
Representative Attention For Vision Transformers
May 14, 2026Yuntong Li, Hainuo Wang, Hengxing Liu +2Self-Supervised Vision Transformers Vision Transformer
SToRe3D: Sparse Token Relevance in ViTs for Efficient Multi-View 3D Object Detection
May 13, 2026Sandro Papais, Lezhou Feng, Charles Cossette +13D Object Detection 3D Object Understanding
Evolving Layer-Specific Scalar Functions for Hardware-Aware Transformer Adaptation
May 13, 2026Kieran Carrigg, Sigur de Vries, Amirhossein Sadough +1Vision Transformer Sparse Deep Neural Network Accelerators
Unified Pix Token And Word Token Generative Language Model
May 13, 2026Haun Leung, ZiNan WangVision Encoders Vision Transformer
Backbone is All You Need: Assessing Vulnerabilities of Frozen Foundation Models in Synthetic Image Forensics
May 13, 2026Chiara Musso, Joy Battocchio, Andrea Montibeller +1Explainable Deepfake Detection Adversarial Examples
Elastic Attention Cores for Scalable Vision Transformers
May 12, 2026Alan Z. Song, Yinjie Chen, Mu Nan +8Self-Supervised Vision Transformers Vision Transformer
On What We Can Learn from Low-Resolution Data
May 12, 2026Theresa Dahl Frehr, Niels Henrik Pontoppidan, Hiba Nassar +1Low-Resolution Downstream Fine-Tuning
Spectral Vision Transformer for Efficient Tokenization with Limited Data
May 12, 2026Alexandra G. Roberts, Maneesh John, Jinwei Zhang +10Self-Supervised Vision Transformers Vision Transformer
What-Where Transformer: A Slot-Centric Visual Backbone for Concurrent Representation and Localization
May 12, 2026Ryota Yoshihashi, Masahiro Kada, Satoshi Ikehata +2Vision Transformer Localization Methods
Vision Transformer-Conditioned UNet for Domain-Adaptive Semantic Segmentation
May 12, 2026Joel Valdivia Ortega, Tingying Peng, Marion JasninSelf-Supervised Vision Transformers U-Net
DORA: Dynamic Online Reinforcement Agent for Token Merging in Vision Transformers
May 12, 2026Kaixuan He, Song Chen, Yi KangVision Transformer Deep Reinforcement Learning
Transformer Interpretability from Perspective of Attention and Gradient
May 12, 2026Yongjin Cui, Xiaohui Fan, Huajun ChenTransformer Attention Vision Transformer
Inducing Spatial Locality in Vision Transformers through the Training Protocol
May 11, 2026Eduardo Santiago Toledo, Asael Fabian MartínezVision Transformer Imagenet
Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs
May 11, 2026Li Shen, Xiaolei Hao, Qinglun Li +3Federated Learning Vision Transformer
bViT: Investigating Single-Block Recurrence in Vision Transformers for Image Recognition
May 11, 2026Michal Byra, Pawel Olszowiec, Grzegorz Stefanski +2Vision Transformer Self-Supervised Vision Transformers
MicroViTv2: Beyond the FLOPS for Edge Energy-Friendly Vision Transformers
May 11, 2026Novendra Setyawan, Chi-Chia Sun, Mao-Hsiu Hsu +2Self-Supervised Vision Transformers Vision Transformer
Dynamic Mode Decomposition along Depth in Vision Transformers
May 8, 2026Nishant Suresh Aswani, Saif Eddin JabariSelf-Supervised Vision Transformers Vision Transformer
Amortized-Precision Quantization for Early-Exit Vision Transformers
May 8, 2026Rui Fang, Hsi-Wen Chen, Ming-Syan ChenMixed-Precision Quantization Self-Supervised Vision Transformers
Beyond ViT Tokens: Masked-Diffusion Pretrained Convolutional Pathology Foundation Model for Cell-Level Dense Prediction
May 8, 2026Weiming Chen, Xitong Ling, Zhenyang Cai +5Pathology Foundation Models Dense Prediction
Attention Transfer Is Not Universally Effective for Vision Transformers
May 8, 2026Huaiyuan Qin, Muli Yang, Gabriel James Goenawan +4Self-Supervised Vision Transformers Vision Transformer
Spark3R: Asymmetric Token Reduction Makes Fast Feed-Forward 3D Reconstruction
May 7, 2026Zecheng Tang, Jiaye Fu, Qiankun Gao +5Feed-Forward 3D Reconstruction 3D Reconstruction
Metonymy in vision models undermines attention-based interpretability
May 7, 2026Ananthu Aniraj, Cassio F. Dantas, Dino Ienco +2Model Interpretability Vision Foundation Models
Architecture-agnostic Lipschitz-constant Bayesian header and its application to resolve semantically proximal classification errors with vision transformers
May 7, 2026Frederik Schäfer, Luis Mandl, Lars Kälber +1Noisy Labels Self-Supervised Vision Transformers
TRAM: Training Approximate Multiplier Structures for Low-Power AI Accelerators
May 6, 2026Chang Meng, Hanyu Wang, Yuyang Ye +3Accelerator Mixed-Precision Quantization
Visual Text Compression as Measure Transport
May 6, 2026Lv Tang, Tianyi Zheng, Yang Liu +2Token Compression Vision Transformer
ViTok-v2: Scaling Native Resolution Auto-Encoders to 5 Billion Parameters
May 6, 2026Philippe Hansen-Estruch, Jiahui Chen, Vivek Ramanujan +9Vision Transformer Autoregressive Image Generation
Taming Outlier Tokens in Diffusion Transformers
May 6, 2026Xiaoyu Wu, Yifei Wang, Tsu-Jui Fu +3Multimodal Diffusion Transformers Vision Transformer
Learngene Search Across Multiple Datasets for Building Variable-Sized Models
May 6, 2026Boyu Shi, Junbo Zhou, Chang Liu +3Model Pretraining Vision Transformer
Undetectable Backdoors in Model Parameters: Hiding Sparse Secrets in High Dimensions
May 5, 2026Sarthak Choudhary, Atharv Singh Patlan, Nils Palumbo +3Backdoor Attacks Detection Framework
Linearizing Vision Transformer with Test-Time Training
May 4, 2026Yining Li, Dongchen Han, Zeyu Liu +3Self-Supervised Vision Transformers Vision Transformer
Deep neural networks with Fisher vector encoding for medical image classification
May 3, 2026Lucas O. Lyra, Antonio E. Fabris, Joao B. FlorindoMedical Image Classification Convolutional Neural Networks
AgriKD: Cross-Architecture Knowledge Distillation for Efficient Leaf Disease Classification
May 2, 2026Minh-Dung Le, Minh-Duc Hoang, Hoang-Vu Truong +1Tomato Diseases Apple Tree Leaf Disease Segmentation Dataset
Colinearity Decay: Training Quantization-Friendly ViTs with Outlier Decay
May 2, 2026Jin Tong, Guang Liang, Peilin Sun +1Vision Transformer Regularization Techniques
Let ViT Speak: Generative Language-Image Pre-training
May 1, 2026Yan Fang, Mengcheng Lan, Zilong Huang +7Large Vision Language Models Vision Encoders
Foundation AI Models for Aerosol Optical Depth Estimation from PACE Satellite Data
May 1, 2026Zahid Hassan Tushar, Sanjay PurushothamAtmospheric Dynamics Satellite Data
Efficient Spatio-Temporal Vegetation Pixel Classification with Vision Transformers
Apr 30, 2026Alan Gomes, Anderson Gonçalves, Samuel Felipe dos Santos +63D Plant Phenotyping Normalized Difference Vegetation Index

May 27, 2026 · cs.CVJ/K move · Enter open · S save

Recursive Vision Transformer with Dynamic Depth and Width Adjustment for Resource-Efficient Image Semantic Communication

Zhilong Zhang, Xinhui Zhang, Gongyu Jin, Sihua Wang+2

Image semantic communication is a critical component in next-generation wireless communication systems. However, such systems typically suffer from large memory footprints and high computational complexity, making them difficult to deploy on resource-constrained devices. To address these challenges, we propose a vision transformer (ViT)-enabled image semantic communication system. In this system, a recursive structure is introduced to iteratively refine semantic features and reduce the parameter count. In addition, three dynamic adjustment strategies are designed to adaptively reduce computational complexity: dynamic depth adjustment, dynamic width adjustment, and joint width-depth optimization. Dynamic depth adjustment adaptively determines the number of recursive modules according to image content and channel conditions, while dynamic width adjustment selectively preserves important neurons and attention heads. The joint width-depth optimization further enables flexible computation configurations. Simulation results verify that the proposed recursive ViT-based system, combined with the three dynamic adjustment strategies, reduces the parameter count by 48.7% and achieves higher reconstruction quality than existing baselines under comparable computational complexity.

Self-Supervised Vision Transformers Vision Transformer Semantic Communications Reconstruction Quality

Open paper Ask about it PDF