Vision Transformer

Also known as ViT

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Latest papers 351

50 papers in the last four weeks, up 39% on the four weeks before. 0.3% of all new papers.

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Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers
Jun 21, 2026Edwin Kwadwo Tenagyei, Lei Wang, Ugochukwu Ejike Akpudo +2Vision Transformer Parameter-Efficient Fine-Tuning
Griffith University, Nathan QLD 4111, Australia · Data61/CSIRO, Canberra ACT 2601, Australia
ScalePredictor: Instance-aware Scale Learning for Accurate Quantization of Vision Transformers
Jun 20, 2026Changjun Li, Runqing Jiang, Lian Xu +3Vision Transformer Activation Quantization
School of Electronics and Communication Engineering, Shenzhen Campus, Sun Yat-sen University, Shenzhen, China · Computer Science and Software Engineering, The University of Western Australia, Perth, Australia · University of Oxford, Oxford, United Kingdom
A scalar per patch from pre-trained ViTs enables fast moving navigation in the real world
Jun 19, 2026Steeven Janny, Leonid Antsfeld, Christian WolfVisual Representation Learning Robot Navigation
NaverLabs Europe, Grenoble, France
Confidence-Aware Automated Assessment of Student-Drawn Scientific Models
Jun 18, 2026Luyang Fang, Yingchuan Zhang, Jongchan Park +3Selective Prediction Vision Transformer
AI4STEM Education Center, Athens, GA, USA · Department of Statistics, University of Georgia, Athens, GA, USA
Transformer Geometry Observatory TGO-I: Spectral Geometry Observatory
Jun 17, 2026Kaustubh Kapil, Kishor P. UplaIntrinsic Dimensionality Neural Representation Geometry
Department of Electronics Engineering Sardar Vallabhai National Institute of Technology (SVNIT), Surat, India
ART-VS: Adaptive Resolution Tiling for Vision Transformer Visual Servoing
Jun 17, 2026Alessandro Scherl, Bernhard Neuberger, Simon Schwaiger +3Vision Transformer Vision-Based Robot Control
Department of Computer Technology, University of Alicante, Spain. · Department of Industrial Engineering, UAS Technikum Vienna, Austria. · Automation and Control Institute, TU Wien, Vienna, Austria. +2
Architectural Bias in Face Presentation Attack Detection: A Comparative Study of Vision Transformers and Convolutional Neural Networks
Jun 16, 2026Ngela Landon Ntung, Floride Tuyisenge, Jema David NdibwileVision Transformer Algorithmic Fairness
College of Engineering, Carnegie Mellon University Africa, Kigali, Rwanda
Gaussian Light Field Splatting: A Physical Prior-Driven Vision Transformer for Unsupervised Low-Light Image Enhancement
Jun 16, 2026Yuhan Chen, Wenxuan Yu, Guofa Li +6Vision Transformer Low-Light Image Enhancement
College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China · Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, USA · Department of Electrical and Computer Engineering, Carnegie Mellon University, Moffett Field, CA 94035, USA +2
Landsat-Sentinel-2 Algal Bloom Mapping Using Vision Transformers: Model Description, Implementation, and Examples
Jun 15, 2026Thainara Lima, Vitor MartinsRemote Sensing Image Understanding Vision Transformer
a Department of Agricultural & Biological Engineering, Mississippi State University (MSU), Starkville, MS 39762, USA.
Explainable Flood Segmentation on Sentinel-1 SAR1 Imagery Using CNN and Transformer Architectures
Jun 15, 2026Arundhuti Banerjee, David DaouRemote Sensing Image Segmentation Explainable Artificial Intelligence
United Nations University’s Institute for Environment and Human Security (UNU-EHS) · Bonn, Germany 53113 · United Nations University’s InstituteBonn,forGermanyEnvironment53113and Human Security (UNU-EHS)
RATS! Patches Talk Through Registers: Emergent Parts in Register Attention Transformers
Jun 12, 2026Timing Yang, Predrag Neskovic, Jansen Seheult +4Visual Attention Visual Representation Learning
1Johns Hopkins University · 2Office of Naval Research, Arlington, VA · Department of Laboratory Medicine and Pathology, Mayo Clinic,FLOPs (G)MN, USA
ViT-Up: Faithful Feature Upsampling for Vision Transformers
Jun 12, 2026Krispin Wandel, Jingchuan Wang, Hesheng WangSemantic Correspondence Vision Transformer
Shanghai Jiao Tong University, Shanghai, China
HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers
Jun 11, 2026Guozhen Zhang, Xuerui Qiu, Yutao Cui +11Unified Multimodal Models Vision Transformer
Nanjing University · Work done during internship at Tencent Hunyuan. · CASIA +3
Vision Transformers for Face Recognition Need More Registers
Jun 10, 2026Tahar Chettaoui, Guray Ozgur, Eduarda Caldeira +2Transformer Interpretability Transformer
Fraunhofer Institute for Computer Graphics Research IGD, Germany · Department of Computer Science, TU Darmstadt, Germany
Precision-Aware Illumination-Disentangled Vision Transformer for Spacecraft 6D Pose Estimation
Jun 10, 2026Zongwu Xie, Yifan Yang, Yonglong Zhang +3Vision Transformer Object Pose Estimation
School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China
Spatial Priors via Space Filling Curves for Small and Limited Data Vision Transformers
Jun 8, 2026Leyla Naz Candogan, Arshia Afzal, Pol Puigdemont +1Vision Transformer Efficient ViTs
LIONS, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Beyond Self-Attention: Sub-Quadratic Vision Transformers for Fast Image Captioning
Jun 7, 2026Chiradeep Ghosh, Dakshina Ranjan KiskuImage Captioning Vision Transformer
Department of Computer Science and Engineering · National Institute of Technology Durgapur · Durgapur, India
RAPID: Layer-Wise Redundancy-Aware Pruning and Importance-Driven Token Merging for Efficient ViT
Jun 6, 2026Kyumin Choi, Ikbeom JangVision Transformer Efficient ViTs
Hankuk University of Foreign Studies Seoul, South Korea
Phase Marginalization for Patch-Grid Instability in Vision Transformers
Jun 6, 2026Oğuzhan ErcanVision Transformer Dense Prediction
Scientific and Technological Research Council of Türkiye
A Unifying View of Attention Sinks: From Mechanisms to Architectural Interventions
Jun 6, 2026Lukas Fesser*, Mozes Jacobs*, Thomas Fel* +2Transformer Interpretability Vision Transformer
Kempner Institute Harvard University Cambridge, MA
When is 3D Worth It? A Resource-Performance Frontier for CNNs and Transformers in Lung CT
Jun 5, 2026Md Enamul Hoq, Sharafat Hossain, Imraul Emmaka +4Vision Transformer 3D Medical Imaging
Department of Biomedical Informatics, University of Arkansas for Medical Sciences · Department of Information Science, University of Arkansas at Little Rock · Department of Neuroscience, University of Arkansas for Medical Sciences
Inside the Visual Mind: Neuroscience-Motivated Concept Circuits for Interpreting and Steering Vision Transformers
Jun 4, 2026Tang Li, Yanlin Chen, Mengmeng Ma +1Transformer Interpretability Vision Transformer
Department of Computer & Information Science, University of Delaware, Newark, DE, USA · Department of Computer Science, University of Virginia, Charlottesville, VA, USA
Selective Coupling of Decoupled Informative Regions: Masked Attention Alignment for Data-Free Quantization of Vision Transformers
Jun 3, 2026Biao Qian, Yang Wang, Yong Wu +1Vision Transformer Synthetic Data Generation
Department of Automation, Tsinghua University, China. · School of Computer Science and Information Engineering, Hefei University of Technology, China. · Li Auto, China.
Formalizing the Binding Problem
Jun 2, 2026Lianghuan Huang, Yihao Li, Saeed Salehi +3Visual Representation Learning Vision Transformer
Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA · Department of Computer and Information Science, University of Pennsylvania · Machine Learning Group, Technical University of Berlin, Berlin, Germany +3
UtVAA: Ultra-tiny Vision Transformer with Affix Attention for Mobile Image Classification
Jun 2, 2026Romiyal George, Sathiyamohan Nishankar, Selvarajah Thuseethan +1Vision Transformer Edge Inference
University of Peradeniya, Sri Lanka · Charles Darwin University, Australia
$A^2$ : Smaller Self-Supervised ViTs Localize Better than Larger Ones
Jun 2, 2026Sreehari Rammohan, Huy Ha, Carl VondrickVisual Attention Vision Transformer
a Columbia University · b Stanford University
Bridging Topology and Deep Representation Learning: A TDA-ViT Fusion Model for Four-Class Brain Tumor Classification
May 30, 2026Faisal AhmedVision Transformer Medical Image Classification
Department of Data Science and Mathematics, Embry-Riddle Aeronautical University, 3700 Willow Creek Rd, Prescott, 86301, Arizona, USA.
SENTRY: Statistical Reliability Analysis of Vision Transformers Under Soft Errors
May 30, 2026Pramit Kumar Bhaduri, Mahdi Taheri, Samira Nazari +3Vision Transformer
Brandenburg University of Technology Cottbus-Senftenberg, Germany · Tallinn University of Technology, Tallinn, Estonia · Zanjan University, Iran
Polyphony: Diffusion-based Dual-Hand Action Segmentation with Alternating Vision Transformer and Semantic Conditioning
May 29, 2026Hao Zheng, Hu Wang, Tiantian Zheng +2Temporal Action Segmentation Vision Transformer
New York University Abu Dhabi, Abu Dhabi, United Arab Emirates · Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates
A Mechanistic Analysis of Adversarial Fine-tuning of Vision Transformers
May 28, 2026Hannah Gao, Isha Agarwal, Dylan Hadfield-Menell +1Adversarial Training Fine-Tuning
Massachusetts Institute of Technology · MIT Cambridge, MA

Jun 21, 2026J/K move · Enter open · S save

Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers

Edwin Kwadwo Tenagyei, Lei Wang, Ugochukwu Ejike Akpudo, Jun Zhou+1

Griffith University, Nathan QLD 4111, Australia · Data61/CSIRO, Canberra ACT 2601, Australia

Vision Transformer Parameter-Efficient Fine-Tuning

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Parameter-efficient fine-tuning (PEFT) has become a practical solution for adapting large pretrained vision transformers (ViTs) to downstream tasks while updating only a small subset of parameters. However, existing adapter-based methods perform adaptation independently for each token, implicitly assuming that token refinements should be learned in isolation. This token-wise formulation overlooks the structured relationships among tokens that naturally arise in visual scenes, potentially leading to redundant updates and spatially inconsistent feature refinement. In this work, we revisit the design of parameter-efficient adapters and propose to perform adaptation in hyperedge space rather than token space. We introduce HyperAdapter, a hypergraph-based adapter architecture that enables structured, group-aware adaptation through soft token routing. HyperAdapter constructs a soft hypergraph over ViT tokens using prototype-based assignments, aggregates token features into latent hyperedge representations, applies lightweight bottleneck adaptation at the hyperedge level, and diffuses the resulting updates back to tokens via the hypergraph incidence structure. This design injects an explicit structural inductive bias into PEFT while preserving the modularity and efficiency of standard adapters. Extensive experiments across diverse visual benchmarks demonstrate that structured hyperedge adaptation consistently outperforms strong PEFT baselines under comparable parameter budgets, with particularly pronounced gains on tasks requiring structured reasoning. Our results suggest that the choice of adaptation space is a critical yet underexplored dimension in parameter-efficient transfer for ViTs.