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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What-Where Transformer: A Slot-Centric Visual Backbone for Concurrent Representation and Localization
May 12, 2026Ryota Yoshihashi, Masahiro Kada, Satoshi Ikehata +2Visual Attention Vision Transformer
Institute of Science Tokyo · 2DENSO IT Laboratory · 3National Institute of Informatics
Transformer Interpretability from Perspective of Attention and Gradient
May 12, 2026Yongjin Cui, Xiaohui Fan, Huajun ChenGradient-Based Attribution Transformer Interpretability
Zhejiang University
Inducing Spatial Locality in Vision Transformers through the Training Protocol
May 11, 2026Eduardo Santiago Toledo, Asael Fabian MartínezVision Transformer Transformer Attention
Universidad Autónoma Metropolitana
Investigating Single-Block Recurrence in Vision Transformers for Image Recognition
May 11, 2026Michal Byra, Pawel Olszowiec, Grzegorz Stefanski +2Vision Transformer Recurrent Transformers
Samsung AI Center, Warsaw, Poland · Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
DynGhost: Temporally-Modelled Transformer for Dynamic Ghost Imagings
May 11, 2026Vittorio Palladino, Ahmet Enis CetinVision Transformer Image Reconstruction
Politecnico di Milano Milan, Italy · University of Illinois at Chicago Chicago, IL, USA · Department of Electrical and Computer Engineering University of Illinois at Chicago Chicago, IL, USA +1
Spatial-Frequency Gated Swin Transformer for Remote Sensing Single-Image Super-Resolution
May 10, 2026Md Aminur Hossain, Parekh Valkesh, Ayush V. Patel +3Remote Sensing Image Understanding Vision Transformer
Space Applications Centre, ISRO, Ahmedabad, India · New L J Institute of Engineering and Technology, Ahmedabad, India · Pandit Deendayal Energy University, Gandhinagar, India +2
MultiMedVision: Multi-Modal Medical Vision Framework
May 9, 2026Frank Li, Bardia Khosravi, Mohammadreza Chavoshi +5Medical Imaging Foundation Models Vision Transformer
Emory University · Yale University
Dynamic Mode Decomposition along Depth in Vision Transformers
May 8, 2026Nishant Suresh Aswani, Saif Eddin JabariTransformer Vision Transformer
NYU Tandon & NYU Abu Dhabi
Amortized-Precision Quantization for Early-Exit Vision Transformers
May 8, 2026Rui Fang, Hsi-Wen Chen, Ming-Syan ChenMixed-Precision Quantization Dynamic Neural Networks
National Taiwan University
TAS-LoRA: Transformer Architecture Search with Mixture-of-LoRA Experts
May 8, 2026Jeimin Jeon, Hyunju Lee, Bumsub HamVision Transformer Neural Architecture Search
Yonsei University · Articron Inc. · Korea Institute of Science and Technology (KIST)
Attention Transfer Is Not Universally Effective for Vision Transformers
May 8, 2026Huaiyuan Qin, Muli Yang, Gabriel James Goenawan +4Self-Attention Vision Transformer
Institute for Infocomm Research (I2R), A*STAR, Singapore · Sichuan University · Shanghai Jiao Tong University
Dynamic Pondering Sparsity-aware Mixture-of-Experts Transformer for Event Stream based Visual Object Tracking
May 7, 2026Shiao Wang, Xiao Wang, Duoqing Yang +5Event-Based Vision Mixture of Experts
School of Computer Science and Technology, Anhui University, Hefei 230601, China · Beijing Institute of Technology, Beijing, China · Peng Cheng Laboratory, Shenzhen, China +2
Metonymy in vision models undermines attention-based interpretability
May 7, 2026Ananthu Aniraj, Cassio F. Dantas, Dino Ienco +2Transformer Interpretability Disentangled Representation Learning
Inria, EVERGREEN, University of Montpellier, 34090 Montpellier, France · INRAE, UMR TETIS, University of Montpellier, 34090 Montpellier, France · University of Trento, Trento, Italy
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 +1Bayesian Neural Networks Vision Transformer
Institute of Structural Mechanics and Dynamics in Aerospace Engineering, University of Stuttgart · Rockwell Collins Deutschland GmbH · Department of Hepatobiliary Surgery and Visceral Transplantation, University of Leipzig Medical Center
Colinearity Decay: Training Quantization-Friendly ViTs with Outlier Decay
May 2, 2026Jin Tong, Guang Liang, Peilin Sun +1Vision Transformer Activation Outliers
State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing 210023, China · School of Artificial Intelligence, Nanjing University, Nanjing 210023, China · Zhongguancun Academy, Beijing 100094, China
Let ViT Speak: Generative Language-Image Pre-training
May 1, 2026Yan Fang, Mengcheng Lan, Zilong Huang +7Cross-Modal Learning Vision Transformer
1Beijing Jiaotong University · 2ByteDance · 3Nanyang Technological University
Foundation AI Models for Aerosol Optical Depth Estimation from PACE Satellite Data
May 1, 2026Zahid Hassan Tushar, Sanjay PurushothamVision Foundation Models Vision Transformer
University of Maryland, Baltimore County, Maryland, USA.
Efficient Spatio-Temporal Vegetation Pixel Classification with Vision Transformers
Apr 30, 2026Alan Gomes, Anderson Gonçalves, Samuel Felipe dos Santos +6Agricultural Remote Sensing Vision Transformer
Department of Computing, Federal University of São Carlos (UFSCar), Sorocaba, 18052-780, Brazil · Center for Research on Biodiversity Dynamics and Climate Change and Department of Biodiversity, Bioscience Institute, São Paulo State University (UNESP), Rio Claro, 13506-900, Brazil · Wageningen University & Research (WUR), Wageningen, 6708PB, Netherlands
Learning Dynamic Evidence Routes for Vision Transformer Probing
Apr 29, 2026Zice Wang, Zhenyu ZhangVision Transformer
School of Future Technology, Northeastern University, Shenyang, China · School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
QFlash: Bridging Quantization and Memory Efficiency in Vision Transformer Attention
Apr 28, 2026Sehyeon Oh, Yongin Kwon, Jemin LeeSoftmax Attention GPU Acceleration
University of Science and Technology, Daejeon, Republic of Korea · Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea · Pusan National University, Busan, Republic of Korea +1
ELSA: Exact Linear-Scan Attention for Fast and Memory-Light Vision Transformers
Apr 26, 2026Chih-Chung Hsu, Xin-Di Ma, Wo-Ting Liao +1Efficient Transformer Inference Softmax Attention
Advanced Computer Vision Laboratory, National Yang Ming Chiao Tung University
From Edges to Depth: Probing the Spatial Hierarchy in Vision Transformers
Apr 25, 2026Jainum SanghaviTransformer Interpretability Visual Spatial Reasoning
Northeastern University
Distilling Vision Transformers for Distortion-Robust Representation Learning
Apr 24, 2026Konstantinos Alexis, Giorgos Giannopoulos, Dimitrios GunopulosRepresentation Learning Visual Representation Learning
Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Greece · National Observatory of Athens, Greece
an interpretable vision transformer framework for automated brain tumor classification
Apr 23, 2026Chinedu Emmanuel Mbonu, Tochukwu Sunday Belonwu, Okwuchukwu Ejike Chukwuogo +1Vision Transformer Medical Image Classification
Department of Computer Science, Nazarbayev University, Astana · Department of Computer Science, Nnamdi Azikiwe University, Nigeria
MLG-Stereo: ViT Based Stereo Matching with Multi-Stage Local-Global Enhancement
Apr 22, 2026Haoyu Zhang, Jingyi Zhou, Peng Ye +4Vision Transformer Multi-Scale Feature Fusion
Embedded Deep Learning and Visual Analysis Laboratory, College of Future Information Technology, Fudan University, Shanghai 200433, China · Embedded Deep Learning and Visual Analysis Laboratory, College of Future Information Technology, Fudan University, Shanghai 200433, China, and also with Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China, and also with the Chinese University of Hong Kong, Hong Kong · Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China +2
Self-supervised pretraining for an iterative image size agnostic vision transformer
Apr 22, 2026Nedyalko Prisadnikov, Danda Pani Paudel, Yuqian Fu +1Self-Supervised Pre-Training Vision Transformer
INSAIT, Sofia University "St. Kliment Ohridski", Bulgaria
Where to Bind Matters: Hebbian Fast Weights in Vision Transformers for Few-Shot Character Recognition
Apr 22, 2026Gavin Money, Sindhuja Penchala, Jiacheng Li +1Hebbian Learning Vision Transformer
Department of Computer Science, The University of Alabama, Tuscaloosa, AL, USA
PASTA: A Patch-Agnostic Twofold-Stealthy Backdoor Attack on Vision Transformers
Apr 21, 2026Dazhuang Liu, Yanqi Qiao, Rui Wang +2Adversarial Attacks Vision Transformer
Delft University of Technology, Delft, the Netherlands. · University of Turku, Turku, Finland
Cognitive Alignment At No Cost: Inducing Human Attention Biases For Interpretable Vision Transformers
Apr 21, 2026Ethan KnightsTransformer Interpretability Visual Attention
Cambridge, UK
Benign Overfitting in Adversarial Training for Vision Transformers
Apr 21, 2026Jiaming Zhang, Meng Ding, Shaopeng Fu +2Adversarial Training Neural Network Generalization
Division of CEMSE, King Abdullah University of Science and Technology · School of Statistics, Renmin University of China · Department of Computer Science and Engineering, State University of New York at Buffalo +1

May 12, 2026J/K move · Enter open · S save

What-Where Transformer: A Slot-Centric Visual Backbone for Concurrent Representation and Localization

Ryota Yoshihashi, Masahiro Kada, Satoshi Ikehata, Rei Kawakami+1

Institute of Science Tokyo · 2DENSO IT Laboratory · 3National Institute of Informatics

Visual Attention Vision Transformer Weakly Supervised Semantic Segmentation Object-Centric Representation Learning Object Detection Semantic Segmentation

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Many image understanding tasks involve identifying what is present and where it appears. However, tasks that address where, such as object discovery, detection, and segmentation, are often considerably more complex than image classification, which primarily focuses on what. One possible reason is that classification-oriented backbones tend to emphasize semantic information about what, while implicitly entangling or suppressing information about where. In this work, we focus on an inductive bias termed what-where separation, which encourages models to represent object appearance and spatial location in a decomposed manner. To incorporate this bias throughout an attentive backbone in the style of Vision Transformer (ViT), we propose the What-Where Transformer (WWT). Our method introduces two key novel designs: (1) it treats tokens as representations of what and attention maps as representations of where, and processes them in concurrent feed-forward modules via a multi-stream, slot-based architecture; (2) it reuses both the final-layer tokens and attention maps for downstream tasks, and directly exposes them to gradients derived from task losses, thereby facilitating more effective and explicit learning of localization. We demonstrate that even under standard single-label classification-based supervision on ImageNet, WWT exhibits emergent multiple object discovery directly from raw attention maps, rather than via additional postprocessing such as token clustering. Furthermore, WWT achieves superior performance compared to ViT-based methods on zero-shot object discovery and weakly supervised semantic segmentation, and it is transferable to various localization setups with minimal modifications. Code will be published after acceptance.