Efficient ViTs

ViT: Vision Transformer

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20 papers in the last four weeks, up 300% on the four weeks before. 0.2% of all new papers.

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Joint Architecture-Token-Bitwidth Multi-Axis Optimization of Vision Transformers for Semiconductor IC Packaging
May 3, 2026Phat Nguyen, Xue Geng, Kaixin Xu +3Efficient Neural Network Inference Efficient ViTs
Singapore University of Technology and Design (SUTD) · Agency for Science, Technology, and Research (A*STAR)
Linear-Time Global Visual Modeling without Explicit Attention
May 3, 2026Ruize He, Dongchen Han, Gao HuangTransformer Transformer Attention
Tsinghua University
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
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
PointTransformerX: Portable and Efficient 3D Point Cloud Processing without Sparse Algorithms
Apr 27, 2026Laurenz Reichardt, Nikolas Ebert, Oliver WasenmüllerRotary Positional Embeddings 3D ViTs
Mannheim University of Applied Sciences, Germany
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
BSViT: A Burst Spiking Vision Transformer for Expressive and Efficient Visual Representation Learning
Apr 25, 2026Hongxiang Peng, Dewei Bai, Hong QuSpiking Transformers Efficient ViTs
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
UHR-DETR: Efficient End-to-End Small Object Detection for Ultra-High-Resolution Remote Sensing Imagery
Apr 23, 2026Jingfang Li, Haoran Zhu, Wen Yang +4Remote Sensing Small Object Detection Small Object Detection
School of Electronic Information, Wuhan University, Wuhan, 430072 China
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
LaplacianFormer:Rethinking Linear Attention with Laplacian Kernel
Apr 22, 2026Zhe Feng, Sen Lian, Changwei Wang +5Efficient ViTs Efficient Attention
1MAIS,Institute of Automation, Chinese Academy of Sciences · usr 3China Electronics Data Corporation · 5The Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology +4
RF-HiT: Rectified Flow Hierarchical Transformer for General Medical Image Segmentation
Apr 21, 2026Ahmed Marouane Djouamaa, Abir Belaala, Abdellah Zakaria Sellam +3Image Segmentation Vision Transformer
Dept. of Computer Science, Mohamed Khider University, Biskra, Algeria · Inst. of Applied Sciences and Intelligent Systems, National Research Council of Italy, 73100 Lecce, Italy · Dept. of Computer Science and AI, University of the Basque Country, UPV/EHU, San Sebastian, Spain +1
Benchmarking Vision Foundation Models for Domain-Generalizable Face Anti-Spoofing
Apr 21, 2026Mika Feng, Pierre Gallin-Martel, Koichi Ito +1Vision Foundation Models Domain Generalization
Graduate School of Information Sciences, Tohoku University, Japan
Advancing Vision Transformer with Enhanced Spatial Priors
Apr 20, 2026Qihang Fan, Huaibo Huang, Mingrui Chen +2Vision Transformer Transformer Attention
MAIS & NLPR, Institude of Automation, Chinese Academic of Science, Beijing 100190, China · School of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China
ESsEN: Training Compact Discriminative Vision-Language Transformers in a Low-Resource Setting
Apr 20, 2026Clayton Fields, Casey KenningtonVision-Language Models Efficient ViTs
Department of Computer Science · Boise State University
CATP: Confidence-Aware Token Pruning for Camouflaged Object Detection
Apr 18, 2026Yuhan Gao, Shuhao Kang, Xin He +3Token Pruning Efficient ViTs
VCIP, College of Computer Science, Nankai University · Academy for Advanced Interdisciplinary Studies, Nankai University · School of Computer Science and Engineering, Tianjin University of Technology +2
SSFT: A Lightweight Spectral-Spatial Fusion Transformer for Generic Hyperspectral Classification
Apr 17, 2026Alexander Musiat, Nikolas Ebert, Oliver WasenmüllerRepresentation Learning Hyperspectral Imaging
Mannheim University of Applied Sciences, Germany
StreamCacheVGGT: Streaming Visual Geometry Transformers with Robust Scoring and Hybrid Cache Compression
Apr 16, 2026Xuanyi Liu, Chunan Yu, Deyi Ji +63D ViTs Streaming 3D Reconstruction
KOKONI 3D, Moxin Technology · Nanjing University of Science and Technology · University of Science and Technology of China +4
Dispatch-Aware Ragged Attention for Pruned Vision Transformers
Apr 16, 2026Seifeldin Abdellatif, Ahmad AlmasriEfficient Transformer Inference Visual Attention
College of Engineering Al Ain University Abu Dhabi, United Arab Emirates
Learning Where to Embed: Noise-Aware Positional Embedding for Query Retrieval in Small-Object Detection
Apr 16, 2026Yangchen Zeng, Zhenyu Yu, Dongming Jiang +5Small Object Detection Efficient Object Detection
Southeast University · Fudan University · The University of Texas at Dallas +6
Revisiting Token Compression for Accelerating ViT-based Sparse Multi-View 3D Object Detectors
Apr 16, 2026Mingqian Ji, Shanshan Zhang, Jian YangMulti-View 3D Object Detection Efficient ViTs
PCA Lab, School of Computer Science and Engineering, Nanjing University of Science and Technology · PCA Lab, School of Intelligence Science and Technology, Nanjing University
Kuramoto Oscillatory Phase Encoding: Neuro-inspired Synchronization for Improved Learning Efficiency
Apr 9, 2026Mingqing Xiao, Yansen Wang, Dongqi Han +2Oscillatory Neural Networks Vision Transformer
Microsoft Research Asia
PDE-SSM: A Spectral State Space Approach to Spatial Mixing in Diffusion Transformers
Mar 14, 2026Eshed Gal, Moshe Eliasof, Eldad HaberDiffusion Transformer Visual SSMs
University of British Columbia, Vancouver, BC Canada · University of Cambridge, Cambridge, United Kingdom
HiAP: A Multi-Granular Stochastic Auto-Pruning Framework for Vision Transformers
Mar 12, 2026Andy Li, Aiden Durrant, Milan Markovic +1Efficient ViTs Attention Head Pruning
School of Natural and Computing Sciences, University of Aberdeen · School of Computing Sciences, University of East Anglia · Department of Physics and Technology, UiT The Arctic University of Norway
StructSAM: Structure- and Spectrum-Preserving Token Merging for Segment Anything Models
Mar 7, 2026Duy M. H. Nguyen, Tuan A. Tran, Duong Nguyen +17Token Merging Image Segmentation
University of Stuttgart · DFKI · Max Planck Research School +10
AFFMAE: Scalable Vision Pre-Training for High-Resolution Microscopy Segmentation on Desktop Hardware
Feb 18, 2026David Smerkous, Zian Wang, Behzad NajafianMasked Autoencoders Self-Supervised Pre-Training
Department of Laboratory Medicine & Pathology, University of Washington, Seattle WA, USA · Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR, USA · Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle WA, USA
From Local Windows to Adaptive Candidates via Individualized Exploratory: Rethinking Attention for Image Super-Resolution
Jan 13, 2026Chunyu Meng, Wei Long, Shuhang GuEfficient ViTs Sparse Attention
University of Electronic Science and Technology of China
Look, Focus, Act: Efficient and Robust Robot Learning via Human Gaze and Foveated Vision Transformers
Jul 21, 2025Ian Chuang, Jinyu Zou, Andrew Lee +2Visuomotor Policy Learning Efficient ViTs
University of California, Berkeley · 2Tongji University · University of California, Davis
DETRPose: Real-Time End-to-End Multi-Person Pose Estimation via Modified Transformer Decoder and Novel Denoising Keypoints
Jun 16, 2025Sebastian Janampa, Marios PattichisDetection Transformer Efficient ViTs
The University of New Mexico
GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers
Jun 13, 2025Guang Liang, Xinyao Liu, Jianxin WuQuantization-Aware Training 4-Bit Quantization
State Key Laboratory for Novel Software Technology, Nanjing University, China · School of Artificial Intelligence, Nanjing University, China · University of Science and Technology of China, Hefei, China
Fast and Accurate Image Restoration and Generation with Rank Enhanced Linear Attention
May 22, 2025Yuang AiEfficient ViTs Linear Attention
University of Chinese Academy of Sciences, Beijing, China

May 3, 2026, cs.CVJ/K move · Enter open · S save

Joint Architecture-Token-Bitwidth Multi-Axis Optimization of Vision Transformers for Semiconductor IC Packaging

Phat Nguyen, Xue Geng, Kaixin Xu, Wang Zhe+2

Singapore University of Technology and Design (SUTD) · Agency for Science, Technology, and Research (A*STAR)

Efficient Neural Network Inference Efficient ViTs

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Vision Transformers (ViTs) have achieved strong performance in visual recognition, yet their deployment in resource-constrained industrial environments remains limited. The main challenges are their high computational cost, memory requirements, and energy consumption. While individual efficiency techniques such as neural architecture search (NAS), token compression, and low-precision inference have been extensively studied, most prior work targets only a single optimization axis, limiting overall deployment gains while preserving accuracy. In this paper, we present one of the first holistic frameworks that jointly optimizes three complementary axes: architecture, token, and bit-width. Specifically, the framework identifies compact backbones via Neural Architecture Search (AutoFormer), reduces information processing via token merging (ToMe), and accelerates per-operation execution via fp16 mixed-precision inference. In our study, we analyze accuracy-efficiency trade-offs on ImageNet-1K under aggressive compression. We then apply the selected configaturions to a real-world in-house 3D X-ray semiconductor defect classification dataset for IC chip packaging inspection. Results show that the proposed multi-axis framework achieves more than

10\times

improvement in throughput along with over

10\times

reductions in parameter count, FLOPs, and energy consumption, while maintaining the required accuracy on the downstream industrial task. To the best of our knowledge, this is among the earliest works to jointly optimize architecture, token, and bit-width dimensions in ViTs and the first such resource-efficient, deployment-focused study tailored to semiconductor manufacturing.

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