Cover Image

I&S-ViT: An Inclusive & Stable Method for Pushing the Limit of Post-Training ViTs Quantization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhong, Yunshan, Hu, Jiawei, lin, Mingbao, Chen, Mengzhao, Ji, Rongrong
Format:	Preprint
Published:	2023
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

CLAMP-ViT: Contrastive Data-Free Learning for Adaptive Post-Training Quantization of ViTs
by: Ramachandran, Akshat, et al.
Published: (2024)

Purrturbed but Stable: Human-Cat Invariant Representations Across CNNs, ViTs and Self-Supervised ViTs
by: Shah, Arya, et al.
Published: (2025)

Colinearity Decay: Training Quantization-Friendly ViTs with Outlier Decay
by: Tong, Jin, et al.
Published: (2026)

STRAP-ViT: Segregated Tokens with Randomized -- Transformations for Defense against Adversarial Patches in ViTs
by: Chattopadhyay, Nandish, et al.
Published: (2026)

Intriguing Frequency Interpretation of Adversarial Robustness for CNNs and ViTs
by: Chen, Lu, et al.
Published: (2025)

U-REPA: Aligning Diffusion U-Nets to ViTs
by: Tian, Yuchuan, et al.
Published: (2025)

MPTQ-ViT: Mixed-Precision Post-Training Quantization for Vision Transformer
by: Tai, Yu-Shan, et al.
Published: (2024)

Training-Free Acceleration of ViTs with Delayed Spatial Merging
by: Heo, Jung Hwan, et al.
Published: (2023)

Elastic ViTs from Pretrained Models without Retraining
by: Simoncini, Walter, et al.
Published: (2025)

Harnessing the Computation Redundancy in ViTs to Boost Adversarial Transferability
by: Liu, Jiani, et al.
Published: (2025)

Pretrained ViTs Yield Versatile Representations For Medical Images
by: Matsoukas, Christos, et al.
Published: (2023)

ADFQ-ViT: Activation-Distribution-Friendly Post-Training Quantization for Vision Transformers
by: Jiang, Yanfeng, et al.
Published: (2024)

APHQ-ViT: Post-Training Quantization with Average Perturbation Hessian Based Reconstruction for Vision Transformers
by: Wu, Zhuguanyu, et al.
Published: (2025)

Octic Vision Transformers: Quicker ViTs Through Equivariance
by: Nordström, David, et al.
Published: (2025)

Unsupervised Object Localization in the Era of Self-Supervised ViTs: A Survey
by: Siméoni, Oriane, et al.
Published: (2023)

Token Cropr: Faster ViTs for Quite a Few Tasks
by: Bergner, Benjamin, et al.
Published: (2024)

Adapting Pretrained ViTs with Convolution Injector for Visuo-Motor Control
by: Hwang, Dongyoon, et al.
Published: (2024)

Trio-ViT: Post-Training Quantization and Acceleration for Softmax-Free Efficient Vision Transformer
by: Shi, Huihong, et al.
Published: (2024)

Deeper Inside Deep ViT
by: Hong, Sungrae
Published: (2025)

ConcatPlexer: Additional Dim1 Batching for Faster ViTs
by: Han, Donghoon, et al.
Published: (2023)

Parameter Efficient Fine-tuning of Self-supervised ViTs without Catastrophic Forgetting
by: Bafghi, Reza Akbarian, et al.
Published: (2024)

VIVID-Med: LLM-Supervised Structured Pretraining for Deployable Medical ViTs
by: Wang, Xiyao, et al.
Published: (2026)

RepViT: Revisiting Mobile CNN From ViT Perspective
by: Wang, Ao, et al.
Published: (2023)

DopQ-ViT: Towards Distribution-Friendly and Outlier-Aware Post-Training Quantization for Vision Transformers
by: Yang, Lianwei, et al.
Published: (2024)

IPTQ-ViT: Post-Training Quantization of Non-linear Functions for Integer-only Vision Transformers
by: Kim, Gihwan, et al.
Published: (2025)

UniRefiner: Teaching Pre-trained ViTs to Self-Dispose Dross via Contrastive Register
by: Qiu, Congpei, et al.
Published: (2026)

ViT$^3$: Unlocking Test-Time Training in Vision
by: Han, Dongchen, et al.
Published: (2025)

AFIDAF: Alternating Fourier and Image Domain Adaptive Filters as an Efficient Alternative to Attention in ViTs
by: Zheng, Yunling, et al.
Published: (2024)

Which Direction to Choose? An Analysis on the Representation Power of Self-Supervised ViTs in Downstream Tasks
by: Kaltampanidis, Yannis, et al.
Published: (2025)

EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation
by: Liu, Longfei, et al.
Published: (2026)

Towards Accurate Post-Training Quantization of Vision Transformers via Error Reduction
by: Zhong, Yunshan, et al.
Published: (2024)

TAP-ViTs: Task-Adaptive Pruning for On-Device Deployment of Vision Transformers
by: Wang, Zhibo, et al.
Published: (2026)

Communication Efficient Split Learning of ViTs with Attention-based Double Compression
by: Alvetreti, Federico, et al.
Published: (2025)

ViTs are Everywhere: A Comprehensive Study Showcasing Vision Transformers in Different Domain
by: Mia, Md Sohag, et al.
Published: (2023)

Exploring the Synergies of Hybrid CNNs and ViTs Architectures for Computer Vision: A survey
by: Yunusa, Haruna, et al.
Published: (2024)

Causality $\neq$ Decodability, and Vice Versa: Lessons from Interpreting Counting ViTs
by: Huang, Lianghuan, et al.
Published: (2025)

CAIT: Triple-Win Compression towards High Accuracy, Fast Inference, and Favorable Transferability For ViTs
by: Wang, Ao, et al.
Published: (2023)

ViTCAE: ViT-based Class-conditioned Autoencoder
by: Jebraeeli, Vahid, et al.
Published: (2025)

DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs
by: Kim, Donghyun, et al.
Published: (2024)

Decomposing and Interpreting Image Representations via Text in ViTs Beyond CLIP
by: Balasubramanian, Sriram, et al.
Published: (2024)