Cover Image

Token Turing Machines are Efficient Vision Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jajal, Purvish, Eliopoulos, Nick John, Chou, Benjamin Shiue-Hal, Thiruvathukal, George K., Davis, James C., Lu, Yung-Hsiang
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

AdaPerceiver: Transformers with Adaptive Width, Depth, and Tokens
by: Jajal, Purvish, et al.
Published: (2025)

Inference-Time Alignment of Diffusion Models via Evolutionary Algorithms
by: Jajal, Purvish, et al.
Published: (2025)

Pruning One More Token is Enough: Leveraging Latency-Workload Non-Linearities for Vision Transformers on the Edge
by: Eliopoulos, Nick John, et al.
Published: (2024)

LadderSym: A Multimodal Interleaved Transformer for Music Practice Error Detection
by: Chou, Benjamin Shiue-Hal, et al.
Published: (2025)

Detecting Music Performance Errors with Transformers
by: Chou, Benjamin Shiue-Hal, et al.
Published: (2025)

Analysis of Failures and Risks in Deep Learning Model Converters: A Case Study in the ONNX Ecosystem
by: Jajal, Purvish, et al.
Published: (2023)

Reusing Deep Learning Models: Challenges and Directions in Software Engineering
by: Davis, James C., et al.
Published: (2024)

Large Language Models for Energy-Efficient Code: Emerging Results and Future Directions
by: Peng, Huiyun, et al.
Published: (2024)

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge
by: Ye, Zihao, et al.
Published: (2026)

Visual Personalization Turing Test
by: Abdal, Rameen, et al.
Published: (2026)

Towards Efficient Vision State Space Models via Token Merging
by: Park, Jinyoung, et al.
Published: (2025)

Skip-Vision: Efficient and Scalable Acceleration of Vision-Language Models via Adaptive Token Skipping
by: Zeng, Weili, et al.
Published: (2025)

Test-Time Consistency in Vision Language Models
by: Chou, Shih-Han, et al.
Published: (2025)

2023 Low-Power Computer Vision Challenge (LPCVC) Summary
by: Chen, Leo, et al.
Published: (2024)

Attention Debiasing for Token Pruning in Vision Language Models
by: Zhao, Kai, et al.
Published: (2025)

SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference
by: Zhang, Yuan, et al.
Published: (2024)

METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models
by: Liu, Yuchen, et al.
Published: (2025)

HiPrune: Hierarchical Attention for Efficient Token Pruning in Vision-Language Models
by: Liu, Jizhihui, et al.
Published: (2025)

PPT: Token Pruning and Pooling for Efficient Vision Transformers
by: Wu, Xinjian, et al.
Published: (2023)

EvoCut: Multi-Layer Evolution-Aware Visual Token Compression for Efficient Large Vision-Language Models
by: Lu, Hongyu, et al.
Published: (2026)

SATA: Spatial Autocorrelation Token Analysis for Enhancing the Robustness of Vision Transformers
by: Nikzad, Nick, et al.
Published: (2024)

PacTure: Efficient PBR Texture Generation on Packed Views with Visual Autoregressive Models
by: Fei, Fan, et al.
Published: (2025)

MM-R$^3$: On (In-)Consistency of Vision-Language Models (VLMs)
by: Chou, Shih-Han, et al.
Published: (2024)

Can Machines Imitate Humans? Integrative Turing-like tests for Language and Vision Demonstrate a Narrowing Gap
by: Zhang, Mengmi, et al.
Published: (2022)

VisionSelector: End-to-End Learnable Visual Token Compression for Efficient Multimodal LLMs
by: Zhu, Jiaying, et al.
Published: (2025)

Vision SmolMamba: Spike-Guided Token Pruning for Energy-Efficient Spiking State-Space Vision Models
by: Bai, Dewei, et al.
Published: (2026)

Multi-Stage Vision Token Dropping: Towards Efficient Multimodal Large Language Model
by: Liu, Ting, et al.
Published: (2024)

EM-KD: Distilling Efficient Multimodal Large Language Model with Unbalanced Vision Tokens
by: Feng, Ze, et al.
Published: (2025)

Delta-LLaVA: Base-then-Specialize Alignment for Token-Efficient Vision-Language Models
by: Zamini, Mohamad, et al.
Published: (2025)

TRIO: Token Reduction via Inference-Objective Guidance for Efficient Vision-Language Models
by: Zhang, Haokui, et al.
Published: (2026)

FASTer: Toward Efficient Autoregressive Vision Language Action Modeling via Neural Action Tokenization
by: Liu, Yicheng, et al.
Published: (2025)

Token-Space Mask Prediction for Efficient Vision Transformer Segmentation
by: Galagain, Calvin, et al.
Published: (2026)

ToSA: Token Selective Attention for Efficient Vision Transformers
by: Singh, Manish Kumar, et al.
Published: (2024)

End-to-End Vision Tokenizer Tuning
by: Wang, Wenxuan, et al.
Published: (2025)

Can Large-Language Models Help us Better Understand and Teach the Development of Energy-Efficient Software?
by: Hasler, Ryan, et al.
Published: (2024)

GTPT: Group-based Token Pruning Transformer for Efficient Human Pose Estimation
by: Wang, Haonan, et al.
Published: (2024)

Beyond Attention Scores: SVD-Based Vision Token Pruning for Efficient Vision-Language Models
by: Apedo, Yvon, et al.
Published: (2026)

HiRED: Attention-Guided Token Dropping for Efficient Inference of High-Resolution Vision-Language Models
by: Arif, Kazi Hasan Ibn, et al.
Published: (2024)

Impact of Architectural Modifications on Deep Learning Adversarial Robustness
by: Juraev, Firuz, et al.
Published: (2024)

MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers
by: Bae, Jongseong, et al.
Published: (2024)