GLF-Q: Global-Local Feature-based Quantization for Vision Transformers

arXiv:2609.34564 · cs.CV · Submitted 2026-09-28 · Read on arXiv

cs.CV

Submitted: 2026-09-28

Updated: 2026-09-28

Code: https://github.com/huggingface/pytorch-image-models

Terminology

• Quantization — 109× in this paper · explained in 12 episodes
• Transformer — 47× in this paper · explained in 2 episodes
• Accuracy — 42× in this paper · explained in 1 episode
• Calibration — 37× in this paper · explained in 7 episodes
• Global-Local Feature — 29× in this paper
• Regularization — 14× in this paper · explained in 3 episodes
• Post-Training Quantization — 13× in this paper
• Instance Segmentation — 8× in this paper · explained in 3 episodes
• Alignment — 7× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 6× in this paper
• Generalization — 6× in this paper · explained in 13 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Ablation Studies — 3× in this paper · explained in 1 episode
• Robustness — 3× in this paper · explained in 5 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Quantization-Aware Training — 2× in this paper · explained in 3 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Fisher Information — 1× in this paper · explained in 3 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Straight-Through Estimator — 1× in this paper
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
• MMDetection: Open MMLab Detection Toolbox and Benchmark
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Learned Step Size Quantization
• BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction
• Nonlinear Bipolar Compensation: Handling Outliers in Post-Training Quantization
• QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization
• DopQ-ViT: Towards Distribution-Friendly and Outlier-Aware Post-Training Quantization for Vision Transformers
• I&S-ViT: An Inclusive & Stable Method for Pushing the Limit of Post-Training ViTs Quantization

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