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cs.CV2026
Nonlinear Bipolar Compensation: Handling Outliers in Post-Training Quantization
Peilin Sun, Jianxin Wu
Network quantization has emerged as one of the most practical model compression techniques, which significantly reduces a model's memory and compute consumption by mapping floating…
cs.CV2026
Colinearity Decay: Training Quantization-Friendly ViTs with Outlier Decay
Jin Tong, Guang Liang, Peilin Sun +1
Low-bit quantization is a practical route for efficiently deploying vision Transformers, yet activation outliers complicate fully quantized deployment. Existing methods either hand…
cs.CV2025
GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers
Guang Liang, Xinyao Liu, Jianxin Wu
Vision Transformers (ViTs) are essential in computer vision but are computationally intensive, too. Model quantization, particularly to low bit-widths like 4-bit, aims to alleviate…