• Introduces AOCQ, a three-level quantization method (operator, framework, loss) that adaptively corrects channel and token outliers in vision Transformers, reducing quantization error.
• Achieves 81.57% accuracy on DeiT-Base with 8-bit post-training quantization, only a 0.28 percentage point drop, and 4× faster runtime.
• Enables ultra-low 4-bit weight quantization for Swin and DeiT across classification and object detection tasks, with a minimal accuracy loss of about 2% and nearly 8× less memory.
• Demonstrates that AOCQ effectively mitigates the uneven activation distributions that limit standard PTQ methods, supporting efficient edge deployment.