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most citedMix-QViT: Mixed-Precision Vision Transformer Quantization Driven by Layer Importance and Quantization Sensitivity

1 citations · 1 across the 4 of their papers we have counts for

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cs.CV2025

Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model

Navin Ranjan, Andreas Savakis

The Segment Anything Model (SAM) is a popular vision foundation model; however, its high computational and memory demands make deployment on resource-constrained devices challengin…

cs.CV2025★ 1 cited

Mix-QViT: Mixed-Precision Vision Transformer Quantization Driven by Layer Importance and Quantization Sensitivity

Navin Ranjan, Andreas Savakis

In this paper, we propose Mix-QViT, an explainability-driven MPQ framework that systematically allocates bit-widths to each layer based on two criteria: layer importance, assessed…