AAAI 2026technical0 citations
SAQ-SAM: Semantically-Aligned Quantization for Segment Anything Model
Jing Zhang, Zhikai Li, Chengzhi Hu, Xuewen Liu, Qingyi Gu
Abstract
Segment Anything Model (SAM) exhibits remarkable zero-shot segmentation capability; however, its prohibitive computational costs make edge deployment challenging. Although post-training quantization (PTQ) offers a promising compression solution, existing methods yield unsatisfactory results when applied to SAM, owing to its specialized model components and promptable workflow: (i) The mask decoder
BibTeX
@inproceedings{aaai2026_saqsamsemantical,
title = {SAQ-SAM: Semantically-Aligned Quantization for Segment Anything Model},
author = {Jing Zhang and Zhikai Li and Chengzhi Hu and Xuewen Liu and Qingyi Gu},
booktitle = {AAAI 2026},
year = {2026}
}